Repository: ivalab/grasp_multiObject_multiGrasp Branch: master Commit: 806ad3d71c2f Files: 86 Total size: 947.1 KB Directory structure: gitextract_6dytcin7/ ├── README.md ├── data/ │ └── scripts/ │ ├── dataPreprocessingTest_fasterrcnn_split.m │ ├── dataPreprocessing_fasterrcnn.m │ └── fetch_faster_rcnn_models.sh ├── experiments/ │ ├── cfgs/ │ │ ├── res101-lg.yml │ │ ├── res101.yml │ │ ├── res50.yml │ │ └── vgg16.yml │ ├── logs/ │ │ └── .gitignore │ └── scripts/ │ ├── convert_vgg16.sh │ ├── test_faster_rcnn.sh │ ├── train_faster_rcnn.sh │ └── train_faster_rcnn.sh~ ├── lib/ │ ├── Makefile │ ├── datasets/ │ │ ├── VOCdevkit-matlab-wrapper/ │ │ │ ├── get_voc_opts.m │ │ │ ├── voc_eval.m │ │ │ └── xVOCap.m │ │ ├── __init__.py │ │ ├── coco.py │ │ ├── ds_utils.py │ │ ├── factory.py │ │ ├── factory.py~ │ │ ├── graspRGB.py │ │ ├── graspRGB.py~ │ │ ├── imdb.py │ │ ├── pascal_voc.py │ │ ├── tools/ │ │ │ └── mcg_munge.py │ │ └── voc_eval.py │ ├── layer_utils/ │ │ ├── __init__.py │ │ ├── anchor_target_layer.py │ │ ├── generate_anchors.py │ │ ├── proposal_layer.py │ │ ├── proposal_target_layer.py │ │ ├── proposal_top_layer.py │ │ └── snippets.py │ ├── model/ │ │ ├── __init__.py │ │ ├── bbox_transform.py │ │ ├── config.py │ │ ├── config.py~ │ │ ├── nms_wrapper.py │ │ ├── test.py │ │ ├── test.py~ │ │ ├── train_val.py │ │ └── train_val.py~ │ ├── nets/ │ │ ├── __init__.py │ │ ├── network.py │ │ ├── resnet_v1.py │ │ ├── resnet_v1.py~ │ │ └── vgg16.py │ ├── nms/ │ │ ├── .gitignore │ │ ├── __init__.py │ │ ├── cpu_nms.c │ │ ├── cpu_nms.pyx │ │ ├── gpu_nms.cpp │ │ ├── gpu_nms.hpp │ │ ├── gpu_nms.pyx │ │ ├── nms_kernel.cu │ │ └── py_cpu_nms.py │ ├── roi_data_layer/ │ │ ├── __init__.py │ │ ├── layer.py │ │ ├── minibatch.py │ │ ├── minibatch.py~ │ │ └── roidb.py │ ├── setup.py │ ├── setup.py~ │ └── utils/ │ ├── .gitignore │ ├── __init__.py │ ├── bbox.pyx │ ├── blob.py │ ├── boxes_grid.py │ ├── nms.py │ ├── nms.pyx │ └── timer.py └── tools/ ├── _init_paths.py ├── demo.py~ ├── demo_graspRGD.py ├── demo_graspRGD.py~ ├── demo_graspRGD_socket.py ├── demo_graspRGD_socket.py~ ├── demo_graspRGD_socket_drawer.py~ ├── demo_graspRGD_socket_save_to_rgbd.py~ ├── demo_graspRGD_vis_mask.py ├── demo_graspRGD_vis_select.py ├── eval_graspRGD.py~ ├── mask_gen.py └── trainval_net.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: README.md ================================================ # grasp_multiObject_multiGrasp This is the implementation of our RA-L work 'Real-world Multi-object, Multi-grasp Detection'. The detector takes RGB-D image input and predicts multiple grasp candidates for a single object or multiple objects, in a single shot. The original arxiv paper can be found [here](https://arxiv.org/pdf/1802.00520.pdf). The final version will be updated after publication process.

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If you find it helpful for your research, please consider citing: @inproceedings{chu2018deep, title = {Real-World Multiobject, Multigrasp Detection}, author = {F. Chu and R. Xu and P. A. Vela}, journal = {IEEE Robotics and Automation Letters}, year = {2018}, volume = {3}, number = {4}, pages = {3355-3362}, DOI = {10.1109/LRA.2018.2852777}, ISSN = {2377-3766}, month = {Oct} } If you encounter any questions, please contact me at fujenchu[at]gatech[dot]edu ### Demo 1. Clone this repository ``` git clone https://github.com/ivalab/grasp_multiObject_multiGrasp.git cd grasp_multiObject_multiGrasp ``` 2. Build Cython modules ``` cd lib make clean make cd .. ``` 3. Install [Python COCO API](https://github.com/cocodataset/cocoapi) ``` cd data git clone https://github.com/pdollar/coco.git cd coco/PythonAPI make cd ../../.. ``` 4. Download pretrained models - trained model for grasp on [dropbox drive](https://www.dropbox.com/s/ldapcpanzqdu7tc/models.zip?dl=0) - put under `output/res50/train/default/` 5. Run demo ``` ./tools/demo_graspRGD.py --net res50 --dataset grasp ``` you can see images pop out. ### Train 1. Generate data 1-1. Download [Cornell Dataset](http://pr.cs.cornell.edu/grasping/rect_data/data.php) 1-2. Run `dataPreprocessingTest_fasterrcnn_split.m` (please modify paths according to your structure) 1-3. Follow 'Format Your Dataset' section [here](https://github.com/zeyuanxy/fast-rcnn/tree/master/help/train) to check if your data follows VOC format 2. Train ``` ./experiments/scripts/train_faster_rcnn.sh 0 graspRGB res50 ``` ### ROS version? Yes! please find it [HERE](https://github.com/ivaROS/ros_deep_grasp) ### Acknowledgment This repo borrows tons of code from - [tf-faster-rcnn](https://github.com/endernewton/tf-faster-rcnn) by endernewton ### Resources - [multi-object grasp dataset](https://github.com/ivalab/grasp_multiObject) - [grasp annotation tool](https://github.com/ivalab/grasp_annotation_tool) ================================================ FILE: data/scripts/dataPreprocessingTest_fasterrcnn_split.m ================================================ %% script to test dataPreprocessing %% created by Fu-Jen Chu on 09/15/2016 close all clear %parpool(4) addpath('/media/fujenchu/home3/data/grasps/') % generate list for splits list = [100:949 1000:1034]; list_idx = randperm(length(list)); train_list_idx = list_idx(length(list)/5+1:end); test_list_idx = list_idx(1:length(list)/5); train_list = list(train_list_idx); test_list = list(test_list_idx); for folder = 1:10 display(['processing folder ' int2str(folder)]) imgDataDir = ['/home/fujenchu/projects/deepLearning/tensorflow-finetune-flickr-style-master/data/grasps/' sprintf('%02d',folder) '_rgd']; txtDataDir = ['/home/fujenchu/projects/deepLearning/tensorflow-finetune-flickr-style-master/data/grasps/' sprintf('%02d',folder)]; %imgDataOutDir = ['/home/fujenchu/projects/deepLearning/tensorflow-finetune-flickr-style-master/data/grasps/' sprintf('%02d',folder) '_Cropped320_rgd']; imgDataOutDir = '/media/fujenchu/home3/fasterrcnn_grasp/rgd_multibbs_5_5_5_tf/data/Images'; annotationDataOutDir = '/media/fujenchu/home3/fasterrcnn_grasp/rgd_multibbs_5_5_5_tf/data/Annotations'; imgSetTrain = '/media/fujenchu/home3/fasterrcnn_grasp/rgd_multibbs_5_5_5_tf/data/ImageSets/train.txt'; imgSetTest = '/media/fujenchu/home3/fasterrcnn_grasp/rgd_multibbs_5_5_5_tf/data/ImageSets/test.txt'; imgFiles = dir([imgDataDir '/*.png']); txtFiles = dir([txtDataDir '/*pos.txt']); logfileID = fopen('log.txt','a'); mainfileID = fopen(['/home/fujenchu/projects/deepLearning/deepGraspExtensiveOffline/data/grasps/scripts/trainttt' sprintf('%02d',folder) '.txt'],'a'); for idx = 1:length(imgFiles) %% display progress tic display(['processing folder: ' sprintf('%02d',folder) ', imgFiles: ' int2str(idx)]) %% reading data imgName = imgFiles(idx).name; [pathstr,imgname] = fileparts(imgName); filenum = str2num(imgname(4:7)); if(any(test_list == filenum)) file_writeID = fopen(imgSetTest,'a'); fprintf(file_writeID, '%s\n', [imgDataDir(1:end-3) 'Cropped320_rgd/' imgname '_preprocessed_1.png' ] ); fclose(file_writeID); continue; end txtName = txtFiles(idx).name; [pathstr,txtname] = fileparts(txtName); img = imread([imgDataDir '/' imgname '.png']); fileID = fopen([txtDataDir '/' txtname '.txt'],'r'); sizeA = [2 100]; bbsIn_all = fscanf(fileID, '%f %f', sizeA); fclose(fileID); %% data pre-processing [imagesOut bbsOut] = dataPreprocessing_fasterrcnn(img, bbsIn_all, 227, 5, 5); % for each augmented image for i = 1:1:size(imagesOut,2) % for each bbs file_writeID = fopen([annotationDataOutDir '/' imgname '_preprocessed_' int2str(i) '.txt'],'w'); printCount = 0; for ibbs = 1:1:size(bbsOut{i},2) A = bbsOut{i}{ibbs}; xy_ctr = sum(A,2)/4; x_ctr = xy_ctr(1); y_ctr = xy_ctr(2); width = sqrt(sum((A(:,1) - A(:,2)).^2)); height = sqrt(sum((A(:,2) - A(:,3)).^2)); if(A(1,1) > A(1,2)) theta = atan((A(2,2)-A(2,1))/(A(1,1)-A(1,2))); else theta = atan((A(2,1)-A(2,2))/(A(1,2)-A(1,1))); % note y is facing down end % process to fasterrcnn x_min = x_ctr - width/2; x_max = x_ctr + width/2; y_min = y_ctr - height/2; y_max = y_ctr + height/2; %if(x_min < 0 || y_min < 0 || x_max > 227 || y_max > 227) display('yoooooooo'); end if((x_min < 0 && x_max < 0) || (y_min > 227 && y_max > 227) || (x_min > 227 && x_max > 227) || (y_min < 0 && y_max < 0)) display('xxxxxxxxx'); break; end cls = round((theta/pi*180+90)/10) + 1; % write as lefttop rightdown, Xmin Ymin Xmax Ymax, ex: 261 109 511 705 (x水平 y垂直) fprintf(file_writeID, '%d %f %f %f %f\n', cls, x_min, y_min, x_max, y_max ); printCount = printCount+1; end if(printCount == 0) fprintf(logfileID, '%s\n', [imgname '_preprocessed_' int2str(i) ]);end fclose(file_writeID); imwrite(imagesOut{i}, [imgDataOutDir '/' imgname '_preprocessed_' int2str(i) '.png']); % write filename to imageSet file_writeID = fopen(imgSetTrain,'a'); fprintf(file_writeID, '%s\n', [imgname '_preprocessed_' int2str(i) ] ); fclose(file_writeID); end toc end fclose(mainfileID); end ================================================ FILE: data/scripts/dataPreprocessing_fasterrcnn.m ================================================ function [imagesOut bbsOut] = dataPreprocessing( imageIn, bbsIn_all, cropSize, translationShiftNumber, roatateAngleNumber) % dataPreprocessing function perfroms % 1) croping % 2) padding % 3) rotatation % 4) shifting % % for a input image with a bbs as 4 points, % dataPreprocessing outputs a set of images with corresponding bbs. % % % Inputs: % imageIn: input image (480 by 640 by 3) % bbsIn: bounding box (2 by 4) % cropSize: output image size % shift: shifting offset % rotate: rotation angle % % Outputs: % imagesOut: output images (n images) % bbsOut: output bbs according to shift and rotation % %% created by Fu-Jen Chu on 09/15/2016 debug_dev = 0; debug = 0; %% show image and bbs if(debug_dev) figure(1); imshow(imageIn); hold on; x = bbsIn_all(1, [1:3]); y = bbsIn_all(2, [1:3]); plot(x,y); hold off; end %% crop image and padding image % cropping to 321 by 321 from center imgCrop = imcrop(imageIn, [145 65 351 351]); % padding to 501 by 501 imgPadding = padarray(imgCrop, [75 75], 'replicate', 'both'); count = 1; for i_rotate = 1:roatateAngleNumber*translationShiftNumber*translationShiftNumber % random roatateAngle theta = randi(360)-1; %theta = 0; % random translationShift dx = randi(101)-51; %dx = 0; %% rotation and shifting % random translationShift dy = randi(101)-51; %dy = 0; imgRotate = imrotate(imgPadding, theta); if(debug_dev)figure(2); imshow(imgRotate);end imgCropRotate = imcrop(imgRotate, [size(imgRotate,1)/2-160-dx size(imgRotate,1)/2-160-dy 320 320]); if(debug_dev)figure(3); imshow(imgCropRotate);end imgResize = imresize(imgCropRotate, [cropSize cropSize]); if(debug)figure(4); imshow(imgResize); hold on;end %% modify bbs [m, n] = size(bbsIn_all); bbsNum = n/4; countbbs = 1; for idx = 1:bbsNum bbsIn = bbsIn_all(:,idx*4-3:idx*4); if(sum(sum(isnan(bbsIn)))) continue; end bbsInShift = bbsIn - repmat([320; 240], 1, 4); R = [cos(theta/180*pi) -sin(theta/180*pi); sin(theta/180*pi) cos(theta/180*pi)]; bbsRotated = (bbsInShift'*R)'; bbsInShiftBack = (bbsRotated + repmat([160; 160], 1, 4) + repmat([dx; dy], 1, 4))*cropSize/320; if(debug) figure(4) x = bbsInShiftBack(1, [1:4 1]); y = bbsInShiftBack(2, [1:4 1]); plot(x,y); hold on; pause(0.01); end bbsOut{count}{countbbs} = bbsInShiftBack; countbbs = countbbs + 1; end imagesOut{count} = imgResize; count = count +1; end end ================================================ FILE: data/scripts/fetch_faster_rcnn_models.sh ================================================ #!/bin/bash DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )/../" && pwd )" cd $DIR NET=res101 FILE=voc_0712_80k-110k.tgz # replace it with gs11655.sp.cs.cmu.edu if ladoga.graphics.cs.cmu.edu does not work URL=http://ladoga.graphics.cs.cmu.edu/xinleic/tf-faster-rcnn/$NET/$FILE CHECKSUM=cb32e9df553153d311cc5095b2f8c340 if [ -f $FILE ]; then echo "File already exists. Checking md5..." os=`uname -s` if [ "$os" = "Linux" ]; then checksum=`md5sum $FILE | awk '{ print $1 }'` elif [ "$os" = "Darwin" ]; then checksum=`cat $FILE | md5` fi if [ "$checksum" = "$CHECKSUM" ]; then echo "Checksum is correct. No need to download." exit 0 else echo "Checksum is incorrect. Need to download again." fi fi echo "Downloading Resnet 101 Faster R-CNN models Pret-trained on VOC 07+12 (340M)..." wget $URL -O $FILE echo "Unzipping..." tar zxvf $FILE echo "Done. Please run this command again to verify that checksum = $CHECKSUM." ================================================ FILE: experiments/cfgs/res101-lg.yml ================================================ EXP_DIR: res101-lg TRAIN: HAS_RPN: True IMS_PER_BATCH: 1 BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True RPN_POSITIVE_OVERLAP: 0.7 RPN_BATCHSIZE: 256 PROPOSAL_METHOD: gt BG_THRESH_LO: 0.0 DISPLAY: 20 BATCH_SIZE: 256 WEIGHT_DECAY: 0.0001 DOUBLE_BIAS: False SNAPSHOT_PREFIX: res101_faster_rcnn SCALES: [800] MAX_SIZE: 1333 TEST: HAS_RPN: True SCALES: [800] MAX_SIZE: 1333 RPN_POST_NMS_TOP_N: 1000 POOLING_MODE: crop ANCHOR_SCALES: [2,4,8,16,32] ================================================ FILE: experiments/cfgs/res101.yml ================================================ EXP_DIR: res101 TRAIN: HAS_RPN: True IMS_PER_BATCH: 1 BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True RPN_POSITIVE_OVERLAP: 0.7 RPN_BATCHSIZE: 256 PROPOSAL_METHOD: gt BG_THRESH_LO: 0.0 DISPLAY: 20 BATCH_SIZE: 256 WEIGHT_DECAY: 0.0001 DOUBLE_BIAS: False SNAPSHOT_PREFIX: res101_faster_rcnn TEST: HAS_RPN: True POOLING_MODE: crop ================================================ FILE: experiments/cfgs/res50.yml ================================================ EXP_DIR: res50 TRAIN: HAS_RPN: True IMS_PER_BATCH: 1 BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True RPN_POSITIVE_OVERLAP: 0.7 RPN_BATCHSIZE: 256 PROPOSAL_METHOD: gt BG_THRESH_LO: 0.0 DISPLAY: 20 BATCH_SIZE: 256 WEIGHT_DECAY: 0.0001 DOUBLE_BIAS: False SNAPSHOT_PREFIX: res50_faster_rcnn TEST: HAS_RPN: True POOLING_MODE: crop ================================================ FILE: experiments/cfgs/vgg16.yml ================================================ EXP_DIR: vgg16 TRAIN: HAS_RPN: True IMS_PER_BATCH: 1 BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True RPN_POSITIVE_OVERLAP: 0.7 RPN_BATCHSIZE: 256 PROPOSAL_METHOD: gt BG_THRESH_LO: 0.0 DISPLAY: 20 BATCH_SIZE: 256 SNAPSHOT_PREFIX: vgg16_faster_rcnn TEST: HAS_RPN: True POOLING_MODE: crop ================================================ FILE: experiments/logs/.gitignore ================================================ *.txt.* ================================================ FILE: experiments/scripts/convert_vgg16.sh ================================================ #!/bin/bash set -x set -e export PYTHONUNBUFFERED="True" GPU_ID=$1 DATASET=$2 NET=vgg16 array=( $@ ) len=${#array[@]} EXTRA_ARGS=${array[@]:2:$len} EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_} case ${DATASET} in pascal_voc) TRAIN_IMDB="voc_2007_trainval" TEST_IMDB="voc_2007_test" STEPSIZE=50000 ITERS=70000 ANCHORS="[8,16,32]" RATIOS="[0.5,1,2]" ;; pascal_voc_0712) TRAIN_IMDB="voc_2007_trainval+voc_2012_trainval" TEST_IMDB="voc_2007_test" STEPSIZE=80000 ITERS=110000 ANCHORS="[8,16,32]" RATIOS="[0.5,1,2]" ;; coco) TRAIN_IMDB="coco_2014_train+coco_2014_valminusminival" TEST_IMDB="coco_2014_minival" STEPSIZE=350000 ITERS=490000 ANCHORS="[4,8,16,32]" RATIOS="[0.5,1,2]" ;; *) echo "No dataset given" exit ;; esac set +x NET_FINAL=${NET}_faster_rcnn_iter_${ITERS} set -x if [ ! -f ${NET_FINAL}.index ]; then if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/convert_from_depre.py \ --snapshot ${NET_FINAL} \ --imdb ${TRAIN_IMDB} \ --iters ${ITERS} \ --cfg experiments/cfgs/${NET}.yml \ --tag ${EXTRA_ARGS_SLUG} \ --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS} else CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/convert_from_depre.py \ --snapshot ${NET_FINAL} \ --imdb ${TRAIN_IMDB} \ --iters ${ITERS} \ --cfg experiments/cfgs/${NET}.yml \ --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS} fi fi ================================================ FILE: experiments/scripts/test_faster_rcnn.sh ================================================ #!/bin/bash set -x set -e export PYTHONUNBUFFERED="True" GPU_ID=$1 DATASET=$2 NET=$3 array=( $@ ) len=${#array[@]} EXTRA_ARGS=${array[@]:3:$len} EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_} case ${DATASET} in pascal_voc) TRAIN_IMDB="voc_2007_trainval" TEST_IMDB="voc_2007_test" ITERS=70000 ANCHORS="[8,16,32]" RATIOS="[0.5,1,2]" ;; pascal_voc_0712) TRAIN_IMDB="voc_2007_trainval+voc_2012_trainval" TEST_IMDB="voc_2007_test" ITERS=110000 ANCHORS="[8,16,32]" RATIOS="[0.5,1,2]" ;; coco) TRAIN_IMDB="coco_2014_train+coco_2014_valminusminival" TEST_IMDB="coco_2014_minival" ITERS=490000 ANCHORS="[4,8,16,32]" RATIOS="[0.5,1,2]" ;; *) echo "No dataset given" exit ;; esac LOG="experiments/logs/test_${NET}_${TRAIN_IMDB}_${EXTRA_ARGS_SLUG}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" exec &> >(tee -a "$LOG") echo Logging output to "$LOG" set +x if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then NET_FINAL=output/${NET}/${TRAIN_IMDB}/${EXTRA_ARGS_SLUG}/${NET}_faster_rcnn_iter_${ITERS}.ckpt else NET_FINAL=output/${NET}/${TRAIN_IMDB}/default/${NET}_faster_rcnn_iter_${ITERS}.ckpt fi set -x if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/test_net.py \ --imdb ${TEST_IMDB} \ --model ${NET_FINAL} \ --cfg experiments/cfgs/${NET}.yml \ --tag ${EXTRA_ARGS_SLUG} \ --net ${NET} \ --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} ${EXTRA_ARGS} else CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/test_net.py \ --imdb ${TEST_IMDB} \ --model ${NET_FINAL} \ --cfg experiments/cfgs/${NET}.yml \ --net ${NET} \ --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} ${EXTRA_ARGS} fi ================================================ FILE: experiments/scripts/train_faster_rcnn.sh ================================================ #!/bin/bash set -x set -e export PYTHONUNBUFFERED="True" GPU_ID=$1 DATASET=$2 NET=$3 array=( $@ ) len=${#array[@]} EXTRA_ARGS=${array[@]:3:$len} EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_} case ${DATASET} in graspRGB) TRAIN_IMDB="graspRGB_train" TEST_IMDB="graspRGB_test" STEPSIZE=50000 ITERS=240000 #ANCHORS="[2,4,8,16,32]" ANCHORS="[8,16,32]" RATIOS="[0.5,1,2]" ;; pascal_voc) TRAIN_IMDB="voc_2007_trainval" TEST_IMDB="voc_2007_test" STEPSIZE=50000 ITERS=70000 ANCHORS="[8,16,32]" RATIOS="[0.5,1,2]" ;; pascal_voc_0712) TRAIN_IMDB="voc_2007_trainval+voc_2012_trainval" TEST_IMDB="voc_2007_test" STEPSIZE=80000 ITERS=110000 ANCHORS="[8,16,32]" RATIOS="[0.5,1,2]" ;; coco) TRAIN_IMDB="coco_2014_train+coco_2014_valminusminival" TEST_IMDB="coco_2014_minival" STEPSIZE=350000 ITERS=490000 ANCHORS="[4,8,16,32]" RATIOS="[0.5,1,2]" ;; *) echo "No dataset given" exit ;; esac LOG="experiments/logs/${NET}_${TRAIN_IMDB}_${EXTRA_ARGS_SLUG}_${NET}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" exec &> >(tee -a "$LOG") echo Logging output to "$LOG" set +x if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then NET_FINAL=output/${NET}/${TRAIN_IMDB}/${EXTRA_ARGS_SLUG}/${NET}_faster_rcnn_iter_${ITERS}.ckpt else NET_FINAL=output/${NET}/${TRAIN_IMDB}/default/${NET}_faster_rcnn_iter_${ITERS}.ckpt fi set -x if [ ! -f ${NET_FINAL}.index ]; then if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/trainval_net.py \ --weight data/imagenet_weights/${NET}.ckpt \ --imdb ${TRAIN_IMDB} \ --imdbval ${TEST_IMDB} \ --iters ${ITERS} \ --cfg experiments/cfgs/${NET}.yml \ --tag ${EXTRA_ARGS_SLUG} \ --net ${NET} \ --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS} else CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/trainval_net.py \ --weight data/imagenet_weights/${NET}.ckpt \ --imdb ${TRAIN_IMDB} \ --imdbval ${TEST_IMDB} \ --iters ${ITERS} \ --cfg experiments/cfgs/${NET}.yml \ --net ${NET} \ --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS} fi fi ./experiments/scripts/test_faster_rcnn.sh $@ ================================================ FILE: experiments/scripts/train_faster_rcnn.sh~ ================================================ #!/bin/bash set -x set -e export PYTHONUNBUFFERED="True" GPU_ID=$1 DATASET=$2 NET=$3 array=( $@ ) len=${#array[@]} EXTRA_ARGS=${array[@]:3:$len} EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_} case ${DATASET} in graspRGB) TRAIN_IMDB="graspRGB_train" TEST_IMDB="graspRGB_test" STEPSIZE=50000 ITERS=160000 #ANCHORS="[2,4,8,16,32]" ANCHORS="[8,16,32]" RATIOS="[0.5,1,2]" ;; pascal_voc) TRAIN_IMDB="voc_2007_trainval" TEST_IMDB="voc_2007_test" STEPSIZE=50000 ITERS=70000 ANCHORS="[8,16,32]" RATIOS="[0.5,1,2]" ;; pascal_voc_0712) TRAIN_IMDB="voc_2007_trainval+voc_2012_trainval" TEST_IMDB="voc_2007_test" STEPSIZE=80000 ITERS=110000 ANCHORS="[8,16,32]" RATIOS="[0.5,1,2]" ;; coco) TRAIN_IMDB="coco_2014_train+coco_2014_valminusminival" TEST_IMDB="coco_2014_minival" STEPSIZE=350000 ITERS=490000 ANCHORS="[4,8,16,32]" RATIOS="[0.5,1,2]" ;; *) echo "No dataset given" exit ;; esac LOG="experiments/logs/${NET}_${TRAIN_IMDB}_${EXTRA_ARGS_SLUG}_${NET}.txt.`date +'%Y-%m-%d_%H-%M-%S'`" exec &> >(tee -a "$LOG") echo Logging output to "$LOG" set +x if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then NET_FINAL=output/${NET}/${TRAIN_IMDB}/${EXTRA_ARGS_SLUG}/${NET}_faster_rcnn_iter_${ITERS}.ckpt else NET_FINAL=output/${NET}/${TRAIN_IMDB}/default/${NET}_faster_rcnn_iter_${ITERS}.ckpt fi set -x if [ ! -f ${NET_FINAL}.index ]; then if [[ ! -z ${EXTRA_ARGS_SLUG} ]]; then CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/trainval_net.py \ --weight data/imagenet_weights/${NET}.ckpt \ --imdb ${TRAIN_IMDB} \ --imdbval ${TEST_IMDB} \ --iters ${ITERS} \ --cfg experiments/cfgs/${NET}.yml \ --tag ${EXTRA_ARGS_SLUG} \ --net ${NET} \ --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS} else CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/trainval_net.py \ --weight data/imagenet_weights/${NET}.ckpt \ --imdb ${TRAIN_IMDB} \ --imdbval ${TEST_IMDB} \ --iters ${ITERS} \ --cfg experiments/cfgs/${NET}.yml \ --net ${NET} \ --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS} fi fi ./experiments/scripts/test_faster_rcnn.sh $@ ================================================ FILE: lib/Makefile ================================================ all: python setup.py build_ext --inplace rm -rf build clean: rm -rf */*.pyc rm -rf */*.so ================================================ FILE: lib/datasets/VOCdevkit-matlab-wrapper/get_voc_opts.m ================================================ function VOCopts = get_voc_opts(path) tmp = pwd; cd(path); try addpath('VOCcode'); VOCinit; catch rmpath('VOCcode'); cd(tmp); error(sprintf('VOCcode directory not found under %s', path)); end rmpath('VOCcode'); cd(tmp); ================================================ FILE: lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m ================================================ function res = voc_eval(path, comp_id, test_set, output_dir) VOCopts = get_voc_opts(path); VOCopts.testset = test_set; for i = 1:length(VOCopts.classes) cls = VOCopts.classes{i}; res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir); end fprintf('\n~~~~~~~~~~~~~~~~~~~~\n'); fprintf('Results:\n'); aps = [res(:).ap]'; fprintf('%.1f\n', aps * 100); fprintf('%.1f\n', mean(aps) * 100); fprintf('~~~~~~~~~~~~~~~~~~~~\n'); function res = voc_eval_cls(cls, VOCopts, comp_id, output_dir) test_set = VOCopts.testset; year = VOCopts.dataset(4:end); addpath(fullfile(VOCopts.datadir, 'VOCcode')); res_fn = sprintf(VOCopts.detrespath, comp_id, cls); recall = []; prec = []; ap = 0; ap_auc = 0; do_eval = (str2num(year) <= 2007) | ~strcmp(test_set, 'test'); if do_eval % Bug in VOCevaldet requires that tic has been called first tic; [recall, prec, ap] = VOCevaldet(VOCopts, comp_id, cls, true); ap_auc = xVOCap(recall, prec); % force plot limits ylim([0 1]); xlim([0 1]); print(gcf, '-djpeg', '-r0', ... [output_dir '/' cls '_pr.jpg']); end fprintf('!!! %s : %.4f %.4f\n', cls, ap, ap_auc); res.recall = recall; res.prec = prec; res.ap = ap; res.ap_auc = ap_auc; save([output_dir '/' cls '_pr.mat'], ... 'res', 'recall', 'prec', 'ap', 'ap_auc'); rmpath(fullfile(VOCopts.datadir, 'VOCcode')); ================================================ FILE: lib/datasets/VOCdevkit-matlab-wrapper/xVOCap.m ================================================ function ap = xVOCap(rec,prec) % From the PASCAL VOC 2011 devkit mrec=[0 ; rec ; 1]; mpre=[0 ; prec ; 0]; for i=numel(mpre)-1:-1:1 mpre(i)=max(mpre(i),mpre(i+1)); end i=find(mrec(2:end)~=mrec(1:end-1))+1; ap=sum((mrec(i)-mrec(i-1)).*mpre(i)); ================================================ FILE: lib/datasets/__init__.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- ================================================ FILE: lib/datasets/coco.py ================================================ # -------------------------------------------------------- # Fast/er R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function from datasets.imdb import imdb import datasets.ds_utils as ds_utils from model.config import cfg import os.path as osp import sys import os import numpy as np import scipy.sparse import scipy.io as sio import pickle import json import uuid # COCO API from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval from pycocotools import mask as COCOmask class coco(imdb): def __init__(self, image_set, year): imdb.__init__(self, 'coco_' + year + '_' + image_set) # COCO specific config options self.config = {'use_salt': True, 'cleanup': True} # name, paths self._year = year self._image_set = image_set self._data_path = osp.join(cfg.DATA_DIR, 'coco') # load COCO API, classes, class <-> id mappings self._COCO = COCO(self._get_ann_file()) cats = self._COCO.loadCats(self._COCO.getCatIds()) self._classes = tuple(['__background__'] + [c['name'] for c in cats]) self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes))))) self._class_to_coco_cat_id = dict(list(zip([c['name'] for c in cats], self._COCO.getCatIds()))) self._image_index = self._load_image_set_index() # Default to roidb handler self.set_proposal_method('gt') self.competition_mode(False) # Some image sets are "views" (i.e. subsets) into others. # For example, minival2014 is a random 5000 image subset of val2014. # This mapping tells us where the view's images and proposals come from. self._view_map = { 'minival2014': 'val2014', # 5k val2014 subset 'valminusminival2014': 'val2014', # val2014 \setminus minival2014 'test-dev2015': 'test2015', } coco_name = image_set + year # e.g., "val2014" self._data_name = (self._view_map[coco_name] if coco_name in self._view_map else coco_name) # Dataset splits that have ground-truth annotations (test splits # do not have gt annotations) self._gt_splits = ('train', 'val', 'minival') def _get_ann_file(self): prefix = 'instances' if self._image_set.find('test') == -1 \ else 'image_info' return osp.join(self._data_path, 'annotations', prefix + '_' + self._image_set + self._year + '.json') def _load_image_set_index(self): """ Load image ids. """ image_ids = self._COCO.getImgIds() return image_ids def _get_widths(self): anns = self._COCO.loadImgs(self._image_index) widths = [ann['width'] for ann in anns] return widths def image_path_at(self, i): """ Return the absolute path to image i in the image sequence. """ return self.image_path_from_index(self._image_index[i]) def image_path_from_index(self, index): """ Construct an image path from the image's "index" identifier. """ # Example image path for index=119993: # images/train2014/COCO_train2014_000000119993.jpg file_name = ('COCO_' + self._data_name + '_' + str(index).zfill(12) + '.jpg') image_path = osp.join(self._data_path, 'images', self._data_name, file_name) assert osp.exists(image_path), \ 'Path does not exist: {}'.format(image_path) return image_path def gt_roidb(self): """ Return the database of ground-truth regions of interest. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = osp.join(self.cache_path, self.name + '_gt_roidb.pkl') if osp.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = pickle.load(fid) print('{} gt roidb loaded from {}'.format(self.name, cache_file)) return roidb gt_roidb = [self._load_coco_annotation(index) for index in self._image_index] with open(cache_file, 'wb') as fid: pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL) print('wrote gt roidb to {}'.format(cache_file)) return gt_roidb def _load_coco_annotation(self, index): """ Loads COCO bounding-box instance annotations. Crowd instances are handled by marking their overlaps (with all categories) to -1. This overlap value means that crowd "instances" are excluded from training. """ im_ann = self._COCO.loadImgs(index)[0] width = im_ann['width'] height = im_ann['height'] annIds = self._COCO.getAnnIds(imgIds=index, iscrowd=None) objs = self._COCO.loadAnns(annIds) # Sanitize bboxes -- some are invalid valid_objs = [] for obj in objs: x1 = np.max((0, obj['bbox'][0])) y1 = np.max((0, obj['bbox'][1])) x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1)))) y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1)))) if obj['area'] > 0 and x2 >= x1 and y2 >= y1: obj['clean_bbox'] = [x1, y1, x2, y2] valid_objs.append(obj) objs = valid_objs num_objs = len(objs) boxes = np.zeros((num_objs, 4), dtype=np.uint16) gt_classes = np.zeros((num_objs), dtype=np.int32) overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) seg_areas = np.zeros((num_objs), dtype=np.float32) # Lookup table to map from COCO category ids to our internal class # indices coco_cat_id_to_class_ind = dict([(self._class_to_coco_cat_id[cls], self._class_to_ind[cls]) for cls in self._classes[1:]]) for ix, obj in enumerate(objs): cls = coco_cat_id_to_class_ind[obj['category_id']] boxes[ix, :] = obj['clean_bbox'] gt_classes[ix] = cls seg_areas[ix] = obj['area'] if obj['iscrowd']: # Set overlap to -1 for all classes for crowd objects # so they will be excluded during training overlaps[ix, :] = -1.0 else: overlaps[ix, cls] = 1.0 ds_utils.validate_boxes(boxes, width=width, height=height) overlaps = scipy.sparse.csr_matrix(overlaps) return {'width': width, 'height': height, 'boxes': boxes, 'gt_classes': gt_classes, 'gt_overlaps': overlaps, 'flipped': False, 'seg_areas': seg_areas} def _get_widths(self): return [r['width'] for r in self.roidb] def append_flipped_images(self): num_images = self.num_images widths = self._get_widths() for i in range(num_images): boxes = self.roidb[i]['boxes'].copy() oldx1 = boxes[:, 0].copy() oldx2 = boxes[:, 2].copy() boxes[:, 0] = widths[i] - oldx2 - 1 boxes[:, 2] = widths[i] - oldx1 - 1 assert (boxes[:, 2] >= boxes[:, 0]).all() entry = {'width': widths[i], 'height': self.roidb[i]['height'], 'boxes': boxes, 'gt_classes': self.roidb[i]['gt_classes'], 'gt_overlaps': self.roidb[i]['gt_overlaps'], 'flipped': True, 'seg_areas': self.roidb[i]['seg_areas']} self.roidb.append(entry) self._image_index = self._image_index * 2 def _get_box_file(self, index): # first 14 chars / first 22 chars / all chars + .mat # COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat file_name = ('COCO_' + self._data_name + '_' + str(index).zfill(12) + '.mat') return osp.join(file_name[:14], file_name[:22], file_name) def _print_detection_eval_metrics(self, coco_eval): IoU_lo_thresh = 0.5 IoU_hi_thresh = 0.95 def _get_thr_ind(coco_eval, thr): ind = np.where((coco_eval.params.iouThrs > thr - 1e-5) & (coco_eval.params.iouThrs < thr + 1e-5))[0][0] iou_thr = coco_eval.params.iouThrs[ind] assert np.isclose(iou_thr, thr) return ind ind_lo = _get_thr_ind(coco_eval, IoU_lo_thresh) ind_hi = _get_thr_ind(coco_eval, IoU_hi_thresh) # precision has dims (iou, recall, cls, area range, max dets) # area range index 0: all area ranges # max dets index 2: 100 per image precision = \ coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, :, 0, 2] ap_default = np.mean(precision[precision > -1]) print(('~~~~ Mean and per-category AP @ IoU=[{:.2f},{:.2f}] ' '~~~~').format(IoU_lo_thresh, IoU_hi_thresh)) print('{:.1f}'.format(100 * ap_default)) for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue # minus 1 because of __background__ precision = coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, cls_ind - 1, 0, 2] ap = np.mean(precision[precision > -1]) print('{:.1f}'.format(100 * ap)) print('~~~~ Summary metrics ~~~~') coco_eval.summarize() def _do_detection_eval(self, res_file, output_dir): ann_type = 'bbox' coco_dt = self._COCO.loadRes(res_file) coco_eval = COCOeval(self._COCO, coco_dt) coco_eval.params.useSegm = (ann_type == 'segm') coco_eval.evaluate() coco_eval.accumulate() self._print_detection_eval_metrics(coco_eval) eval_file = osp.join(output_dir, 'detection_results.pkl') with open(eval_file, 'wb') as fid: pickle.dump(coco_eval, fid, pickle.HIGHEST_PROTOCOL) print('Wrote COCO eval results to: {}'.format(eval_file)) def _coco_results_one_category(self, boxes, cat_id): results = [] for im_ind, index in enumerate(self.image_index): dets = boxes[im_ind].astype(np.float) if dets == []: continue scores = dets[:, -1] xs = dets[:, 0] ys = dets[:, 1] ws = dets[:, 2] - xs + 1 hs = dets[:, 3] - ys + 1 results.extend( [{'image_id': index, 'category_id': cat_id, 'bbox': [xs[k], ys[k], ws[k], hs[k]], 'score': scores[k]} for k in range(dets.shape[0])]) return results def _write_coco_results_file(self, all_boxes, res_file): # [{"image_id": 42, # "category_id": 18, # "bbox": [258.15,41.29,348.26,243.78], # "score": 0.236}, ...] results = [] for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue print('Collecting {} results ({:d}/{:d})'.format(cls, cls_ind, self.num_classes - 1)) coco_cat_id = self._class_to_coco_cat_id[cls] results.extend(self._coco_results_one_category(all_boxes[cls_ind], coco_cat_id)) print('Writing results json to {}'.format(res_file)) with open(res_file, 'w') as fid: json.dump(results, fid) def evaluate_detections(self, all_boxes, output_dir): res_file = osp.join(output_dir, ('detections_' + self._image_set + self._year + '_results')) if self.config['use_salt']: res_file += '_{}'.format(str(uuid.uuid4())) res_file += '.json' self._write_coco_results_file(all_boxes, res_file) # Only do evaluation on non-test sets if self._image_set.find('test') == -1: self._do_detection_eval(res_file, output_dir) # Optionally cleanup results json file if self.config['cleanup']: os.remove(res_file) def competition_mode(self, on): if on: self.config['use_salt'] = False self.config['cleanup'] = False else: self.config['use_salt'] = True self.config['cleanup'] = True ================================================ FILE: lib/datasets/ds_utils.py ================================================ # -------------------------------------------------------- # Fast/er R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np def unique_boxes(boxes, scale=1.0): """Return indices of unique boxes.""" v = np.array([1, 1e3, 1e6, 1e9]) hashes = np.round(boxes * scale).dot(v) _, index = np.unique(hashes, return_index=True) return np.sort(index) def xywh_to_xyxy(boxes): """Convert [x y w h] box format to [x1 y1 x2 y2] format.""" return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1)) def xyxy_to_xywh(boxes): """Convert [x1 y1 x2 y2] box format to [x y w h] format.""" return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1)) def validate_boxes(boxes, width=0, height=0): """Check that a set of boxes are valid.""" x1 = boxes[:, 0] y1 = boxes[:, 1] x2 = boxes[:, 2] y2 = boxes[:, 3] assert (x1 >= 0).all() assert (y1 >= 0).all() assert (x2 >= x1).all() assert (y2 >= y1).all() assert (x2 < width).all() assert (y2 < height).all() def filter_small_boxes(boxes, min_size): w = boxes[:, 2] - boxes[:, 0] h = boxes[:, 3] - boxes[:, 1] keep = np.where((w >= min_size) & (h > min_size))[0] return keep ================================================ FILE: lib/datasets/factory.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- """Factory method for easily getting imdbs by name.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from datasets.graspRGB import graspRGB __sets = {} from datasets.pascal_voc import pascal_voc from datasets.coco import coco import numpy as np # Set up voc__ for year in ['2007', '2012']: for split in ['train', 'val', 'trainval', 'test']: name = 'voc_{}_{}'.format(year, split) __sets[name] = (lambda split=split, year=year: pascal_voc(split, year)) # Set up coco_2014_ for year in ['2014']: for split in ['train', 'val', 'minival', 'valminusminival', 'trainval']: name = 'coco_{}_{}'.format(year, split) __sets[name] = (lambda split=split, year=year: coco(split, year)) # Set up coco_2015_ for year in ['2015']: for split in ['test', 'test-dev']: name = 'coco_{}_{}'.format(year, split) __sets[name] = (lambda split=split, year=year: coco(split, year)) # Set up graspRGB_ using selective search "fast" mode # added by FC graspRGB_devkit_path = '/media/fujenchu/home3/fasterrcnn_grasp/rgb_multibbs_5_5_5_object_tf' for split in ['train', 'test']: name = '{}_{}'.format('graspRGB', split) __sets[name] = (lambda split=split: graspRGB(split, graspRGB_devkit_path)) def get_imdb(name): """Get an imdb (image database) by name.""" if name not in __sets: raise KeyError('Unknown dataset: {}'.format(name)) return __sets[name]() def list_imdbs(): """List all registered imdbs.""" return list(__sets.keys()) ================================================ FILE: lib/datasets/factory.py~ ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- """Factory method for easily getting imdbs by name.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from datasets.graspRGB import graspRGB __sets = {} from datasets.pascal_voc import pascal_voc from datasets.coco import coco import numpy as np # Set up voc__ for year in ['2007', '2012']: for split in ['train', 'val', 'trainval', 'test']: name = 'voc_{}_{}'.format(year, split) __sets[name] = (lambda split=split, year=year: pascal_voc(split, year)) # Set up coco_2014_ for year in ['2014']: for split in ['train', 'val', 'minival', 'valminusminival', 'trainval']: name = 'coco_{}_{}'.format(year, split) __sets[name] = (lambda split=split, year=year: coco(split, year)) # Set up coco_2015_ for year in ['2015']: for split in ['test', 'test-dev']: name = 'coco_{}_{}'.format(year, split) __sets[name] = (lambda split=split, year=year: coco(split, year)) # Set up graspRGB_ using selective search "fast" mode # added by FC graspRGB_devkit_path = '/media/fujenchu/home3/fasterrcnn_grasp/rgd_multibbs_5_5_5_object_tf' for split in ['train', 'test']: name = '{}_{}'.format('graspRGB', split) __sets[name] = (lambda split=split: graspRGB(split, graspRGB_devkit_path)) def get_imdb(name): """Get an imdb (image database) by name.""" if name not in __sets: raise KeyError('Unknown dataset: {}'.format(name)) return __sets[name]() def list_imdbs(): """List all registered imdbs.""" return list(__sets.keys()) ================================================ FILE: lib/datasets/graspRGB.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import datasets import datasets.graspRGB import os from datasets.imdb import imdb import datasets.ds_utils as ds_utils import xml.etree.ElementTree as ET import numpy as np import scipy.sparse import scipy.io as sio import utils.cython_bbox import cPickle import subprocess import uuid from voc_eval import voc_eval class graspRGB(imdb): def __init__(self, image_set, devkit_path): imdb.__init__(self, image_set) self._image_set = image_set self._devkit_path = devkit_path self._data_path = os.path.join(self._devkit_path, 'data') self._classes = ('__background__', # always index 0 'angle_01', 'angle_02', 'angle_03', 'angle_04', 'angle_05', 'angle_06', 'angle_07', 'angle_08', 'angle_09', 'angle_10', 'angle_11', 'angle_12', 'angle_13', 'angle_14', 'angle_15', 'angle_16', 'angle_17', 'angle_18', 'angle_19') self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes))) self._image_ext = ['.jpg', '.png'] self._image_index = self._load_image_set_index() # Default to roidb handler self._roidb_handler = self.selective_search_roidb self._salt = str(uuid.uuid4()) self._comp_id = 'comp4' # Specific config options self.config = {'cleanup' : True, 'use_salt' : True, 'use_diff' : False, 'matlab_eval' : False, 'rpn_file' : None, 'min_size' : 2} assert os.path.exists(self._devkit_path), \ 'Devkit path does not exist: {}'.format(self._devkit_path) assert os.path.exists(self._data_path), \ 'Path does not exist: {}'.format(self._data_path) def image_path_at(self, i): """ Return the absolute path to image i in the image sequence. """ return self.image_path_from_index(self._image_index[i]) def image_path_from_index(self, index): """ Construct an image path from the image's "index" identifier. """ for ext in self._image_ext: image_path = os.path.join(self._data_path, 'Images', index + ext) if os.path.exists(image_path): break assert os.path.exists(image_path), \ 'Path does not exist: {}'.format(image_path) return image_path def _load_image_set_index(self): """ Load the indexes listed in this dataset's image set file. """ # Example path to image set file: # self._data_path + /ImageSets/val.txt image_set_file = os.path.join(self._data_path, 'ImageSets', self._image_set + '.txt') assert os.path.exists(image_set_file), \ 'Path does not exist: {}'.format(image_set_file) with open(image_set_file) as f: image_index = [x.strip() for x in f.readlines()] # if you output image_index, it's ['I00001', 'I00002', ..] all the file names, not just numbers return image_index def gt_roidb(self): """ Return the database of ground-truth regions of interest. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} gt roidb loaded from {}'.format(self.name, cache_file) return roidb gt_roidb = [self._load_graspRGB_annotation(index) for index in self.image_index] with open(cache_file, 'wb') as fid: cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote gt roidb to {}'.format(cache_file) return gt_roidb def selective_search_roidb(self): """ Return the database of selective search regions of interest. Ground-truth ROIs are also included. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, self.name + '_selective_search_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} ss roidb loaded from {}'.format(self.name, cache_file) return roidb if self._image_set != 'test': gt_roidb = self.gt_roidb() ss_roidb = self._load_selective_search_roidb(gt_roidb) roidb = imdb.merge_roidbs(gt_roidb, ss_roidb) else: roidb = self._load_selective_search_roidb(None) print len(roidb) with open(cache_file, 'wb') as fid: cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote ss roidb to {}'.format(cache_file) return roidb def _load_selective_search_roidb(self, gt_roidb): filename = os.path.abspath(os.path.join(self._devkit_path, self.name + '.mat')) assert os.path.exists(filename), \ 'Selective search data not found at: {}'.format(filename) print filename raw_data = sio.loadmat(filename)['all_boxes'].ravel() box_list = [] for i in xrange(raw_data.shape[0]): box_list.append(raw_data[i][:, (1, 0, 3, 2)] - 1) return self.create_roidb_from_box_list(box_list, gt_roidb) def selective_search_IJCV_roidb(self): """ eturn the database of selective search regions of interest. Ground-truth ROIs are also included. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, '{:s}_selective_search_IJCV_top_{:d}_roidb.pkl'. format(self.name, self.config['top_k'])) if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} ss roidb loaded from {}'.format(self.name, cache_file) return roidb gt_roidb = self.gt_roidb() ss_roidb = self._load_selective_search_IJCV_roidb(gt_roidb) roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb) with open(cache_file, 'wb') as fid: cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote ss roidb to {}'.format(cache_file) return roidb def _load_selective_search_IJCV_roidb(self, gt_roidb): IJCV_path = os.path.abspath(os.path.join(self.cache_path, '..', 'selective_search_IJCV_data', self.name)) assert os.path.exists(IJCV_path), \ 'Selective search IJCV data not found at: {}'.format(IJCV_path) top_k = self.config['top_k'] box_list = [] for i in xrange(self.num_images): filename = os.path.join(IJCV_path, self.image_index[i] + '.mat') raw_data = sio.loadmat(filename) box_list.append((raw_data['boxes'][:top_k, :]-1).astype(np.uint16)) return self.create_roidb_from_box_list(box_list, gt_roidb) def _load_graspRGB_annotation(self, index): """ Load image and bounding boxes info from txt files of graspRGB. """ filename = os.path.join(self._data_path, 'Annotations', index + '.txt') print 'Loading: {}'.format(filename) with open(filename) as f: data = f.readlines() num_objs = len(data) print len(data) if len(data) == 0: print 'yooooooooo' import sys sys.exit() boxes = np.zeros((num_objs, 4), dtype=np.uint16) gt_classes = np.zeros((num_objs), dtype=np.int32) overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) # "Seg" area for pascal is just the box area seg_areas = np.zeros((num_objs), dtype=np.float32) # Load object bounding boxes into a data frame. for ix, aline in enumerate(data): # Make pixel indexes 0-based tokens = aline.strip().split() if len(tokens) != 5: continue cls = int(tokens[0]) # this file uses 0 as the background x1 = float(tokens[1]) y1 = float(tokens[2]) x2 = float(tokens[3]) y2 = float(tokens[4]) # if not doing this, there is negative value when bbs around boundary of image, and when it got read back, it becomes 655xx if (x1<0 and x2<0) or (y1<0 and y2<0): print 'yooooooooo' import sys sys.exit() if x1 < 0: x1 = 0 if x2 < 0: x2 = 0 if y1 < 0: y1 = 0 if y2 < 0: y2 = 0 gt_classes[ix] = cls boxes[ix, :] = [x1, y1, x2, y2] overlaps[ix, cls] = 1.0 seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1) overlaps = scipy.sparse.csr_matrix(overlaps) return {'boxes' : boxes, 'gt_classes': gt_classes, 'gt_overlaps' : overlaps, 'flipped' : False, 'seg_areas' : seg_areas} def _write_graspRGB_results_file(self, all_boxes): use_salt = self.config['use_salt'] comp_id = 'comp4' if use_salt: comp_id += '-{}'.format(os.getpid()) # VOCdevkit/results/comp4-44503_det_test_aeroplane.txt path = os.path.join(self._devkit_path, 'results', self.name, comp_id + '_') for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue print 'Writing {} results file'.format(cls) filename = path + 'det_' + self._image_set + '_' + cls + '.txt' with open(filename, 'wt') as f: for im_ind, index in enumerate(self.image_index): dets = all_boxes[cls_ind][im_ind] if dets == []: continue # the VOCdevkit expects 1-based indices for k in xrange(dets.shape[0]): f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'. format(index, dets[k, -1], dets[k, 0] + 1, dets[k, 1] + 1, dets[k, 2] + 1, dets[k, 3] + 1)) return comp_id def _do_matlab_eval(self, comp_id, output_dir='output'): rm_results = self.config['cleanup'] path = os.path.join(os.path.dirname(__file__), 'VOCdevkit-matlab-wrapper') cmd = 'cd {} && '.format(path) cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB) cmd += '-r "dbstop if error; ' cmd += 'setenv(\'LC_ALL\',\'C\'); voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',{:d}); quit;"' \ .format(self._devkit_path, comp_id, self._image_set, output_dir, int(rm_results)) print('Running:\n{}'.format(cmd)) status = subprocess.call(cmd, shell=True) def evaluate_detections(self, all_boxes, output_dir): comp_id = self._write_graspRGB_results_file(all_boxes) self._do_matlab_eval(comp_id, output_dir) def competition_mode(self, on): if on: self.config['use_salt'] = False self.config['cleanup'] = False else: self.config['use_salt'] = True self.config['cleanup'] = True if __name__ == '__main__': d = datasets.graspRGB('train', '') res = d.roidb from IPython import embed; embed() ================================================ FILE: lib/datasets/graspRGB.py~ ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import datasets import datasets.graspRGB import os from datasets.imdb import imdb import datasets.ds_utils as ds_utils import xml.etree.ElementTree as ET import numpy as np import scipy.sparse import scipy.io as sio import utils.cython_bbox import cPickle import subprocess import uuid from voc_eval import voc_eval class graspRGB(imdb): def __init__(self, image_set, devkit_path): imdb.__init__(self, image_set) self._image_set = image_set self._devkit_path = devkit_path self._data_path = os.path.join(self._devkit_path, 'data') self._classes = ('__background__', # always index 0 'angle_01', 'angle_02', 'angle_03', 'angle_04', 'angle_05', 'angle_06', 'angle_07', 'angle_08', 'angle_09', 'angle_10', 'angle_11', 'angle_12', 'angle_13', 'angle_14', 'angle_15', 'angle_16', 'angle_17', 'angle_18', 'angle_19') self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes))) self._image_ext = ['.jpg', '.png'] self._image_index = self._load_image_set_index() # Default to roidb handler self._roidb_handler = self.selective_search_roidb self._salt = str(uuid.uuid4()) self._comp_id = 'comp4' # Specific config options self.config = {'cleanup' : True, 'use_salt' : True, 'use_diff' : False, 'matlab_eval' : False, 'rpn_file' : None, 'min_size' : 2} assert os.path.exists(self._devkit_path), \ 'Devkit path does not exist: {}'.format(self._devkit_path) assert os.path.exists(self._data_path), \ 'Path does not exist: {}'.format(self._data_path) def image_path_at(self, i): """ Return the absolute path to image i in the image sequence. """ return self.image_path_from_index(self._image_index[i]) def image_path_from_index(self, index): """ Construct an image path from the image's "index" identifier. """ for ext in self._image_ext: image_path = os.path.join(self._data_path, 'Images', index + ext) if os.path.exists(image_path): break assert os.path.exists(image_path), \ 'Path does not exist: {}'.format(image_path) return image_path def _load_image_set_index(self): """ Load the indexes listed in this dataset's image set file. """ # Example path to image set file: # self._data_path + /ImageSets/val.txt image_set_file = os.path.join(self._data_path, 'ImageSets', self._image_set + '.txt') assert os.path.exists(image_set_file), \ 'Path does not exist: {}'.format(image_set_file) with open(image_set_file) as f: image_index = [x.strip() for x in f.readlines()] # if you output image_index, it's ['I00001', 'I00002', ..] all the file names, not just numbers return image_index def gt_roidb(self): """ Return the database of ground-truth regions of interest. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} gt roidb loaded from {}'.format(self.name, cache_file) return roidb gt_roidb = [self._load_graspRGB_annotation(index) for index in self.image_index] with open(cache_file, 'wb') as fid: cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote gt roidb to {}'.format(cache_file) return gt_roidb def selective_search_roidb(self): """ Return the database of selective search regions of interest. Ground-truth ROIs are also included. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, self.name + '_selective_search_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} ss roidb loaded from {}'.format(self.name, cache_file) return roidb if self._image_set != 'test': gt_roidb = self.gt_roidb() ss_roidb = self._load_selective_search_roidb(gt_roidb) roidb = imdb.merge_roidbs(gt_roidb, ss_roidb) else: roidb = self._load_selective_search_roidb(None) print len(roidb) with open(cache_file, 'wb') as fid: cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote ss roidb to {}'.format(cache_file) return roidb def _load_selective_search_roidb(self, gt_roidb): filename = os.path.abspath(os.path.join(self._devkit_path, self.name + '.mat')) assert os.path.exists(filename), \ 'Selective search data not found at: {}'.format(filename) print filename raw_data = sio.loadmat(filename)['all_boxes'].ravel() box_list = [] for i in xrange(raw_data.shape[0]): box_list.append(raw_data[i][:, (1, 0, 3, 2)] - 1) return self.create_roidb_from_box_list(box_list, gt_roidb) def selective_search_IJCV_roidb(self): """ eturn the database of selective search regions of interest. Ground-truth ROIs are also included. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, '{:s}_selective_search_IJCV_top_{:d}_roidb.pkl'. format(self.name, self.config['top_k'])) if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) print '{} ss roidb loaded from {}'.format(self.name, cache_file) return roidb gt_roidb = self.gt_roidb() ss_roidb = self._load_selective_search_IJCV_roidb(gt_roidb) roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb) with open(cache_file, 'wb') as fid: cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote ss roidb to {}'.format(cache_file) return roidb def _load_selective_search_IJCV_roidb(self, gt_roidb): IJCV_path = os.path.abspath(os.path.join(self.cache_path, '..', 'selective_search_IJCV_data', self.name)) assert os.path.exists(IJCV_path), \ 'Selective search IJCV data not found at: {}'.format(IJCV_path) top_k = self.config['top_k'] box_list = [] for i in xrange(self.num_images): filename = os.path.join(IJCV_path, self.image_index[i] + '.mat') raw_data = sio.loadmat(filename) box_list.append((raw_data['boxes'][:top_k, :]-1).astype(np.uint16)) return self.create_roidb_from_box_list(box_list, gt_roidb) def _load_graspRGB_annotation(self, index): """ Load image and bounding boxes info from txt files of graspRGB. """ filename = os.path.join(self._data_path, 'Annotations', index + '.txt') print 'Loading: {}'.format(filename) with open(filename) as f: data = f.readlines() num_objs = len(data) print len(data) if len(data) == 0: print 'yooooooooo' import sys sys.exit() boxes = np.zeros((num_objs, 4), dtype=np.uint16) gt_classes = np.zeros((num_objs), dtype=np.int32) overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) # "Seg" area for pascal is just the box area seg_areas = np.zeros((num_objs), dtype=np.float32) # Load object bounding boxes into a data frame. for ix, aline in enumerate(data): # Make pixel indexes 0-based tokens = aline.strip().split() if len(tokens) != 5: continue cls = float(tokens[0]) # this file uses 0 as the background x1 = float(tokens[1]) y1 = float(tokens[2]) x2 = float(tokens[3]) y2 = float(tokens[4]) # if not doing this, there is negative value when bbs around boundary of image, and when it got read back, it becomes 655xx if (x1<0 and x2<0) or (y1<0 and y2<0): print 'yooooooooo' import sys sys.exit() if x1 < 0: x1 = 0 if x2 < 0: x2 = 0 if y1 < 0: y1 = 0 if y2 < 0: y2 = 0 gt_classes[ix] = cls boxes[ix, :] = [x1, y1, x2, y2] overlaps[ix, cls] = 1.0 seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1) overlaps = scipy.sparse.csr_matrix(overlaps) return {'boxes' : boxes, 'gt_classes': gt_classes, 'gt_overlaps' : overlaps, 'flipped' : False, 'seg_areas' : seg_areas} def _write_graspRGB_results_file(self, all_boxes): use_salt = self.config['use_salt'] comp_id = 'comp4' if use_salt: comp_id += '-{}'.format(os.getpid()) # VOCdevkit/results/comp4-44503_det_test_aeroplane.txt path = os.path.join(self._devkit_path, 'results', self.name, comp_id + '_') for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue print 'Writing {} results file'.format(cls) filename = path + 'det_' + self._image_set + '_' + cls + '.txt' with open(filename, 'wt') as f: for im_ind, index in enumerate(self.image_index): dets = all_boxes[cls_ind][im_ind] if dets == []: continue # the VOCdevkit expects 1-based indices for k in xrange(dets.shape[0]): f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'. format(index, dets[k, -1], dets[k, 0] + 1, dets[k, 1] + 1, dets[k, 2] + 1, dets[k, 3] + 1)) return comp_id def _do_matlab_eval(self, comp_id, output_dir='output'): rm_results = self.config['cleanup'] path = os.path.join(os.path.dirname(__file__), 'VOCdevkit-matlab-wrapper') cmd = 'cd {} && '.format(path) cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB) cmd += '-r "dbstop if error; ' cmd += 'setenv(\'LC_ALL\',\'C\'); voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',{:d}); quit;"' \ .format(self._devkit_path, comp_id, self._image_set, output_dir, int(rm_results)) print('Running:\n{}'.format(cmd)) status = subprocess.call(cmd, shell=True) def evaluate_detections(self, all_boxes, output_dir): comp_id = self._write_graspRGB_results_file(all_boxes) self._do_matlab_eval(comp_id, output_dir) def competition_mode(self, on): if on: self.config['use_salt'] = False self.config['cleanup'] = False else: self.config['use_salt'] = True self.config['cleanup'] = True if __name__ == '__main__': d = datasets.graspRGB('train', '') res = d.roidb from IPython import embed; embed() ================================================ FILE: lib/datasets/imdb.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import os.path as osp import PIL from utils.cython_bbox import bbox_overlaps import numpy as np import scipy.sparse from model.config import cfg class imdb(object): """Image database.""" def __init__(self, name, classes=None): self._name = name self._num_classes = 0 if not classes: self._classes = [] else: self._classes = classes self._image_index = [] self._obj_proposer = 'gt' self._roidb = None self._roidb_handler = self.default_roidb # Use this dict for storing dataset specific config options self.config = {} @property def name(self): return self._name @property def num_classes(self): return len(self._classes) @property def classes(self): return self._classes @property def image_index(self): return self._image_index @property def roidb_handler(self): return self._roidb_handler @roidb_handler.setter def roidb_handler(self, val): self._roidb_handler = val def set_proposal_method(self, method): method = eval('self.' + method + '_roidb') self.roidb_handler = method @property def roidb(self): # A roidb is a list of dictionaries, each with the following keys: # boxes # gt_overlaps # gt_classes # flipped if self._roidb is not None: return self._roidb self._roidb = self.roidb_handler() return self._roidb @property def cache_path(self): cache_path = osp.abspath(osp.join(cfg.DATA_DIR, 'cache')) if not os.path.exists(cache_path): os.makedirs(cache_path) return cache_path @property def num_images(self): return len(self.image_index) def image_path_at(self, i): raise NotImplementedError def default_roidb(self): raise NotImplementedError def evaluate_detections(self, all_boxes, output_dir=None): """ all_boxes is a list of length number-of-classes. Each list element is a list of length number-of-images. Each of those list elements is either an empty list [] or a numpy array of detection. all_boxes[class][image] = [] or np.array of shape #dets x 5 """ raise NotImplementedError def _get_widths(self): return [PIL.Image.open(self.image_path_at(i)).size[0] for i in range(self.num_images)] def append_flipped_images(self): num_images = self.num_images widths = self._get_widths() for i in range(num_images): boxes = self.roidb[i]['boxes'].copy() oldx1 = boxes[:, 0].copy() oldx2 = boxes[:, 2].copy() boxes[:, 0] = widths[i] - oldx2 - 1 boxes[:, 2] = widths[i] - oldx1 - 1 assert (boxes[:, 2] >= boxes[:, 0]).all() entry = {'boxes': boxes, 'gt_overlaps': self.roidb[i]['gt_overlaps'], 'gt_classes': self.roidb[i]['gt_classes'], 'flipped': True} self.roidb.append(entry) self._image_index = self._image_index * 2 def evaluate_recall(self, candidate_boxes=None, thresholds=None, area='all', limit=None): """Evaluate detection proposal recall metrics. Returns: results: dictionary of results with keys 'ar': average recall 'recalls': vector recalls at each IoU overlap threshold 'thresholds': vector of IoU overlap thresholds 'gt_overlaps': vector of all ground-truth overlaps """ # Record max overlap value for each gt box # Return vector of overlap values areas = {'all': 0, 'small': 1, 'medium': 2, 'large': 3, '96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7} area_ranges = [[0 ** 2, 1e5 ** 2], # all [0 ** 2, 32 ** 2], # small [32 ** 2, 96 ** 2], # medium [96 ** 2, 1e5 ** 2], # large [96 ** 2, 128 ** 2], # 96-128 [128 ** 2, 256 ** 2], # 128-256 [256 ** 2, 512 ** 2], # 256-512 [512 ** 2, 1e5 ** 2], # 512-inf ] assert area in areas, 'unknown area range: {}'.format(area) area_range = area_ranges[areas[area]] gt_overlaps = np.zeros(0) num_pos = 0 for i in range(self.num_images): # Checking for max_overlaps == 1 avoids including crowd annotations # (...pretty hacking :/) max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1) gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) & (max_gt_overlaps == 1))[0] gt_boxes = self.roidb[i]['boxes'][gt_inds, :] gt_areas = self.roidb[i]['seg_areas'][gt_inds] valid_gt_inds = np.where((gt_areas >= area_range[0]) & (gt_areas <= area_range[1]))[0] gt_boxes = gt_boxes[valid_gt_inds, :] num_pos += len(valid_gt_inds) if candidate_boxes is None: # If candidate_boxes is not supplied, the default is to use the # non-ground-truth boxes from this roidb non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0] boxes = self.roidb[i]['boxes'][non_gt_inds, :] else: boxes = candidate_boxes[i] if boxes.shape[0] == 0: continue if limit is not None and boxes.shape[0] > limit: boxes = boxes[:limit, :] overlaps = bbox_overlaps(boxes.astype(np.float), gt_boxes.astype(np.float)) _gt_overlaps = np.zeros((gt_boxes.shape[0])) for j in range(gt_boxes.shape[0]): # find which proposal box maximally covers each gt box argmax_overlaps = overlaps.argmax(axis=0) # and get the iou amount of coverage for each gt box max_overlaps = overlaps.max(axis=0) # find which gt box is 'best' covered (i.e. 'best' = most iou) gt_ind = max_overlaps.argmax() gt_ovr = max_overlaps.max() assert (gt_ovr >= 0) # find the proposal box that covers the best covered gt box box_ind = argmax_overlaps[gt_ind] # record the iou coverage of this gt box _gt_overlaps[j] = overlaps[box_ind, gt_ind] assert (_gt_overlaps[j] == gt_ovr) # mark the proposal box and the gt box as used overlaps[box_ind, :] = -1 overlaps[:, gt_ind] = -1 # append recorded iou coverage level gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps)) gt_overlaps = np.sort(gt_overlaps) if thresholds is None: step = 0.05 thresholds = np.arange(0.5, 0.95 + 1e-5, step) recalls = np.zeros_like(thresholds) # compute recall for each iou threshold for i, t in enumerate(thresholds): recalls[i] = (gt_overlaps >= t).sum() / float(num_pos) # ar = 2 * np.trapz(recalls, thresholds) ar = recalls.mean() return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds, 'gt_overlaps': gt_overlaps} def create_roidb_from_box_list(self, box_list, gt_roidb): assert len(box_list) == self.num_images, \ 'Number of boxes must match number of ground-truth images' roidb = [] for i in range(self.num_images): boxes = box_list[i] num_boxes = boxes.shape[0] overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32) if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0: gt_boxes = gt_roidb[i]['boxes'] gt_classes = gt_roidb[i]['gt_classes'] gt_overlaps = bbox_overlaps(boxes.astype(np.float), gt_boxes.astype(np.float)) argmaxes = gt_overlaps.argmax(axis=1) maxes = gt_overlaps.max(axis=1) I = np.where(maxes > 0)[0] overlaps[I, gt_classes[argmaxes[I]]] = maxes[I] overlaps = scipy.sparse.csr_matrix(overlaps) roidb.append({ 'boxes': boxes, 'gt_classes': np.zeros((num_boxes,), dtype=np.int32), 'gt_overlaps': overlaps, 'flipped': False, 'seg_areas': np.zeros((num_boxes,), dtype=np.float32), }) return roidb @staticmethod def merge_roidbs(a, b): assert len(a) == len(b) for i in range(len(a)): a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes'])) a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'], b[i]['gt_classes'])) a[i]['gt_overlaps'] = scipy.sparse.vstack([a[i]['gt_overlaps'], b[i]['gt_overlaps']]) a[i]['seg_areas'] = np.hstack((a[i]['seg_areas'], b[i]['seg_areas'])) return a def competition_mode(self, on): """Turn competition mode on or off.""" pass ================================================ FILE: lib/datasets/pascal_voc.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from datasets.imdb import imdb import datasets.ds_utils as ds_utils import xml.etree.ElementTree as ET import numpy as np import scipy.sparse import scipy.io as sio import utils.cython_bbox import pickle import subprocess import uuid from .voc_eval import voc_eval from model.config import cfg class pascal_voc(imdb): def __init__(self, image_set, year, devkit_path=None): imdb.__init__(self, 'voc_' + year + '_' + image_set) self._year = year self._image_set = image_set self._devkit_path = self._get_default_path() if devkit_path is None \ else devkit_path self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year) self._classes = ('__background__', # always index 0 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor') self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes))))) self._image_ext = '.jpg' self._image_index = self._load_image_set_index() # Default to roidb handler self._roidb_handler = self.gt_roidb self._salt = str(uuid.uuid4()) self._comp_id = 'comp4' # PASCAL specific config options self.config = {'cleanup': True, 'use_salt': True, 'use_diff': False, 'matlab_eval': False, 'rpn_file': None} assert os.path.exists(self._devkit_path), \ 'VOCdevkit path does not exist: {}'.format(self._devkit_path) assert os.path.exists(self._data_path), \ 'Path does not exist: {}'.format(self._data_path) def image_path_at(self, i): """ Return the absolute path to image i in the image sequence. """ return self.image_path_from_index(self._image_index[i]) def image_path_from_index(self, index): """ Construct an image path from the image's "index" identifier. """ image_path = os.path.join(self._data_path, 'JPEGImages', index + self._image_ext) assert os.path.exists(image_path), \ 'Path does not exist: {}'.format(image_path) return image_path def _load_image_set_index(self): """ Load the indexes listed in this dataset's image set file. """ # Example path to image set file: # self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main', self._image_set + '.txt') assert os.path.exists(image_set_file), \ 'Path does not exist: {}'.format(image_set_file) with open(image_set_file) as f: image_index = [x.strip() for x in f.readlines()] return image_index def _get_default_path(self): """ Return the default path where PASCAL VOC is expected to be installed. """ return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year) def gt_roidb(self): """ Return the database of ground-truth regions of interest. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: try: roidb = pickle.load(fid) except: roidb = pickle.load(fid, encoding='bytes') print('{} gt roidb loaded from {}'.format(self.name, cache_file)) return roidb gt_roidb = [self._load_pascal_annotation(index) for index in self.image_index] with open(cache_file, 'wb') as fid: pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL) print('wrote gt roidb to {}'.format(cache_file)) return gt_roidb def rpn_roidb(self): if int(self._year) == 2007 or self._image_set != 'test': gt_roidb = self.gt_roidb() rpn_roidb = self._load_rpn_roidb(gt_roidb) roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb) else: roidb = self._load_rpn_roidb(None) return roidb def _load_rpn_roidb(self, gt_roidb): filename = self.config['rpn_file'] print('loading {}'.format(filename)) assert os.path.exists(filename), \ 'rpn data not found at: {}'.format(filename) with open(filename, 'rb') as f: box_list = pickle.load(f) return self.create_roidb_from_box_list(box_list, gt_roidb) def _load_pascal_annotation(self, index): """ Load image and bounding boxes info from XML file in the PASCAL VOC format. """ filename = os.path.join(self._data_path, 'Annotations', index + '.xml') tree = ET.parse(filename) objs = tree.findall('object') if not self.config['use_diff']: # Exclude the samples labeled as difficult non_diff_objs = [ obj for obj in objs if int(obj.find('difficult').text) == 0] # if len(non_diff_objs) != len(objs): # print 'Removed {} difficult objects'.format( # len(objs) - len(non_diff_objs)) objs = non_diff_objs num_objs = len(objs) boxes = np.zeros((num_objs, 4), dtype=np.uint16) gt_classes = np.zeros((num_objs), dtype=np.int32) overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) # "Seg" area for pascal is just the box area seg_areas = np.zeros((num_objs), dtype=np.float32) # Load object bounding boxes into a data frame. for ix, obj in enumerate(objs): bbox = obj.find('bndbox') # Make pixel indexes 0-based x1 = float(bbox.find('xmin').text) - 1 y1 = float(bbox.find('ymin').text) - 1 x2 = float(bbox.find('xmax').text) - 1 y2 = float(bbox.find('ymax').text) - 1 cls = self._class_to_ind[obj.find('name').text.lower().strip()] boxes[ix, :] = [x1, y1, x2, y2] gt_classes[ix] = cls overlaps[ix, cls] = 1.0 seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1) overlaps = scipy.sparse.csr_matrix(overlaps) return {'boxes': boxes, 'gt_classes': gt_classes, 'gt_overlaps': overlaps, 'flipped': False, 'seg_areas': seg_areas} def _get_comp_id(self): comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt'] else self._comp_id) return comp_id def _get_voc_results_file_template(self): # VOCdevkit/results/VOC2007/Main/_det_test_aeroplane.txt filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt' path = os.path.join( self._devkit_path, 'results', 'VOC' + self._year, 'Main', filename) return path def _write_voc_results_file(self, all_boxes): for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue print('Writing {} VOC results file'.format(cls)) filename = self._get_voc_results_file_template().format(cls) with open(filename, 'wt') as f: for im_ind, index in enumerate(self.image_index): dets = all_boxes[cls_ind][im_ind] if dets == []: continue # the VOCdevkit expects 1-based indices for k in range(dets.shape[0]): f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'. format(index, dets[k, -1], dets[k, 0] + 1, dets[k, 1] + 1, dets[k, 2] + 1, dets[k, 3] + 1)) def _do_python_eval(self, output_dir='output'): annopath = os.path.join( self._devkit_path, 'VOC' + self._year, 'Annotations', '{:s}.xml') imagesetfile = os.path.join( self._devkit_path, 'VOC' + self._year, 'ImageSets', 'Main', self._image_set + '.txt') cachedir = os.path.join(self._devkit_path, 'annotations_cache') aps = [] # The PASCAL VOC metric changed in 2010 use_07_metric = True if int(self._year) < 2010 else False print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No')) if not os.path.isdir(output_dir): os.mkdir(output_dir) for i, cls in enumerate(self._classes): if cls == '__background__': continue filename = self._get_voc_results_file_template().format(cls) rec, prec, ap = voc_eval( filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5, use_07_metric=use_07_metric) aps += [ap] print(('AP for {} = {:.4f}'.format(cls, ap))) with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f: pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f) print(('Mean AP = {:.4f}'.format(np.mean(aps)))) print('~~~~~~~~') print('Results:') for ap in aps: print(('{:.3f}'.format(ap))) print(('{:.3f}'.format(np.mean(aps)))) print('~~~~~~~~') print('') print('--------------------------------------------------------------') print('Results computed with the **unofficial** Python eval code.') print('Results should be very close to the official MATLAB eval code.') print('Recompute with `./tools/reval.py --matlab ...` for your paper.') print('-- Thanks, The Management') print('--------------------------------------------------------------') def _do_matlab_eval(self, output_dir='output'): print('-----------------------------------------------------') print('Computing results with the official MATLAB eval code.') print('-----------------------------------------------------') path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets', 'VOCdevkit-matlab-wrapper') cmd = 'cd {} && '.format(path) cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB) cmd += '-r "dbstop if error; ' cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \ .format(self._devkit_path, self._get_comp_id(), self._image_set, output_dir) print(('Running:\n{}'.format(cmd))) status = subprocess.call(cmd, shell=True) def evaluate_detections(self, all_boxes, output_dir): self._write_voc_results_file(all_boxes) self._do_python_eval(output_dir) if self.config['matlab_eval']: self._do_matlab_eval(output_dir) if self.config['cleanup']: for cls in self._classes: if cls == '__background__': continue filename = self._get_voc_results_file_template().format(cls) os.remove(filename) def competition_mode(self, on): if on: self.config['use_salt'] = False self.config['cleanup'] = False else: self.config['use_salt'] = True self.config['cleanup'] = True if __name__ == '__main__': from datasets.pascal_voc import pascal_voc d = pascal_voc('trainval', '2007') res = d.roidb from IPython import embed; embed() ================================================ FILE: lib/datasets/tools/mcg_munge.py ================================================ import os import sys """Hacky tool to convert file system layout of MCG boxes downloaded from http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/ so that it's consistent with those computed by Jan Hosang (see: http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal- computing/research/object-recognition-and-scene-understanding/how- good-are-detection-proposals-really/) NB: Boxes from the MCG website are in (y1, x1, y2, x2) order. Boxes from Hosang et al. are in (x1, y1, x2, y2) order. """ def munge(src_dir): # stored as: ./MCG-COCO-val2014-boxes/COCO_val2014_000000193401.mat # want: ./MCG/mat/COCO_val2014_0/COCO_val2014_000000141/COCO_val2014_000000141334.mat files = os.listdir(src_dir) for fn in files: base, ext = os.path.splitext(fn) # first 14 chars / first 22 chars / all chars + .mat # COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat first = base[:14] second = base[:22] dst_dir = os.path.join('MCG', 'mat', first, second) if not os.path.exists(dst_dir): os.makedirs(dst_dir) src = os.path.join(src_dir, fn) dst = os.path.join(dst_dir, fn) print 'MV: {} -> {}'.format(src, dst) os.rename(src, dst) if __name__ == '__main__': # src_dir should look something like: # src_dir = 'MCG-COCO-val2014-boxes' src_dir = sys.argv[1] munge(src_dir) ================================================ FILE: lib/datasets/voc_eval.py ================================================ # -------------------------------------------------------- # Fast/er R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Bharath Hariharan # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import xml.etree.ElementTree as ET import os import pickle import numpy as np def parse_rec(filename): """ Parse a PASCAL VOC xml file """ tree = ET.parse(filename) objects = [] for obj in tree.findall('object'): obj_struct = {} obj_struct['name'] = obj.find('name').text obj_struct['pose'] = obj.find('pose').text obj_struct['truncated'] = int(obj.find('truncated').text) obj_struct['difficult'] = int(obj.find('difficult').text) bbox = obj.find('bndbox') obj_struct['bbox'] = [int(bbox.find('xmin').text), int(bbox.find('ymin').text), int(bbox.find('xmax').text), int(bbox.find('ymax').text)] objects.append(obj_struct) return objects def voc_ap(rec, prec, use_07_metric=False): """ ap = voc_ap(rec, prec, [use_07_metric]) Compute VOC AP given precision and recall. If use_07_metric is true, uses the VOC 07 11 point method (default:False). """ if use_07_metric: # 11 point metric ap = 0. for t in np.arange(0., 1.1, 0.1): if np.sum(rec >= t) == 0: p = 0 else: p = np.max(prec[rec >= t]) ap = ap + p / 11. else: # correct AP calculation # first append sentinel values at the end mrec = np.concatenate(([0.], rec, [1.])) mpre = np.concatenate(([0.], prec, [0.])) # compute the precision envelope for i in range(mpre.size - 1, 0, -1): mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) # to calculate area under PR curve, look for points # where X axis (recall) changes value i = np.where(mrec[1:] != mrec[:-1])[0] # and sum (\Delta recall) * prec ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap def voc_eval(detpath, annopath, imagesetfile, classname, cachedir, ovthresh=0.5, use_07_metric=False): """rec, prec, ap = voc_eval(detpath, annopath, imagesetfile, classname, [ovthresh], [use_07_metric]) Top level function that does the PASCAL VOC evaluation. detpath: Path to detections detpath.format(classname) should produce the detection results file. annopath: Path to annotations annopath.format(imagename) should be the xml annotations file. imagesetfile: Text file containing the list of images, one image per line. classname: Category name (duh) cachedir: Directory for caching the annotations [ovthresh]: Overlap threshold (default = 0.5) [use_07_metric]: Whether to use VOC07's 11 point AP computation (default False) """ # assumes detections are in detpath.format(classname) # assumes annotations are in annopath.format(imagename) # assumes imagesetfile is a text file with each line an image name # cachedir caches the annotations in a pickle file # first load gt if not os.path.isdir(cachedir): os.mkdir(cachedir) cachefile = os.path.join(cachedir, 'annots.pkl') # read list of images with open(imagesetfile, 'r') as f: lines = f.readlines() imagenames = [x.strip() for x in lines] if not os.path.isfile(cachefile): # load annots recs = {} for i, imagename in enumerate(imagenames): recs[imagename] = parse_rec(annopath.format(imagename)) if i % 100 == 0: print('Reading annotation for {:d}/{:d}'.format( i + 1, len(imagenames))) # save print('Saving cached annotations to {:s}'.format(cachefile)) with open(cachefile, 'w') as f: pickle.dump(recs, f) else: # load with open(cachefile, 'rb') as f: try: recs = pickle.load(f) except: recs = pickle.load(f, encoding='bytes') # extract gt objects for this class class_recs = {} npos = 0 for imagename in imagenames: R = [obj for obj in recs[imagename] if obj['name'] == classname] bbox = np.array([x['bbox'] for x in R]) difficult = np.array([x['difficult'] for x in R]).astype(np.bool) det = [False] * len(R) npos = npos + sum(~difficult) class_recs[imagename] = {'bbox': bbox, 'difficult': difficult, 'det': det} # read dets detfile = detpath.format(classname) with open(detfile, 'r') as f: lines = f.readlines() splitlines = [x.strip().split(' ') for x in lines] image_ids = [x[0] for x in splitlines] confidence = np.array([float(x[1]) for x in splitlines]) BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) nd = len(image_ids) tp = np.zeros(nd) fp = np.zeros(nd) if BB.shape[0] > 0: # sort by confidence sorted_ind = np.argsort(-confidence) sorted_scores = np.sort(-confidence) BB = BB[sorted_ind, :] image_ids = [image_ids[x] for x in sorted_ind] # go down dets and mark TPs and FPs for d in range(nd): R = class_recs[image_ids[d]] bb = BB[d, :].astype(float) ovmax = -np.inf BBGT = R['bbox'].astype(float) if BBGT.size > 0: # compute overlaps # intersection ixmin = np.maximum(BBGT[:, 0], bb[0]) iymin = np.maximum(BBGT[:, 1], bb[1]) ixmax = np.minimum(BBGT[:, 2], bb[2]) iymax = np.minimum(BBGT[:, 3], bb[3]) iw = np.maximum(ixmax - ixmin + 1., 0.) ih = np.maximum(iymax - iymin + 1., 0.) inters = iw * ih # union uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + (BBGT[:, 2] - BBGT[:, 0] + 1.) * (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) overlaps = inters / uni ovmax = np.max(overlaps) jmax = np.argmax(overlaps) if ovmax > ovthresh: if not R['difficult'][jmax]: if not R['det'][jmax]: tp[d] = 1. R['det'][jmax] = 1 else: fp[d] = 1. else: fp[d] = 1. # compute precision recall fp = np.cumsum(fp) tp = np.cumsum(tp) rec = tp / float(npos) # avoid divide by zero in case the first detection matches a difficult # ground truth prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) ap = voc_ap(rec, prec, use_07_metric) return rec, prec, ap ================================================ FILE: lib/layer_utils/__init__.py ================================================ ================================================ FILE: lib/layer_utils/anchor_target_layer.py ================================================ # -------------------------------------------------------- # Faster R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from model.config import cfg import numpy as np import numpy.random as npr from utils.cython_bbox import bbox_overlaps from model.bbox_transform import bbox_transform def anchor_target_layer(rpn_cls_score, gt_boxes, im_info, _feat_stride, all_anchors, num_anchors): """Same as the anchor target layer in original Fast/er RCNN """ A = num_anchors total_anchors = all_anchors.shape[0] K = total_anchors / num_anchors im_info = im_info[0] # allow boxes to sit over the edge by a small amount _allowed_border = 0 # map of shape (..., H, W) height, width = rpn_cls_score.shape[1:3] # only keep anchors inside the image inds_inside = np.where( (all_anchors[:, 0] >= -_allowed_border) & (all_anchors[:, 1] >= -_allowed_border) & (all_anchors[:, 2] < im_info[1] + _allowed_border) & # width (all_anchors[:, 3] < im_info[0] + _allowed_border) # height )[0] # keep only inside anchors anchors = all_anchors[inds_inside, :] # label: 1 is positive, 0 is negative, -1 is dont care labels = np.empty((len(inds_inside),), dtype=np.float32) labels.fill(-1) # overlaps between the anchors and the gt boxes # overlaps (ex, gt) overlaps = bbox_overlaps( np.ascontiguousarray(anchors, dtype=np.float), np.ascontiguousarray(gt_boxes, dtype=np.float)) argmax_overlaps = overlaps.argmax(axis=1) max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps] gt_argmax_overlaps = overlaps.argmax(axis=0) gt_max_overlaps = overlaps[gt_argmax_overlaps, np.arange(overlaps.shape[1])] gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0] if not cfg.TRAIN.RPN_CLOBBER_POSITIVES: # assign bg labels first so that positive labels can clobber them # first set the negatives labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 # fg label: for each gt, anchor with highest overlap labels[gt_argmax_overlaps] = 1 # fg label: above threshold IOU labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1 if cfg.TRAIN.RPN_CLOBBER_POSITIVES: # assign bg labels last so that negative labels can clobber positives labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 # subsample positive labels if we have too many num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE) fg_inds = np.where(labels == 1)[0] if len(fg_inds) > num_fg: disable_inds = npr.choice( fg_inds, size=(len(fg_inds) - num_fg), replace=False) labels[disable_inds] = -1 # subsample negative labels if we have too many num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1) bg_inds = np.where(labels == 0)[0] if len(bg_inds) > num_bg: disable_inds = npr.choice( bg_inds, size=(len(bg_inds) - num_bg), replace=False) labels[disable_inds] = -1 bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32) bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :]) bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32) # only the positive ones have regression targets bbox_inside_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS) bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32) if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0: # uniform weighting of examples (given non-uniform sampling) num_examples = np.sum(labels >= 0) positive_weights = np.ones((1, 4)) * 1.0 / num_examples negative_weights = np.ones((1, 4)) * 1.0 / num_examples else: assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) & (cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1)) positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT / np.sum(labels == 1)) negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) / np.sum(labels == 0)) bbox_outside_weights[labels == 1, :] = positive_weights bbox_outside_weights[labels == 0, :] = negative_weights # map up to original set of anchors labels = _unmap(labels, total_anchors, inds_inside, fill=-1) bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0) bbox_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0) bbox_outside_weights = _unmap(bbox_outside_weights, total_anchors, inds_inside, fill=0) # labels labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2) labels = labels.reshape((1, 1, A * height, width)) rpn_labels = labels # bbox_targets bbox_targets = bbox_targets \ .reshape((1, height, width, A * 4)) rpn_bbox_targets = bbox_targets # bbox_inside_weights bbox_inside_weights = bbox_inside_weights \ .reshape((1, height, width, A * 4)) rpn_bbox_inside_weights = bbox_inside_weights # bbox_outside_weights bbox_outside_weights = bbox_outside_weights \ .reshape((1, height, width, A * 4)) rpn_bbox_outside_weights = bbox_outside_weights return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights def _unmap(data, count, inds, fill=0): """ Unmap a subset of item (data) back to the original set of items (of size count) """ if len(data.shape) == 1: ret = np.empty((count,), dtype=np.float32) ret.fill(fill) ret[inds] = data else: ret = np.empty((count,) + data.shape[1:], dtype=np.float32) ret.fill(fill) ret[inds, :] = data return ret def _compute_targets(ex_rois, gt_rois): """Compute bounding-box regression targets for an image.""" assert ex_rois.shape[0] == gt_rois.shape[0] assert ex_rois.shape[1] == 4 assert gt_rois.shape[1] == 5 return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False) ================================================ FILE: lib/layer_utils/generate_anchors.py ================================================ # -------------------------------------------------------- # Faster R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Sean Bell # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np # Verify that we compute the same anchors as Shaoqing's matlab implementation: # # >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat # >> anchors # # anchors = # # -83 -39 100 56 # -175 -87 192 104 # -359 -183 376 200 # -55 -55 72 72 # -119 -119 136 136 # -247 -247 264 264 # -35 -79 52 96 # -79 -167 96 184 # -167 -343 184 360 # array([[ -83., -39., 100., 56.], # [-175., -87., 192., 104.], # [-359., -183., 376., 200.], # [ -55., -55., 72., 72.], # [-119., -119., 136., 136.], # [-247., -247., 264., 264.], # [ -35., -79., 52., 96.], # [ -79., -167., 96., 184.], # [-167., -343., 184., 360.]]) def generate_anchors(base_size=16, ratios=[0.5, 1, 2], scales=2 ** np.arange(3, 6)): """ Generate anchor (reference) windows by enumerating aspect ratios X scales wrt a reference (0, 0, 15, 15) window. """ base_anchor = np.array([1, 1, base_size, base_size]) - 1 ratio_anchors = _ratio_enum(base_anchor, ratios) anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales) for i in range(ratio_anchors.shape[0])]) return anchors def _whctrs(anchor): """ Return width, height, x center, and y center for an anchor (window). """ w = anchor[2] - anchor[0] + 1 h = anchor[3] - anchor[1] + 1 x_ctr = anchor[0] + 0.5 * (w - 1) y_ctr = anchor[1] + 0.5 * (h - 1) return w, h, x_ctr, y_ctr def _mkanchors(ws, hs, x_ctr, y_ctr): """ Given a vector of widths (ws) and heights (hs) around a center (x_ctr, y_ctr), output a set of anchors (windows). """ ws = ws[:, np.newaxis] hs = hs[:, np.newaxis] anchors = np.hstack((x_ctr - 0.5 * (ws - 1), y_ctr - 0.5 * (hs - 1), x_ctr + 0.5 * (ws - 1), y_ctr + 0.5 * (hs - 1))) return anchors def _ratio_enum(anchor, ratios): """ Enumerate a set of anchors for each aspect ratio wrt an anchor. """ w, h, x_ctr, y_ctr = _whctrs(anchor) size = w * h size_ratios = size / ratios ws = np.round(np.sqrt(size_ratios)) hs = np.round(ws * ratios) anchors = _mkanchors(ws, hs, x_ctr, y_ctr) return anchors def _scale_enum(anchor, scales): """ Enumerate a set of anchors for each scale wrt an anchor. """ w, h, x_ctr, y_ctr = _whctrs(anchor) ws = w * scales hs = h * scales anchors = _mkanchors(ws, hs, x_ctr, y_ctr) return anchors if __name__ == '__main__': import time t = time.time() a = generate_anchors() print(time.time() - t) print(a) from IPython import embed; embed() ================================================ FILE: lib/layer_utils/proposal_layer.py ================================================ # -------------------------------------------------------- # Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from model.config import cfg from model.bbox_transform import bbox_transform_inv, clip_boxes from model.nms_wrapper import nms def proposal_layer(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors): """A simplified version compared to fast/er RCNN For details please see the technical report """ if type(cfg_key) == bytes: cfg_key = cfg_key.decode('utf-8') pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N nms_thresh = cfg[cfg_key].RPN_NMS_THRESH im_info = im_info[0] # Get the scores and bounding boxes scores = rpn_cls_prob[:, :, :, num_anchors:] rpn_bbox_pred = rpn_bbox_pred.reshape((-1, 4)) scores = scores.reshape((-1, 1)) proposals = bbox_transform_inv(anchors, rpn_bbox_pred) proposals = clip_boxes(proposals, im_info[:2]) # Pick the top region proposals order = scores.ravel().argsort()[::-1] if pre_nms_topN > 0: order = order[:pre_nms_topN] proposals = proposals[order, :] scores = scores[order] # Non-maximal suppression keep = nms(np.hstack((proposals, scores)), nms_thresh) # Pick th top region proposals after NMS if post_nms_topN > 0: keep = keep[:post_nms_topN] proposals = proposals[keep, :] scores = scores[keep] # Only support single image as input batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32) blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False))) return blob, scores ================================================ FILE: lib/layer_utils/proposal_target_layer.py ================================================ # -------------------------------------------------------- # Faster R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick, Sean Bell and Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import numpy.random as npr from model.config import cfg from model.bbox_transform import bbox_transform from utils.cython_bbox import bbox_overlaps def proposal_target_layer(rpn_rois, rpn_scores, gt_boxes, _num_classes): """ Assign object detection proposals to ground-truth targets. Produces proposal classification labels and bounding-box regression targets. """ # Proposal ROIs (0, x1, y1, x2, y2) coming from RPN # (i.e., rpn.proposal_layer.ProposalLayer), or any other source all_rois = rpn_rois all_scores = rpn_scores # Include ground-truth boxes in the set of candidate rois if cfg.TRAIN.USE_GT: zeros = np.zeros((gt_boxes.shape[0], 1), dtype=gt_boxes.dtype) all_rois = np.vstack( (all_rois, np.hstack((zeros, gt_boxes[:, :-1]))) ) # not sure if it a wise appending, but anyway i am not using it all_scores = np.vstack((all_scores, zeros)) num_images = 1 rois_per_image = cfg.TRAIN.BATCH_SIZE / num_images fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image) # Sample rois with classification labels and bounding box regression # targets labels, rois, roi_scores, bbox_targets, bbox_inside_weights = _sample_rois( all_rois, all_scores, gt_boxes, fg_rois_per_image, rois_per_image, _num_classes) rois = rois.reshape(-1, 5) roi_scores = roi_scores.reshape(-1) labels = labels.reshape(-1, 1) bbox_targets = bbox_targets.reshape(-1, _num_classes * 4) bbox_inside_weights = bbox_inside_weights.reshape(-1, _num_classes * 4) bbox_outside_weights = np.array(bbox_inside_weights > 0).astype(np.float32) return rois, roi_scores, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights def _get_bbox_regression_labels(bbox_target_data, num_classes): """Bounding-box regression targets (bbox_target_data) are stored in a compact form N x (class, tx, ty, tw, th) This function expands those targets into the 4-of-4*K representation used by the network (i.e. only one class has non-zero targets). Returns: bbox_target (ndarray): N x 4K blob of regression targets bbox_inside_weights (ndarray): N x 4K blob of loss weights """ clss = bbox_target_data[:, 0] bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32) bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32) inds = np.where(clss > 0)[0] for ind in inds: cls = clss[ind] start = int(4 * cls) end = start + 4 bbox_targets[ind, start:end] = bbox_target_data[ind, 1:] bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS return bbox_targets, bbox_inside_weights def _compute_targets(ex_rois, gt_rois, labels): """Compute bounding-box regression targets for an image.""" assert ex_rois.shape[0] == gt_rois.shape[0] assert ex_rois.shape[1] == 4 assert gt_rois.shape[1] == 4 targets = bbox_transform(ex_rois, gt_rois) if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED: # Optionally normalize targets by a precomputed mean and stdev targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS)) / np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS)) return np.hstack( (labels[:, np.newaxis], targets)).astype(np.float32, copy=False) def _sample_rois(all_rois, all_scores, gt_boxes, fg_rois_per_image, rois_per_image, num_classes): """Generate a random sample of RoIs comprising foreground and background examples. """ # overlaps: (rois x gt_boxes) overlaps = bbox_overlaps( np.ascontiguousarray(all_rois[:, 1:5], dtype=np.float), np.ascontiguousarray(gt_boxes[:, :4], dtype=np.float)) gt_assignment = overlaps.argmax(axis=1) max_overlaps = overlaps.max(axis=1) labels = gt_boxes[gt_assignment, 4] # Select foreground RoIs as those with >= FG_THRESH overlap fg_inds = np.where(max_overlaps >= cfg.TRAIN.FG_THRESH)[0] # Guard against the case when an image has fewer than fg_rois_per_image # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI) bg_inds = np.where((max_overlaps < cfg.TRAIN.BG_THRESH_HI) & (max_overlaps >= cfg.TRAIN.BG_THRESH_LO))[0] # Small modification to the original version where we ensure a fixed number of regions are sampled if fg_inds.size > 0 and bg_inds.size > 0: fg_rois_per_image = min(fg_rois_per_image, fg_inds.size) fg_inds = npr.choice(fg_inds, size=int(fg_rois_per_image), replace=False) bg_rois_per_image = rois_per_image - fg_rois_per_image to_replace = bg_inds.size < bg_rois_per_image bg_inds = npr.choice(bg_inds, size=int(bg_rois_per_image), replace=to_replace) elif fg_inds.size > 0: to_replace = fg_inds.size < rois_per_image fg_inds = npr.choice(fg_inds, size=int(rois_per_image), replace=to_replace) fg_rois_per_image = rois_per_image elif bg_inds.size > 0: to_replace = bg_inds.size < rois_per_image bg_inds = npr.choice(bg_inds, size=int(rois_per_image), replace=to_replace) fg_rois_per_image = 0 else: import pdb pdb.set_trace() # The indices that we're selecting (both fg and bg) keep_inds = np.append(fg_inds, bg_inds) # Select sampled values from various arrays: labels = labels[keep_inds] # Clamp labels for the background RoIs to 0 labels[int(fg_rois_per_image):] = 0 rois = all_rois[keep_inds] roi_scores = all_scores[keep_inds] bbox_target_data = _compute_targets( rois[:, 1:5], gt_boxes[gt_assignment[keep_inds], :4], labels) bbox_targets, bbox_inside_weights = \ _get_bbox_regression_labels(bbox_target_data, num_classes) return labels, rois, roi_scores, bbox_targets, bbox_inside_weights ================================================ FILE: lib/layer_utils/proposal_top_layer.py ================================================ # -------------------------------------------------------- # Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from model.config import cfg from model.bbox_transform import bbox_transform_inv, clip_boxes import numpy.random as npr def proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, im_info, _feat_stride, anchors, num_anchors): """A layer that just selects the top region proposals without using non-maximal suppression, For details please see the technical report """ rpn_top_n = cfg.TEST.RPN_TOP_N im_info = im_info[0] scores = rpn_cls_prob[:, :, :, num_anchors:] rpn_bbox_pred = rpn_bbox_pred.reshape((-1, 4)) scores = scores.reshape((-1, 1)) length = scores.shape[0] if length < rpn_top_n: # Random selection, maybe unnecessary and loses good proposals # But such case rarely happens top_inds = npr.choice(length, size=rpn_top_n, replace=True) else: top_inds = scores.argsort(0)[::-1] top_inds = top_inds[:rpn_top_n] top_inds = top_inds.reshape(rpn_top_n, ) # Do the selection here anchors = anchors[top_inds, :] rpn_bbox_pred = rpn_bbox_pred[top_inds, :] scores = scores[top_inds] # Convert anchors into proposals via bbox transformations proposals = bbox_transform_inv(anchors, rpn_bbox_pred) # Clip predicted boxes to image proposals = clip_boxes(proposals, im_info[:2]) # Output rois blob # Our RPN implementation only supports a single input image, so all # batch inds are 0 batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32) blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False))) return blob, scores ================================================ FILE: lib/layer_utils/snippets.py ================================================ # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import numpy.random as npr from model.config import cfg from layer_utils.generate_anchors import generate_anchors from model.bbox_transform import bbox_transform_inv, clip_boxes from utils.cython_bbox import bbox_overlaps def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratios=(0.5,1,2)): """ A wrapper function to generate anchors given different scales Also return the number of anchors in variable 'length' """ anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales)) A = anchors.shape[0] shift_x = np.arange(0, width) * feat_stride shift_y = np.arange(0, height) * feat_stride shift_x, shift_y = np.meshgrid(shift_x, shift_y) shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose() K = shifts.shape[0] # width changes faster, so here it is H, W, C anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2)) anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False) length = np.int32(anchors.shape[0]) return anchors, length ================================================ FILE: lib/model/__init__.py ================================================ from . import config ================================================ FILE: lib/model/bbox_transform.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np def bbox_transform(ex_rois, gt_rois): ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0 ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0 ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0 gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0 gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights targets_dw = np.log(gt_widths / ex_widths) targets_dh = np.log(gt_heights / ex_heights) targets = np.vstack( (targets_dx, targets_dy, targets_dw, targets_dh)).transpose() return targets def bbox_transform_inv(boxes, deltas): if boxes.shape[0] == 0: return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype) boxes = boxes.astype(deltas.dtype, copy=False) widths = boxes[:, 2] - boxes[:, 0] + 1.0 heights = boxes[:, 3] - boxes[:, 1] + 1.0 ctr_x = boxes[:, 0] + 0.5 * widths ctr_y = boxes[:, 1] + 0.5 * heights dx = deltas[:, 0::4] dy = deltas[:, 1::4] dw = deltas[:, 2::4] dh = deltas[:, 3::4] pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis] pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis] pred_w = np.exp(dw) * widths[:, np.newaxis] pred_h = np.exp(dh) * heights[:, np.newaxis] pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype) # x1 pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w # y1 pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h # x2 pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w # y2 pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h return pred_boxes def clip_boxes(boxes, im_shape): """ Clip boxes to image boundaries. """ # x1 >= 0 boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0) # y1 >= 0 boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0) # x2 < im_shape[1] boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0) # y2 < im_shape[0] boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0) return boxes ================================================ FILE: lib/model/config.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import os.path as osp import numpy as np # `pip install easydict` if you don't have it from easydict import EasyDict as edict __C = edict() # Consumers can get config by: # from fast_rcnn_config import cfg cfg = __C # # Training options # __C.TRAIN = edict() # Initial learning rate __C.TRAIN.LEARNING_RATE = 0.0001 # Momentum __C.TRAIN.MOMENTUM = 0.9 # Weight decay, for regularization __C.TRAIN.WEIGHT_DECAY = 0.0005 # Factor for reducing the learning rate __C.TRAIN.GAMMA = 0.1 # Step size for reducing the learning rate, currently only support one step __C.TRAIN.STEPSIZE = 30000 # Iteration intervals for showing the loss during training, on command line interface __C.TRAIN.DISPLAY = 10 # Whether to double the learning rate for bias __C.TRAIN.DOUBLE_BIAS = True # Whether to initialize the weights with truncated normal distribution __C.TRAIN.TRUNCATED = False # Whether to have weight decay on bias as well __C.TRAIN.BIAS_DECAY = False # Whether to add ground truth boxes to the pool when sampling regions __C.TRAIN.USE_GT = False # Whether to use aspect-ratio grouping of training images, introduced merely for saving # GPU memory __C.TRAIN.ASPECT_GROUPING = False # The number of snapshots kept, older ones are deleted to save space __C.TRAIN.SNAPSHOT_KEPT = 3 # The time interval for saving tensorflow summaries __C.TRAIN.SUMMARY_INTERVAL = 180 # Scale to use during training (can list multiple scales) # The scale is the pixel size of an image's shortest side __C.TRAIN.SCALES = (600,) # Max pixel size of the longest side of a scaled input image __C.TRAIN.MAX_SIZE = 1000 # Images to use per minibatch __C.TRAIN.IMS_PER_BATCH = 1 # Minibatch size (number of regions of interest [ROIs]) __C.TRAIN.BATCH_SIZE = 128 # Fraction of minibatch that is labeled foreground (i.e. class > 0) __C.TRAIN.FG_FRACTION = 0.25 # Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH) __C.TRAIN.FG_THRESH = 0.5 # Overlap threshold for a ROI to be considered background (class = 0 if # overlap in [LO, HI)) __C.TRAIN.BG_THRESH_HI = 0.5 __C.TRAIN.BG_THRESH_LO = 0.1 # Use horizontally-flipped images during training? #__C.TRAIN.USE_FLIPPED = True __C.TRAIN.USE_FLIPPED = False # Train bounding-box regressors __C.TRAIN.BBOX_REG = True # Overlap required between a ROI and ground-truth box in order for that ROI to # be used as a bounding-box regression training example __C.TRAIN.BBOX_THRESH = 0.5 # Iterations between snapshots __C.TRAIN.SNAPSHOT_ITERS = 3000 # solver.prototxt specifies the snapshot path prefix, this adds an optional # infix to yield the path: [_]_iters_XYZ.caffemodel __C.TRAIN.SNAPSHOT_PREFIX = 'res101_faster_rcnn' # __C.TRAIN.SNAPSHOT_INFIX = '' # Use a prefetch thread in roi_data_layer.layer # So far I haven't found this useful; likely more engineering work is required # __C.TRAIN.USE_PREFETCH = False # Normalize the targets (subtract empirical mean, divide by empirical stddev) __C.TRAIN.BBOX_NORMALIZE_TARGETS = True # Deprecated (inside weights) __C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0) # Normalize the targets using "precomputed" (or made up) means and stdevs # (BBOX_NORMALIZE_TARGETS must also be True) __C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = True __C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0) __C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2) # Train using these proposals __C.TRAIN.PROPOSAL_METHOD = 'gt' # Make minibatches from images that have similar aspect ratios (i.e. both # tall and thin or both short and wide) in order to avoid wasting computation # on zero-padding. # Use RPN to detect objects __C.TRAIN.HAS_RPN = True # IOU >= thresh: positive example __C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7 # IOU < thresh: negative example __C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3 # If an anchor statisfied by positive and negative conditions set to negative __C.TRAIN.RPN_CLOBBER_POSITIVES = False # Max number of foreground examples __C.TRAIN.RPN_FG_FRACTION = 0.5 # Total number of examples __C.TRAIN.RPN_BATCHSIZE = 256 # NMS threshold used on RPN proposals __C.TRAIN.RPN_NMS_THRESH = 0.7 # Number of top scoring boxes to keep before apply NMS to RPN proposals __C.TRAIN.RPN_PRE_NMS_TOP_N = 12000 # Number of top scoring boxes to keep after applying NMS to RPN proposals __C.TRAIN.RPN_POST_NMS_TOP_N = 2000 # Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale) # __C.TRAIN.RPN_MIN_SIZE = 16 # Deprecated (outside weights) __C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0) # Give the positive RPN examples weight of p * 1 / {num positives} # and give negatives a weight of (1 - p) # Set to -1.0 to use uniform example weighting __C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0 # Whether to use all ground truth bounding boxes for training, # For COCO, setting USE_ALL_GT to False will exclude boxes that are flagged as ''iscrowd'' __C.TRAIN.USE_ALL_GT = True # # Testing options # __C.TEST = edict() # Scale to use during testing (can NOT list multiple scales) # The scale is the pixel size of an image's shortest side __C.TEST.SCALES = (600,) # Max pixel size of the longest side of a scaled input image __C.TEST.MAX_SIZE = 1000 # Overlap threshold used for non-maximum suppression (suppress boxes with # IoU >= this threshold) __C.TEST.NMS = 0.3 # Experimental: treat the (K+1) units in the cls_score layer as linear # predictors (trained, eg, with one-vs-rest SVMs). __C.TEST.SVM = False # Test using bounding-box regressors __C.TEST.BBOX_REG = True # Propose boxes __C.TEST.HAS_RPN = False # Test using these proposals __C.TEST.PROPOSAL_METHOD = 'gt' ## NMS threshold used on RPN proposals __C.TEST.RPN_NMS_THRESH = 0.7 ## Number of top scoring boxes to keep before apply NMS to RPN proposals __C.TEST.RPN_PRE_NMS_TOP_N = 6000 ## Number of top scoring boxes to keep after applying NMS to RPN proposals __C.TEST.RPN_POST_NMS_TOP_N = 300 # Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale) # __C.TEST.RPN_MIN_SIZE = 16 # Testing mode, default to be 'nms', 'top' is slower but better # See report for details __C.TEST.MODE = 'nms' # Only useful when TEST.MODE is 'top', specifies the number of top proposals to select __C.TEST.RPN_TOP_N = 5000 # # ResNet options # __C.RESNET = edict() # Option to set if max-pooling is appended after crop_and_resize. # if true, the region will be resized to a squre of 2xPOOLING_SIZE, # then 2x2 max-pooling is applied; otherwise the region will be directly # resized to a square of POOLING_SIZE __C.RESNET.MAX_POOL = False # Number of fixed blocks during finetuning, by default the first of all 4 blocks is fixed # Range: 0 (none) to 3 (all) __C.RESNET.FIXED_BLOCKS = 1 # Whether to tune the batch nomalization parameters during training __C.RESNET.BN_TRAIN = False # # MISC # # The mapping from image coordinates to feature map coordinates might cause # some boxes that are distinct in image space to become identical in feature # coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor # for identifying duplicate boxes. # 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16 __C.DEDUP_BOXES = 1. / 16. # Pixel mean values (BGR order) as a (1, 1, 3) array # We use the same pixel mean for all networks even though it's not exactly what # they were trained with __C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]]) # For reproducibility __C.RNG_SEED = 3 # A small number that's used many times __C.EPS = 1e-14 # Root directory of project __C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..')) # Data directory __C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) # Name (or path to) the matlab executable __C.MATLAB = 'matlab' # Place outputs under an experiments directory __C.EXP_DIR = 'default' # Use GPU implementation of non-maximum suppression __C.USE_GPU_NMS = True # Default GPU device id __C.GPU_ID = 0 # Default pooling mode, only 'crop' is available __C.POOLING_MODE = 'crop' # Size of the pooled region after RoI pooling __C.POOLING_SIZE = 7 # Anchor scales for RPN __C.ANCHOR_SCALES = [8,16,32] # Anchor ratios for RPN __C.ANCHOR_RATIOS = [0.5,1,2] def get_output_dir(imdb, weights_filename): """Return the directory where experimental artifacts are placed. If the directory does not exist, it is created. A canonical path is built using the name from an imdb and a network (if not None). """ outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name)) if weights_filename is None: weights_filename = 'default' outdir = osp.join(outdir, weights_filename) if not os.path.exists(outdir): os.makedirs(outdir) return outdir def get_output_tb_dir(imdb, weights_filename): """Return the directory where tensorflow summaries are placed. If the directory does not exist, it is created. A canonical path is built using the name from an imdb and a network (if not None). """ outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'tensorboard', __C.EXP_DIR, imdb.name)) if weights_filename is None: weights_filename = 'default' outdir = osp.join(outdir, weights_filename) if not os.path.exists(outdir): os.makedirs(outdir) return outdir def _merge_a_into_b(a, b): """Merge config dictionary a into config dictionary b, clobbering the options in b whenever they are also specified in a. """ if type(a) is not edict: return for k, v in a.items(): # a must specify keys that are in b if k not in b: raise KeyError('{} is not a valid config key'.format(k)) # the types must match, too old_type = type(b[k]) if old_type is not type(v): if isinstance(b[k], np.ndarray): v = np.array(v, dtype=b[k].dtype) else: raise ValueError(('Type mismatch ({} vs. {}) ' 'for config key: {}').format(type(b[k]), type(v), k)) # recursively merge dicts if type(v) is edict: try: _merge_a_into_b(a[k], b[k]) except: print(('Error under config key: {}'.format(k))) raise else: b[k] = v def cfg_from_file(filename): """Load a config file and merge it into the default options.""" import yaml with open(filename, 'r') as f: yaml_cfg = edict(yaml.load(f)) _merge_a_into_b(yaml_cfg, __C) def cfg_from_list(cfg_list): """Set config keys via list (e.g., from command line).""" from ast import literal_eval assert len(cfg_list) % 2 == 0 for k, v in zip(cfg_list[0::2], cfg_list[1::2]): key_list = k.split('.') d = __C for subkey in key_list[:-1]: assert subkey in d d = d[subkey] subkey = key_list[-1] assert subkey in d try: value = literal_eval(v) except: # handle the case when v is a string literal value = v assert type(value) == type(d[subkey]), \ 'type {} does not match original type {}'.format( type(value), type(d[subkey])) d[subkey] = value ================================================ FILE: lib/model/config.py~ ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import os.path as osp import numpy as np # `pip install easydict` if you don't have it from easydict import EasyDict as edict __C = edict() # Consumers can get config by: # from fast_rcnn_config import cfg cfg = __C # # Training options # __C.TRAIN = edict() # Initial learning rate __C.TRAIN.LEARNING_RATE = 0.001 # Momentum __C.TRAIN.MOMENTUM = 0.9 # Weight decay, for regularization __C.TRAIN.WEIGHT_DECAY = 0.0005 # Factor for reducing the learning rate __C.TRAIN.GAMMA = 0.1 # Step size for reducing the learning rate, currently only support one step __C.TRAIN.STEPSIZE = 30000 # Iteration intervals for showing the loss during training, on command line interface __C.TRAIN.DISPLAY = 10 # Whether to double the learning rate for bias __C.TRAIN.DOUBLE_BIAS = True # Whether to initialize the weights with truncated normal distribution __C.TRAIN.TRUNCATED = False # Whether to have weight decay on bias as well __C.TRAIN.BIAS_DECAY = False # Whether to add ground truth boxes to the pool when sampling regions __C.TRAIN.USE_GT = False # Whether to use aspect-ratio grouping of training images, introduced merely for saving # GPU memory __C.TRAIN.ASPECT_GROUPING = False # The number of snapshots kept, older ones are deleted to save space __C.TRAIN.SNAPSHOT_KEPT = 3 # The time interval for saving tensorflow summaries __C.TRAIN.SUMMARY_INTERVAL = 180 # Scale to use during training (can list multiple scales) # The scale is the pixel size of an image's shortest side __C.TRAIN.SCALES = (600,) # Max pixel size of the longest side of a scaled input image __C.TRAIN.MAX_SIZE = 1000 # Images to use per minibatch __C.TRAIN.IMS_PER_BATCH = 1 # Minibatch size (number of regions of interest [ROIs]) __C.TRAIN.BATCH_SIZE = 128 # Fraction of minibatch that is labeled foreground (i.e. class > 0) __C.TRAIN.FG_FRACTION = 0.25 # Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH) __C.TRAIN.FG_THRESH = 0.5 # Overlap threshold for a ROI to be considered background (class = 0 if # overlap in [LO, HI)) __C.TRAIN.BG_THRESH_HI = 0.5 __C.TRAIN.BG_THRESH_LO = 0.1 # Use horizontally-flipped images during training? #__C.TRAIN.USE_FLIPPED = True __C.TRAIN.USE_FLIPPED = False # Train bounding-box regressors __C.TRAIN.BBOX_REG = True # Overlap required between a ROI and ground-truth box in order for that ROI to # be used as a bounding-box regression training example __C.TRAIN.BBOX_THRESH = 0.5 # Iterations between snapshots __C.TRAIN.SNAPSHOT_ITERS = 3000 # solver.prototxt specifies the snapshot path prefix, this adds an optional # infix to yield the path: [_]_iters_XYZ.caffemodel __C.TRAIN.SNAPSHOT_PREFIX = 'res101_faster_rcnn' # __C.TRAIN.SNAPSHOT_INFIX = '' # Use a prefetch thread in roi_data_layer.layer # So far I haven't found this useful; likely more engineering work is required # __C.TRAIN.USE_PREFETCH = False # Normalize the targets (subtract empirical mean, divide by empirical stddev) __C.TRAIN.BBOX_NORMALIZE_TARGETS = True # Deprecated (inside weights) __C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0) # Normalize the targets using "precomputed" (or made up) means and stdevs # (BBOX_NORMALIZE_TARGETS must also be True) __C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = True __C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0) __C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2) # Train using these proposals __C.TRAIN.PROPOSAL_METHOD = 'gt' # Make minibatches from images that have similar aspect ratios (i.e. both # tall and thin or both short and wide) in order to avoid wasting computation # on zero-padding. # Use RPN to detect objects __C.TRAIN.HAS_RPN = True # IOU >= thresh: positive example __C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7 # IOU < thresh: negative example __C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3 # If an anchor statisfied by positive and negative conditions set to negative __C.TRAIN.RPN_CLOBBER_POSITIVES = False # Max number of foreground examples __C.TRAIN.RPN_FG_FRACTION = 0.5 # Total number of examples __C.TRAIN.RPN_BATCHSIZE = 256 # NMS threshold used on RPN proposals __C.TRAIN.RPN_NMS_THRESH = 0.7 # Number of top scoring boxes to keep before apply NMS to RPN proposals __C.TRAIN.RPN_PRE_NMS_TOP_N = 12000 # Number of top scoring boxes to keep after applying NMS to RPN proposals __C.TRAIN.RPN_POST_NMS_TOP_N = 2000 # Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale) # __C.TRAIN.RPN_MIN_SIZE = 16 # Deprecated (outside weights) __C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0) # Give the positive RPN examples weight of p * 1 / {num positives} # and give negatives a weight of (1 - p) # Set to -1.0 to use uniform example weighting __C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0 # Whether to use all ground truth bounding boxes for training, # For COCO, setting USE_ALL_GT to False will exclude boxes that are flagged as ''iscrowd'' __C.TRAIN.USE_ALL_GT = True # # Testing options # __C.TEST = edict() # Scale to use during testing (can NOT list multiple scales) # The scale is the pixel size of an image's shortest side __C.TEST.SCALES = (600,) # Max pixel size of the longest side of a scaled input image __C.TEST.MAX_SIZE = 1000 # Overlap threshold used for non-maximum suppression (suppress boxes with # IoU >= this threshold) __C.TEST.NMS = 0.3 # Experimental: treat the (K+1) units in the cls_score layer as linear # predictors (trained, eg, with one-vs-rest SVMs). __C.TEST.SVM = False # Test using bounding-box regressors __C.TEST.BBOX_REG = True # Propose boxes __C.TEST.HAS_RPN = False # Test using these proposals __C.TEST.PROPOSAL_METHOD = 'gt' ## NMS threshold used on RPN proposals __C.TEST.RPN_NMS_THRESH = 0.7 ## Number of top scoring boxes to keep before apply NMS to RPN proposals __C.TEST.RPN_PRE_NMS_TOP_N = 6000 ## Number of top scoring boxes to keep after applying NMS to RPN proposals __C.TEST.RPN_POST_NMS_TOP_N = 300 # Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale) # __C.TEST.RPN_MIN_SIZE = 16 # Testing mode, default to be 'nms', 'top' is slower but better # See report for details __C.TEST.MODE = 'nms' # Only useful when TEST.MODE is 'top', specifies the number of top proposals to select __C.TEST.RPN_TOP_N = 5000 # # ResNet options # __C.RESNET = edict() # Option to set if max-pooling is appended after crop_and_resize. # if true, the region will be resized to a squre of 2xPOOLING_SIZE, # then 2x2 max-pooling is applied; otherwise the region will be directly # resized to a square of POOLING_SIZE __C.RESNET.MAX_POOL = False # Number of fixed blocks during finetuning, by default the first of all 4 blocks is fixed # Range: 0 (none) to 3 (all) __C.RESNET.FIXED_BLOCKS = 1 # Whether to tune the batch nomalization parameters during training __C.RESNET.BN_TRAIN = False # # MISC # # The mapping from image coordinates to feature map coordinates might cause # some boxes that are distinct in image space to become identical in feature # coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor # for identifying duplicate boxes. # 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16 __C.DEDUP_BOXES = 1. / 16. # Pixel mean values (BGR order) as a (1, 1, 3) array # We use the same pixel mean for all networks even though it's not exactly what # they were trained with __C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]]) # For reproducibility __C.RNG_SEED = 3 # A small number that's used many times __C.EPS = 1e-14 # Root directory of project __C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..')) # Data directory __C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) # Name (or path to) the matlab executable __C.MATLAB = 'matlab' # Place outputs under an experiments directory __C.EXP_DIR = 'default' # Use GPU implementation of non-maximum suppression __C.USE_GPU_NMS = True # Default GPU device id __C.GPU_ID = 0 # Default pooling mode, only 'crop' is available __C.POOLING_MODE = 'crop' # Size of the pooled region after RoI pooling __C.POOLING_SIZE = 7 # Anchor scales for RPN __C.ANCHOR_SCALES = [8,16,32] # Anchor ratios for RPN __C.ANCHOR_RATIOS = [0.5,1,2] def get_output_dir(imdb, weights_filename): """Return the directory where experimental artifacts are placed. If the directory does not exist, it is created. A canonical path is built using the name from an imdb and a network (if not None). """ outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name)) if weights_filename is None: weights_filename = 'default' outdir = osp.join(outdir, weights_filename) if not os.path.exists(outdir): os.makedirs(outdir) return outdir def get_output_tb_dir(imdb, weights_filename): """Return the directory where tensorflow summaries are placed. If the directory does not exist, it is created. A canonical path is built using the name from an imdb and a network (if not None). """ outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'tensorboard', __C.EXP_DIR, imdb.name)) if weights_filename is None: weights_filename = 'default' outdir = osp.join(outdir, weights_filename) if not os.path.exists(outdir): os.makedirs(outdir) return outdir def _merge_a_into_b(a, b): """Merge config dictionary a into config dictionary b, clobbering the options in b whenever they are also specified in a. """ if type(a) is not edict: return for k, v in a.items(): # a must specify keys that are in b if k not in b: raise KeyError('{} is not a valid config key'.format(k)) # the types must match, too old_type = type(b[k]) if old_type is not type(v): if isinstance(b[k], np.ndarray): v = np.array(v, dtype=b[k].dtype) else: raise ValueError(('Type mismatch ({} vs. {}) ' 'for config key: {}').format(type(b[k]), type(v), k)) # recursively merge dicts if type(v) is edict: try: _merge_a_into_b(a[k], b[k]) except: print(('Error under config key: {}'.format(k))) raise else: b[k] = v def cfg_from_file(filename): """Load a config file and merge it into the default options.""" import yaml with open(filename, 'r') as f: yaml_cfg = edict(yaml.load(f)) _merge_a_into_b(yaml_cfg, __C) def cfg_from_list(cfg_list): """Set config keys via list (e.g., from command line).""" from ast import literal_eval assert len(cfg_list) % 2 == 0 for k, v in zip(cfg_list[0::2], cfg_list[1::2]): key_list = k.split('.') d = __C for subkey in key_list[:-1]: assert subkey in d d = d[subkey] subkey = key_list[-1] assert subkey in d try: value = literal_eval(v) except: # handle the case when v is a string literal value = v assert type(value) == type(d[subkey]), \ 'type {} does not match original type {}'.format( type(value), type(d[subkey])) d[subkey] = value ================================================ FILE: lib/model/nms_wrapper.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function from model.config import cfg from nms.gpu_nms import gpu_nms from nms.cpu_nms import cpu_nms def nms(dets, thresh, force_cpu=False): """Dispatch to either CPU or GPU NMS implementations.""" if dets.shape[0] == 0: return [] if cfg.USE_GPU_NMS and not force_cpu: return gpu_nms(dets, thresh, device_id=cfg.GPU_ID) else: return cpu_nms(dets, thresh) ================================================ FILE: lib/model/test.py ================================================ # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import cv2 import numpy as np try: import cPickle as pickle except ImportError: import pickle import os import math from utils.timer import Timer from utils.cython_nms import nms, nms_new from utils.boxes_grid import get_boxes_grid from utils.blob import im_list_to_blob from model.config import cfg, get_output_dir from model.bbox_transform import clip_boxes, bbox_transform_inv def _get_image_blob(im): """Converts an image into a network input. Arguments: im (ndarray): a color image in BGR order Returns: blob (ndarray): a data blob holding an image pyramid im_scale_factors (list): list of image scales (relative to im) used in the image pyramid """ im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) processed_ims = [] im_scale_factors = [] for target_size in cfg.TEST.SCALES: im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE: im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max) im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) im_scale_factors.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, np.array(im_scale_factors) def _get_blobs(im): """Convert an image and RoIs within that image into network inputs.""" blobs = {} blobs['data'], im_scale_factors = _get_image_blob(im) return blobs, im_scale_factors def _clip_boxes(boxes, im_shape): """Clip boxes to image boundaries.""" # x1 >= 0 boxes[:, 0::4] = np.maximum(boxes[:, 0::4], 0) # y1 >= 0 boxes[:, 1::4] = np.maximum(boxes[:, 1::4], 0) # x2 < im_shape[1] boxes[:, 2::4] = np.minimum(boxes[:, 2::4], im_shape[1] - 1) # y2 < im_shape[0] boxes[:, 3::4] = np.minimum(boxes[:, 3::4], im_shape[0] - 1) return boxes def _rescale_boxes(boxes, inds, scales): """Rescale boxes according to image rescaling.""" for i in range(boxes.shape[0]): boxes[i,:] = boxes[i,:] / scales[int(inds[i])] return boxes def im_detect(sess, net, im): blobs, im_scales = _get_blobs(im) assert len(im_scales) == 1, "Only single-image batch implemented" im_blob = blobs['data'] # seems to have height, width, and image scales # still not sure about the scale, maybe full image it is 1. blobs['im_info'] = np.array([[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32) _, scores, bbox_pred, rois = net.test_image(sess, blobs['data'], blobs['im_info']) boxes = rois[:, 1:5] / im_scales[0] # print(scores.shape, bbox_pred.shape, rois.shape, boxes.shape) scores = np.reshape(scores, [scores.shape[0], -1]) bbox_pred = np.reshape(bbox_pred, [bbox_pred.shape[0], -1]) if cfg.TEST.BBOX_REG: # Apply bounding-box regression deltas box_deltas = bbox_pred pred_boxes = bbox_transform_inv(boxes, box_deltas) pred_boxes = _clip_boxes(pred_boxes, im.shape) else: # Simply repeat the boxes, once for each class pred_boxes = np.tile(boxes, (1, scores.shape[1])) return scores, pred_boxes def apply_nms(all_boxes, thresh): """Apply non-maximum suppression to all predicted boxes output by the test_net method. """ num_classes = len(all_boxes) num_images = len(all_boxes[0]) nms_boxes = [[[] for _ in range(num_images)] for _ in range(num_classes)] for cls_ind in range(num_classes): for im_ind in range(num_images): dets = all_boxes[cls_ind][im_ind] if dets == []: continue x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] inds = np.where((x2 > x1) & (y2 > y1) & (scores > cfg.TEST.DET_THRESHOLD))[0] dets = dets[inds,:] if dets == []: continue keep = nms(dets, thresh) if len(keep) == 0: continue nms_boxes[cls_ind][im_ind] = dets[keep, :].copy() return nms_boxes def test_net(sess, net, imdb, weights_filename, max_per_image=100, thresh=0.05): np.random.seed(cfg.RNG_SEED) """Test a Fast R-CNN network on an image database.""" num_images = len(imdb.image_index) # all detections are collected into: # all_boxes[cls][image] = N x 5 array of detections in # (x1, y1, x2, y2, score) all_boxes = [[[] for _ in range(num_images)] for _ in range(imdb.num_classes)] output_dir = get_output_dir(imdb, weights_filename) # timers _t = {'im_detect' : Timer(), 'misc' : Timer()} for i in range(num_images): im = cv2.imread(imdb.image_path_at(i)) _t['im_detect'].tic() scores, boxes = im_detect(sess, net, im) _t['im_detect'].toc() _t['misc'].tic() # skip j = 0, because it's the background class for j in range(1, imdb.num_classes): inds = np.where(scores[:, j] > thresh)[0] cls_scores = scores[inds, j] cls_boxes = boxes[inds, j*4:(j+1)*4] cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \ .astype(np.float32, copy=False) keep = nms(cls_dets, cfg.TEST.NMS) cls_dets = cls_dets[keep, :] all_boxes[j][i] = cls_dets # Limit to max_per_image detections *over all classes* if max_per_image > 0: image_scores = np.hstack([all_boxes[j][i][:, -1] for j in range(1, imdb.num_classes)]) if len(image_scores) > max_per_image: image_thresh = np.sort(image_scores)[-max_per_image] for j in range(1, imdb.num_classes): keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0] all_boxes[j][i] = all_boxes[j][i][keep, :] _t['misc'].toc() print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \ .format(i + 1, num_images, _t['im_detect'].average_time, _t['misc'].average_time)) det_file = os.path.join(output_dir, 'detections.pkl') with open(det_file, 'wb') as f: pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL) print('Evaluating detections') imdb.evaluate_detections(all_boxes, output_dir) ================================================ FILE: lib/model/test.py~ ================================================ # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import cv2 import numpy as np try: import cPickle as pickle except ImportError: import pickle import os import math from utils.timer import Timer from utils.cython_nms import nms, nms_new from utils.boxes_grid import get_boxes_grid from utils.blob import im_list_to_blob from model.config import cfg, get_output_dir from model.bbox_transform import clip_boxes, bbox_transform_inv def _get_image_blob(im): """Converts an image into a network input. Arguments: im (ndarray): a color image in BGR order Returns: blob (ndarray): a data blob holding an image pyramid im_scale_factors (list): list of image scales (relative to im) used in the image pyramid """ im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) processed_ims = [] im_scale_factors = [] for target_size in cfg.TEST.SCALES: im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE: im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max) im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) im_scale_factors.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, np.array(im_scale_factors) def _get_blobs(im): """Convert an image and RoIs within that image into network inputs.""" blobs = {} blobs['data'], im_scale_factors = _get_image_blob(im) return blobs, im_scale_factors def _clip_boxes(boxes, im_shape): """Clip boxes to image boundaries.""" # x1 >= 0 boxes[:, 0::4] = np.maximum(boxes[:, 0::4], 0) # y1 >= 0 boxes[:, 1::4] = np.maximum(boxes[:, 1::4], 0) # x2 < im_shape[1] boxes[:, 2::4] = np.minimum(boxes[:, 2::4], im_shape[1] - 1) # y2 < im_shape[0] boxes[:, 3::4] = np.minimum(boxes[:, 3::4], im_shape[0] - 1) return boxes def _rescale_boxes(boxes, inds, scales): """Rescale boxes according to image rescaling.""" for i in range(boxes.shape[0]): boxes[i,:] = boxes[i,:] / scales[int(inds[i])] return boxes def im_detect(sess, net, im): blobs, im_scales = _get_blobs(im) assert len(im_scales) == 1, "Only single-image batch implemented" print (im_scales) im_blob = blobs['data'] # seems to have height, width, and image scales # still not sure about the scale, maybe full image it is 1. blobs['im_info'] = np.array([[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32) _, scores, bbox_pred, rois = net.test_image(sess, blobs['data'], blobs['im_info']) boxes = rois[:, 1:5] / im_scales[0] # print(scores.shape, bbox_pred.shape, rois.shape, boxes.shape) scores = np.reshape(scores, [scores.shape[0], -1]) bbox_pred = np.reshape(bbox_pred, [bbox_pred.shape[0], -1]) if cfg.TEST.BBOX_REG: # Apply bounding-box regression deltas box_deltas = bbox_pred pred_boxes = bbox_transform_inv(boxes, box_deltas) pred_boxes = _clip_boxes(pred_boxes, im.shape) else: # Simply repeat the boxes, once for each class pred_boxes = np.tile(boxes, (1, scores.shape[1])) return scores, pred_boxes def apply_nms(all_boxes, thresh): """Apply non-maximum suppression to all predicted boxes output by the test_net method. """ num_classes = len(all_boxes) num_images = len(all_boxes[0]) nms_boxes = [[[] for _ in range(num_images)] for _ in range(num_classes)] for cls_ind in range(num_classes): for im_ind in range(num_images): dets = all_boxes[cls_ind][im_ind] if dets == []: continue x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] inds = np.where((x2 > x1) & (y2 > y1) & (scores > cfg.TEST.DET_THRESHOLD))[0] dets = dets[inds,:] if dets == []: continue keep = nms(dets, thresh) if len(keep) == 0: continue nms_boxes[cls_ind][im_ind] = dets[keep, :].copy() return nms_boxes def test_net(sess, net, imdb, weights_filename, max_per_image=100, thresh=0.05): np.random.seed(cfg.RNG_SEED) """Test a Fast R-CNN network on an image database.""" num_images = len(imdb.image_index) # all detections are collected into: # all_boxes[cls][image] = N x 5 array of detections in # (x1, y1, x2, y2, score) all_boxes = [[[] for _ in range(num_images)] for _ in range(imdb.num_classes)] output_dir = get_output_dir(imdb, weights_filename) # timers _t = {'im_detect' : Timer(), 'misc' : Timer()} for i in range(num_images): im = cv2.imread(imdb.image_path_at(i)) _t['im_detect'].tic() scores, boxes = im_detect(sess, net, im) _t['im_detect'].toc() _t['misc'].tic() # skip j = 0, because it's the background class for j in range(1, imdb.num_classes): inds = np.where(scores[:, j] > thresh)[0] cls_scores = scores[inds, j] cls_boxes = boxes[inds, j*4:(j+1)*4] cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \ .astype(np.float32, copy=False) keep = nms(cls_dets, cfg.TEST.NMS) cls_dets = cls_dets[keep, :] all_boxes[j][i] = cls_dets # Limit to max_per_image detections *over all classes* if max_per_image > 0: image_scores = np.hstack([all_boxes[j][i][:, -1] for j in range(1, imdb.num_classes)]) if len(image_scores) > max_per_image: image_thresh = np.sort(image_scores)[-max_per_image] for j in range(1, imdb.num_classes): keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0] all_boxes[j][i] = all_boxes[j][i][keep, :] _t['misc'].toc() print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \ .format(i + 1, num_images, _t['im_detect'].average_time, _t['misc'].average_time)) det_file = os.path.join(output_dir, 'detections.pkl') with open(det_file, 'wb') as f: pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL) print('Evaluating detections') imdb.evaluate_detections(all_boxes, output_dir) ================================================ FILE: lib/model/train_val.py ================================================ # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen and Zheqi He # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function from model.config import cfg import roi_data_layer.roidb as rdl_roidb from roi_data_layer.layer import RoIDataLayer from utils.timer import Timer try: import cPickle as pickle except ImportError: import pickle import numpy as np import os import sys import glob import time import tensorflow as tf from tensorflow.python import pywrap_tensorflow class SolverWrapper(object): """ A wrapper class for the training process """ def __init__(self, sess, network, imdb, roidb, valroidb, output_dir, tbdir, pretrained_model=None): self.net = network self.imdb = imdb self.roidb = roidb self.valroidb = valroidb self.output_dir = output_dir self.tbdir = tbdir # Simply put '_val' at the end to save the summaries from the validation set self.tbvaldir = tbdir + '_val' if not os.path.exists(self.tbvaldir): os.makedirs(self.tbvaldir) self.pretrained_model = pretrained_model def snapshot(self, sess, iter): net = self.net if not os.path.exists(self.output_dir): os.makedirs(self.output_dir) # Store the model snapshot filename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.ckpt' filename = os.path.join(self.output_dir, filename) self.saver.save(sess, filename) print('Wrote snapshot to: {:s}'.format(filename)) # Also store some meta information, random state, etc. nfilename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.pkl' nfilename = os.path.join(self.output_dir, nfilename) # current state of numpy random st0 = np.random.get_state() # current position in the database cur = self.data_layer._cur # current shuffled indeces of the database perm = self.data_layer._perm # current position in the validation database cur_val = self.data_layer_val._cur # current shuffled indeces of the validation database perm_val = self.data_layer_val._perm # Dump the meta info with open(nfilename, 'wb') as fid: pickle.dump(st0, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(cur, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(perm, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(cur_val, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(perm_val, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(iter, fid, pickle.HIGHEST_PROTOCOL) return filename, nfilename def get_variables_in_checkpoint_file(self, file_name): try: reader = pywrap_tensorflow.NewCheckpointReader(file_name) var_to_shape_map = reader.get_variable_to_shape_map() return var_to_shape_map except Exception as e: # pylint: disable=broad-except print(str(e)) if "corrupted compressed block contents" in str(e): print("It's likely that your checkpoint file has been compressed " "with SNAPPY.") def train_model(self, sess, max_iters): # Build data layers for both training and validation set self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes) self.data_layer_val = RoIDataLayer(self.valroidb, self.imdb.num_classes, random=True) # Determine different scales for anchors, see paper with sess.graph.as_default(): # Set the random seed for tensorflow tf.set_random_seed(cfg.RNG_SEED) # Build the main computation graph layers = self.net.create_architecture(sess, 'TRAIN', self.imdb.num_classes, tag='default', anchor_scales=cfg.ANCHOR_SCALES, anchor_ratios=cfg.ANCHOR_RATIOS) # Define the loss loss = layers['total_loss'] # Set learning rate and momentum lr = tf.Variable(cfg.TRAIN.LEARNING_RATE, trainable=False) momentum = cfg.TRAIN.MOMENTUM self.optimizer = tf.train.MomentumOptimizer(lr, momentum) # Compute the gradients wrt the loss gvs = self.optimizer.compute_gradients(loss) # Double the gradient of the bias if set if cfg.TRAIN.DOUBLE_BIAS: final_gvs = [] with tf.variable_scope('Gradient_Mult') as scope: for grad, var in gvs: scale = 1. if cfg.TRAIN.DOUBLE_BIAS and '/biases:' in var.name: scale *= 2. if not np.allclose(scale, 1.0): grad = tf.multiply(grad, scale) final_gvs.append((grad, var)) train_op = self.optimizer.apply_gradients(final_gvs) else: train_op = self.optimizer.apply_gradients(gvs) # We will handle the snapshots ourselves self.saver = tf.train.Saver(max_to_keep=100000) # Write the train and validation information to tensorboard self.writer = tf.summary.FileWriter(self.tbdir, sess.graph) self.valwriter = tf.summary.FileWriter(self.tbvaldir) # Find previous snapshots if there is any to restore from sfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.ckpt.meta') sfiles = glob.glob(sfiles) sfiles.sort(key=os.path.getmtime) # Get the snapshot name in TensorFlow redstr = '_iter_{:d}.'.format(cfg.TRAIN.STEPSIZE+1) sfiles = [ss.replace('.meta', '') for ss in sfiles] sfiles = [ss for ss in sfiles if redstr not in ss] nfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.pkl') nfiles = glob.glob(nfiles) nfiles.sort(key=os.path.getmtime) nfiles = [nn for nn in nfiles if redstr not in nn] lsf = len(sfiles) assert len(nfiles) == lsf np_paths = nfiles ss_paths = sfiles if lsf == 0: # Fresh train directly from ImageNet weights print('Loading initial model weights from {:s}'.format(self.pretrained_model)) variables = tf.global_variables() # Initialize all variables first sess.run(tf.variables_initializer(variables, name='init')) var_keep_dic = self.get_variables_in_checkpoint_file(self.pretrained_model) # Get the variables to restore, ignorizing the variables to fix variables_to_restore = self.net.get_variables_to_restore(variables, var_keep_dic) restorer = tf.train.Saver(variables_to_restore) restorer.restore(sess, self.pretrained_model) print('Loaded.') # Need to fix the variables before loading, so that the RGB weights are changed to BGR # For VGG16 it also changes the convolutional weights fc6 and fc7 to # fully connected weights self.net.fix_variables(sess, self.pretrained_model) print('Fixed.') sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE)) last_snapshot_iter = 0 else: # Get the most recent snapshot and restore ss_paths = [ss_paths[-1]] np_paths = [np_paths[-1]] print('Restorining model snapshots from {:s}'.format(sfiles[-1])) self.saver.restore(sess, str(sfiles[-1])) print('Restored.') # Needs to restore the other hyperparameters/states for training, (TODO xinlei) I have # tried my best to find the random states so that it can be recovered exactly # However the Tensorflow state is currently not available with open(str(nfiles[-1]), 'rb') as fid: st0 = pickle.load(fid) cur = pickle.load(fid) perm = pickle.load(fid) cur_val = pickle.load(fid) perm_val = pickle.load(fid) last_snapshot_iter = pickle.load(fid) np.random.set_state(st0) self.data_layer._cur = cur self.data_layer._perm = perm self.data_layer_val._cur = cur_val self.data_layer_val._perm = perm_val # Set the learning rate, only reduce once if last_snapshot_iter > cfg.TRAIN.STEPSIZE: sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE * cfg.TRAIN.GAMMA)) else: sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE)) a = np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()]) print(a) timer = Timer() iter = last_snapshot_iter + 1 last_summary_time = time.time() while iter < max_iters + 1: # Learning rate if iter == cfg.TRAIN.STEPSIZE + 1: # Add snapshot here before reducing the learning rate self.snapshot(sess, iter) sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE * cfg.TRAIN.GAMMA)) timer.tic() # Get training data, one batch at a time blobs = self.data_layer.forward() now = time.time() if now - last_summary_time > cfg.TRAIN.SUMMARY_INTERVAL: # Compute the graph with summary rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, total_loss, summary = \ self.net.train_step_with_summary(sess, blobs, train_op) self.writer.add_summary(summary, float(iter)) # Also check the summary on the validation set blobs_val = self.data_layer_val.forward() summary_val = self.net.get_summary(sess, blobs_val) self.valwriter.add_summary(summary_val, float(iter)) last_summary_time = now else: # Compute the graph without summary rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, total_loss = \ self.net.train_step(sess, blobs, train_op) timer.toc() # Display training information if iter % (cfg.TRAIN.DISPLAY) == 0: print('iter: %d / %d, total loss: %.6f\n >>> rpn_loss_cls: %.6f\n ' '>>> rpn_loss_box: %.6f\n >>> loss_cls: %.6f\n >>> loss_box: %.6f\n >>> lr: %f' % \ (iter, max_iters, total_loss, rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, lr.eval())) print('speed: {:.3f}s / iter'.format(timer.average_time)) if iter % cfg.TRAIN.SNAPSHOT_ITERS == 0: last_snapshot_iter = iter snapshot_path, np_path = self.snapshot(sess, iter) np_paths.append(np_path) ss_paths.append(snapshot_path) # Remove the old snapshots if there are too many if len(np_paths) > cfg.TRAIN.SNAPSHOT_KEPT: to_remove = len(np_paths) - cfg.TRAIN.SNAPSHOT_KEPT for c in range(to_remove): nfile = np_paths[0] os.remove(str(nfile)) np_paths.remove(nfile) if len(ss_paths) > cfg.TRAIN.SNAPSHOT_KEPT: to_remove = len(ss_paths) - cfg.TRAIN.SNAPSHOT_KEPT for c in range(to_remove): sfile = ss_paths[0] # To make the code compatible to earlier versions of Tensorflow, # where the naming tradition for checkpoints are different if os.path.exists(str(sfile)): os.remove(str(sfile)) else: os.remove(str(sfile + '.data-00000-of-00001')) os.remove(str(sfile + '.index')) sfile_meta = sfile + '.meta' os.remove(str(sfile_meta)) ss_paths.remove(sfile) iter += 1 if last_snapshot_iter != iter - 1: self.snapshot(sess, iter - 1) self.writer.close() self.valwriter.close() def get_training_roidb(imdb): """Returns a roidb (Region of Interest database) for use in training.""" if cfg.TRAIN.USE_FLIPPED: print('Appending horizontally-flipped training examples...') imdb.append_flipped_images() print('done') print('Preparing training data...') rdl_roidb.prepare_roidb(imdb) print('done') return imdb.roidb def filter_roidb(roidb): """Remove roidb entries that have no usable RoIs.""" def is_valid(entry): # Valid images have: # (1) At least one foreground RoI OR # (2) At least one background RoI overlaps = entry['max_overlaps'] # find boxes with sufficient overlap fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0] # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI) bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) & (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0] # image is only valid if such boxes exist valid = len(fg_inds) > 0 or len(bg_inds) > 0 return valid num = len(roidb) filtered_roidb = [entry for entry in roidb if is_valid(entry)] num_after = len(filtered_roidb) print('Filtered {} roidb entries: {} -> {}'.format(num - num_after, num, num_after)) return filtered_roidb def train_net(network, imdb, roidb, valroidb, output_dir, tb_dir, pretrained_model=None, max_iters=40000): """Train a Fast R-CNN network.""" roidb = filter_roidb(roidb) valroidb = filter_roidb(valroidb) tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth = True with tf.Session(config=tfconfig) as sess: sw = SolverWrapper(sess, network, imdb, roidb, valroidb, output_dir, tb_dir, pretrained_model=pretrained_model) print('Solving...') sw.train_model(sess, max_iters) print('done solving') ================================================ FILE: lib/model/train_val.py~ ================================================ # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen and Zheqi He # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function from model.config import cfg import roi_data_layer.roidb as rdl_roidb from roi_data_layer.layer import RoIDataLayer from utils.timer import Timer try: import cPickle as pickle except ImportError: import pickle import numpy as np import os import sys import glob import time import tensorflow as tf from tensorflow.python import pywrap_tensorflow class SolverWrapper(object): """ A wrapper class for the training process """ def __init__(self, sess, network, imdb, roidb, valroidb, output_dir, tbdir, pretrained_model=None): self.net = network self.imdb = imdb self.roidb = roidb self.valroidb = valroidb self.output_dir = output_dir self.tbdir = tbdir # Simply put '_val' at the end to save the summaries from the validation set self.tbvaldir = tbdir + '_val' if not os.path.exists(self.tbvaldir): os.makedirs(self.tbvaldir) self.pretrained_model = pretrained_model def snapshot(self, sess, iter): net = self.net if not os.path.exists(self.output_dir): os.makedirs(self.output_dir) # Store the model snapshot filename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.ckpt' filename = os.path.join(self.output_dir, filename) self.saver.save(sess, filename) print('Wrote snapshot to: {:s}'.format(filename)) # Also store some meta information, random state, etc. nfilename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.pkl' nfilename = os.path.join(self.output_dir, nfilename) # current state of numpy random st0 = np.random.get_state() # current position in the database cur = self.data_layer._cur # current shuffled indeces of the database perm = self.data_layer._perm # current position in the validation database cur_val = self.data_layer_val._cur # current shuffled indeces of the validation database perm_val = self.data_layer_val._perm # Dump the meta info with open(nfilename, 'wb') as fid: pickle.dump(st0, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(cur, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(perm, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(cur_val, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(perm_val, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(iter, fid, pickle.HIGHEST_PROTOCOL) return filename, nfilename def get_variables_in_checkpoint_file(self, file_name): try: reader = pywrap_tensorflow.NewCheckpointReader(file_name) var_to_shape_map = reader.get_variable_to_shape_map() return var_to_shape_map except Exception as e: # pylint: disable=broad-except print(str(e)) if "corrupted compressed block contents" in str(e): print("It's likely that your checkpoint file has been compressed " "with SNAPPY.") def train_model(self, sess, max_iters): # Build data layers for both training and validation set self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes) self.data_layer_val = RoIDataLayer(self.valroidb, self.imdb.num_classes, random=True) # Determine different scales for anchors, see paper with sess.graph.as_default(): # Set the random seed for tensorflow tf.set_random_seed(cfg.RNG_SEED) # Build the main computation graph layers = self.net.create_architecture(sess, 'TRAIN', self.imdb.num_classes, tag='default', anchor_scales=cfg.ANCHOR_SCALES, anchor_ratios=cfg.ANCHOR_RATIOS) # Define the loss loss = layers['total_loss'] # Set learning rate and momentum lr = tf.Variable(cfg.TRAIN.LEARNING_RATE, trainable=False) momentum = cfg.TRAIN.MOMENTUM self.optimizer = tf.train.MomentumOptimizer(lr, momentum) # Compute the gradients wrt the loss gvs = self.optimizer.compute_gradients(loss) # Double the gradient of the bias if set if cfg.TRAIN.DOUBLE_BIAS: final_gvs = [] with tf.variable_scope('Gradient_Mult') as scope: for grad, var in gvs: scale = 1. if cfg.TRAIN.DOUBLE_BIAS and '/biases:' in var.name: scale *= 2. if not np.allclose(scale, 1.0): grad = tf.multiply(grad, scale) final_gvs.append((grad, var)) train_op = self.optimizer.apply_gradients(final_gvs) else: train_op = self.optimizer.apply_gradients(gvs) # We will handle the snapshots ourselves self.saver = tf.train.Saver(max_to_keep=100000) # Write the train and validation information to tensorboard self.writer = tf.summary.FileWriter(self.tbdir, sess.graph) self.valwriter = tf.summary.FileWriter(self.tbvaldir) # Find previous snapshots if there is any to restore from sfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.ckpt.meta') sfiles = glob.glob(sfiles) sfiles.sort(key=os.path.getmtime) # Get the snapshot name in TensorFlow redstr = '_iter_{:d}.'.format(cfg.TRAIN.STEPSIZE+1) sfiles = [ss.replace('.meta', '') for ss in sfiles] sfiles = [ss for ss in sfiles if redstr not in ss] nfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.pkl') nfiles = glob.glob(nfiles) nfiles.sort(key=os.path.getmtime) nfiles = [nn for nn in nfiles if redstr not in nn] lsf = len(sfiles) assert len(nfiles) == lsf np_paths = nfiles ss_paths = sfiles if lsf == 0: # Fresh train directly from ImageNet weights print('Loading initial model weights from {:s}'.format(self.pretrained_model)) variables = tf.global_variables() # Initialize all variables first sess.run(tf.variables_initializer(variables, name='init')) var_keep_dic = self.get_variables_in_checkpoint_file(self.pretrained_model) # Get the variables to restore, ignorizing the variables to fix variables_to_restore = self.net.get_variables_to_restore(variables, var_keep_dic) restorer = tf.train.Saver(variables_to_restore) restorer.restore(sess, self.pretrained_model) print('Loaded.') # Need to fix the variables before loading, so that the RGB weights are changed to BGR # For VGG16 it also changes the convolutional weights fc6 and fc7 to # fully connected weights self.net.fix_variables(sess, self.pretrained_model) print('Fixed.') sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE)) last_snapshot_iter = 0 else: # Get the most recent snapshot and restore ss_paths = [ss_paths[-1]] np_paths = [np_paths[-1]] print('Restorining model snapshots from {:s}'.format(sfiles[-1])) self.saver.restore(sess, str(sfiles[-1])) print('Restored.') # Needs to restore the other hyperparameters/states for training, (TODO xinlei) I have # tried my best to find the random states so that it can be recovered exactly # However the Tensorflow state is currently not available with open(str(nfiles[-1]), 'rb') as fid: st0 = pickle.load(fid) cur = pickle.load(fid) perm = pickle.load(fid) cur_val = pickle.load(fid) perm_val = pickle.load(fid) last_snapshot_iter = pickle.load(fid) np.random.set_state(st0) self.data_layer._cur = cur self.data_layer._perm = perm self.data_layer_val._cur = cur_val self.data_layer_val._perm = perm_val # Set the learning rate, only reduce once if last_snapshot_iter > cfg.TRAIN.STEPSIZE: sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE * cfg.TRAIN.GAMMA)) else: sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE)) np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()]) timer = Timer() iter = last_snapshot_iter + 1 last_summary_time = time.time() while iter < max_iters + 1: # Learning rate if iter == cfg.TRAIN.STEPSIZE + 1: # Add snapshot here before reducing the learning rate self.snapshot(sess, iter) sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE * cfg.TRAIN.GAMMA)) timer.tic() # Get training data, one batch at a time blobs = self.data_layer.forward() now = time.time() if now - last_summary_time > cfg.TRAIN.SUMMARY_INTERVAL: # Compute the graph with summary rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, total_loss, summary = \ self.net.train_step_with_summary(sess, blobs, train_op) self.writer.add_summary(summary, float(iter)) # Also check the summary on the validation set blobs_val = self.data_layer_val.forward() summary_val = self.net.get_summary(sess, blobs_val) self.valwriter.add_summary(summary_val, float(iter)) last_summary_time = now else: # Compute the graph without summary rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, total_loss = \ self.net.train_step(sess, blobs, train_op) timer.toc() # Display training information if iter % (cfg.TRAIN.DISPLAY) == 0: print('iter: %d / %d, total loss: %.6f\n >>> rpn_loss_cls: %.6f\n ' '>>> rpn_loss_box: %.6f\n >>> loss_cls: %.6f\n >>> loss_box: %.6f\n >>> lr: %f' % \ (iter, max_iters, total_loss, rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, lr.eval())) print('speed: {:.3f}s / iter'.format(timer.average_time)) if iter % cfg.TRAIN.SNAPSHOT_ITERS == 0: last_snapshot_iter = iter snapshot_path, np_path = self.snapshot(sess, iter) np_paths.append(np_path) ss_paths.append(snapshot_path) # Remove the old snapshots if there are too many if len(np_paths) > cfg.TRAIN.SNAPSHOT_KEPT: to_remove = len(np_paths) - cfg.TRAIN.SNAPSHOT_KEPT for c in range(to_remove): nfile = np_paths[0] os.remove(str(nfile)) np_paths.remove(nfile) if len(ss_paths) > cfg.TRAIN.SNAPSHOT_KEPT: to_remove = len(ss_paths) - cfg.TRAIN.SNAPSHOT_KEPT for c in range(to_remove): sfile = ss_paths[0] # To make the code compatible to earlier versions of Tensorflow, # where the naming tradition for checkpoints are different if os.path.exists(str(sfile)): os.remove(str(sfile)) else: os.remove(str(sfile + '.data-00000-of-00001')) os.remove(str(sfile + '.index')) sfile_meta = sfile + '.meta' os.remove(str(sfile_meta)) ss_paths.remove(sfile) iter += 1 if last_snapshot_iter != iter - 1: self.snapshot(sess, iter - 1) self.writer.close() self.valwriter.close() def get_training_roidb(imdb): """Returns a roidb (Region of Interest database) for use in training.""" if cfg.TRAIN.USE_FLIPPED: print('Appending horizontally-flipped training examples...') imdb.append_flipped_images() print('done') print('Preparing training data...') rdl_roidb.prepare_roidb(imdb) print('done') return imdb.roidb def filter_roidb(roidb): """Remove roidb entries that have no usable RoIs.""" def is_valid(entry): # Valid images have: # (1) At least one foreground RoI OR # (2) At least one background RoI overlaps = entry['max_overlaps'] # find boxes with sufficient overlap fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0] # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI) bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) & (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0] # image is only valid if such boxes exist valid = len(fg_inds) > 0 or len(bg_inds) > 0 return valid num = len(roidb) filtered_roidb = [entry for entry in roidb if is_valid(entry)] num_after = len(filtered_roidb) print('Filtered {} roidb entries: {} -> {}'.format(num - num_after, num, num_after)) return filtered_roidb def train_net(network, imdb, roidb, valroidb, output_dir, tb_dir, pretrained_model=None, max_iters=40000): """Train a Fast R-CNN network.""" roidb = filter_roidb(roidb) valroidb = filter_roidb(valroidb) tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth = True with tf.Session(config=tfconfig) as sess: sw = SolverWrapper(sess, network, imdb, roidb, valroidb, output_dir, tb_dir, pretrained_model=pretrained_model) print('Solving...') sw.train_model(sess, max_iters) print('done solving') ================================================ FILE: lib/nets/__init__.py ================================================ ================================================ FILE: lib/nets/network.py ================================================ # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf import tensorflow.contrib.slim as slim from tensorflow.contrib.slim import losses from tensorflow.contrib.slim import arg_scope import numpy as np from layer_utils.snippets import generate_anchors_pre from layer_utils.proposal_layer import proposal_layer from layer_utils.proposal_top_layer import proposal_top_layer from layer_utils.anchor_target_layer import anchor_target_layer from layer_utils.proposal_target_layer import proposal_target_layer from model.config import cfg class Network(object): def __init__(self, batch_size=1): self._feat_stride = [16, ] self._feat_compress = [1. / 16., ] self._batch_size = batch_size self._predictions = {} self._losses = {} self._anchor_targets = {} self._proposal_targets = {} self._layers = {} self._act_summaries = [] self._score_summaries = {} self._train_summaries = [] self._event_summaries = {} self._variables_to_fix = {} def _add_image_summary(self, image, boxes): # add back mean image += cfg.PIXEL_MEANS # bgr to rgb (opencv uses bgr) channels = tf.unstack (image, axis=-1) image = tf.stack ([channels[2], channels[1], channels[0]], axis=-1) # dims for normalization width = tf.to_float(tf.shape(image)[2]) height = tf.to_float(tf.shape(image)[1]) # from [x1, y1, x2, y2, cls] to normalized [y1, x1, y1, x1] cols = tf.unstack(boxes, axis=1) boxes = tf.stack([cols[1] / height, cols[0] / width, cols[3] / height, cols[2] / width], axis=1) # add batch dimension (assume batch_size==1) assert image.get_shape()[0] == 1 boxes = tf.expand_dims(boxes, dim=0) image = tf.image.draw_bounding_boxes(image, boxes) return tf.summary.image('ground_truth', image) def _add_act_summary(self, tensor): tf.summary.histogram('ACT/' + tensor.op.name + '/activations', tensor) tf.summary.scalar('ACT/' + tensor.op.name + '/zero_fraction', tf.nn.zero_fraction(tensor)) def _add_score_summary(self, key, tensor): tf.summary.histogram('SCORE/' + tensor.op.name + '/' + key + '/scores', tensor) def _add_train_summary(self, var): tf.summary.histogram('TRAIN/' + var.op.name, var) def _reshape_layer(self, bottom, num_dim, name): input_shape = tf.shape(bottom) with tf.variable_scope(name) as scope: # change the channel to the caffe format to_caffe = tf.transpose(bottom, [0, 3, 1, 2]) # then force it to have channel 2 reshaped = tf.reshape(to_caffe, tf.concat(axis=0, values=[[self._batch_size], [num_dim, -1], [input_shape[2]]])) # then swap the channel back to_tf = tf.transpose(reshaped, [0, 2, 3, 1]) return to_tf def _softmax_layer(self, bottom, name): if name == 'rpn_cls_prob_reshape': input_shape = tf.shape(bottom) bottom_reshaped = tf.reshape(bottom, [-1, input_shape[-1]]) reshaped_score = tf.nn.softmax(bottom_reshaped, name=name) return tf.reshape(reshaped_score, input_shape) return tf.nn.softmax(bottom, name=name) def _proposal_top_layer(self, rpn_cls_prob, rpn_bbox_pred, name): with tf.variable_scope(name) as scope: rois, rpn_scores = tf.py_func(proposal_top_layer, [rpn_cls_prob, rpn_bbox_pred, self._im_info, self._feat_stride, self._anchors, self._num_anchors], [tf.float32, tf.float32]) rois.set_shape([cfg.TEST.RPN_TOP_N, 5]) rpn_scores.set_shape([cfg.TEST.RPN_TOP_N, 1]) return rois, rpn_scores def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name): with tf.variable_scope(name) as scope: rois, rpn_scores = tf.py_func(proposal_layer, [rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode, self._feat_stride, self._anchors, self._num_anchors], [tf.float32, tf.float32]) rois.set_shape([None, 5]) rpn_scores.set_shape([None, 1]) return rois, rpn_scores # Only use it if you have roi_pooling op written in tf.image def _roi_pool_layer(self, bootom, rois, name): with tf.variable_scope(name) as scope: return tf.image.roi_pooling(bootom, rois, pooled_height=cfg.POOLING_SIZE, pooled_width=cfg.POOLING_SIZE, spatial_scale=1. / 16.)[0] def _crop_pool_layer(self, bottom, rois, name): with tf.variable_scope(name) as scope: batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1]) # Get the normalized coordinates of bboxes bottom_shape = tf.shape(bottom) height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0]) width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0]) x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height # Won't be backpropagated to rois anyway, but to save time bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], axis=1)) pre_pool_size = cfg.POOLING_SIZE * 2 crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [pre_pool_size, pre_pool_size], name="crops") return slim.max_pool2d(crops, [2, 2], padding='SAME') def _dropout_layer(self, bottom, name, ratio=0.5): return tf.nn.dropout(bottom, ratio, name=name) def _anchor_target_layer(self, rpn_cls_score, name): with tf.variable_scope(name) as scope: rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = tf.py_func( anchor_target_layer, [rpn_cls_score, self._gt_boxes, self._im_info, self._feat_stride, self._anchors, self._num_anchors], [tf.float32, tf.float32, tf.float32, tf.float32]) rpn_labels.set_shape([1, 1, None, None]) rpn_bbox_targets.set_shape([1, None, None, self._num_anchors * 4]) rpn_bbox_inside_weights.set_shape([1, None, None, self._num_anchors * 4]) rpn_bbox_outside_weights.set_shape([1, None, None, self._num_anchors * 4]) rpn_labels = tf.to_int32(rpn_labels, name="to_int32") self._anchor_targets['rpn_labels'] = rpn_labels self._anchor_targets['rpn_bbox_targets'] = rpn_bbox_targets self._anchor_targets['rpn_bbox_inside_weights'] = rpn_bbox_inside_weights self._anchor_targets['rpn_bbox_outside_weights'] = rpn_bbox_outside_weights self._score_summaries.update(self._anchor_targets) return rpn_labels def _proposal_target_layer(self, rois, roi_scores, name): with tf.variable_scope(name) as scope: rois, roi_scores, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights = tf.py_func( proposal_target_layer, [rois, roi_scores, self._gt_boxes, self._num_classes], [tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32]) rois.set_shape([cfg.TRAIN.BATCH_SIZE, 5]) roi_scores.set_shape([cfg.TRAIN.BATCH_SIZE]) labels.set_shape([cfg.TRAIN.BATCH_SIZE, 1]) bbox_targets.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4]) bbox_inside_weights.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4]) bbox_outside_weights.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4]) self._proposal_targets['rois'] = rois self._proposal_targets['labels'] = tf.to_int32(labels, name="to_int32") self._proposal_targets['bbox_targets'] = bbox_targets self._proposal_targets['bbox_inside_weights'] = bbox_inside_weights self._proposal_targets['bbox_outside_weights'] = bbox_outside_weights self._score_summaries.update(self._proposal_targets) return rois, roi_scores def _anchor_component(self): with tf.variable_scope('ANCHOR_' + self._tag) as scope: # just to get the shape right height = tf.to_int32(tf.ceil(self._im_info[0, 0] / np.float32(self._feat_stride[0]))) width = tf.to_int32(tf.ceil(self._im_info[0, 1] / np.float32(self._feat_stride[0]))) anchors, anchor_length = tf.py_func(generate_anchors_pre, [height, width, self._feat_stride, self._anchor_scales, self._anchor_ratios], [tf.float32, tf.int32], name="generate_anchors") anchors.set_shape([None, 4]) anchor_length.set_shape([]) self._anchors = anchors self._anchor_length = anchor_length def build_network(self, sess, is_training=True): raise NotImplementedError def _smooth_l1_loss(self, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights, sigma=1.0, dim=[1]): sigma_2 = sigma ** 2 box_diff = bbox_pred - bbox_targets in_box_diff = bbox_inside_weights * box_diff abs_in_box_diff = tf.abs(in_box_diff) smoothL1_sign = tf.stop_gradient(tf.to_float(tf.less(abs_in_box_diff, 1. / sigma_2))) in_loss_box = tf.pow(in_box_diff, 2) * (sigma_2 / 2.) * smoothL1_sign \ + (abs_in_box_diff - (0.5 / sigma_2)) * (1. - smoothL1_sign) out_loss_box = bbox_outside_weights * in_loss_box loss_box = tf.reduce_mean(tf.reduce_sum( out_loss_box, axis=dim )) return loss_box def _add_losses(self, sigma_rpn=3.0): with tf.variable_scope('loss_' + self._tag) as scope: # RPN, class loss rpn_cls_score = tf.reshape(self._predictions['rpn_cls_score_reshape'], [-1, 2]) rpn_label = tf.reshape(self._anchor_targets['rpn_labels'], [-1]) rpn_select = tf.where(tf.not_equal(rpn_label, -1)) rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2]) rpn_label = tf.reshape(tf.gather(rpn_label, rpn_select), [-1]) rpn_cross_entropy = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, labels=rpn_label)) # RPN, bbox loss rpn_bbox_pred = self._predictions['rpn_bbox_pred'] rpn_bbox_targets = self._anchor_targets['rpn_bbox_targets'] rpn_bbox_inside_weights = self._anchor_targets['rpn_bbox_inside_weights'] rpn_bbox_outside_weights = self._anchor_targets['rpn_bbox_outside_weights'] rpn_loss_box = self._smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights, sigma=sigma_rpn, dim=[1, 2, 3]) # RCNN, class loss cls_score = self._predictions["cls_score"] label = tf.reshape(self._proposal_targets["labels"], [-1]) cross_entropy = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits( logits=tf.reshape(cls_score, [-1, self._num_classes]), labels=label)) # RCNN, bbox loss bbox_pred = self._predictions['bbox_pred'] bbox_targets = self._proposal_targets['bbox_targets'] bbox_inside_weights = self._proposal_targets['bbox_inside_weights'] bbox_outside_weights = self._proposal_targets['bbox_outside_weights'] loss_box = self._smooth_l1_loss(bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights) self._losses['cross_entropy'] = cross_entropy self._losses['loss_box'] = loss_box self._losses['rpn_cross_entropy'] = rpn_cross_entropy self._losses['rpn_loss_box'] = rpn_loss_box loss = cross_entropy + loss_box + rpn_cross_entropy + rpn_loss_box self._losses['total_loss'] = loss self._event_summaries.update(self._losses) return loss def create_architecture(self, sess, mode, num_classes, tag=None, anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)): self._image = tf.placeholder(tf.float32, shape=[self._batch_size, None, None, 3]) self._im_info = tf.placeholder(tf.float32, shape=[self._batch_size, 3]) self._gt_boxes = tf.placeholder(tf.float32, shape=[None, 5]) self._tag = tag self._num_classes = num_classes self._mode = mode self._anchor_scales = anchor_scales self._num_scales = len(anchor_scales) self._anchor_ratios = anchor_ratios self._num_ratios = len(anchor_ratios) self._num_anchors = self._num_scales * self._num_ratios training = mode == 'TRAIN' testing = mode == 'TEST' assert tag != None # handle most of the regularizers here weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY) if cfg.TRAIN.BIAS_DECAY: biases_regularizer = weights_regularizer else: biases_regularizer = tf.no_regularizer # list as many types of layers as possible, even if they are not used now with arg_scope([slim.conv2d, slim.conv2d_in_plane, \ slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], weights_regularizer=weights_regularizer, biases_regularizer=biases_regularizer, biases_initializer=tf.constant_initializer(0.0)): rois, cls_prob, bbox_pred = self.build_network(sess, training) layers_to_output = {'rois': rois} layers_to_output.update(self._predictions) for var in tf.trainable_variables(): self._train_summaries.append(var) if mode == 'TEST': stds = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS), (self._num_classes)) means = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS), (self._num_classes)) self._predictions["bbox_pred"] *= stds self._predictions["bbox_pred"] += means else: self._add_losses() layers_to_output.update(self._losses) val_summaries = [] with tf.device("/cpu:0"): val_summaries.append(self._add_image_summary(self._image, self._gt_boxes)) for key, var in self._event_summaries.items(): val_summaries.append(tf.summary.scalar(key, var)) for key, var in self._score_summaries.items(): self._add_score_summary(key, var) for var in self._act_summaries: self._add_act_summary(var) for var in self._train_summaries: self._add_train_summary(var) self._summary_op = tf.summary.merge_all() if not testing: self._summary_op_val = tf.summary.merge(val_summaries) return layers_to_output def get_variables_to_restore(self, variables, var_keep_dic): raise NotImplementedError def fix_variables(self, sess, pretrained_model): raise NotImplementedError # Extract the head feature maps, for example for vgg16 it is conv5_3 # only useful during testing mode def extract_head(self, sess, image): feed_dict = {self._image: image} feat = sess.run(self._layers["head"], feed_dict=feed_dict) return feat # only useful during testing mode def test_image(self, sess, image, im_info): feed_dict = {self._image: image, self._im_info: im_info} cls_score, cls_prob, bbox_pred, rois = sess.run([self._predictions["cls_score"], self._predictions['cls_prob'], self._predictions['bbox_pred'], self._predictions['rois']], feed_dict=feed_dict) return cls_score, cls_prob, bbox_pred, rois def get_summary(self, sess, blobs): feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], self._gt_boxes: blobs['gt_boxes']} summary = sess.run(self._summary_op_val, feed_dict=feed_dict) return summary def train_step(self, sess, blobs, train_op): feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], self._gt_boxes: blobs['gt_boxes']} rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, _ = sess.run([self._losses["rpn_cross_entropy"], self._losses['rpn_loss_box'], self._losses['cross_entropy'], self._losses['loss_box'], self._losses['total_loss'], train_op], feed_dict=feed_dict) return rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss def train_step_with_summary(self, sess, blobs, train_op): feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], self._gt_boxes: blobs['gt_boxes']} rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, summary, _ = sess.run([self._losses["rpn_cross_entropy"], self._losses['rpn_loss_box'], self._losses['cross_entropy'], self._losses['loss_box'], self._losses['total_loss'], self._summary_op, train_op], feed_dict=feed_dict) return rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, summary def train_step_no_return(self, sess, blobs, train_op): feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'], self._gt_boxes: blobs['gt_boxes']} sess.run([train_op], feed_dict=feed_dict) ================================================ FILE: lib/nets/resnet_v1.py ================================================ # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Zheqi He and Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf import tensorflow.contrib.slim as slim from tensorflow.contrib.slim import losses from tensorflow.contrib.slim import arg_scope from tensorflow.contrib.slim.python.slim.nets import resnet_utils from tensorflow.contrib.slim.python.slim.nets import resnet_v1 import numpy as np from nets.network import Network from tensorflow.python.framework import ops from tensorflow.contrib.layers.python.layers import regularizers from tensorflow.python.ops import nn_ops from tensorflow.contrib.layers.python.layers import initializers from tensorflow.contrib.layers.python.layers import layers from model.config import cfg def resnet_arg_scope(is_training=True, weight_decay=cfg.TRAIN.WEIGHT_DECAY, batch_norm_decay=0.997, batch_norm_epsilon=1e-5, batch_norm_scale=True): batch_norm_params = { # NOTE 'is_training' here does not work because inside resnet it gets reset: # https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v1.py#L187 'is_training': False, 'decay': batch_norm_decay, 'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale, 'trainable': cfg.RESNET.BN_TRAIN, 'updates_collections': ops.GraphKeys.UPDATE_OPS } with arg_scope( [slim.conv2d], weights_regularizer=regularizers.l2_regularizer(weight_decay), weights_initializer=initializers.variance_scaling_initializer(), trainable=is_training, activation_fn=nn_ops.relu, normalizer_fn=layers.batch_norm, normalizer_params=batch_norm_params): with arg_scope([layers.batch_norm], **batch_norm_params) as arg_sc: return arg_sc class resnetv1(Network): def __init__(self, batch_size=1, num_layers=50): Network.__init__(self, batch_size=batch_size) self._num_layers = num_layers self._resnet_scope = 'resnet_v1_%d' % num_layers def _crop_pool_layer(self, bottom, rois, name): with tf.variable_scope(name) as scope: batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1]) # Get the normalized coordinates of bboxes bottom_shape = tf.shape(bottom) height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0]) width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0]) x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height # Won't be backpropagated to rois anyway, but to save time bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], 1)) if cfg.RESNET.MAX_POOL: pre_pool_size = cfg.POOLING_SIZE * 2 crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [pre_pool_size, pre_pool_size], name="crops") crops = slim.max_pool2d(crops, [2, 2], padding='SAME') else: crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [cfg.POOLING_SIZE, cfg.POOLING_SIZE], name="crops") return crops # Do the first few layers manually, because 'SAME' padding can behave inconsistently # for images of different sizes: sometimes 0, sometimes 1 def build_base(self): with tf.variable_scope(self._resnet_scope, self._resnet_scope): net = resnet_utils.conv2d_same(self._image, 64, 7, stride=2, scope='conv1') net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]]) net = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1') return net def build_network(self, sess, is_training=True): # select initializers if cfg.TRAIN.TRUNCATED: initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01) initializer_bbox = tf.truncated_normal_initializer(mean=0.0, stddev=0.001) else: initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01) initializer_bbox = tf.random_normal_initializer(mean=0.0, stddev=0.001) bottleneck = resnet_v1.bottleneck # choose different blocks for different number of layers if self._num_layers == 50: blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), # Use stride-1 for the last conv4 layer resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 5 + [(1024, 256, 1)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] elif self._num_layers == 101: blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), # Use stride-1 for the last conv4 layer resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 22 + [(1024, 256, 1)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] elif self._num_layers == 152: blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 7 + [(512, 128, 2)]), # Use stride-1 for the last conv4 layer resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 1)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] else: # other numbers are not supported raise NotImplementedError assert (0 <= cfg.RESNET.FIXED_BLOCKS < 4) if cfg.RESNET.FIXED_BLOCKS == 3: with slim.arg_scope(resnet_arg_scope(is_training=False)): net = self.build_base() net_conv4, _ = resnet_v1.resnet_v1(net, blocks[0:cfg.RESNET.FIXED_BLOCKS], global_pool=False, include_root_block=False, scope=self._resnet_scope) elif cfg.RESNET.FIXED_BLOCKS > 0: with slim.arg_scope(resnet_arg_scope(is_training=False)): net = self.build_base() net, _ = resnet_v1.resnet_v1(net, blocks[0:cfg.RESNET.FIXED_BLOCKS], global_pool=False, include_root_block=False, scope=self._resnet_scope) with slim.arg_scope(resnet_arg_scope(is_training=is_training)): net_conv4, _ = resnet_v1.resnet_v1(net, blocks[cfg.RESNET.FIXED_BLOCKS:-1], global_pool=False, include_root_block=False, scope=self._resnet_scope) else: # cfg.RESNET.FIXED_BLOCKS == 0 with slim.arg_scope(resnet_arg_scope(is_training=is_training)): net = self.build_base() net_conv4, _ = resnet_v1.resnet_v1(net, blocks[0:-1], global_pool=False, include_root_block=False, scope=self._resnet_scope) self._act_summaries.append(net_conv4) self._layers['head'] = net_conv4 with tf.variable_scope(self._resnet_scope, self._resnet_scope): # build the anchors for the image self._anchor_component() # rpn rpn = slim.conv2d(net_conv4, 512, [3, 3], trainable=is_training, weights_initializer=initializer, scope="rpn_conv/3x3") self._act_summaries.append(rpn) rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=is_training, weights_initializer=initializer, padding='VALID', activation_fn=None, scope='rpn_cls_score') # change it so that the score has 2 as its channel size rpn_cls_score_reshape = self._reshape_layer(rpn_cls_score, 2, 'rpn_cls_score_reshape') rpn_cls_prob_reshape = self._softmax_layer(rpn_cls_score_reshape, "rpn_cls_prob_reshape") rpn_cls_prob = self._reshape_layer(rpn_cls_prob_reshape, self._num_anchors * 2, "rpn_cls_prob") rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=is_training, weights_initializer=initializer, padding='VALID', activation_fn=None, scope='rpn_bbox_pred') if is_training: rois, roi_scores = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois") rpn_labels = self._anchor_target_layer(rpn_cls_score, "anchor") # Try to have a determinestic order for the computing graph, for reproducibility with tf.control_dependencies([rpn_labels]): rois, _ = self._proposal_target_layer(rois, roi_scores, "rpn_rois") else: if cfg.TEST.MODE == 'nms': rois, _ = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois") elif cfg.TEST.MODE == 'top': rois, _ = self._proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, "rois") else: raise NotImplementedError # rcnn if cfg.POOLING_MODE == 'crop': pool5 = self._crop_pool_layer(net_conv4, rois, "pool5") else: raise NotImplementedError with slim.arg_scope(resnet_arg_scope(is_training=is_training)): fc7, _ = resnet_v1.resnet_v1(pool5, blocks[-1:], global_pool=False, include_root_block=False, scope=self._resnet_scope) with tf.variable_scope(self._resnet_scope, self._resnet_scope): # Average pooling done by reduce_mean fc7 = tf.reduce_mean(fc7, axis=[1, 2]) cls_score = slim.fully_connected(fc7, self._num_classes, weights_initializer=initializer, trainable=is_training, activation_fn=None, scope='cls_score') cls_prob = self._softmax_layer(cls_score, "cls_prob") bbox_pred = slim.fully_connected(fc7, self._num_classes * 4, weights_initializer=initializer_bbox, trainable=is_training, activation_fn=None, scope='bbox_pred') self._predictions["rpn_cls_score"] = rpn_cls_score self._predictions["rpn_cls_score_reshape"] = rpn_cls_score_reshape self._predictions["rpn_cls_prob"] = rpn_cls_prob self._predictions["rpn_bbox_pred"] = rpn_bbox_pred self._predictions["cls_score"] = cls_score self._predictions["cls_prob"] = cls_prob self._predictions["bbox_pred"] = bbox_pred self._predictions["rois"] = rois self._score_summaries.update(self._predictions) return rois, cls_prob, bbox_pred def get_variables_to_restore(self, variables, var_keep_dic): variables_to_restore = [] for v in variables: # exclude the first conv layer to swap RGB to BGR if v.name == (self._resnet_scope + '/conv1/weights:0'): self._variables_to_fix[v.name] = v continue if v.name.split(':')[0] in var_keep_dic: #if v.name == 'resnet_v1_50/cls_score/weights/Momentum:0' \ # or v.name == 'resnet_v1_50/cls_score/biases/Momentum:0' \ # or v.name == 'resnet_v1_50/bbox_pred/weights/Momentum:0' \ # or v.name == 'resnet_v1_50/bbox_pred/biases/Momentum:0' \ # or v.name == 'resnet_v1_50/cls_score/weights:0' \ # or v.name == 'resnet_v1_50/cls_score/biases:0' \ # or v.name == 'resnet_v1_50/bbox_pred/weights:0' \ # or v.name == 'resnet_v1_50/bbox_pred/biases:0': # continue print('Varibles restored: %s' % v.name) variables_to_restore.append(v) return variables_to_restore def fix_variables(self, sess, pretrained_model): print('Fix Resnet V1 layers..') with tf.variable_scope('Fix_Resnet_V1') as scope: with tf.device("/cpu:0"): # fix RGB to BGR conv1_rgb = tf.get_variable("conv1_rgb", [7, 7, 3, 64], trainable=False) restorer_fc = tf.train.Saver({self._resnet_scope + "/conv1/weights": conv1_rgb}) restorer_fc.restore(sess, pretrained_model) sess.run(tf.assign(self._variables_to_fix[self._resnet_scope + '/conv1/weights:0'], tf.reverse(conv1_rgb, [2]))) ================================================ FILE: lib/nets/resnet_v1.py~ ================================================ # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Zheqi He and Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf import tensorflow.contrib.slim as slim from tensorflow.contrib.slim import losses from tensorflow.contrib.slim import arg_scope from tensorflow.contrib.slim.python.slim.nets import resnet_utils from tensorflow.contrib.slim.python.slim.nets import resnet_v1 import numpy as np from nets.network import Network from tensorflow.python.framework import ops from tensorflow.contrib.layers.python.layers import regularizers from tensorflow.python.ops import nn_ops from tensorflow.contrib.layers.python.layers import initializers from tensorflow.contrib.layers.python.layers import layers from model.config import cfg def resnet_arg_scope(is_training=True, weight_decay=cfg.TRAIN.WEIGHT_DECAY, batch_norm_decay=0.997, batch_norm_epsilon=1e-5, batch_norm_scale=True): batch_norm_params = { # NOTE 'is_training' here does not work because inside resnet it gets reset: # https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v1.py#L187 'is_training': False, 'decay': batch_norm_decay, 'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale, 'trainable': cfg.RESNET.BN_TRAIN, 'updates_collections': ops.GraphKeys.UPDATE_OPS } with arg_scope( [slim.conv2d], weights_regularizer=regularizers.l2_regularizer(weight_decay), weights_initializer=initializers.variance_scaling_initializer(), trainable=is_training, activation_fn=nn_ops.relu, normalizer_fn=layers.batch_norm, normalizer_params=batch_norm_params): with arg_scope([layers.batch_norm], **batch_norm_params) as arg_sc: return arg_sc class resnetv1(Network): def __init__(self, batch_size=1, num_layers=50): Network.__init__(self, batch_size=batch_size) self._num_layers = num_layers self._resnet_scope = 'resnet_v1_%d' % num_layers def _crop_pool_layer(self, bottom, rois, name): with tf.variable_scope(name) as scope: batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1]) # Get the normalized coordinates of bboxes bottom_shape = tf.shape(bottom) height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0]) width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0]) x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height # Won't be backpropagated to rois anyway, but to save time bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], 1)) if cfg.RESNET.MAX_POOL: pre_pool_size = cfg.POOLING_SIZE * 2 crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [pre_pool_size, pre_pool_size], name="crops") crops = slim.max_pool2d(crops, [2, 2], padding='SAME') else: crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [cfg.POOLING_SIZE, cfg.POOLING_SIZE], name="crops") return crops # Do the first few layers manually, because 'SAME' padding can behave inconsistently # for images of different sizes: sometimes 0, sometimes 1 def build_base(self): with tf.variable_scope(self._resnet_scope, self._resnet_scope): net = resnet_utils.conv2d_same(self._image, 64, 7, stride=2, scope='conv1') net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]]) net = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1') return net def build_network(self, sess, is_training=True): # select initializers if cfg.TRAIN.TRUNCATED: initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01) initializer_bbox = tf.truncated_normal_initializer(mean=0.0, stddev=0.001) else: initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01) initializer_bbox = tf.random_normal_initializer(mean=0.0, stddev=0.001) bottleneck = resnet_v1.bottleneck # choose different blocks for different number of layers if self._num_layers == 50: blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), # Use stride-1 for the last conv4 layer resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 5 + [(1024, 256, 1)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] elif self._num_layers == 101: blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), # Use stride-1 for the last conv4 layer resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 22 + [(1024, 256, 1)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] elif self._num_layers == 152: blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 7 + [(512, 128, 2)]), # Use stride-1 for the last conv4 layer resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 1)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] else: # other numbers are not supported raise NotImplementedError assert (0 <= cfg.RESNET.FIXED_BLOCKS < 4) if cfg.RESNET.FIXED_BLOCKS == 3: with slim.arg_scope(resnet_arg_scope(is_training=False)): net = self.build_base() net_conv4, _ = resnet_v1.resnet_v1(net, blocks[0:cfg.RESNET.FIXED_BLOCKS], global_pool=False, include_root_block=False, scope=self._resnet_scope) elif cfg.RESNET.FIXED_BLOCKS > 0: with slim.arg_scope(resnet_arg_scope(is_training=False)): net = self.build_base() net, _ = resnet_v1.resnet_v1(net, blocks[0:cfg.RESNET.FIXED_BLOCKS], global_pool=False, include_root_block=False, scope=self._resnet_scope) with slim.arg_scope(resnet_arg_scope(is_training=is_training)): net_conv4, _ = resnet_v1.resnet_v1(net, blocks[cfg.RESNET.FIXED_BLOCKS:-1], global_pool=False, include_root_block=False, scope=self._resnet_scope) else: # cfg.RESNET.FIXED_BLOCKS == 0 with slim.arg_scope(resnet_arg_scope(is_training=is_training)): net = self.build_base() net_conv4, _ = resnet_v1.resnet_v1(net, blocks[0:-1], global_pool=False, include_root_block=False, scope=self._resnet_scope) self._act_summaries.append(net_conv4) self._layers['head'] = net_conv4 with tf.variable_scope(self._resnet_scope, self._resnet_scope): # build the anchors for the image self._anchor_component() # rpn rpn = slim.conv2d(net_conv4, 512, [3, 3], trainable=is_training, weights_initializer=initializer, scope="rpn_conv/3x3") self._act_summaries.append(rpn) rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=is_training, weights_initializer=initializer, padding='VALID', activation_fn=None, scope='rpn_cls_score') # change it so that the score has 2 as its channel size rpn_cls_score_reshape = self._reshape_layer(rpn_cls_score, 2, 'rpn_cls_score_reshape') rpn_cls_prob_reshape = self._softmax_layer(rpn_cls_score_reshape, "rpn_cls_prob_reshape") rpn_cls_prob = self._reshape_layer(rpn_cls_prob_reshape, self._num_anchors * 2, "rpn_cls_prob") rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=is_training, weights_initializer=initializer, padding='VALID', activation_fn=None, scope='rpn_bbox_pred') if is_training: rois, roi_scores = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois") rpn_labels = self._anchor_target_layer(rpn_cls_score, "anchor") # Try to have a determinestic order for the computing graph, for reproducibility with tf.control_dependencies([rpn_labels]): rois, _ = self._proposal_target_layer(rois, roi_scores, "rpn_rois") else: if cfg.TEST.MODE == 'nms': rois, _ = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois") elif cfg.TEST.MODE == 'top': rois, _ = self._proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, "rois") else: raise NotImplementedError # rcnn if cfg.POOLING_MODE == 'crop': pool5 = self._crop_pool_layer(net_conv4, rois, "pool5") else: raise NotImplementedError with slim.arg_scope(resnet_arg_scope(is_training=is_training)): fc7, _ = resnet_v1.resnet_v1(pool5, blocks[-1:], global_pool=False, include_root_block=False, scope=self._resnet_scope) with tf.variable_scope(self._resnet_scope, self._resnet_scope): # Average pooling done by reduce_mean fc7 = tf.reduce_mean(fc7, axis=[1, 2]) cls_score = slim.fully_connected(fc7, self._num_classes, weights_initializer=initializer, trainable=is_training, activation_fn=None, scope='cls_score') cls_prob = self._softmax_layer(cls_score, "cls_prob") bbox_pred = slim.fully_connected(fc7, self._num_classes * 4, weights_initializer=initializer_bbox, trainable=is_training, activation_fn=None, scope='bbox_pred') self._predictions["rpn_cls_score"] = rpn_cls_score self._predictions["rpn_cls_score_reshape"] = rpn_cls_score_reshape self._predictions["rpn_cls_prob"] = rpn_cls_prob self._predictions["rpn_bbox_pred"] = rpn_bbox_pred self._predictions["cls_score"] = cls_score self._predictions["cls_prob"] = cls_prob self._predictions["bbox_pred"] = bbox_pred self._predictions["rois"] = rois self._score_summaries.update(self._predictions) return rois, cls_prob, bbox_pred def get_variables_to_restore(self, variables, var_keep_dic): variables_to_restore = [] for v in variables: # exclude the first conv layer to swap RGB to BGR if v.name == (self._resnet_scope + '/conv1/weights:0'): self._variables_to_fix[v.name] = v continue if v.name.split(':')[0] in var_keep_dic: if v.name == 'resnet_v1_50/cls_score/weights/Momentum:0' \ or v.name == 'resnet_v1_50/cls_score/biases/Momentum:0' \ or v.name == 'resnet_v1_50/bbox_pred/weights/Momentum:0' \ or v.name == 'resnet_v1_50/bbox_pred/biases/Momentum:0' \ or v.name == 'resnet_v1_50/cls_score/weights:0' \ or v.name == 'resnet_v1_50/cls_score/biases:0' \ or v.name == 'resnet_v1_50/bbox_pred/weights:0' \ or v.name == 'resnet_v1_50/bbox_pred/biases:0': continue print('Varibles restored: %s' % v.name) variables_to_restore.append(v) return variables_to_restore def fix_variables(self, sess, pretrained_model): print('Fix Resnet V1 layers..') with tf.variable_scope('Fix_Resnet_V1') as scope: with tf.device("/cpu:0"): # fix RGB to BGR conv1_rgb = tf.get_variable("conv1_rgb", [7, 7, 3, 64], trainable=False) restorer_fc = tf.train.Saver({self._resnet_scope + "/conv1/weights": conv1_rgb}) restorer_fc.restore(sess, pretrained_model) sess.run(tf.assign(self._variables_to_fix[self._resnet_scope + '/conv1/weights:0'], tf.reverse(conv1_rgb, [2]))) ================================================ FILE: lib/nets/vgg16.py ================================================ # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf import tensorflow.contrib.slim as slim from tensorflow.contrib.slim import losses from tensorflow.contrib.slim import arg_scope import numpy as np from nets.network import Network from model.config import cfg class vgg16(Network): def __init__(self, batch_size=1): Network.__init__(self, batch_size=batch_size) def build_network(self, sess, is_training=True): with tf.variable_scope('vgg_16', 'vgg_16'): # select initializers if cfg.TRAIN.TRUNCATED: initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01) initializer_bbox = tf.truncated_normal_initializer(mean=0.0, stddev=0.001) else: initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01) initializer_bbox = tf.random_normal_initializer(mean=0.0, stddev=0.001) net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3], trainable=False, scope='conv1') net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1') net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], trainable=False, scope='conv2') net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2') net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], trainable=is_training, scope='conv3') net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3') net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv4') net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4') net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv5') self._act_summaries.append(net) self._layers['head'] = net # build the anchors for the image self._anchor_component() # rpn rpn = slim.conv2d(net, 512, [3, 3], trainable=is_training, weights_initializer=initializer, scope="rpn_conv/3x3") self._act_summaries.append(rpn) rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=is_training, weights_initializer=initializer, padding='VALID', activation_fn=None, scope='rpn_cls_score') # change it so that the score has 2 as its channel size rpn_cls_score_reshape = self._reshape_layer(rpn_cls_score, 2, 'rpn_cls_score_reshape') rpn_cls_prob_reshape = self._softmax_layer(rpn_cls_score_reshape, "rpn_cls_prob_reshape") rpn_cls_prob = self._reshape_layer(rpn_cls_prob_reshape, self._num_anchors * 2, "rpn_cls_prob") rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=is_training, weights_initializer=initializer, padding='VALID', activation_fn=None, scope='rpn_bbox_pred') if is_training: rois, roi_scores = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois") rpn_labels = self._anchor_target_layer(rpn_cls_score, "anchor") # Try to have a determinestic order for the computing graph, for reproducibility with tf.control_dependencies([rpn_labels]): rois, _ = self._proposal_target_layer(rois, roi_scores, "rpn_rois") else: if cfg.TEST.MODE == 'nms': rois, _ = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois") elif cfg.TEST.MODE == 'top': rois, _ = self._proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, "rois") else: raise NotImplementedError # rcnn if cfg.POOLING_MODE == 'crop': pool5 = self._crop_pool_layer(net, rois, "pool5") else: raise NotImplementedError pool5_flat = slim.flatten(pool5, scope='flatten') fc6 = slim.fully_connected(pool5_flat, 4096, scope='fc6') if is_training: fc6 = slim.dropout(fc6, keep_prob=0.5, is_training=True, scope='dropout6') fc7 = slim.fully_connected(fc6, 4096, scope='fc7') if is_training: fc7 = slim.dropout(fc7, keep_prob=0.5, is_training=True, scope='dropout7') cls_score = slim.fully_connected(fc7, self._num_classes, weights_initializer=initializer, trainable=is_training, activation_fn=None, scope='cls_score') cls_prob = self._softmax_layer(cls_score, "cls_prob") bbox_pred = slim.fully_connected(fc7, self._num_classes * 4, weights_initializer=initializer_bbox, trainable=is_training, activation_fn=None, scope='bbox_pred') self._predictions["rpn_cls_score"] = rpn_cls_score self._predictions["rpn_cls_score_reshape"] = rpn_cls_score_reshape self._predictions["rpn_cls_prob"] = rpn_cls_prob self._predictions["rpn_bbox_pred"] = rpn_bbox_pred self._predictions["cls_score"] = cls_score self._predictions["cls_prob"] = cls_prob self._predictions["bbox_pred"] = bbox_pred self._predictions["rois"] = rois self._score_summaries.update(self._predictions) return rois, cls_prob, bbox_pred def get_variables_to_restore(self, variables, var_keep_dic): variables_to_restore = [] for v in variables: # exclude the conv weights that are fc weights in vgg16 if v.name == 'vgg_16/fc6/weights:0' or v.name == 'vgg_16/fc7/weights:0': self._variables_to_fix[v.name] = v continue # exclude the first conv layer to swap RGB to BGR if v.name == 'vgg_16/conv1/conv1_1/weights:0': self._variables_to_fix[v.name] = v continue if v.name.split(':')[0] in var_keep_dic: print('Varibles restored: %s' % v.name) variables_to_restore.append(v) return variables_to_restore def fix_variables(self, sess, pretrained_model): print('Fix VGG16 layers..') with tf.variable_scope('Fix_VGG16') as scope: with tf.device("/cpu:0"): # fix the vgg16 issue from conv weights to fc weights # fix RGB to BGR fc6_conv = tf.get_variable("fc6_conv", [7, 7, 512, 4096], trainable=False) fc7_conv = tf.get_variable("fc7_conv", [1, 1, 4096, 4096], trainable=False) conv1_rgb = tf.get_variable("conv1_rgb", [3, 3, 3, 64], trainable=False) restorer_fc = tf.train.Saver({"vgg_16/fc6/weights": fc6_conv, "vgg_16/fc7/weights": fc7_conv, "vgg_16/conv1/conv1_1/weights": conv1_rgb}) restorer_fc.restore(sess, pretrained_model) sess.run(tf.assign(self._variables_to_fix['vgg_16/fc6/weights:0'], tf.reshape(fc6_conv, self._variables_to_fix['vgg_16/fc6/weights:0'].get_shape()))) sess.run(tf.assign(self._variables_to_fix['vgg_16/fc7/weights:0'], tf.reshape(fc7_conv, self._variables_to_fix['vgg_16/fc7/weights:0'].get_shape()))) sess.run(tf.assign(self._variables_to_fix['vgg_16/conv1/conv1_1/weights:0'], tf.reverse(conv1_rgb, [2]))) ================================================ FILE: lib/nms/.gitignore ================================================ ================================================ FILE: lib/nms/__init__.py ================================================ ================================================ FILE: lib/nms/cpu_nms.c ================================================ /* Generated by Cython 0.20.1 on Wed Oct 5 13:15:30 2016 */ #define PY_SSIZE_T_CLEAN #ifndef CYTHON_USE_PYLONG_INTERNALS #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 0 #else #include "pyconfig.h" #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 1 #else #define CYTHON_USE_PYLONG_INTERNALS 0 #endif #endif #endif #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02040000 #error Cython requires Python 2.4+. #else #define CYTHON_ABI "0_20_1" #include /* For offsetof */ #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #endif #if CYTHON_COMPILING_IN_PYPY #define Py_OptimizeFlag 0 #endif #if PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN #define PY_FORMAT_SIZE_T "" #define CYTHON_FORMAT_SSIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) #define PyInt_AsSsize_t(o) __Pyx_PyInt_As_int(o) #define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? 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/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":730 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":731 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; 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if (likely(tp->tp_getattro)) return tp->tp_getattro(obj, attr_name); #if PY_MAJOR_VERSION < 3 if (likely(tp->tp_getattr)) return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); #endif return PyObject_GetAttr(obj, attr_name); } #else #define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) #endif static PyObject *__Pyx_GetBuiltinName(PyObject *name); /*proto*/ static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/ static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/ static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \ const char* function_name); /*proto*/ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, const char *name, int exact); /*proto*/ static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); /*proto*/ #else #define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) #endif static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); /*proto*/ static void __Pyx_RaiseBufferIndexError(int axis); /*proto*/ #define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0) #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); Py_SIZE(list) = len+1; return 0; } return PyList_Append(list, x); } #else #define __Pyx_PyList_Append(L,x) PyList_Append(L,x) #endif #ifndef __PYX_FORCE_INIT_THREADS #define __PYX_FORCE_INIT_THREADS 0 #endif static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; typedef struct { size_t refcount; Py_buffer pybuffer; } __Pyx_Buffer; typedef struct { __Pyx_Buffer *rcbuffer; char *data; __Pyx_Buf_DimInfo diminfo[8]; } __Pyx_LocalBuf_ND; #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); static void __Pyx_ReleaseBuffer(Py_buffer *view); #else #define __Pyx_GetBuffer PyObject_GetBuffer #define __Pyx_ReleaseBuffer PyBuffer_Release #endif static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /*proto*/ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); #if CYTHON_CCOMPLEX #ifdef __cplusplus #define __Pyx_CREAL(z) ((z).real()) #define __Pyx_CIMAG(z) ((z).imag()) #else #define __Pyx_CREAL(z) (__real__(z)) #define __Pyx_CIMAG(z) (__imag__(z)) #endif #else #define __Pyx_CREAL(z) ((z).real) #define __Pyx_CIMAG(z) ((z).imag) #endif #if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX #define __Pyx_SET_CREAL(z,x) ((z).real(x)) #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) #else #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) #endif static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); #if CYTHON_CCOMPLEX #define __Pyx_c_eqf(a, b) ((a)==(b)) #define __Pyx_c_sumf(a, b) ((a)+(b)) #define __Pyx_c_difff(a, b) ((a)-(b)) #define __Pyx_c_prodf(a, b) ((a)*(b)) #define __Pyx_c_quotf(a, b) ((a)/(b)) #define __Pyx_c_negf(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zerof(z) ((z)==(float)0) #define __Pyx_c_conjf(z) (::std::conj(z)) #if 1 #define __Pyx_c_absf(z) (::std::abs(z)) #define __Pyx_c_powf(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zerof(z) ((z)==0) #define __Pyx_c_conjf(z) (conjf(z)) #if 1 #define __Pyx_c_absf(z) (cabsf(z)) #define __Pyx_c_powf(a, b) (cpowf(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); #if CYTHON_CCOMPLEX #define __Pyx_c_eq(a, b) ((a)==(b)) #define __Pyx_c_sum(a, b) ((a)+(b)) #define __Pyx_c_diff(a, b) ((a)-(b)) #define __Pyx_c_prod(a, b) ((a)*(b)) #define __Pyx_c_quot(a, b) ((a)/(b)) #define __Pyx_c_neg(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero(z) ((z)==(double)0) #define __Pyx_c_conj(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs(z) (::std::abs(z)) #define __Pyx_c_pow(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero(z) ((z)==0) #define __Pyx_c_conj(z) (conj(z)) #if 1 #define __Pyx_c_abs(z) (cabs(z)) #define __Pyx_c_pow(a, b) (cpow(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); static int __Pyx_check_binary_version(void); #if !defined(__Pyx_PyIdentifier_FromString) #if PY_MAJOR_VERSION < 3 #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) #else #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) #endif #endif static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*proto*/ typedef struct { int code_line; PyCodeObject* code_object; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; int max_count; __Pyx_CodeObjectCacheEntry* entries; }; static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); /*proto*/ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ /* Module declarations from 'cpython.buffer' */ /* Module declarations from 'cpython.ref' */ /* Module declarations from 'libc.string' */ /* Module declarations from 'libc.stdio' */ /* Module declarations from 'cpython.object' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.type' */ static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; 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static char __pyx_k_h[] = "h"; static char __pyx_k_i[] = "i"; static char __pyx_k_j[] = "_j"; static char __pyx_k_l[] = "l"; static char __pyx_k_q[] = "q"; static char __pyx_k_w[] = "w"; static char __pyx_k_Zd[] = "Zd"; static char __pyx_k_Zf[] = "Zf"; static char __pyx_k_Zg[] = "Zg"; static char __pyx_k_np[] = "np"; static char __pyx_k_x1[] = "x1"; static char __pyx_k_x2[] = "x2"; static char __pyx_k_y1[] = "y1"; static char __pyx_k_y2[] = "y2"; static char __pyx_k_i_2[] = "_i"; static char __pyx_k_int[] = "int"; static char __pyx_k_ix1[] = "ix1"; static char __pyx_k_ix2[] = "ix2"; static char __pyx_k_iy1[] = "iy1"; static char __pyx_k_iy2[] = "iy2"; static char __pyx_k_j_2[] = "j"; static char __pyx_k_ovr[] = "ovr"; static char __pyx_k_xx1[] = "xx1"; static char __pyx_k_xx2[] = "xx2"; static char __pyx_k_yy1[] = "yy1"; static char __pyx_k_yy2[] = "yy2"; static char __pyx_k_dets[] = "dets"; static char __pyx_k_keep[] = "keep"; static char __pyx_k_main[] = "__main__"; static char __pyx_k_test[] = "__test__"; 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#else "name '%.200s' is not defined", PyString_AS_STRING(name)); #endif } return result; } static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found) { Py_ssize_t num_expected; const char *more_or_less; if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; } else { num_expected = num_max; more_or_less = "at most"; } if (exact) { more_or_less = "exactly"; } PyErr_Format(PyExc_TypeError, "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", func_name, more_or_less, num_expected, (num_expected == 1) ? "" : "s", num_found); } static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AsString(kw_name)); #endif } static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; continue; } name = first_kw_arg; #if PY_MAJOR_VERSION < 3 if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { while (*name) { if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) && _PyString_Eq(**name, key)) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { if ((**argname == key) || ( (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) && _PyString_Eq(**argname, key))) { goto arg_passed_twice; } argname++; } } } else #endif if (likely(PyUnicode_Check(key))) { while (*name) { int cmp = (**name == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : #endif PyUnicode_Compare(**name, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { int cmp = (**argname == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : #endif PyUnicode_Compare(**argname, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) goto arg_passed_twice; argname++; } } } else goto invalid_keyword_type; if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, key); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%.200s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%.200s() got an unexpected keyword argument '%.200s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { PyErr_Format(PyExc_TypeError, "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", name, type->tp_name, Py_TYPE(obj)->tp_name); } static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, const char *name, int exact) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (none_allowed && obj == Py_None) return 1; else if (exact) { if (likely(Py_TYPE(obj) == type)) return 1; #if PY_MAJOR_VERSION == 2 else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; #endif } else { if (likely(PyObject_TypeCheck(obj, type))) return 1; } __Pyx_RaiseArgumentTypeInvalid(name, obj, type); return 0; } static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { unsigned int n = 1; return *(unsigned char*)(&n) != 0; } static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t < '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) /* First char was not a digit */ PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case 'c': return "'char'"; case 'b': return "'signed char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; /* Consume from buffer string */ while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; /* breaks both loops as ctx->enc_count == 0 */ } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; /* empty struct */ field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static CYTHON_INLINE PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; /* not a 'break' in the loop */ } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case 10: case 13: ++ts; break; case '<': if (!__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': /* substruct */ { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': /* end of substruct; either repeat or move on */ { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } /* fall through */ case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 's': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; } else { if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; } ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { buf->buf = NULL; buf->obj = NULL; buf->strides = __Pyx_zeros; buf->shape = __Pyx_zeros; buf->suboffsets = __Pyx_minusones; } static CYTHON_INLINE int __Pyx_GetBufferAndValidate( Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None || obj == NULL) { __Pyx_ZeroBuffer(buf); return 0; } buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", nd, buf->ndim); goto fail; } if (!cast) { __Pyx_BufFmt_Context ctx; __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned)buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { if (info->buf == NULL) return; if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; __Pyx_ReleaseBuffer(info); } static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(PyObject_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); #if PY_VERSION_HEX >= 0x02060000 if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; #endif result = (*call)(func, arg, kw); #if PY_VERSION_HEX >= 0x02060000 Py_LeaveRecursiveCall(); #endif if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { PyObject *result; #if CYTHON_COMPILING_IN_CPYTHON result = PyDict_GetItem(__pyx_d, name); if (result) { Py_INCREF(result); } else { #else result = PyObject_GetItem(__pyx_d, name); if (!result) { PyErr_Clear(); #endif result = __Pyx_GetBuiltinName(name); } return result; } static void __Pyx_RaiseBufferIndexError(int axis) { PyErr_Format(PyExc_IndexError, "Out of bounds on buffer access (axis %d)", axis); } static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_Restore(type, value, tb); #endif } static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(type, value, tb); #endif } #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } #if PY_VERSION_HEX < 0x02050000 if (PyClass_Check(type)) { #else if (PyType_Check(type)) { #endif #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; #if PY_VERSION_HEX < 0x02050000 if (PyInstance_Check(type)) { type = (PyObject*) ((PyInstanceObject*)type)->in_class; Py_INCREF(type); } else { type = 0; PyErr_SetString(PyExc_TypeError, "raise: exception must be an old-style class or instance"); goto raise_error; } #else type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } #endif } __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else /* Python 3+ */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { if (PyObject_IsSubclass(instance_class, type)) { type = instance_class; } else { instance_class = NULL; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } #if PY_VERSION_HEX >= 0x03030000 if (cause) { #else if (cause && cause != Py_None) { #endif PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } } bad: Py_XDECREF(owned_instance); return; } #endif static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict) { PyObject *getbuffer_cobj = PyObject_GetItem( obj->ob_type->tp_dict, __pyx_n_s_pyx_getbuffer); if (getbuffer_cobj) { getbufferproc func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj); Py_DECREF(getbuffer_cobj); if (!func) goto fail; return func(obj, view, flags); } else { PyErr_Clear(); } } #endif PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); #if PY_VERSION_HEX < 0x02060000 fail: #endif return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict) { PyObject *releasebuffer_cobj = PyObject_GetItem( obj->ob_type->tp_dict, __pyx_n_s_pyx_releasebuffer); if (releasebuffer_cobj) { releasebufferproc func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj); Py_DECREF(releasebuffer_cobj); if (!func) goto fail; func(obj, view); return; } else { PyErr_Clear(); } } #endif goto nofail; #if PY_VERSION_HEX < 0x02060000 fail: #endif PyErr_WriteUnraisable(obj); nofail: Py_DECREF(obj); view->obj = NULL; } #endif /* PY_MAJOR_VERSION < 3 */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_VERSION_HEX < 0x03030000 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; #if PY_VERSION_HEX >= 0x02050000 { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if (strchr(__Pyx_MODULE_NAME, '.')) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(1); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); #endif if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; /* try absolute import on failure */ } #endif if (!module) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } #else if (level>0) { PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); goto bad; } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); #endif bad: #if PY_VERSION_HEX < 0x03030000 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(int) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func) \ { \ func_type value = func(x); \ if (sizeof(target_type) < sizeof(func_type)) { \ if (unlikely(value != (func_type) (target_type) value)) { \ func_type zero = 0; \ PyErr_SetString(PyExc_OverflowError, \ (is_unsigned && unlikely(value < zero)) ? \ "can't convert negative value to " #target_type : \ "value too large to convert to " #target_type); \ return (target_type) -1; \ } \ } \ return (target_type) value; \ } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(int)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return (int) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong) } else if (sizeof(int) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(int)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return +(int) ((PyLongObject*)x)->ob_digit[0]; case -1: return -(int) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong) } else if (sizeof(int) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(long) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(a, a); case 3: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, a); case 4: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_absf(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(a, a); case 3: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, a); case 4: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_abs(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(long)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return (long) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong) } else if (sizeof(long) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(long)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return +(long) ((PyLongObject*)x)->ob_digit[0]; case -1: return -(long) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong) } else if (sizeof(long) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); #if PY_VERSION_HEX < 0x02050000 return PyErr_Warn(NULL, message); #else return PyErr_WarnEx(NULL, message, 1); #endif } return 0; } #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; py_name = __Pyx_PyIdentifier_FromString(name); if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; Py_ssize_t basicsize; #ifdef Py_LIMITED_API PyObject *py_basicsize; #endif py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; py_name = __Pyx_PyIdentifier_FromString(class_name); if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%.200s.%.200s is not a type object", module_name, class_name); goto bad; } #ifndef Py_LIMITED_API basicsize = ((PyTypeObject *)result)->tp_basicsize; #else py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); if (!py_basicsize) goto bad; basicsize = PyLong_AsSsize_t(py_basicsize); Py_DECREF(py_basicsize); py_basicsize = 0; if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; #endif if (!strict && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 if (PyErr_Warn(NULL, warning) < 0) goto bad; #else if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } else if ((size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = (start + end) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, /*int argcount,*/ 0, /*int kwonlyargcount,*/ 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, /*int firstlineno,*/ __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_globals = 0; PyFrameObject *py_frame = 0; py_code = __pyx_find_code_object(c_line ? c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? c_line : py_line, py_code); } py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ py_globals, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = py_line; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) { return __Pyx_PyUnicode_FromStringAndSize(c_str, strlen(c_str)); } static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { Py_ssize_t ignore; return __Pyx_PyObject_AsStringAndSize(o, &ignore); } static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT if ( #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII __Pyx_sys_getdefaultencoding_not_ascii && #endif PyUnicode_Check(o)) { #if PY_VERSION_HEX < 0x03030000 char* defenc_c; PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); if (!defenc) return NULL; defenc_c = PyBytes_AS_STRING(defenc); #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII { char* end = defenc_c + PyBytes_GET_SIZE(defenc); char* c; for (c = defenc_c; c < end; c++) { if ((unsigned char) (*c) >= 128) { PyUnicode_AsASCIIString(o); return NULL; } } } #endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/ *length = PyBytes_GET_SIZE(defenc); return defenc_c; #else /* PY_VERSION_HEX < 0x03030000 */ if (PyUnicode_READY(o) == -1) return NULL; #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII if (PyUnicode_IS_ASCII(o)) { *length = PyUnicode_GET_DATA_SIZE(o); return PyUnicode_AsUTF8(o); } else { PyUnicode_AsASCIIString(o); return NULL; } #else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ return PyUnicode_AsUTF8AndSize(o, length); #endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ #endif /* PY_VERSION_HEX < 0x03030000 */ } else #endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */ #if !CYTHON_COMPILING_IN_PYPY #if PY_VERSION_HEX >= 0x02060000 if (PyByteArray_Check(o)) { *length = PyByteArray_GET_SIZE(o); return PyByteArray_AS_STRING(o); } else #endif #endif { char* result; int r = PyBytes_AsStringAndSize(o, &result, length); if (unlikely(r < 0)) { return NULL; } else { return result; } } } static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_MAJOR_VERSION < 3 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) return PyInt_AS_LONG(b); #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(b)) { case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0]; case 0: return 0; case 1: return ((PyLongObject*)b)->ob_digit[0]; } #endif #endif #if PY_VERSION_HEX < 0x02060000 return PyInt_AsSsize_t(b); #else return PyLong_AsSsize_t(b); #endif } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { #if PY_VERSION_HEX < 0x02050000 if (ival <= LONG_MAX) return PyInt_FromLong((long)ival); else { unsigned char *bytes = (unsigned char *) &ival; int one = 1; int little = (int)*(unsigned char*)&one; return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); } #else return PyInt_FromSize_t(ival); #endif } #endif /* Py_PYTHON_H */ ================================================ FILE: lib/nms/cpu_nms.pyx ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import numpy as np cimport numpy as np cdef inline np.float32_t max(np.float32_t a, np.float32_t b): return a if a >= b else b cdef inline np.float32_t min(np.float32_t a, np.float32_t b): return a if a <= b else b def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1] cdef int ndets = dets.shape[0] cdef np.ndarray[np.int_t, ndim=1] suppressed = \ np.zeros((ndets), dtype=np.int) # nominal indices cdef int _i, _j # sorted indices cdef int i, j # temp variables for box i's (the box currently under consideration) cdef np.float32_t ix1, iy1, ix2, iy2, iarea # variables for computing overlap with box j (lower scoring box) cdef np.float32_t xx1, yy1, xx2, yy2 cdef np.float32_t w, h cdef np.float32_t inter, ovr keep = [] for _i in range(ndets): i = order[_i] if suppressed[i] == 1: continue keep.append(i) ix1 = x1[i] iy1 = y1[i] ix2 = x2[i] iy2 = y2[i] iarea = areas[i] for _j in range(_i + 1, ndets): j = order[_j] if suppressed[j] == 1: continue xx1 = max(ix1, x1[j]) yy1 = max(iy1, y1[j]) xx2 = min(ix2, x2[j]) yy2 = min(iy2, y2[j]) w = max(0.0, xx2 - xx1 + 1) h = max(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (iarea + areas[j] - inter) if ovr >= thresh: suppressed[j] = 1 return keep ================================================ FILE: lib/nms/gpu_nms.cpp ================================================ /* Generated by Cython 0.20.1 on Wed Oct 5 13:15:30 2016 */ #define PY_SSIZE_T_CLEAN #ifndef CYTHON_USE_PYLONG_INTERNALS #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 0 #else #include "pyconfig.h" #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 1 #else #define CYTHON_USE_PYLONG_INTERNALS 0 #endif #endif #endif #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02040000 #error Cython requires Python 2.4+. #else #define CYTHON_ABI "0_20_1" #include /* For offsetof */ #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #endif #if CYTHON_COMPILING_IN_PYPY #define Py_OptimizeFlag 0 #endif #if PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN #define PY_FORMAT_SIZE_T "" #define CYTHON_FORMAT_SSIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) #define PyInt_AsSsize_t(o) __Pyx_PyInt_As_int(o) #define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? 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PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) #define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) #else #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) #endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject #define PyString_Type PyUnicode_Type #define PyString_Check PyUnicode_Check #define PyString_CheckExact PyUnicode_CheckExact #endif #if PY_VERSION_HEX < 0x02060000 #define PyBytesObject PyStringObject #define PyBytes_Type PyString_Type #define PyBytes_Check PyString_Check #define PyBytes_CheckExact PyString_CheckExact #define PyBytes_FromString PyString_FromString #define PyBytes_FromStringAndSize PyString_FromStringAndSize #define PyBytes_FromFormat PyString_FromFormat #define PyBytes_DecodeEscape PyString_DecodeEscape #define PyBytes_AsString PyString_AsString #define PyBytes_AsStringAndSize PyString_AsStringAndSize #define PyBytes_Size PyString_Size #define PyBytes_AS_STRING PyString_AS_STRING #define PyBytes_GET_SIZE PyString_GET_SIZE #define PyBytes_Repr PyString_Repr #define PyBytes_Concat PyString_Concat #define PyBytes_ConcatAndDel PyString_ConcatAndDel #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) #else #define __Pyx_PyBaseString_Check(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj) || \ PyString_Check(obj) || PyUnicode_Check(obj)) #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) #endif #if PY_VERSION_HEX < 0x02060000 #define PySet_Check(obj) PyObject_TypeCheck(obj, &PySet_Type) #define PyFrozenSet_Check(obj) PyObject_TypeCheck(obj, &PyFrozenSet_Type) #endif #ifndef PySet_CheckExact #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type #define PyInt_Check(op) PyLong_Check(op) #define PyInt_CheckExact(op) PyLong_CheckExact(op) #define PyInt_FromString PyLong_FromString #define PyInt_FromUnicode PyLong_FromUnicode #define PyInt_FromLong PyLong_FromLong #define PyInt_FromSize_t PyLong_FromSize_t #define PyInt_FromSsize_t PyLong_FromSsize_t #define PyInt_AsLong PyLong_AsLong #define PyInt_AS_LONG PyLong_AS_LONG #define PyInt_AsSsize_t PyLong_AsSsize_t #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #define PyNumber_Int PyNumber_Long #endif #if PY_MAJOR_VERSION >= 3 #define PyBoolObject PyLongObject #endif #if PY_VERSION_HEX < 0x030200A4 typedef long Py_hash_t; #define __Pyx_PyInt_FromHash_t PyInt_FromLong #define __Pyx_PyInt_AsHash_t PyInt_AsLong #else #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t #endif #if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300) #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b) #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value) #define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b) #else #define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \ (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \ (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \ (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0))) #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \ (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \ (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1))) #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \ (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \ (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1))) #endif #if PY_MAJOR_VERSION >= 3 #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) #endif #if PY_VERSION_HEX < 0x02050000 #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n))) #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a)) #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),((char *)(n))) #else #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),(n)) #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a)) #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n)) #endif #if PY_VERSION_HEX < 0x02050000 #define __Pyx_NAMESTR(n) ((char *)(n)) #define __Pyx_DOCSTR(n) ((char *)(n)) #else #define __Pyx_NAMESTR(n) (n) #define __Pyx_DOCSTR(n) (n) #endif #ifndef CYTHON_INLINE #if defined(__GNUC__) #define CYTHON_INLINE __inline__ #elif defined(_MSC_VER) #define CYTHON_INLINE __inline #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_INLINE inline #else #define CYTHON_INLINE #endif #endif #ifndef CYTHON_RESTRICT #if defined(__GNUC__) #define CYTHON_RESTRICT __restrict__ #elif defined(_MSC_VER) && _MSC_VER >= 1400 #define CYTHON_RESTRICT __restrict #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_RESTRICT restrict #else #define CYTHON_RESTRICT #endif #endif #ifdef NAN #define __PYX_NAN() ((float) NAN) #else static CYTHON_INLINE float __PYX_NAN() { /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is a quiet NaN. */ float value; memset(&value, 0xFF, sizeof(value)); return value; } #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) #endif #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include #define __PYX_HAVE__nms__gpu_nms #define __PYX_HAVE_API__nms__gpu_nms #include "string.h" #include "stdio.h" #include "stdlib.h" #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" #include "gpu_nms.hpp" #ifdef _OPENMP #include #endif /* _OPENMP */ #ifdef PYREX_WITHOUT_ASSERTIONS #define CYTHON_WITHOUT_ASSERTIONS #endif #ifndef CYTHON_UNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif # elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif #endif typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/ #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 #define __PYX_DEFAULT_STRING_ENCODING "" #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #define __Pyx_fits_Py_ssize_t(v, type, is_signed) ( \ (sizeof(type) < sizeof(Py_ssize_t)) || \ (sizeof(type) > sizeof(Py_ssize_t) && \ likely(v < (type)PY_SSIZE_T_MAX || \ v == (type)PY_SSIZE_T_MAX) && \ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN || \ v == (type)PY_SSIZE_T_MIN))) || \ (sizeof(type) == sizeof(Py_ssize_t) && \ (is_signed || likely(v < (type)PY_SSIZE_T_MAX || \ v == (type)PY_SSIZE_T_MAX))) ) static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); #define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) #define __Pyx_PyBytes_FromString PyBytes_FromString #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char*); #if PY_MAJOR_VERSION < 3 #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #else #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize #endif #define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_FromUString(s) __Pyx_PyObject_FromString((char*)s) #define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((char*)s) #define __Pyx_PyByteArray_FromUString(s) __Pyx_PyByteArray_FromString((char*)s) #define __Pyx_PyStr_FromUString(s) __Pyx_PyStr_FromString((char*)s) #define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((char*)s) #if PY_MAJOR_VERSION < 3 static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { const Py_UNICODE *u_end = u; while (*u_end++) ; return u_end - u - 1; } #else #define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen #endif #define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode #define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode #define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None) #define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); #if CYTHON_COMPILING_IN_CPYTHON #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys = NULL; PyObject* default_encoding = NULL; PyObject* ascii_chars_u = NULL; PyObject* ascii_chars_b = NULL; sys = PyImport_ImportModule("sys"); if (sys == NULL) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); if (default_encoding == NULL) goto bad; if (strcmp(PyBytes_AsString(default_encoding), "ascii") == 0) { __Pyx_sys_getdefaultencoding_not_ascii = 0; } else { const char* default_encoding_c = PyBytes_AS_STRING(default_encoding); char ascii_chars[128]; int c; for (c = 0; c < 128; c++) { ascii_chars[c] = c; } __Pyx_sys_getdefaultencoding_not_ascii = 1; ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); if (ascii_chars_u == NULL) goto bad; ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); if (ascii_chars_b == NULL || strncmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { PyErr_Format( PyExc_ValueError, "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", default_encoding_c); goto bad; } } Py_XDECREF(sys); Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return 0; bad: Py_XDECREF(sys); Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return -1; } #endif #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) #else #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT static char* __PYX_DEFAULT_STRING_ENCODING; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys = NULL; PyObject* default_encoding = NULL; char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (sys == NULL) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); if (default_encoding == NULL) goto bad; default_encoding_c = PyBytes_AS_STRING(default_encoding); __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); Py_DECREF(sys); Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(sys); Py_XDECREF(default_encoding); return -1; } #endif #endif #ifdef __GNUC__ /* Test for GCC > 2.95 */ #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #else /* __GNUC__ > 2 ... */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ > 2 ... */ #else /* __GNUC__ */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ */ static PyObject *__pyx_m; static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 #elif defined(_Complex_I) #define CYTHON_CCOMPLEX 1 #else #define CYTHON_CCOMPLEX 0 #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus #include #else #include #endif #endif #if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) #undef _Complex_I #define _Complex_I 1.0fj #endif static const char *__pyx_f[] = { "gpu_nms.pyx", "__init__.pxd", "type.pxd", }; #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) typedef struct { const char* name; /* for error messages only */ struct __Pyx_StructField_* fields; size_t size; /* sizeof(type) */ size_t arraysize[8]; 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/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":730 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":731 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< * ctypedef npy_uint64 uint64_t * #ctypedef npy_uint96 uint96_t */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< * #ctypedef npy_uint96 uint96_t * #ctypedef npy_uint128 uint128_t */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":737 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< * ctypedef npy_float64 float64_t * #ctypedef npy_float80 float80_t */ typedef npy_float32 __pyx_t_5numpy_float32_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":738 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< * #ctypedef npy_float80 float80_t * #ctypedef npy_float128 float128_t */ typedef npy_float64 __pyx_t_5numpy_float64_t; 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/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":758 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t */ typedef npy_double __pyx_t_5numpy_float_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":759 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< * ctypedef npy_longdouble longdouble_t * */ typedef npy_double __pyx_t_5numpy_double_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":760 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< float > __pyx_t_float_complex; #else typedef float _Complex __pyx_t_float_complex; #endif #else typedef struct { float real, imag; 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This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; /* Consume from buffer string */ while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; /* breaks both loops as ctx->enc_count == 0 */ } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; /* empty struct */ field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static CYTHON_INLINE PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; /* not a 'break' in the loop */ } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case 10: case 13: ++ts; break; case '<': if (!__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': /* substruct */ { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': /* end of substruct; either repeat or move on */ { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } /* fall through */ case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 's': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; } else { if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; } ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { buf->buf = NULL; buf->obj = NULL; buf->strides = __Pyx_zeros; buf->shape = __Pyx_zeros; buf->suboffsets = __Pyx_minusones; } static CYTHON_INLINE int __Pyx_GetBufferAndValidate( Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None || obj == NULL) { __Pyx_ZeroBuffer(buf); return 0; } buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", nd, buf->ndim); goto fail; } if (!cast) { __Pyx_BufFmt_Context ctx; __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned)buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { if (info->buf == NULL) return; if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; __Pyx_ReleaseBuffer(info); } static PyObject *__Pyx_GetBuiltinName(PyObject *name) { PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); if (unlikely(!result)) { PyErr_Format(PyExc_NameError, #if PY_MAJOR_VERSION >= 3 "name '%U' is not defined", name); #else "name '%.200s' is not defined", PyString_AS_STRING(name)); #endif } return result; } static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { PyObject *result; #if CYTHON_COMPILING_IN_CPYTHON result = PyDict_GetItem(__pyx_d, name); if (result) { Py_INCREF(result); } else { #else result = PyObject_GetItem(__pyx_d, name); if (!result) { PyErr_Clear(); #endif result = __Pyx_GetBuiltinName(name); } return result; } #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); #if PY_VERSION_HEX >= 0x02060000 if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; #endif result = (*call)(func, arg, kw); #if PY_VERSION_HEX >= 0x02060000 Py_LeaveRecursiveCall(); #endif if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(PyObject_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } static void __Pyx_RaiseBufferIndexError(int axis) { PyErr_Format(PyExc_IndexError, "Out of bounds on buffer access (axis %d)", axis); } static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice( PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop, PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice, int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) { #if CYTHON_COMPILING_IN_CPYTHON PyMappingMethods* mp; #if PY_MAJOR_VERSION < 3 PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence; if (likely(ms && ms->sq_slice)) { if (!has_cstart) { if (_py_start && (*_py_start != Py_None)) { cstart = __Pyx_PyIndex_AsSsize_t(*_py_start); if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; } else cstart = 0; } if (!has_cstop) { if (_py_stop && (*_py_stop != Py_None)) { cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop); if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; } else cstop = PY_SSIZE_T_MAX; } if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) { Py_ssize_t l = ms->sq_length(obj); if (likely(l >= 0)) { if (cstop < 0) { cstop += l; if (cstop < 0) cstop = 0; } if (cstart < 0) { cstart += l; if (cstart < 0) cstart = 0; } } else { if (PyErr_ExceptionMatches(PyExc_OverflowError)) PyErr_Clear(); else goto bad; } } return ms->sq_slice(obj, cstart, cstop); } #endif mp = Py_TYPE(obj)->tp_as_mapping; if (likely(mp && mp->mp_subscript)) #endif { PyObject* result; PyObject *py_slice, *py_start, *py_stop; if (_py_slice) { py_slice = *_py_slice; } else { PyObject* owned_start = NULL; PyObject* owned_stop = NULL; if (_py_start) { py_start = *_py_start; } else { if (has_cstart) { owned_start = py_start = PyInt_FromSsize_t(cstart); if (unlikely(!py_start)) goto bad; } else py_start = Py_None; } if (_py_stop) { py_stop = *_py_stop; } else { if (has_cstop) { owned_stop = py_stop = PyInt_FromSsize_t(cstop); if (unlikely(!py_stop)) { Py_XDECREF(owned_start); goto bad; } } else py_stop = Py_None; } py_slice = PySlice_New(py_start, py_stop, Py_None); Py_XDECREF(owned_start); Py_XDECREF(owned_stop); if (unlikely(!py_slice)) goto bad; } #if CYTHON_COMPILING_IN_CPYTHON result = mp->mp_subscript(obj, py_slice); #else result = PyObject_GetItem(obj, py_slice); #endif if (!_py_slice) { Py_DECREF(py_slice); } return result; } PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name); bad: return NULL; } static void __Pyx_RaiseBufferFallbackError(void) { PyErr_SetString(PyExc_ValueError, "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); } static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_Restore(type, value, tb); #endif } static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(type, value, tb); #endif } #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } #if PY_VERSION_HEX < 0x02050000 if (PyClass_Check(type)) { #else if (PyType_Check(type)) { #endif #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; #if PY_VERSION_HEX < 0x02050000 if (PyInstance_Check(type)) { type = (PyObject*) ((PyInstanceObject*)type)->in_class; Py_INCREF(type); } else { type = 0; PyErr_SetString(PyExc_TypeError, "raise: exception must be an old-style class or instance"); goto raise_error; } #else type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } #endif } __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else /* Python 3+ */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { if (PyObject_IsSubclass(instance_class, type)) { type = instance_class; } else { instance_class = NULL; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } #if PY_VERSION_HEX >= 0x03030000 if (cause) { #else if (cause && cause != Py_None) { #endif PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } } bad: Py_XDECREF(owned_instance); return; } #endif static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict) { PyObject *getbuffer_cobj = PyObject_GetItem( obj->ob_type->tp_dict, __pyx_n_s_pyx_getbuffer); if (getbuffer_cobj) { getbufferproc func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj); Py_DECREF(getbuffer_cobj); if (!func) goto fail; return func(obj, view, flags); } else { PyErr_Clear(); } } #endif PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); #if PY_VERSION_HEX < 0x02060000 fail: #endif return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict) { PyObject *releasebuffer_cobj = PyObject_GetItem( obj->ob_type->tp_dict, __pyx_n_s_pyx_releasebuffer); if (releasebuffer_cobj) { releasebufferproc func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj); Py_DECREF(releasebuffer_cobj); if (!func) goto fail; func(obj, view); return; } else { PyErr_Clear(); } } #endif goto nofail; #if PY_VERSION_HEX < 0x02060000 fail: #endif PyErr_WriteUnraisable(obj); nofail: Py_DECREF(obj); view->obj = NULL; } #endif /* PY_MAJOR_VERSION < 3 */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_VERSION_HEX < 0x03030000 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; #if PY_VERSION_HEX >= 0x02050000 { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if (strchr(__Pyx_MODULE_NAME, '.')) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(1); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); #endif if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; /* try absolute import on failure */ } #endif if (!module) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } #else if (level>0) { PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); goto bad; } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); #endif bad: #if PY_VERSION_HEX < 0x03030000 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func) \ { \ func_type value = func(x); \ if (sizeof(target_type) < sizeof(func_type)) { \ if (unlikely(value != (func_type) (target_type) value)) { \ func_type zero = 0; \ PyErr_SetString(PyExc_OverflowError, \ (is_unsigned && unlikely(value < zero)) ? \ "can't convert negative value to " #target_type : \ "value too large to convert to " #target_type); \ return (target_type) -1; \ } \ } \ return (target_type) value; \ } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *x) { const npy_int32 neg_one = (npy_int32) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(npy_int32) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(npy_int32, long, PyInt_AS_LONG) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to npy_int32"); return (npy_int32) -1; } return (npy_int32) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(npy_int32)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return (npy_int32) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to npy_int32"); return (npy_int32) -1; } if (sizeof(npy_int32) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, PyLong_AsUnsignedLong) } else if (sizeof(npy_int32) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long long, PyLong_AsUnsignedLongLong) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(npy_int32)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return +(npy_int32) ((PyLongObject*)x)->ob_digit[0]; case -1: return -(npy_int32) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (sizeof(npy_int32) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(npy_int32, long, PyLong_AsLong) } else if (sizeof(npy_int32) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(npy_int32, long long, PyLong_AsLongLong) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else npy_int32 val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (npy_int32) -1; } } else { npy_int32 val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (npy_int32) -1; val = __Pyx_PyInt_As_npy_int32(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(int) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(a, a); case 3: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, a); case 4: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_absf(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(a, a); case 3: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, a); case 4: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_abs(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(int)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return (int) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong) } else if (sizeof(int) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(int)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return +(int) ((PyLongObject*)x)->ob_digit[0]; case -1: return -(int) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong) } else if (sizeof(int) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(long) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(long)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return (long) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong) } else if (sizeof(long) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(long)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return +(long) ((PyLongObject*)x)->ob_digit[0]; case -1: return -(long) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong) } else if (sizeof(long) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); #if PY_VERSION_HEX < 0x02050000 return PyErr_Warn(NULL, message); #else return PyErr_WarnEx(NULL, message, 1); #endif } return 0; } #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; py_name = __Pyx_PyIdentifier_FromString(name); if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; Py_ssize_t basicsize; #ifdef Py_LIMITED_API PyObject *py_basicsize; #endif py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; py_name = __Pyx_PyIdentifier_FromString(class_name); if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%.200s.%.200s is not a type object", module_name, class_name); goto bad; } #ifndef Py_LIMITED_API basicsize = ((PyTypeObject *)result)->tp_basicsize; #else py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); if (!py_basicsize) goto bad; basicsize = PyLong_AsSsize_t(py_basicsize); Py_DECREF(py_basicsize); py_basicsize = 0; if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; #endif if (!strict && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 if (PyErr_Warn(NULL, warning) < 0) goto bad; #else if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } else if ((size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = (start + end) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, /*int argcount,*/ 0, /*int kwonlyargcount,*/ 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, /*int firstlineno,*/ __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_globals = 0; PyFrameObject *py_frame = 0; py_code = __pyx_find_code_object(c_line ? c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? c_line : py_line, py_code); } py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ py_globals, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = py_line; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) { return __Pyx_PyUnicode_FromStringAndSize(c_str, strlen(c_str)); } static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { Py_ssize_t ignore; return __Pyx_PyObject_AsStringAndSize(o, &ignore); } static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT if ( #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII __Pyx_sys_getdefaultencoding_not_ascii && #endif PyUnicode_Check(o)) { #if PY_VERSION_HEX < 0x03030000 char* defenc_c; PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); if (!defenc) return NULL; defenc_c = PyBytes_AS_STRING(defenc); #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII { char* end = defenc_c + PyBytes_GET_SIZE(defenc); char* c; for (c = defenc_c; c < end; c++) { if ((unsigned char) (*c) >= 128) { PyUnicode_AsASCIIString(o); return NULL; } } } #endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/ *length = PyBytes_GET_SIZE(defenc); return defenc_c; #else /* PY_VERSION_HEX < 0x03030000 */ if (PyUnicode_READY(o) == -1) return NULL; #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII if (PyUnicode_IS_ASCII(o)) { *length = PyUnicode_GET_DATA_SIZE(o); return PyUnicode_AsUTF8(o); } else { PyUnicode_AsASCIIString(o); return NULL; } #else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ return PyUnicode_AsUTF8AndSize(o, length); #endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ #endif /* PY_VERSION_HEX < 0x03030000 */ } else #endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */ #if !CYTHON_COMPILING_IN_PYPY #if PY_VERSION_HEX >= 0x02060000 if (PyByteArray_Check(o)) { *length = PyByteArray_GET_SIZE(o); return PyByteArray_AS_STRING(o); } else #endif #endif { char* result; int r = PyBytes_AsStringAndSize(o, &result, length); if (unlikely(r < 0)) { return NULL; } else { return result; } } } static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_MAJOR_VERSION < 3 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) return PyInt_AS_LONG(b); #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(b)) { case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0]; case 0: return 0; case 1: return ((PyLongObject*)b)->ob_digit[0]; } #endif #endif #if PY_VERSION_HEX < 0x02060000 return PyInt_AsSsize_t(b); #else return PyLong_AsSsize_t(b); #endif } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { #if PY_VERSION_HEX < 0x02050000 if (ival <= LONG_MAX) return PyInt_FromLong((long)ival); else { unsigned char *bytes = (unsigned char *) &ival; int one = 1; int little = (int)*(unsigned char*)&one; return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); } #else return PyInt_FromSize_t(ival); #endif } #endif /* Py_PYTHON_H */ ================================================ FILE: lib/nms/gpu_nms.hpp ================================================ void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, int boxes_dim, float nms_overlap_thresh, int device_id); ================================================ FILE: lib/nms/gpu_nms.pyx ================================================ # -------------------------------------------------------- # Faster R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import numpy as np cimport numpy as np assert sizeof(int) == sizeof(np.int32_t) cdef extern from "gpu_nms.hpp": void _nms(np.int32_t*, int*, np.float32_t*, int, int, float, int) def gpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh, np.int32_t device_id=0): cdef int boxes_num = dets.shape[0] cdef int boxes_dim = dets.shape[1] cdef int num_out cdef np.ndarray[np.int32_t, ndim=1] \ keep = np.zeros(boxes_num, dtype=np.int32) cdef np.ndarray[np.float32_t, ndim=1] \ scores = dets[:, 4] cdef np.ndarray[np.int_t, ndim=1] \ order = scores.argsort()[::-1] cdef np.ndarray[np.float32_t, ndim=2] \ sorted_dets = dets[order, :] _nms(&keep[0], &num_out, &sorted_dets[0, 0], boxes_num, boxes_dim, thresh, device_id) keep = keep[:num_out] return list(order[keep]) ================================================ FILE: lib/nms/nms_kernel.cu ================================================ // ------------------------------------------------------------------ // Faster R-CNN // Copyright (c) 2015 Microsoft // Licensed under The MIT License [see fast-rcnn/LICENSE for details] // Written by Shaoqing Ren // ------------------------------------------------------------------ #include "gpu_nms.hpp" #include #include #define CUDA_CHECK(condition) \ /* Code block avoids redefinition of cudaError_t error */ \ do { \ cudaError_t error = condition; \ if (error != cudaSuccess) { \ std::cout << cudaGetErrorString(error) << std::endl; \ } \ } while (0) #define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) int const threadsPerBlock = sizeof(unsigned long long) * 8; __device__ inline float devIoU(float const * const a, float const * const b) { float left = max(a[0], b[0]), right = min(a[2], b[2]); float top = max(a[1], b[1]), bottom = min(a[3], b[3]); float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f); float interS = width * height; float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1); float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1); return interS / (Sa + Sb - interS); } __global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh, const float *dev_boxes, unsigned long long *dev_mask) { const int row_start = blockIdx.y; const int col_start = blockIdx.x; // if (row_start > col_start) return; const int row_size = min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); const int col_size = min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); __shared__ float block_boxes[threadsPerBlock * 5]; if (threadIdx.x < col_size) { block_boxes[threadIdx.x * 5 + 0] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; block_boxes[threadIdx.x * 5 + 1] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; block_boxes[threadIdx.x * 5 + 2] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; block_boxes[threadIdx.x * 5 + 3] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; block_boxes[threadIdx.x * 5 + 4] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; } __syncthreads(); if (threadIdx.x < row_size) { const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; const float *cur_box = dev_boxes + cur_box_idx * 5; int i = 0; unsigned long long t = 0; int start = 0; if (row_start == col_start) { start = threadIdx.x + 1; } for (i = start; i < col_size; i++) { if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { t |= 1ULL << i; } } const int col_blocks = DIVUP(n_boxes, threadsPerBlock); dev_mask[cur_box_idx * col_blocks + col_start] = t; } } void _set_device(int device_id) { int current_device; CUDA_CHECK(cudaGetDevice(¤t_device)); if (current_device == device_id) { return; } // The call to cudaSetDevice must come before any calls to Get, which // may perform initialization using the GPU. CUDA_CHECK(cudaSetDevice(device_id)); } void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, int boxes_dim, float nms_overlap_thresh, int device_id) { _set_device(device_id); float* boxes_dev = NULL; unsigned long long* mask_dev = NULL; const int col_blocks = DIVUP(boxes_num, threadsPerBlock); CUDA_CHECK(cudaMalloc(&boxes_dev, boxes_num * boxes_dim * sizeof(float))); CUDA_CHECK(cudaMemcpy(boxes_dev, boxes_host, boxes_num * boxes_dim * sizeof(float), cudaMemcpyHostToDevice)); CUDA_CHECK(cudaMalloc(&mask_dev, boxes_num * col_blocks * sizeof(unsigned long long))); dim3 blocks(DIVUP(boxes_num, threadsPerBlock), DIVUP(boxes_num, threadsPerBlock)); dim3 threads(threadsPerBlock); nms_kernel<<>>(boxes_num, nms_overlap_thresh, boxes_dev, mask_dev); std::vector mask_host(boxes_num * col_blocks); CUDA_CHECK(cudaMemcpy(&mask_host[0], mask_dev, sizeof(unsigned long long) * boxes_num * col_blocks, cudaMemcpyDeviceToHost)); std::vector remv(col_blocks); memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); int num_to_keep = 0; for (int i = 0; i < boxes_num; i++) { int nblock = i / threadsPerBlock; int inblock = i % threadsPerBlock; if (!(remv[nblock] & (1ULL << inblock))) { keep_out[num_to_keep++] = i; unsigned long long *p = &mask_host[0] + i * col_blocks; for (int j = nblock; j < col_blocks; j++) { remv[j] |= p[j]; } } } *num_out = num_to_keep; CUDA_CHECK(cudaFree(boxes_dev)); CUDA_CHECK(cudaFree(mask_dev)); } ================================================ FILE: lib/nms/py_cpu_nms.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import numpy as np def py_cpu_nms(dets, thresh): """Pure Python NMS baseline.""" x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] areas = (x2 - x1 + 1) * (y2 - y1 + 1) order = scores.argsort()[::-1] keep = [] while order.size > 0: i = order[0] keep.append(i) xx1 = np.maximum(x1[i], x1[order[1:]]) yy1 = np.maximum(y1[i], y1[order[1:]]) xx2 = np.minimum(x2[i], x2[order[1:]]) yy2 = np.minimum(y2[i], y2[order[1:]]) w = np.maximum(0.0, xx2 - xx1 + 1) h = np.maximum(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (areas[i] + areas[order[1:]] - inter) inds = np.where(ovr <= thresh)[0] order = order[inds + 1] return keep ================================================ FILE: lib/roi_data_layer/__init__.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- ================================================ FILE: lib/roi_data_layer/layer.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Xinlei Chen # -------------------------------------------------------- """The data layer used during training to train a Fast R-CNN network. RoIDataLayer implements a Caffe Python layer. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from model.config import cfg from roi_data_layer.minibatch import get_minibatch import numpy as np import time class RoIDataLayer(object): """Fast R-CNN data layer used for training.""" def __init__(self, roidb, num_classes, random=False): """Set the roidb to be used by this layer during training.""" self._roidb = roidb self._num_classes = num_classes # Also set a random flag self._random = random self._shuffle_roidb_inds() def _shuffle_roidb_inds(self): """Randomly permute the training roidb.""" # If the random flag is set, # then the database is shuffled according to system time # Useful for the validation set if self._random: st0 = np.random.get_state() millis = int(round(time.time() * 1000)) % 4294967295 np.random.seed(millis) if cfg.TRAIN.ASPECT_GROUPING: widths = np.array([r['width'] for r in self._roidb]) heights = np.array([r['height'] for r in self._roidb]) horz = (widths >= heights) vert = np.logical_not(horz) horz_inds = np.where(horz)[0] vert_inds = np.where(vert)[0] inds = np.hstack(( np.random.permutation(horz_inds), np.random.permutation(vert_inds))) inds = np.reshape(inds, (-1, 2)) row_perm = np.random.permutation(np.arange(inds.shape[0])) inds = np.reshape(inds[row_perm, :], (-1,)) self._perm = inds else: self._perm = np.random.permutation(np.arange(len(self._roidb))) # Restore the random state if self._random: np.random.set_state(st0) self._cur = 0 def _get_next_minibatch_inds(self): """Return the roidb indices for the next minibatch.""" if self._cur + cfg.TRAIN.IMS_PER_BATCH >= len(self._roidb): self._shuffle_roidb_inds() #print (len(self._perm)) db_inds = self._perm[self._cur:self._cur + cfg.TRAIN.IMS_PER_BATCH] self._cur += cfg.TRAIN.IMS_PER_BATCH return db_inds def _get_next_minibatch(self): """Return the blobs to be used for the next minibatch. If cfg.TRAIN.USE_PREFETCH is True, then blobs will be computed in a separate process and made available through self._blob_queue. """ db_inds = self._get_next_minibatch_inds() minibatch_db = [self._roidb[i] for i in db_inds] return get_minibatch(minibatch_db, self._num_classes) def forward(self): """Get blobs and copy them into this layer's top blob vector.""" blobs = self._get_next_minibatch() return blobs ================================================ FILE: lib/roi_data_layer/minibatch.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Xinlei Chen # -------------------------------------------------------- """Compute minibatch blobs for training a Fast R-CNN network.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import numpy.random as npr import cv2 from model.config import cfg from utils.blob import prep_im_for_blob, im_list_to_blob def get_minibatch(roidb, num_classes): """Given a roidb, construct a minibatch sampled from it.""" num_images = len(roidb) # Sample random scales to use for each image in this batch random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES), size=num_images) assert(cfg.TRAIN.BATCH_SIZE % num_images == 0), \ 'num_images ({}) must divide BATCH_SIZE ({})'. \ format(num_images, cfg.TRAIN.BATCH_SIZE) # Get the input image blob, formatted for caffe im_blob, im_scales = _get_image_blob(roidb, random_scale_inds) blobs = {'data': im_blob} assert len(im_scales) == 1, "Single batch only" assert len(roidb) == 1, "Single batch only" # gt boxes: (x1, y1, x2, y2, cls) if cfg.TRAIN.USE_ALL_GT: # Include all ground truth boxes gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0] else: # For the COCO ground truth boxes, exclude the ones that are ''iscrowd'' gt_inds = np.where(roidb[0]['gt_classes'] != 0 & np.all(roidb[0]['gt_overlaps'].toarray() > -1.0, axis=1))[0] gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32) gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :] * im_scales[0] gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds] blobs['gt_boxes'] = gt_boxes blobs['im_info'] = np.array( [[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32) return blobs def _get_image_blob(roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] for i in range(num_images): im = cv2.imread(roidb[i]['image']) if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE) im_scales.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales ================================================ FILE: lib/roi_data_layer/minibatch.py~ ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Xinlei Chen # -------------------------------------------------------- """Compute minibatch blobs for training a Fast R-CNN network.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import numpy.random as npr import cv2 from model.config import cfg from utils.blob import prep_im_for_blob, im_list_to_blob def get_minibatch(roidb, num_classes): """Given a roidb, construct a minibatch sampled from it.""" num_images = len(roidb) # Sample random scales to use for each image in this batch random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES), size=num_images) print (num_images) print cfg.TRAIN.BATCH_SIZE assert(cfg.TRAIN.BATCH_SIZE % num_images == 0), \ 'num_images ({}) must divide BATCH_SIZE ({})'. \ format(num_images, cfg.TRAIN.BATCH_SIZE) # Get the input image blob, formatted for caffe im_blob, im_scales = _get_image_blob(roidb, random_scale_inds) blobs = {'data': im_blob} assert len(im_scales) == 1, "Single batch only" assert len(roidb) == 1, "Single batch only" # gt boxes: (x1, y1, x2, y2, cls) if cfg.TRAIN.USE_ALL_GT: # Include all ground truth boxes gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0] else: # For the COCO ground truth boxes, exclude the ones that are ''iscrowd'' gt_inds = np.where(roidb[0]['gt_classes'] != 0 & np.all(roidb[0]['gt_overlaps'].toarray() > -1.0, axis=1))[0] gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32) gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :] * im_scales[0] gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds] blobs['gt_boxes'] = gt_boxes blobs['im_info'] = np.array( [[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32) return blobs def _get_image_blob(roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] for i in range(num_images): im = cv2.imread(roidb[i]['image']) if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE) im_scales.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales ================================================ FILE: lib/roi_data_layer/roidb.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- """Transform a roidb into a trainable roidb by adding a bunch of metadata.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from model.config import cfg from model.bbox_transform import bbox_transform from utils.cython_bbox import bbox_overlaps import PIL def prepare_roidb(imdb): """Enrich the imdb's roidb by adding some derived quantities that are useful for training. This function precomputes the maximum overlap, taken over ground-truth boxes, between each ROI and each ground-truth box. The class with maximum overlap is also recorded. """ roidb = imdb.roidb if not (imdb.name.startswith('coco')): sizes = [PIL.Image.open(imdb.image_path_at(i)).size for i in range(imdb.num_images)] for i in range(len(imdb.image_index)): roidb[i]['image'] = imdb.image_path_at(i) if not (imdb.name.startswith('coco')): roidb[i]['width'] = sizes[i][0] roidb[i]['height'] = sizes[i][1] # need gt_overlaps as a dense array for argmax gt_overlaps = roidb[i]['gt_overlaps'].toarray() # max overlap with gt over classes (columns) max_overlaps = gt_overlaps.max(axis=1) # gt class that had the max overlap max_classes = gt_overlaps.argmax(axis=1) roidb[i]['max_classes'] = max_classes roidb[i]['max_overlaps'] = max_overlaps # sanity checks # max overlap of 0 => class should be zero (background) zero_inds = np.where(max_overlaps == 0)[0] assert all(max_classes[zero_inds] == 0) # max overlap > 0 => class should not be zero (must be a fg class) nonzero_inds = np.where(max_overlaps > 0)[0] assert all(max_classes[nonzero_inds] != 0) ================================================ FILE: lib/setup.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import os from os.path import join as pjoin import numpy as np from distutils.core import setup from distutils.extension import Extension from Cython.Distutils import build_ext def find_in_path(name, path): "Find a file in a search path" #adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ for dir in path.split(os.pathsep): binpath = pjoin(dir, name) if os.path.exists(binpath): return os.path.abspath(binpath) return None def locate_cuda(): """Locate the CUDA environment on the system Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' and values giving the absolute path to each directory. Starts by looking for the CUDAHOME env variable. If not found, everything is based on finding 'nvcc' in the PATH. """ # first check if the CUDAHOME env variable is in use if 'CUDAHOME' in os.environ: home = os.environ['CUDAHOME'] nvcc = pjoin(home, 'bin', 'nvcc') else: # otherwise, search the PATH for NVCC default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) if nvcc is None: raise EnvironmentError('The nvcc binary could not be ' 'located in your $PATH. Either add it to your path, or set $CUDAHOME') home = os.path.dirname(os.path.dirname(nvcc)) cudaconfig = {'home':home, 'nvcc':nvcc, 'include': pjoin(home, 'include'), 'lib64': pjoin(home, 'lib64')} for k, v in cudaconfig.items(): if not os.path.exists(v): raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) return cudaconfig CUDA = locate_cuda() # Obtain the numpy include directory. This logic works across numpy versions. try: numpy_include = np.get_include() except AttributeError: numpy_include = np.get_numpy_include() def customize_compiler_for_nvcc(self): """inject deep into distutils to customize how the dispatch to gcc/nvcc works. If you subclass UnixCCompiler, it's not trivial to get your subclass injected in, and still have the right customizations (i.e. distutils.sysconfig.customize_compiler) run on it. So instead of going the OO route, I have this. Note, it's kindof like a wierd functional subclassing going on.""" # tell the compiler it can processes .cu self.src_extensions.append('.cu') # save references to the default compiler_so and _comple methods default_compiler_so = self.compiler_so super = self._compile # now redefine the _compile method. This gets executed for each # object but distutils doesn't have the ability to change compilers # based on source extension: we add it. def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): print(extra_postargs) if os.path.splitext(src)[1] == '.cu': # use the cuda for .cu files self.set_executable('compiler_so', CUDA['nvcc']) # use only a subset of the extra_postargs, which are 1-1 translated # from the extra_compile_args in the Extension class postargs = extra_postargs['nvcc'] else: postargs = extra_postargs['gcc'] super(obj, src, ext, cc_args, postargs, pp_opts) # reset the default compiler_so, which we might have changed for cuda self.compiler_so = default_compiler_so # inject our redefined _compile method into the class self._compile = _compile # run the customize_compiler class custom_build_ext(build_ext): def build_extensions(self): customize_compiler_for_nvcc(self.compiler) build_ext.build_extensions(self) ext_modules = [ Extension( "utils.cython_bbox", ["utils/bbox.pyx"], extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, include_dirs = [numpy_include] ), Extension( "utils.cython_nms", ["utils/nms.pyx"], extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, include_dirs = [numpy_include] ), Extension( "nms.cpu_nms", ["nms/cpu_nms.pyx"], extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, include_dirs = [numpy_include] ), Extension('nms.gpu_nms', ['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'], library_dirs=[CUDA['lib64']], libraries=['cudart'], language='c++', runtime_library_dirs=[CUDA['lib64']], # this syntax is specific to this build system # we're only going to use certain compiler args with nvcc and not with gcc # the implementation of this trick is in customize_compiler() below extra_compile_args={'gcc': ["-Wno-unused-function"], 'nvcc': ['-arch=sm_52', '--ptxas-options=-v', '-c', '--compiler-options', "'-fPIC'"]}, include_dirs = [numpy_include, CUDA['include']] ) ] setup( name='tf_faster_rcnn', ext_modules=ext_modules, # inject our custom trigger cmdclass={'build_ext': custom_build_ext}, ) ================================================ FILE: lib/setup.py~ ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import os from os.path import join as pjoin import numpy as np from distutils.core import setup from distutils.extension import Extension from Cython.Distutils import build_ext def find_in_path(name, path): "Find a file in a search path" #adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ for dir in path.split(os.pathsep): binpath = pjoin(dir, name) if os.path.exists(binpath): return os.path.abspath(binpath) return None def locate_cuda(): """Locate the CUDA environment on the system Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' and values giving the absolute path to each directory. Starts by looking for the CUDAHOME env variable. If not found, everything is based on finding 'nvcc' in the PATH. """ # first check if the CUDAHOME env variable is in use if 'CUDAHOME' in os.environ: home = os.environ['CUDAHOME'] nvcc = pjoin(home, 'bin', 'nvcc') else: # otherwise, search the PATH for NVCC default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) if nvcc is None: raise EnvironmentError('The nvcc binary could not be ' 'located in your $PATH. Either add it to your path, or set $CUDAHOME') home = os.path.dirname(os.path.dirname(nvcc)) cudaconfig = {'home':home, 'nvcc':nvcc, 'include': pjoin(home, 'include'), 'lib64': pjoin(home, 'lib64')} for k, v in cudaconfig.items(): if not os.path.exists(v): raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) return cudaconfig CUDA = locate_cuda() # Obtain the numpy include directory. This logic works across numpy versions. try: numpy_include = np.get_include() except AttributeError: numpy_include = np.get_numpy_include() def customize_compiler_for_nvcc(self): """inject deep into distutils to customize how the dispatch to gcc/nvcc works. If you subclass UnixCCompiler, it's not trivial to get your subclass injected in, and still have the right customizations (i.e. distutils.sysconfig.customize_compiler) run on it. So instead of going the OO route, I have this. Note, it's kindof like a wierd functional subclassing going on.""" # tell the compiler it can processes .cu self.src_extensions.append('.cu') # save references to the default compiler_so and _comple methods default_compiler_so = self.compiler_so super = self._compile # now redefine the _compile method. This gets executed for each # object but distutils doesn't have the ability to change compilers # based on source extension: we add it. def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): print(extra_postargs) if os.path.splitext(src)[1] == '.cu': # use the cuda for .cu files self.set_executable('compiler_so', CUDA['nvcc']) # use only a subset of the extra_postargs, which are 1-1 translated # from the extra_compile_args in the Extension class postargs = extra_postargs['nvcc'] else: postargs = extra_postargs['gcc'] super(obj, src, ext, cc_args, postargs, pp_opts) # reset the default compiler_so, which we might have changed for cuda self.compiler_so = default_compiler_so # inject our redefined _compile method into the class self._compile = _compile # run the customize_compiler class custom_build_ext(build_ext): def build_extensions(self): customize_compiler_for_nvcc(self.compiler) build_ext.build_extensions(self) ext_modules = [ Extension( "utils.cython_bbox", ["utils/bbox.pyx"], extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, include_dirs = [numpy_include] ), Extension( "utils.cython_nms", ["utils/nms.pyx"], extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, include_dirs = [numpy_include] ), Extension( "nms.cpu_nms", ["nms/cpu_nms.pyx"], extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, include_dirs = [numpy_include] ), Extension('nms.gpu_nms', ['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'], library_dirs=[CUDA['lib64']], libraries=['cudart'], language='c++', runtime_library_dirs=[CUDA['lib64']], # this syntax is specific to this build system # we're only going to use certain compiler args with nvcc and not with gcc # the implementation of this trick is in customize_compiler() below extra_compile_args={'gcc': ["-Wno-unused-function"], 'nvcc': ['-arch=sm_52', '--ptxas-options=-v', '-c', '--compiler-options', "'-fPIC'"]}, include_dirs = [numpy_include, CUDA['include']] ) ] setup( name='tf_faster_rcnn', ext_modules=ext_modules, # inject our custom trigger cmdclass={'build_ext': custom_build_ext}, ) ================================================ FILE: lib/utils/.gitignore ================================================ *.c *.cpp *.h *.hpp ================================================ FILE: lib/utils/__init__.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- ================================================ FILE: lib/utils/bbox.pyx ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Sergey Karayev # -------------------------------------------------------- cimport cython import numpy as np cimport numpy as np DTYPE = np.float ctypedef np.float_t DTYPE_t def bbox_overlaps( np.ndarray[DTYPE_t, ndim=2] boxes, np.ndarray[DTYPE_t, ndim=2] query_boxes): """ Parameters ---------- boxes: (N, 4) ndarray of float query_boxes: (K, 4) ndarray of float Returns ------- overlaps: (N, K) ndarray of overlap between boxes and query_boxes """ cdef unsigned int N = boxes.shape[0] cdef unsigned int K = query_boxes.shape[0] cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) cdef DTYPE_t iw, ih, box_area cdef DTYPE_t ua cdef unsigned int k, n for k in range(K): box_area = ( (query_boxes[k, 2] - query_boxes[k, 0] + 1) * (query_boxes[k, 3] - query_boxes[k, 1] + 1) ) for n in range(N): iw = ( min(boxes[n, 2], query_boxes[k, 2]) - max(boxes[n, 0], query_boxes[k, 0]) + 1 ) if iw > 0: ih = ( min(boxes[n, 3], query_boxes[k, 3]) - max(boxes[n, 1], query_boxes[k, 1]) + 1 ) if ih > 0: ua = float( (boxes[n, 2] - boxes[n, 0] + 1) * (boxes[n, 3] - boxes[n, 1] + 1) + box_area - iw * ih ) overlaps[n, k] = iw * ih / ua return overlaps def bbox_overlaps_self( np.ndarray[DTYPE_t, ndim=2] boxes, np.ndarray[DTYPE_t, ndim=2] query_boxes): """ Parameters ---------- boxes: (N, 4) ndarray of float query_boxes: (K, 4) ndarray of float Returns ------- overlaps: (N, K) ndarray of overlap between boxes and query_boxes """ cdef unsigned int N = boxes.shape[0] cdef unsigned int K = query_boxes.shape[0] cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) cdef DTYPE_t iw, ih, box_area cdef DTYPE_t ua cdef unsigned int k, n for k in range(K): box_area = ( (query_boxes[k, 2] - query_boxes[k, 0] + 1) * (query_boxes[k, 3] - query_boxes[k, 1] + 1) ) for n in range(N): iw = ( min(boxes[n, 2], query_boxes[k, 2]) - max(boxes[n, 0], query_boxes[k, 0]) + 1 ) if iw > 0: ih = ( min(boxes[n, 3], query_boxes[k, 3]) - max(boxes[n, 1], query_boxes[k, 1]) + 1 ) if ih > 0: ua = float(box_area) overlaps[n, k] = iw * ih / ua return overlaps ================================================ FILE: lib/utils/blob.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- """Blob helper functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import cv2 def im_list_to_blob(ims): """Convert a list of images into a network input. Assumes images are already prepared (means subtracted, BGR order, ...). """ max_shape = np.array([im.shape for im in ims]).max(axis=0) num_images = len(ims) blob = np.zeros((num_images, max_shape[0], max_shape[1], 3), dtype=np.float32) for i in range(num_images): im = ims[i] blob[i, 0:im.shape[0], 0:im.shape[1], :] = im return blob def prep_im_for_blob(im, pixel_means, target_size, max_size): """Mean subtract and scale an image for use in a blob.""" im = im.astype(np.float32, copy=False) im -= pixel_means im_shape = im.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > max_size: im_scale = float(max_size) / float(im_size_max) im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) return im, im_scale ================================================ FILE: lib/utils/boxes_grid.py ================================================ # -------------------------------------------------------- # Subcategory CNN # Copyright (c) 2015 CVGL Stanford # Licensed under The MIT License [see LICENSE for details] # Written by Yu Xiang # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import math from model.config import cfg def get_boxes_grid(image_height, image_width): """ Return the boxes on image grid. """ # height and width of the heatmap if cfg.NET_NAME == 'CaffeNet': height = np.floor((image_height * max(cfg.TRAIN.SCALES) - 1) / 4.0 + 1) height = np.floor((height - 1) / 2.0 + 1 + 0.5) height = np.floor((height - 1) / 2.0 + 1 + 0.5) width = np.floor((image_width * max(cfg.TRAIN.SCALES) - 1) / 4.0 + 1) width = np.floor((width - 1) / 2.0 + 1 + 0.5) width = np.floor((width - 1) / 2.0 + 1 + 0.5) elif cfg.NET_NAME == 'VGGnet': height = np.floor(image_height * max(cfg.TRAIN.SCALES) / 2.0 + 0.5) height = np.floor(height / 2.0 + 0.5) height = np.floor(height / 2.0 + 0.5) height = np.floor(height / 2.0 + 0.5) width = np.floor(image_width * max(cfg.TRAIN.SCALES) / 2.0 + 0.5) width = np.floor(width / 2.0 + 0.5) width = np.floor(width / 2.0 + 0.5) width = np.floor(width / 2.0 + 0.5) else: assert (1), 'The network architecture is not supported in utils.get_boxes_grid!' # compute the grid box centers h = np.arange(height) w = np.arange(width) y, x = np.meshgrid(h, w, indexing='ij') centers = np.dstack((x, y)) centers = np.reshape(centers, (-1, 2)) num = centers.shape[0] # compute width and height of grid box area = cfg.TRAIN.KERNEL_SIZE * cfg.TRAIN.KERNEL_SIZE aspect = cfg.TRAIN.ASPECTS # height / width num_aspect = len(aspect) widths = np.zeros((1, num_aspect), dtype=np.float32) heights = np.zeros((1, num_aspect), dtype=np.float32) for i in range(num_aspect): widths[0, i] = math.sqrt(area / aspect[i]) heights[0, i] = widths[0, i] * aspect[i] # construct grid boxes centers = np.repeat(centers, num_aspect, axis=0) widths = np.tile(widths, num).transpose() heights = np.tile(heights, num).transpose() x1 = np.reshape(centers[:, 0], (-1, 1)) - widths * 0.5 x2 = np.reshape(centers[:, 0], (-1, 1)) + widths * 0.5 y1 = np.reshape(centers[:, 1], (-1, 1)) - heights * 0.5 y2 = np.reshape(centers[:, 1], (-1, 1)) + heights * 0.5 boxes_grid = np.hstack((x1, y1, x2, y2)) / cfg.TRAIN.SPATIAL_SCALE return boxes_grid, centers[:, 0], centers[:, 1] ================================================ FILE: lib/utils/nms.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import numpy as np def nms(dets, thresh): x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] areas = (x2 - x1 + 1) * (y2 - y1 + 1) order = scores.argsort()[::-1] keep = [] while order.size > 0: i = order[0] keep.append(i) xx1 = np.maximum(x1[i], x1[order[1:]]) yy1 = np.maximum(y1[i], y1[order[1:]]) xx2 = np.minimum(x2[i], x2[order[1:]]) yy2 = np.minimum(y2[i], y2[order[1:]]) w = np.maximum(0.0, xx2 - xx1 + 1) h = np.maximum(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (areas[i] + areas[order[1:]] - inter) inds = np.where(ovr <= thresh)[0] order = order[inds + 1] return keep ================================================ FILE: lib/utils/nms.pyx ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import numpy as np cimport numpy as np cdef inline np.float32_t max(np.float32_t a, np.float32_t b): return a if a >= b else b cdef inline np.float32_t min(np.float32_t a, np.float32_t b): return a if a <= b else b def nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1] cdef int ndets = dets.shape[0] cdef np.ndarray[np.int_t, ndim=1] suppressed = \ np.zeros((ndets), dtype=np.int) # nominal indices cdef int _i, _j # sorted indices cdef int i, j # temp variables for box i's (the box currently under consideration) cdef np.float32_t ix1, iy1, ix2, iy2, iarea # variables for computing overlap with box j (lower scoring box) cdef np.float32_t xx1, yy1, xx2, yy2 cdef np.float32_t w, h cdef np.float32_t inter, ovr keep = [] for _i in range(ndets): i = order[_i] if suppressed[i] == 1: continue keep.append(i) ix1 = x1[i] iy1 = y1[i] ix2 = x2[i] iy2 = y2[i] iarea = areas[i] for _j in range(_i + 1, ndets): j = order[_j] if suppressed[j] == 1: continue xx1 = max(ix1, x1[j]) yy1 = max(iy1, y1[j]) xx2 = min(ix2, x2[j]) yy2 = min(iy2, y2[j]) w = max(0.0, xx2 - xx1 + 1) h = max(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (iarea + areas[j] - inter) if ovr >= thresh: suppressed[j] = 1 return keep def nms_new(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1] cdef int ndets = dets.shape[0] cdef np.ndarray[np.int_t, ndim=1] suppressed = \ np.zeros((ndets), dtype=np.int) # nominal indices cdef int _i, _j # sorted indices cdef int i, j # temp variables for box i's (the box currently under consideration) cdef np.float32_t ix1, iy1, ix2, iy2, iarea # variables for computing overlap with box j (lower scoring box) cdef np.float32_t xx1, yy1, xx2, yy2 cdef np.float32_t w, h cdef np.float32_t inter, ovr keep = [] for _i in range(ndets): i = order[_i] if suppressed[i] == 1: continue keep.append(i) ix1 = x1[i] iy1 = y1[i] ix2 = x2[i] iy2 = y2[i] iarea = areas[i] for _j in range(_i + 1, ndets): j = order[_j] if suppressed[j] == 1: continue xx1 = max(ix1, x1[j]) yy1 = max(iy1, y1[j]) xx2 = min(ix2, x2[j]) yy2 = min(iy2, y2[j]) w = max(0.0, xx2 - xx1 + 1) h = max(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (iarea + areas[j] - inter) ovr1 = inter / iarea ovr2 = inter / areas[j] if ovr >= thresh or ovr1 > 0.95 or ovr2 > 0.95: suppressed[j] = 1 return keep ================================================ FILE: lib/utils/timer.py ================================================ # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import time class Timer(object): """A simple timer.""" def __init__(self): self.total_time = 0. self.calls = 0 self.start_time = 0. self.diff = 0. self.average_time = 0. def tic(self): # using time.time instead of time.clock because time time.clock # does not normalize for multithreading self.start_time = time.time() def toc(self, average=True): self.diff = time.time() - self.start_time self.total_time += self.diff self.calls += 1 self.average_time = self.total_time / self.calls if average: return self.average_time else: return self.diff ================================================ FILE: tools/_init_paths.py ================================================ import os.path as osp import sys def add_path(path): if path not in sys.path: sys.path.insert(0, path) this_dir = osp.dirname(__file__) # Add lib to PYTHONPATH lib_path = osp.join(this_dir, '..', 'lib') add_path(lib_path) coco_path = osp.join(this_dir, '..', 'data', 'coco', 'PythonAPI') add_path(coco_path) ================================================ FILE: tools/demo.py~ ================================================ #!/usr/bin/env python # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- """ Demo script showing detections in sample images. See README.md for installation instructions before running. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.config import cfg from model.test import im_detect from model.nms_wrapper import nms from utils.timer import Timer import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os, cv2 import argparse from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 CLASSES = ('__background__', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor') NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',)} DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)} def vis_detections(im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: return im = im[:, :, (2, 1, 0)] fig, ax = plt.subplots(figsize=(12, 12)) ax.imshow(im, aspect='equal') for i in inds: bbox = dets[i, :4] score = dets[i, -1] ax.add_patch( plt.Rectangle((bbox[0], bbox[1]), bbox[2] - bbox[0], bbox[3] - bbox[1], fill=False, edgecolor='red', linewidth=3.5) ) ax.text(bbox[0], bbox[1] - 2, '{:s} {:.3f}'.format(class_name, score), bbox=dict(facecolor='blue', alpha=0.5), fontsize=14, color='white') ax.set_title(('{} detections with ' 'p({} | box) >= {:.1f}').format(class_name, class_name, thresh), fontsize=14) plt.axis('off') plt.tight_layout() plt.draw() def demo(sess, net, image_name): """Detect object classes in an image using pre-computed object proposals.""" # Load the demo image im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) im = cv2.imread(im_file) # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(sess, net, im) timer.toc() print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) # Visualize detections for each class CONF_THRESH = 0.8 NMS_THRESH = 0.3 for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] vis_detections(im, cls, dets, thresh=CONF_THRESH) def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', choices=NETS.keys(), default='res101') parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', choices=DATASETS.keys(), default='pascal_voc_0712') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() # model path demonet = args.demo_net dataset = args.dataset tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0]) if not os.path.isfile(tfmodel + '.meta'): raise IOError(('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(tfmodel + '.meta')) # set config tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth=True # init session sess = tf.Session(config=tfconfig) # load network if demonet == 'vgg16': net = vgg16(batch_size=1) elif demonet == 'res101': net = resnetv1(batch_size=1, num_layers=101) else: raise NotImplementedError net.create_architecture(sess, "TEST", 21, tag='default', anchor_scales=[8, 16, 32]) saver = tf.train.Saver() saver.restore(sess, tfmodel) print('Loaded network {:s}'.format(tfmodel)) im_names = ['0010.png','0012.png','0027.png','0038.png','0039.png','000456.jpg', '000542.jpg', '001150.jpg', '001763.jpg', '004545.jpg'] for im_name in im_names: print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') print('Demo for data/demo/{}'.format(im_name)) demo(sess, net, im_name) plt.show() ================================================ FILE: tools/demo_graspRGD.py ================================================ #!/usr/bin/env python # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- """ Demo script showing detections in sample images. See README.md for installation instructions before running. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.config import cfg from model.test import im_detect from model.nms_wrapper import nms from utils.timer import Timer import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os, cv2 import argparse from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 import scipy from shapely.geometry import Polygon pi = scipy.pi dot = scipy.dot sin = scipy.sin cos = scipy.cos ar = scipy.array CLASSES = ('__background__', 'angle_01', 'angle_02', 'angle_03', 'angle_04', 'angle_05', 'angle_06', 'angle_07', 'angle_08', 'angle_09', 'angle_10', 'angle_11', 'angle_12', 'angle_13', 'angle_14', 'angle_15', 'angle_16', 'angle_17', 'angle_18', 'angle_19') NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',),'res50': ('res50_faster_rcnn_iter_240000.ckpt',)} DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',),'grasp': ('train',)} def Rotate2D(pts,cnt,ang=scipy.pi/4): '''pts = {} Rotates points(nx2) about center cnt(2) by angle ang(1) in radian''' return dot(pts-cnt,ar([[cos(ang),sin(ang)],[-sin(ang),cos(ang)]]))+cnt def vis_detections(ax, image_name, im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: return im = im[:, :, (2, 1, 0)] #fig, ax = plt.subplots(figsize=(12, 12)) ax.imshow(im, aspect='equal') for i in inds: bbox = dets[i, :4] score = dets[i, -1] #ax.add_patch( # plt.Rectangle((bbox[0], bbox[1]), # bbox[2] - bbox[0], # bbox[3] - bbox[1], fill=False, # edgecolor='red', linewidth=3.5) # ) # plot rotated rectangles pts = ar([[bbox[0],bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]]) cnt = ar([(bbox[0] + bbox[2])/2, (bbox[1] + bbox[3])/2]) angle = int(class_name[6:]) r_bbox = Rotate2D(pts, cnt, -pi/2-pi/20*(angle-1)) pred_label_polygon = Polygon([(r_bbox[0,0],r_bbox[0,1]), (r_bbox[1,0], r_bbox[1,1]), (r_bbox[2,0], r_bbox[2,1]), (r_bbox[3,0], r_bbox[3,1])]) pred_x, pred_y = pred_label_polygon.exterior.xy plt.plot(pred_x[0:2],pred_y[0:2], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[1:3],pred_y[1:3], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) plt.plot(pred_x[2:4],pred_y[2:4], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[3:5],pred_y[3:5], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) #ax.text(bbox[0], bbox[1] - 2, # '{:s} {:.3f}'.format(class_name, score), # bbox=dict(facecolor='blue', alpha=0.5), # fontsize=14, color='white') #ax.set_title(('{} detections with ' # 'p({} | box) >= {:.1f}').format(class_name, class_name, # thresh), # fontsize=14) #plt.axis('off') #plt.tight_layout() #save result #savepath = './data/demo/results/' + str(image_name) + str(class_name) + '.png' #plt.savefig(savepath) #plt.draw() def demo(sess, net, image_name): """Detect object classes in an image using pre-computed object proposals.""" # Load the demo image im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) im = cv2.imread(im_file) #print(im) # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(sess, net, im) #scores_max = scores[:,1:-1].max(axis=1) #scores_max_idx = np.argmax(scores_max) #scores = scores[scores_max_idx:scores_max_idx+1,:] #boxes = boxes[scores_max_idx:scores_max_idx+1, :] #im = cv2.imread('/home/fujenchu/projects/deepLearning/tensorflow-finetune-flickr-style-master/data/grasps_ivalab/rgb_cropped320/rgb_0076Cropped320.png') timer.toc() print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) fig, ax = plt.subplots(figsize=(12, 12)) # Visualize detections for each class CONF_THRESH = 0.1 NMS_THRESH = 0.3 for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] vis_detections(ax, image_name, im, cls, dets, thresh=CONF_THRESH) #tmp = max(cls_scores) plt.axis('off') plt.tight_layout() #cv2.imshow('deepGrasp_top_score', im) #choice = cv2.waitKey(100) #save result savepath = './data/demo/results_all_cls/' + str(image_name) + '.png' plt.savefig(savepath) plt.draw() def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', choices=NETS.keys(), default='res101') parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', choices=DATASETS.keys(), default='pascal_voc_0712') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() # model path demonet = args.demo_net dataset = args.dataset tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0]) if not os.path.isfile(tfmodel + '.meta'): raise IOError(('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(tfmodel + '.meta')) # set config tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth=True # init session sess = tf.Session(config=tfconfig) # load network if demonet == 'vgg16': net = vgg16(batch_size=1) elif demonet == 'res101': net = resnetv1(batch_size=1, num_layers=101) elif demonet == 'res50': net = resnetv1(batch_size=1, num_layers=50) else: raise NotImplementedError net.create_architecture(sess, "TEST", 20, tag='default', anchor_scales=[8, 16, 32]) saver = tf.train.Saver() saver.restore(sess, tfmodel) print('Loaded network {:s}'.format(tfmodel)) #im_names = ['rgd_0076Cropped320.png','rgd_0095.png','pcd0122r_rgd_preprocessed_1.png','pcd0875r_rgd_preprocessed_1.png','resized_0875_2.png'] im_names = ['pcd0100r_rgd_preprocessed_1.png','pcd0266r_rgd_preprocessed_1.png','pcd0882r_rgd_preprocessed_1.png','rgd_0000Cropped320.png'] for im_name in im_names: print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') print('Demo for data/demo/{}'.format(im_name)) demo(sess, net, im_name) plt.show() ================================================ FILE: tools/demo_graspRGD.py~ ================================================ #!/usr/bin/env python # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- """ Demo script showing detections in sample images. See README.md for installation instructions before running. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.config import cfg from model.test import im_detect from model.nms_wrapper import nms from utils.timer import Timer import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os, cv2 import argparse from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 import scipy from shapely.geometry import Polygon pi = scipy.pi dot = scipy.dot sin = scipy.sin cos = scipy.cos ar = scipy.array CLASSES = ('__background__', 'angle_01', 'angle_02', 'angle_03', 'angle_04', 'angle_05', 'angle_06', 'angle_07', 'angle_08', 'angle_09', 'angle_10', 'angle_11', 'angle_12', 'angle_13', 'angle_14', 'angle_15', 'angle_16', 'angle_17', 'angle_18', 'angle_19') NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',),'res50': ('res50_faster_rcnn_iter_240000.ckpt',)} DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',),'grasp': ('train',)} def Rotate2D(pts,cnt,ang=scipy.pi/4): '''pts = {} Rotates points(nx2) about center cnt(2) by angle ang(1) in radian''' return dot(pts-cnt,ar([[cos(ang),sin(ang)],[-sin(ang),cos(ang)]]))+cnt def vis_detections(ax, image_name, im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: return im = im[:, :, (2, 1, 0)] #fig, ax = plt.subplots(figsize=(12, 12)) ax.imshow(im, aspect='equal') for i in inds: bbox = dets[i, :4] score = dets[i, -1] #ax.add_patch( # plt.Rectangle((bbox[0], bbox[1]), # bbox[2] - bbox[0], # bbox[3] - bbox[1], fill=False, # edgecolor='red', linewidth=3.5) # ) # plot rotated rectangles pts = ar([[bbox[0],bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]]) cnt = ar([(bbox[0] + bbox[2])/2, (bbox[1] + bbox[3])/2]) angle = int(class_name[6:]) r_bbox = Rotate2D(pts, cnt, -pi/2-pi/20*(angle-1)) pred_label_polygon = Polygon([(r_bbox[0,0],r_bbox[0,1]), (r_bbox[1,0], r_bbox[1,1]), (r_bbox[2,0], r_bbox[2,1]), (r_bbox[3,0], r_bbox[3,1])]) pred_x, pred_y = pred_label_polygon.exterior.xy plt.plot(pred_x[0:2],pred_y[0:2], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[1:3],pred_y[1:3], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) plt.plot(pred_x[2:4],pred_y[2:4], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[3:5],pred_y[3:5], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) #ax.text(bbox[0], bbox[1] - 2, # '{:s} {:.3f}'.format(class_name, score), # bbox=dict(facecolor='blue', alpha=0.5), # fontsize=14, color='white') #ax.set_title(('{} detections with ' # 'p({} | box) >= {:.1f}').format(class_name, class_name, # thresh), # fontsize=14) #plt.axis('off') #plt.tight_layout() #save result #savepath = './data/demo/results/' + str(image_name) + str(class_name) + '.png' #plt.savefig(savepath) #plt.draw() def demo(sess, net, image_name): """Detect object classes in an image using pre-computed object proposals.""" # Load the demo image im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) im = cv2.imread(im_file) #print(im) # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(sess, net, im) #scores_max = scores[:,1:-1].max(axis=1) #scores_max_idx = np.argmax(scores_max) #scores = scores[scores_max_idx:scores_max_idx+1,:] #boxes = boxes[scores_max_idx:scores_max_idx+1, :] #im = cv2.imread('/home/fujenchu/projects/deepLearning/tensorflow-finetune-flickr-style-master/data/grasps_ivalab/rgb_cropped320/rgb_0076Cropped320.png') timer.toc() print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) fig, ax = plt.subplots(figsize=(12, 12)) # Visualize detections for each class CONF_THRESH = 0.1 NMS_THRESH = 0.3 for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] vis_detections(ax, image_name, im, cls, dets, thresh=CONF_THRESH) #tmp = max(cls_scores) plt.axis('off') plt.tight_layout() #cv2.imshow('deepGrasp_top_score', im) #choice = cv2.waitKey(100) #save result savepath = './data/demo/results_all_cls/' + str(image_name) + '.png' plt.savefig(savepath) plt.draw() def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', choices=NETS.keys(), default='res101') parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', choices=DATASETS.keys(), default='pascal_voc_0712') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() # model path demonet = args.demo_net dataset = args.dataset tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0]) if not os.path.isfile(tfmodel + '.meta'): raise IOError(('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(tfmodel + '.meta')) # set config tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth=True # init session sess = tf.Session(config=tfconfig) # load network if demonet == 'vgg16': net = vgg16(batch_size=1) elif demonet == 'res101': net = resnetv1(batch_size=1, num_layers=101) elif demonet == 'res50': net = resnetv1(batch_size=1, num_layers=50) else: raise NotImplementedError net.create_architecture(sess, "TEST", 20, tag='default', anchor_scales=[8, 16, 32]) saver = tf.train.Saver() saver.restore(sess, tfmodel) print('Loaded network {:s}'.format(tfmodel)) #im_names = ['rgd_0076Cropped320.png','rgd_0095.png','pcd0122r_rgd_preprocessed_1.png','pcd0875r_rgd_preprocessed_1.png','resized_0875_2.png'] im_names = ['0010.png','0012.png','0027.png','0038.png','0039.png','pcd0875r_rgd_preprocessed_1.png','pic_0010.png'] for im_name in im_names: print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') print('Demo for data/demo/{}'.format(im_name)) demo(sess, net, im_name) plt.show() ================================================ FILE: tools/demo_graspRGD_socket.py ================================================ #!/usr/bin/env python # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- """ Demo script showing detections in sample images. See README.md for installation instructions before running. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.config import cfg from model.test import im_detect from model.nms_wrapper import nms from utils.timer import Timer import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os, cv2 import argparse import threading import socket import struct import time import cv2.aruco as aruco from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 import scipy from shapely.geometry import Polygon pi = scipy.pi dot = scipy.dot sin = scipy.sin cos = scipy.cos ar = scipy.array frame_current=[] pred_x, pred_y = np.asarray([1,50,100,200]), np.asarray([50, 100, 200, 400]) CLASSES = ('__background__', 'angle_01', 'angle_02', 'angle_03', 'angle_04', 'angle_05', 'angle_06', 'angle_07', 'angle_08', 'angle_09', 'angle_10', 'angle_11', 'angle_12', 'angle_13', 'angle_14', 'angle_15', 'angle_16', 'angle_17', 'angle_18', 'angle_19') NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',),'res50': ('res50_faster_rcnn_iter_240000.ckpt',)} DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',),'grasp': ('train',)} ANGLES = (-1000, 0, -10, -20, -30, -40, -50, -60, -70, -80, -90, 80, 70, 60, 50, 40, 30, 20, 10, 0) def Rotate2D(pts,cnt,ang=scipy.pi/4): '''pts = {} Rotates points(nx2) about center cnt(2) by angle ang(1) in radian''' return dot(pts-cnt,ar([[cos(ang),sin(ang)],[-sin(ang),cos(ang)]]))+cnt def vis_detections(ax, im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: return im = im[:, :, (2, 1, 0)] #fig, ax = plt.subplots(figsize=(12, 12)) ax.imshow(im, aspect='equal') for i in inds: bbox = dets[i, :4] score = dets[i, -1] #ax.add_patch( # plt.Rectangle((bbox[0], bbox[1]), # bbox[2] - bbox[0], # bbox[3] - bbox[1], fill=False, # edgecolor='red', linewidth=3.5) # ) # plot rotated rectangles pts = ar([[bbox[0],bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]]) cnt = ar([(bbox[0] + bbox[2])/2, (bbox[1] + bbox[3])/2]) angle = int(class_name[6:]) r_bbox = Rotate2D(pts, cnt, -pi/2-pi/20*(angle-1)) pred_label_polygon = Polygon([(r_bbox[0,0],r_bbox[0,1]), (r_bbox[1,0], r_bbox[1,1]), (r_bbox[2,0], r_bbox[2,1]), (r_bbox[3,0], r_bbox[3,1])]) pred_x, pred_y = pred_label_polygon.exterior.xy plt.plot(pred_x[0:2],pred_y[0:2], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[1:3],pred_y[1:3], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) plt.plot(pred_x[2:4],pred_y[2:4], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[3:5],pred_y[3:5], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) #ax.text(bbox[0], bbox[1] - 2, # '{:s} {:.3f}'.format(class_name, score), # bbox=dict(facecolor='blue', alpha=0.5), # fontsize=14, color='white') #ax.set_title(('{} detections with ' # 'p({} | box) >= {:.1f}').format(class_name, class_name, # thresh), # fontsize=14) #plt.axis('off') #plt.tight_layout() #save result #savepath = './data/demo/results/' + str(image_name) + str(class_name) + '.png' #plt.savefig(savepath) #plt.draw() def compute_imgRot(frame): # aim to find a ARUCO marker and compute the camera rotation on XY plane # camera matrix markerLength = 0.06 # old camera matrix used by Yufeng # cameraMatrix = np.array([[297.47608, 0.0, 320], [0.0, 297.14815, 240], [0.0, 0.0, 1.0]]) # camera frame from socket # distCoeffs = np.array([0.15190073, -0.8267655, 0.00985276, -0.00435892, 1.58437205]) # fake value # new camera matrix obtained at 01/2018 cameraMatrix = np.array( [[592.90077, 0.0, 327.06503], [0.0, 591.07515, 239.40367], [0.0, 0.0, 1.0]]) # camera frame from socket distCoeffs = np.array([-0.02067, 0.06351, -0.00285, 0.00083, 0.00000]) # fake value # find ARUCO marker gray = frame * 255 # print(gray) gray = gray.astype(np.uint8) gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) # print (gray) # gray = gray.astype(np.uint8) aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) parameters = aruco.DetectorParameters_create() #P_reshape = np.reshape(P, (7*7*2*2)) #top = P_reshape.argsort()[-10:][::-1] #index = top[3] corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters) print('detected aruco mark (table angle compensation): ' + str(ids)) gray = aruco.drawDetectedMarkers(gray, corners) dst = 0 tvec = 0 T = 0 angle = 0.0 if ids is not None: point0 = corners[0][0][0] point3 = corners[0][0][3] #if point0[1] >= point3[1]: # rotate counter-clock wise angle = np.arctan((point0[1] - point3[1]) / (point3[0] - point0[0])) print('counter-clock rotate angle in degree: ' + str(angle/3.14*180)) print('(simply added to detected degree to compensate the table)') return angle/3.14*180 # else: # rotate clock wise # angle = np.arctan((point3[1] - point0[1]) / (point3[0] - point0[0])) # print('clock') # print(angle/3.14*180) else: return angle def coordinate_img2table(frame, u, v, rot): """project found u v coordinate on image to x y coordinate on table with ARUCO marker.""" # camera matrix markerLength = 0.06 # old camera matrix used by Yufeng #cameraMatrix = np.array([[297.47608, 0.0, 320], [0.0, 297.14815, 240], [0.0, 0.0, 1.0]]) # camera frame from socket #distCoeffs = np.array([0.15190073, -0.8267655, 0.00985276, -0.00435892, 1.58437205]) # fake value # new camera matrix obtained at 01/2018 cameraMatrix = np.array([[592.90077, 0.0, 327.06503], [0.0, 591.07515, 239.40367], [0.0, 0.0, 1.0]]) # camera frame from socket distCoeffs = np.array([-0.02067, 0.06351, -0.00285, 0.00083, 0.00000]) # fake value # find ARUCO marker gray = frame*255 #print(gray) gray = gray.astype(np.uint8) gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) #print (gray) #gray = gray.astype(np.uint8) aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) parameters = aruco.DetectorParameters_create() corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters) print('detected aruco mark (table location): ' + str(ids)) gray = aruco.drawDetectedMarkers(gray, corners) dst = 0 tvec = 0 T = 0 point_new = np.zeros((3, 1)) if ids is not None: rvec, tvec, _ = aruco.estimatePoseSingleMarkers(corners[0], markerLength, cameraMatrix, distCoeffs) dst, jacobian = cv2.Rodrigues(rvec) T = np.zeros((4, 4)) T[:3, :3] = dst T[:3, 3] = tvec T[3, :] = np.array([0, 0, 0, 1]) # projection imagePts = np.array([u, v, 1]) normal_old = np.array([0, 0, 1]) ray_center = np.array([0, 0, 0]) distance_old = 0 normal_new = np.dot(dst, normal_old) #(3,) normal_new = np.expand_dims(normal_new, 1) #(3,1) translation_old = tvec #(1,1,3) translation_old = np.squeeze(translation_old, 0) #(1,3) distance_new = -(distance_old + np.dot(translation_old, normal_new)) ray = np.dot(np.linalg.inv(cameraMatrix), imagePts) #(3,) t = -(np.dot(normal_new.transpose(), ray_center) + distance_new) / np.dot(normal_new.transpose(), ray) # (1,1) intersection = np.multiply(ray, t) #(1,3) intersection_homo = np.array([intersection[0,0], intersection[0,1], intersection[0,2], 1]) point_new = np.dot(np.linalg.inv(T), intersection_homo) #print(point_new[0] + 0.36)#0.3 #print(point_new[1] + 0.35)#0.4# print('location on table: ' + str(point_new[0] + 0.30))#0.3 print('location on table: ' + str(point_new[1] + 0.30))#0.4# return point_new[0] + 0.30, point_new[1] + 0.30, rot def demo_process(sess, net): """Detect object classes in an image using pre-computed object proposals.""" count = 0 bbs_array = np.array([], dtype=np.float32).reshape(0, 5) im = [] tmp_g = [] scores= [] boxes = [] #fig, ax = plt.subplots(figsize=(12, 12)) while True: if frame_current != []: print('\n') print('############ Start detection on a new frame ############') bbs_array = np.array([], dtype=np.float32).reshape(0, 5) # Load the demo image #im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) #im = cv2.imread(im_file) #print (frame_current) im = frame_current tmp_g = im[:,:,1] im[:,:,1] = im[:,:,2] im[:,:,2] = tmp_g im = im*255 #img = im.astype('uint8') #ax.imshow(img) # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(sess, net, im) #scores_max = scores[:,1:-1].max(axis=1) #scores_max_idx = np.argmax(scores_max) #scores = scores[scores_max_idx:scores_max_idx+1,:] #boxes = boxes[scores_max_idx:scores_max_idx+1, :] timer.toc() print('deepGrasp detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) #fig, ax = plt.subplots(figsize=(12, 12)) #uncommand this when writing files into disk # Visualize detections for each class CONF_THRESH = 0.001 CONF_THRESH = 0.1 # demo for spoon 2018/08 NMS_THRESH = 0.3 top_score = 0; top_cls = 0; angle_compensated = 0.0 top_boxes = np.zeros(2) top_boxes_coor = np.zeros(4) for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] # stack all det > threshold dets_th = np.delete(dets, np.where(dets[:, -1] < CONF_THRESH)[0], axis=0) bbs_array = np.vstack((bbs_array, dets_th)) #vis_detections(ax, im, cls, dets, thresh=CONF_THRESH) #uncommand if you want to visualize if ( max(cls_scores) > top_score): #print (max(cls_scores)) #print(np.amax(cls_scores)) #print (np.argmax(cls_scores)) #print (cls_boxes[np.argmax(cls_scores),:]) top_boxes_coor = cls_boxes[np.argmax(cls_scores),:] top_boxes[0] = (top_boxes_coor[0] + top_boxes_coor[2]) / 2 top_boxes[1] = (top_boxes_coor[1] + top_boxes_coor[3]) / 2 top_score = max(cls_scores) top_cls = cls_ind x_table = 0 y_table = 0 if bbs_array.shape[0] != 0: print('candadiates grasp above threshold: ' + str(CONF_THRESH) + ' are found!') bbs_array_cnt = np.transpose(np.vstack(( bbs_array[:,0]/2+bbs_array[:,2]/2, bbs_array[:,1]/2+bbs_array[:,3]/2))) bbs_array_cnt_mean = bbs_array_cnt.mean(axis=0, keepdims=True) bbs_array_dist = np.sum(np.square(bbs_array_cnt - bbs_array_cnt_mean), axis=1) bbs_array_ins = np.argmin(bbs_array_dist) #circle2 = plt.Circle((bbs_array[bbs_array_ins,0]/2+bbs_array[bbs_array_ins,2]/2, bbs_array[bbs_array_ins,1]/2+bbs_array[bbs_array_ins,3]/2), 2, color='g') #ax.add_artist(circle2) USE_AVERAGE = False if USE_AVERAGE: # if need top 10, use this: x_table, y_table, _ = coordinate_img2table(frame_current, bbs_array[bbs_array_ins,0]/2+bbs_array[bbs_array_ins,2]/2, bbs_array[bbs_array_ins,1]/2+bbs_array[bbs_array_ins,3]/2, top_cls) bbox = top_boxes_coor else: # if need top 1, use this x_table, y_table, _ = coordinate_img2table(frame_current, top_boxes[0], top_boxes[1], top_cls) bbox = bbs_array[bbs_array_ins, :4] # (1, 5) = (x_min, y_min, x_max, y_max, score) angle_compensated = compute_imgRot(frame_current) ## plot grasp, need to use change global pred_x and pred_y pts = ar([[bbox[0],bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]]) cnt = ar([(bbox[0] + bbox[2])/2, (bbox[1] + bbox[3])/2]) r_bbox = Rotate2D(pts, cnt, -pi/2-pi/20*(top_cls-1)) pred_label_polygon = Polygon([(r_bbox[0,0],r_bbox[0,1]), (r_bbox[1,0], r_bbox[1,1]), (r_bbox[2,0], r_bbox[2,1]), (r_bbox[3,0], r_bbox[3,1])]) global pred_x global pred_y pred_x, pred_y = pred_label_polygon.exterior.xy else: print('no candidates above threshold: ' + str(CONF_THRESH) + 'found, lower down threshold') print("top class is: " + str(top_cls)) print("top class angle is: " + str(ANGLES[top_cls] + angle_compensated)) print("top class location is: " + str(top_boxes[0]) + " " + str(top_boxes[1])) x_table_center, y_table_center, _ = coordinate_img2table(frame_current, 320, 240, 0) print('camera is facing to '+str(x_table_center)+' '+str(y_table_center)+' on the table') #circle1 = plt.Circle((top_boxes[0], top_boxes[1]), 2, color='y') #ax.add_artist(circle1) #plt.axis('off') #plt.tight_layout() #save result count = count +1 #savepath = './data/demo/results_all_cls/' + str(count) + '.png' #plt.savefig(savepath) #plt.cla() file = open('/home/fujenchu/projects/robotArm/toy-opencv-mat-socket-server-master_pcl/bbs/rotation.txt', "w") file.write(str(x_table) + '\n') file.write(str(y_table) + '\n') file.write(str(ANGLES[top_cls] + angle_compensated) + '\n') file.close() file = open('/home/fujenchu/projects/robotArm/toy-opencv-mat-socket-server-master_pcl/bbs/cam_facing.txt', "w") file.write(str(x_table_center) + '\n') file.write(str(y_table_center) + '\n') #file.write(str(ANGLES[top_cls] + angle_compensated) + '\n') file.close() #plt.draw() #plt.show() #plt.clf() #cv2.imshow('deepGrasp_top_score', frame_current) #choice = cv2.waitKey(20) #if choice == 27: # break def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', choices=NETS.keys(), default='res101') parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', choices=DATASETS.keys(), default='pascal_voc_0712') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() # model path demonet = args.demo_net dataset = args.dataset tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0]) if not os.path.isfile(tfmodel + '.meta'): raise IOError(('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(tfmodel + '.meta')) # set config tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth=True #tfconfig = tf.ConfigProto(device_count={'GPU': 0}) # init session sess = tf.Session(config=tfconfig) # load network if demonet == 'vgg16': net = vgg16(batch_size=1) elif demonet == 'res101': net = resnetv1(batch_size=1, num_layers=101) elif demonet == 'res50': net = resnetv1(batch_size=1, num_layers=50) else: raise NotImplementedError net.create_architecture(sess, "TEST", 20, tag='default', anchor_scales=[8, 16, 32]) saver = tf.train.Saver() saver.restore(sess, tfmodel) print('Loaded network {:s}'.format(tfmodel)) demo_process = threading.Thread(target=demo_process, args=(sess,net)) demo_process.start() # TCP/IP HOST='' PORT=2330 #PORT = 2325 s=socket.socket(socket.AF_INET,socket.SOCK_STREAM) print('Socket created') s.bind((HOST,PORT)) print('Socket bind complete') s.listen(10) print('Socket now listening') client,addr=s.accept() #count = 0 frame = np.zeros((480, 640, 3)) while True: start = time.time() frame = np.zeros((480, 640, 3)) show_frame = np.zeros((480, 640, 3)) frame = np.reshape(frame, (480*640*3)) i=0 while i < 480*640*3: data = client.recv(640*480*3 - i) datalen = len(data) #if len(data) != 1024: datalen_str = str(len(data)) datalen_str = datalen_str + 'B' data_up = struct.unpack(datalen_str, data) data_up_np = np.asarray(data_up) frame[i:i + len(data)] = data_up_np i += len(data) # frame_depth = np.zeros((240, 320, 1)) # show_frame_depth = np.zeros((240, 320, 1)) # frame_depth = np.reshape(frame_depth, (320*240*1)) # i=0 # while i < 240*640*1: # data = client.recv(240*640*1 - i) # # datalen = len(data) # # print(datalen) # # #if len(data) != 1024: # #datalen_str = str(len(data)/2) # #datalen_str = datalen_str + 'H' # #data_up = struct.unpack(datalen_str, data) # #data_up_np = np.asarray(data_up) # data_up_np = np.fromstring(data, dtype='>H') # frame_depth[i:i + 76800] = data_up_np # # i += len(data) ########## save images for calibration ####################### # if cv2.waitKey(10) == ord('s'): # count = count +1 # savepath = './data/demo/live/' + str(count) + '.png' # cv2.imwrite(savepath, np.reshape(frame, (480, 640, 3))) # # savepath = './data/demo/live/' + str(count) + '_d.png' # cv2.imwrite(savepath, np.reshape(frame_depth, (240, 320, 1))/ np.max(frame_depth)) ############################################################## frame_current = np.reshape(frame, (480, 640, 3))/255.0 #frame_current_depth = 1-(np.reshape(frame_depth, (240, 320, 1)) / np.max(frame_depth)) duration = time.time()-start #print("processed time main =" + str(duration)) color = (255/255.0, 255/255.0, 255/255.0) thickness = 1 start_point, end_point = (int(pred_x[0]), int(pred_y[0])), (int(pred_x[1]), int(pred_y[1])) cv2.line(frame_current, start_point, end_point, color, thickness) start_point, end_point = (int(pred_x[2]), int(pred_y[2])), (int(pred_x[3]), int(pred_y[3])) cv2.line(frame_current, start_point, end_point, color, thickness) color = (0, 0, 255/255.0) thickness = 2 start_point, end_point = (int(pred_x[1]), int(pred_y[1])), (int(pred_x[2]), int(pred_y[2])) cv2.line(frame_current, start_point, end_point, color, thickness) start_point, end_point = (int(pred_x[3]), int(pred_y[3])), (int(pred_x[0]), int(pred_y[0])) cv2.line(frame_current, start_point, end_point, color, thickness) cv2.imshow('frame',frame_current) #cv2.imshow('frame_depth', frame_current_depth) cv2.waitKey(1) #im_names = ['rgd_0076Cropped320.png','rgd_0095.png','pcd0122r_rgd_preprocessed_1.png','pcd0875r_rgd_preprocessed_1.png','resized_0875_2.png'] #im_names = ['pcd0875r_rgd_preprocessed_1.png','pic_0010.png'] #for im_name in im_names: # print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') # print('Demo for data/demo/{}'.format(im_name)) # demo(sess, net, im_name) #plt.show() ================================================ FILE: tools/demo_graspRGD_socket.py~ ================================================ #!/usr/bin/env python # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- """ Demo script showing detections in sample images. See README.md for installation instructions before running. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.config import cfg from model.test import im_detect from model.nms_wrapper import nms from utils.timer import Timer import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os, cv2 import argparse import threading import socket import struct import time import cv2.aruco as aruco from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 import scipy from shapely.geometry import Polygon pi = scipy.pi dot = scipy.dot sin = scipy.sin cos = scipy.cos ar = scipy.array frame_current=[] CLASSES = ('__background__', 'angle_01', 'angle_02', 'angle_03', 'angle_04', 'angle_05', 'angle_06', 'angle_07', 'angle_08', 'angle_09', 'angle_10', 'angle_11', 'angle_12', 'angle_13', 'angle_14', 'angle_15', 'angle_16', 'angle_17', 'angle_18', 'angle_19') NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',),'res50': ('res50_faster_rcnn_iter_240000.ckpt',)} DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',),'grasp': ('train',)} ANGLES = (-1000, 0, -10, -20, -30, -40, -50, -60, -70, -80, -90, 80, 70, 60, 50, 40, 30, 20, 10, 0) def Rotate2D(pts,cnt,ang=scipy.pi/4): '''pts = {} Rotates points(nx2) about center cnt(2) by angle ang(1) in radian''' return dot(pts-cnt,ar([[cos(ang),sin(ang)],[-sin(ang),cos(ang)]]))+cnt def vis_detections(ax, im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: return im = im[:, :, (2, 1, 0)] #fig, ax = plt.subplots(figsize=(12, 12)) ax.imshow(im, aspect='equal') for i in inds: bbox = dets[i, :4] score = dets[i, -1] #ax.add_patch( # plt.Rectangle((bbox[0], bbox[1]), # bbox[2] - bbox[0], # bbox[3] - bbox[1], fill=False, # edgecolor='red', linewidth=3.5) # ) # plot rotated rectangles pts = ar([[bbox[0],bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]]) cnt = ar([(bbox[0] + bbox[2])/2, (bbox[1] + bbox[3])/2]) angle = int(class_name[6:]) r_bbox = Rotate2D(pts, cnt, -pi/2-pi/20*(angle-1)) pred_label_polygon = Polygon([(r_bbox[0,0],r_bbox[0,1]), (r_bbox[1,0], r_bbox[1,1]), (r_bbox[2,0], r_bbox[2,1]), (r_bbox[3,0], r_bbox[3,1])]) pred_x, pred_y = pred_label_polygon.exterior.xy plt.plot(pred_x[0:2],pred_y[0:2], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[1:3],pred_y[1:3], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) plt.plot(pred_x[2:4],pred_y[2:4], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[3:5],pred_y[3:5], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) #ax.text(bbox[0], bbox[1] - 2, # '{:s} {:.3f}'.format(class_name, score), # bbox=dict(facecolor='blue', alpha=0.5), # fontsize=14, color='white') #ax.set_title(('{} detections with ' # 'p({} | box) >= {:.1f}').format(class_name, class_name, # thresh), # fontsize=14) #plt.axis('off') #plt.tight_layout() #save result #savepath = './data/demo/results/' + str(image_name) + str(class_name) + '.png' #plt.savefig(savepath) #plt.draw() def compute_imgRot(frame): # aim to find a ARUCO marker and compute the camera rotation on XY plane # camera matrix markerLength = 0.06 # old camera matrix used by Yufeng # cameraMatrix = np.array([[297.47608, 0.0, 320], [0.0, 297.14815, 240], [0.0, 0.0, 1.0]]) # camera frame from socket # distCoeffs = np.array([0.15190073, -0.8267655, 0.00985276, -0.00435892, 1.58437205]) # fake value # new camera matrix obtained at 01/2018 cameraMatrix = np.array( [[592.90077, 0.0, 327.06503], [0.0, 591.07515, 239.40367], [0.0, 0.0, 1.0]]) # camera frame from socket distCoeffs = np.array([-0.02067, 0.06351, -0.00285, 0.00083, 0.00000]) # fake value # find ARUCO marker gray = frame * 255 # print(gray) gray = gray.astype(np.uint8) gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) # print (gray) # gray = gray.astype(np.uint8) aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) parameters = aruco.DetectorParameters_create() #P_reshape = np.reshape(P, (7*7*2*2)) #top = P_reshape.argsort()[-10:][::-1] #index = top[3] corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters) print(ids) gray = aruco.drawDetectedMarkers(gray, corners) dst = 0 tvec = 0 T = 0 angle = 0.0 if ids is not None: point0 = corners[0][0][0] point3 = corners[0][0][3] #if point0[1] >= point3[1]: # rotate counter-clock wise angle = np.arctan((point0[1] - point3[1]) / (point3[0] - point0[0])) print('counter-clock rotate angle in degree: (just added to detected degree to compensate the table)') print(angle/3.14*180) return angle/3.14*180 # else: # rotate clock wise # angle = np.arctan((point3[1] - point0[1]) / (point3[0] - point0[0])) # print('clock') # print(angle/3.14*180) else: return angle def coordinate_img2table(frame, u, v, rot): """project found u v coordinate on image to x y coordinate on table with ARUCO marker.""" # camera matrix markerLength = 0.06 # old camera matrix used by Yufeng #cameraMatrix = np.array([[297.47608, 0.0, 320], [0.0, 297.14815, 240], [0.0, 0.0, 1.0]]) # camera frame from socket #distCoeffs = np.array([0.15190073, -0.8267655, 0.00985276, -0.00435892, 1.58437205]) # fake value # new camera matrix obtained at 01/2018 cameraMatrix = np.array([[592.90077, 0.0, 327.06503], [0.0, 591.07515, 239.40367], [0.0, 0.0, 1.0]]) # camera frame from socket distCoeffs = np.array([-0.02067, 0.06351, -0.00285, 0.00083, 0.00000]) # fake value # find ARUCO marker gray = frame*255 #print(gray) gray = gray.astype(np.uint8) gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) #print (gray) #gray = gray.astype(np.uint8) aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) parameters = aruco.DetectorParameters_create() corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters) print(ids) gray = aruco.drawDetectedMarkers(gray, corners) dst = 0 tvec = 0 T = 0 point_new = np.zeros((3, 1)) if ids is not None: rvec, tvec, _ = aruco.estimatePoseSingleMarkers(corners[0], markerLength, cameraMatrix, distCoeffs) dst, jacobian = cv2.Rodrigues(rvec) T = np.zeros((4, 4)) T[:3, :3] = dst T[:3, 3] = tvec T[3, :] = np.array([0, 0, 0, 1]) # projection imagePts = np.array([u, v, 1]) normal_old = np.array([0, 0, 1]) ray_center = np.array([0, 0, 0]) distance_old = 0 normal_new = np.dot(dst, normal_old) #(3,) normal_new = np.expand_dims(normal_new, 1) #(3,1) translation_old = tvec #(1,1,3) translation_old = np.squeeze(translation_old, 0) #(1,3) distance_new = -(distance_old + np.dot(translation_old, normal_new)) ray = np.dot(np.linalg.inv(cameraMatrix), imagePts) #(3,) t = -(np.dot(normal_new.transpose(), ray_center) + distance_new) / np.dot(normal_new.transpose(), ray) # (1,1) intersection = np.multiply(ray, t) #(1,3) intersection_homo = np.array([intersection[0,0], intersection[0,1], intersection[0,2], 1]) point_new = np.dot(np.linalg.inv(T), intersection_homo) #print(point_new[0] + 0.36)#0.3 #print(point_new[1] + 0.35)#0.4# print(point_new[0] + 0.30)#0.3 print(point_new[1] + 0.30)#0.4# return point_new[0] + 0.30, point_new[1] + 0.30, rot def demo_process(sess, net): """Detect object classes in an image using pre-computed object proposals.""" count = 0 bbs_array = np.array([], dtype=np.float32).reshape(0, 5) im = [] tmp_g = [] scores= [] boxes = [] fig, ax = plt.subplots(figsize=(12, 12)) while True: if frame_current != []: print('flag') bbs_array = np.array([], dtype=np.float32).reshape(0, 5) # Load the demo image #im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) #im = cv2.imread(im_file) #print (frame_current) im = frame_current tmp_g = im[:,:,1] im[:,:,1] = im[:,:,2] im[:,:,2] = tmp_g im = im*255 img = im.astype('uint8') ax.imshow(img) # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(sess, net, im) #scores_max = scores[:,1:-1].max(axis=1) #scores_max_idx = np.argmax(scores_max) #scores = scores[scores_max_idx:scores_max_idx+1,:] #boxes = boxes[scores_max_idx:scores_max_idx+1, :] timer.toc() print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) #fig, ax = plt.subplots(figsize=(12, 12)) #uncommand this when writing files into disk # Visualize detections for each class CONF_THRESH = 0.001 CONF_THRESH = 0.5 # demo for spoon 2018/08 NMS_THRESH = 0.3 top_score = 0; top_cls = 0; angle_compensated = 0.0 top_boxes = np.zeros(2) for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] # stack all det > threshold dets_th = np.delete(dets, np.where(dets[:, -1] < CONF_THRESH)[0], axis=0) bbs_array = np.vstack((bbs_array, dets_th)) #vis_detections(ax, im, cls, dets, thresh=CONF_THRESH) #uncommand if you want to visualize if ( max(cls_scores) > top_score): #print (max(cls_scores)) #print(np.amax(cls_scores)) #print (np.argmax(cls_scores)) #print (cls_boxes[np.argmax(cls_scores),:]) tmp_top_boxes = cls_boxes[np.argmax(cls_scores),:] top_boxes[0] = (tmp_top_boxes[0] + tmp_top_boxes[2]) / 2 top_boxes[1] = (tmp_top_boxes[1] + tmp_top_boxes[3]) / 2 top_score = max(cls_scores) top_cls = cls_ind x_table = 0 y_table = 0 if bbs_array.shape[0] != 0: print('array got~') bbs_array_cnt = np.transpose(np.vstack(( bbs_array[:,0]/2+bbs_array[:,2]/2, bbs_array[:,1]/2+bbs_array[:,3]/2))) bbs_array_cnt_mean = bbs_array_cnt.mean(axis=0, keepdims=True) bbs_array_dist = np.sum(np.square(bbs_array_cnt - bbs_array_cnt_mean), axis=1) bbs_array_ins = np.argmin(bbs_array_dist) circle2 = plt.Circle((bbs_array[bbs_array_ins,0]/2+bbs_array[bbs_array_ins,2]/2, bbs_array[bbs_array_ins,1]/2+bbs_array[bbs_array_ins,3]/2), 2, color='g') ax.add_artist(circle2) # if need top 10, use this: #x_table, y_table, _ = coordinate_img2table(frame_current, bbs_array[bbs_array_ins,0]/2+bbs_array[bbs_array_ins,2]/2, bbs_array[bbs_array_ins,1]/2+bbs_array[bbs_array_ins,3]/2, top_cls) # if need top 1, use this x_table, y_table, _ = coordinate_img2table(frame_current, top_boxes[0], top_boxes[1], top_cls) angle_compensated = compute_imgRot(frame_current) else: print('no array!!') print("top class is: " + str(top_cls)) print("top class angle is: " + str(ANGLES[top_cls] + angle_compensated)) print("top class location is: " + str(top_boxes[0]) + " " + str(top_boxes[1])) #coordinate_img2table(frame_current, top_boxes[0], top_boxes[1], top_cls) circle1 = plt.Circle((top_boxes[0], top_boxes[1]), 2, color='y') ax.add_artist(circle1) plt.axis('off') plt.tight_layout() #save result count = count +1 #savepath = './data/demo/results_all_cls/' + str(count) + '.png' #plt.savefig(savepath) #plt.cla() file = open('/home/fujenchu/projects/robotArm/toy-opencv-mat-socket-server-master_pcl/bbs/rotation.txt', "w") file.write(str(x_table) + '\n') file.write(str(y_table) + '\n') file.write(str(ANGLES[top_cls] + angle_compensated) + '\n') file.close() #plt.draw() plt.show() #plt.clf() #cv2.imshow('deepGrasp_top_score', frame_current) #choice = cv2.waitKey(20) #if choice == 27: # break def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', choices=NETS.keys(), default='res101') parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', choices=DATASETS.keys(), default='pascal_voc_0712') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() # model path demonet = args.demo_net dataset = args.dataset tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0]) if not os.path.isfile(tfmodel + '.meta'): raise IOError(('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(tfmodel + '.meta')) # set config tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth=True #tfconfig = tf.ConfigProto(device_count={'GPU': 0}) # init session sess = tf.Session(config=tfconfig) # load network if demonet == 'vgg16': net = vgg16(batch_size=1) elif demonet == 'res101': net = resnetv1(batch_size=1, num_layers=101) elif demonet == 'res50': net = resnetv1(batch_size=1, num_layers=50) else: raise NotImplementedError net.create_architecture(sess, "TEST", 20, tag='default', anchor_scales=[8, 16, 32]) saver = tf.train.Saver() saver.restore(sess, tfmodel) print('Loaded network {:s}'.format(tfmodel)) demo_process = threading.Thread(target=demo_process, args=(sess,net)) demo_process.start() # TCP/IP HOST='' PORT=2330 #PORT = 2325 s=socket.socket(socket.AF_INET,socket.SOCK_STREAM) print('Socket created') s.bind((HOST,PORT)) print('Socket bind complete') s.listen(10) print('Socket now listening') client,addr=s.accept() #count = 0 frame = np.zeros((480, 640, 3)) while True: start = time.time() frame = np.zeros((480, 640, 3)) show_frame = np.zeros((480, 640, 3)) frame = np.reshape(frame, (480*640*3)) i=0 while i < 480*640*3: data = client.recv(640*480*3 - i) datalen = len(data) #if len(data) != 1024: datalen_str = str(len(data)) datalen_str = datalen_str + 'B' data_up = struct.unpack(datalen_str, data) data_up_np = np.asarray(data_up) frame[i:i + len(data)] = data_up_np i += len(data) # frame_depth = np.zeros((240, 320, 1)) # show_frame_depth = np.zeros((240, 320, 1)) # frame_depth = np.reshape(frame_depth, (320*240*1)) # i=0 # while i < 240*640*1: # data = client.recv(240*640*1 - i) # # datalen = len(data) # # print(datalen) # # #if len(data) != 1024: # #datalen_str = str(len(data)/2) # #datalen_str = datalen_str + 'H' # #data_up = struct.unpack(datalen_str, data) # #data_up_np = np.asarray(data_up) # data_up_np = np.fromstring(data, dtype='>H') # frame_depth[i:i + 76800] = data_up_np # # i += len(data) ########## save images for calibration ####################### # if cv2.waitKey(10) == ord('s'): # count = count +1 # savepath = './data/demo/live/' + str(count) + '.png' # cv2.imwrite(savepath, np.reshape(frame, (480, 640, 3))) # # savepath = './data/demo/live/' + str(count) + '_d.png' # cv2.imwrite(savepath, np.reshape(frame_depth, (240, 320, 1))/ np.max(frame_depth)) ############################################################## frame_current = np.reshape(frame, (480, 640, 3))/255.0 #frame_current_depth = 1-(np.reshape(frame_depth, (240, 320, 1)) / np.max(frame_depth)) duration = time.time()-start #print("processed time main =" + str(duration)) cv2.imshow('frame',frame_current) #cv2.imshow('frame_depth', frame_current_depth) cv2.waitKey(1) #im_names = ['rgd_0076Cropped320.png','rgd_0095.png','pcd0122r_rgd_preprocessed_1.png','pcd0875r_rgd_preprocessed_1.png','resized_0875_2.png'] #im_names = ['pcd0875r_rgd_preprocessed_1.png','pic_0010.png'] #for im_name in im_names: # print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') # print('Demo for data/demo/{}'.format(im_name)) # demo(sess, net, im_name) #plt.show() ================================================ FILE: tools/demo_graspRGD_socket_drawer.py~ ================================================ #!/usr/bin/env python # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- """ Demo script showing detections in sample images. See README.md for installation instructions before running. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.config import cfg from model.test import im_detect from model.nms_wrapper import nms from utils.timer import Timer import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os, cv2 import argparse import threading import socket import struct import time import cv2.aruco as aruco from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 import scipy from shapely.geometry import Polygon pi = scipy.pi dot = scipy.dot sin = scipy.sin cos = scipy.cos ar = scipy.array frame_current=[] CLASSES = ('__background__', 'angle_01', 'angle_02', 'angle_03', 'angle_04', 'angle_05', 'angle_06', 'angle_07', 'angle_08', 'angle_09', 'angle_10', 'angle_11', 'angle_12', 'angle_13', 'angle_14', 'angle_15', 'angle_16', 'angle_17', 'angle_18', 'angle_19') NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',),'res50': ('res50_faster_rcnn_iter_240000.ckpt',)} DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',),'grasp': ('train',)} ANGLES = (-1000, 0, -10, -20, -30, -40, -50, -60, -70, -80, -90, 80, 70, 60, 50, 40, 30, 20, 10, 0) def Rotate2D(pts,cnt,ang=scipy.pi/4): '''pts = {} Rotates points(nx2) about center cnt(2) by angle ang(1) in radian''' return dot(pts-cnt,ar([[cos(ang),sin(ang)],[-sin(ang),cos(ang)]]))+cnt def vis_detections(ax, im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: return im = im[:, :, (2, 1, 0)] #fig, ax = plt.subplots(figsize=(12, 12)) ax.imshow(im, aspect='equal') for i in inds: bbox = dets[i, :4] score = dets[i, -1] #ax.add_patch( # plt.Rectangle((bbox[0], bbox[1]), # bbox[2] - bbox[0], # bbox[3] - bbox[1], fill=False, # edgecolor='red', linewidth=3.5) # ) # plot rotated rectangles pts = ar([[bbox[0],bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]]) cnt = ar([(bbox[0] + bbox[2])/2, (bbox[1] + bbox[3])/2]) angle = int(class_name[6:]) r_bbox = Rotate2D(pts, cnt, -pi/2-pi/20*(angle-1)) pred_label_polygon = Polygon([(r_bbox[0,0],r_bbox[0,1]), (r_bbox[1,0], r_bbox[1,1]), (r_bbox[2,0], r_bbox[2,1]), (r_bbox[3,0], r_bbox[3,1])]) pred_x, pred_y = pred_label_polygon.exterior.xy plt.plot(pred_x[0:2],pred_y[0:2], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[1:3],pred_y[1:3], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) plt.plot(pred_x[2:4],pred_y[2:4], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[3:5],pred_y[3:5], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) #ax.text(bbox[0], bbox[1] - 2, # '{:s} {:.3f}'.format(class_name, score), # bbox=dict(facecolor='blue', alpha=0.5), # fontsize=14, color='white') #ax.set_title(('{} detections with ' # 'p({} | box) >= {:.1f}').format(class_name, class_name, # thresh), # fontsize=14) #plt.axis('off') #plt.tight_layout() #save result #savepath = './data/demo/results/' + str(image_name) + str(class_name) + '.png' #plt.savefig(savepath) #plt.draw() def compute_imgRot(frame): # aim to find a ARUCO marker and compute the camera rotation on XY plane # camera matrix markerLength = 0.06 # old camera matrix used by Yufeng # cameraMatrix = np.array([[297.47608, 0.0, 320], [0.0, 297.14815, 240], [0.0, 0.0, 1.0]]) # camera frame from socket # distCoeffs = np.array([0.15190073, -0.8267655, 0.00985276, -0.00435892, 1.58437205]) # fake value # new camera matrix obtained at 01/2018 cameraMatrix = np.array( [[592.90077, 0.0, 327.06503], [0.0, 591.07515, 239.40367], [0.0, 0.0, 1.0]]) # camera frame from socket distCoeffs = np.array([-0.02067, 0.06351, -0.00285, 0.00083, 0.00000]) # fake value # find ARUCO marker gray = frame * 255 # print(gray) gray = gray.astype(np.uint8) gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) # print (gray) # gray = gray.astype(np.uint8) aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) parameters = aruco.DetectorParameters_create() corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters) print(ids) gray = aruco.drawDetectedMarkers(gray, corners) dst = 0 tvec = 0 T = 0 angle = 0.0 if ids is not None: point0 = corners[0][0][0] point3 = corners[0][0][3] #if point0[1] >= point3[1]: # rotate counter-clock wise angle = np.arctan((point0[1] - point3[1]) / (point3[0] - point0[0])) print('counter-clock rotate angle in degree: (just added to detected degree to compensate the table)') print(angle/3.14*180) return angle/3.14*180 # else: # rotate clock wise # angle = np.arctan((point3[1] - point0[1]) / (point3[0] - point0[0])) # print('clock') # print(angle/3.14*180) else: return angle def coordinate_drawerTop2table(frame): """find the marker on the drawer and its location relative to robot base (and marker on the table)""" gray = frame*255 gray = gray.astype(np.uint8) gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) # shared camera info cameraMatrix = np.array([[592.90077, 0.0, 327.06503], [0.0, 591.07515, 239.40367], [0.0, 0.0, 1.0]]) # camera frame from socket distCoeffs = np.array([-0.02067, 0.06351, -0.00285, 0.00083, 0.00000]) # fake value # find marker on the table (T_o1_c) aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) parameters = aruco.DetectorParameters_create() corners_o1, ids_o1, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters) markerLength_o1 = 0.06 T_o1_c = np.random.rand(4,4) if ids_o1 is not None and ids_o1[0] == 466: print(ids_o1) rvec, tvec, _ = aruco.estimatePoseSingleMarkers(corners_o1[0], markerLength_o1, cameraMatrix, distCoeffs) dst, jacobian = cv2.Rodrigues(rvec) T_o1_c[:3, :3] = dst T_o1_c[:3, 3] = tvec T_o1_c[3, :] = np.array([0, 0, 0, 1]) aruco.drawAxis(gray, cameraMatrix, distCoeffs, rvec, tvec, markerLength_o1) # find marker on the drawer (T_o2_c) aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_o2, ids_o2, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters) markerLength_o2 = 0.03 T_o2_c = np.random.rand(4,4) if ids_o2 is not None and ids_o2[0] == 4: print(ids_o2) rvec, tvec, _ = aruco.estimatePoseSingleMarkers(corners_o2[0], markerLength_o2, cameraMatrix, distCoeffs) dst, jacobian = cv2.Rodrigues(rvec) T_o2_c[:3, :3] = dst T_o2_c[:3, 3] = tvec T_o2_c[3, :] = np.array([0, 0, 0, 1]) aruco.drawAxis(gray, cameraMatrix, distCoeffs, rvec, tvec, markerLength_o2) if ids_o1 is not None and ids_o2 is not None: # find q_o1 = T_o2_o1 * q_o2 = T_c_o1 * T_o2_c * q_o2 = T_o1_c' * T_o2_c * q_o2 (with q_o2 = [0,0,0,1]') q_o2 = np.array((0.0, 0.0, 0.0, 1.0)) q_o1 = np.dot(np.linalg.inv(T_o1_c), np.dot(T_o2_c, q_o2)) q_o1 = q_o1/q_o1[3] print(q_o1[0] + 0.40) print(q_o1[1] + 0.30) print(q_o1[2] - 0.033) file = open('/home/fujenchu/projects/robotArm/toy-opencv-mat-socket-server-master_pcl/bbs/3Dlocations_drawer.txt', "w") file.write(str(class_name)) file.write("\n") file.write(str(xmin)) file.write("\n") file.write(str(ymin)) file.write("\n") file.write(str(xmax)) file.write("\n") file.write(str(ymax)) file.write("\n") file.close() angle = 0.0 vector_o1 = corners_o1[0][0][3] - corners_o1[0][0][1] vector_o2 = corners_o2[0][0][3] - corners_o2[0][0][1] L_o1 = np.sqrt(vector_o1.dot(vector_o1)) L_o2 = np.sqrt(vector_o2.dot(vector_o2)) cos_angle = vector_o1.dot(vector_o2)/(L_o1*L_o2) angle = np.arccos(cos_angle)/3.14*180 print(angle) def coordinate_img2table(frame, u, v, rot): """project found u v coordinate on image to x y coordinate on table with ARUCO marker.""" # camera matrix markerLength = 0.06 # old camera matrix used by Yufeng #cameraMatrix = np.array([[297.47608, 0.0, 320], [0.0, 297.14815, 240], [0.0, 0.0, 1.0]]) # camera frame from socket #distCoeffs = np.array([0.15190073, -0.8267655, 0.00985276, -0.00435892, 1.58437205]) # fake value # new camera matrix obtained at 01/2018 cameraMatrix = np.array([[592.90077, 0.0, 327.06503], [0.0, 591.07515, 239.40367], [0.0, 0.0, 1.0]]) # camera frame from socket distCoeffs = np.array([-0.02067, 0.06351, -0.00285, 0.00083, 0.00000]) # fake value # find ARUCO marker gray = frame*255 #print(gray) gray = gray.astype(np.uint8) gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) #print (gray) #gray = gray.astype(np.uint8) aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) parameters = aruco.DetectorParameters_create() corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters) print(ids) gray = aruco.drawDetectedMarkers(gray, corners) dst = 0 tvec = 0 T = 0 point_new = np.zeros((3, 1)) if ids is not None: rvec, tvec, _ = aruco.estimatePoseSingleMarkers(corners[0], markerLength, cameraMatrix, distCoeffs) dst, jacobian = cv2.Rodrigues(rvec) T = np.zeros((4, 4)) T[:3, :3] = dst T[:3, 3] = tvec T[3, :] = np.array([0, 0, 0, 1]) # projection imagePts = np.array([u, v, 1]) normal_old = np.array([0, 0, 1]) ray_center = np.array([0, 0, 0]) distance_old = 0 normal_new = np.dot(dst, normal_old) #(3,) normal_new = np.expand_dims(normal_new, 1) #(3,1) translation_old = tvec #(1,1,3) translation_old = np.squeeze(translation_old, 0) #(1,3) distance_new = -(distance_old + np.dot(translation_old, normal_new)) ray = np.dot(np.linalg.inv(cameraMatrix), imagePts) #(3,) t = -(np.dot(normal_new.transpose(), ray_center) + distance_new) / np.dot(normal_new.transpose(), ray) # (1,1) intersection = np.multiply(ray, t) #(1,3) intersection_homo = np.array([intersection[0,0], intersection[0,1], intersection[0,2], 1]) point_new = np.dot(np.linalg.inv(T), intersection_homo) #print(point_new[0] + 0.36)#0.3 #print(point_new[1] + 0.35)#0.4# print(point_new[0] + 0.40)#0.3 print(point_new[1] + 0.30)#0.4# return point_new[0] + 0.40, point_new[1] + 0.30, rot def demo_process(sess, net): """Detect object classes in an image using pre-computed object proposals.""" count = 0 bbs_array = np.array([], dtype=np.float32).reshape(0, 5) im = [] tmp_g = [] scores= [] boxes = [] fig, ax = plt.subplots(figsize=(12, 12)) while True: if frame_current != []: print('flag') bbs_array = np.array([], dtype=np.float32).reshape(0, 5) # Load the demo image #im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) #im = cv2.imread(im_file) #print (frame_current) im = frame_current tmp_g = im[:,:,1] im[:,:,1] = im[:,:,2] im[:,:,2] = tmp_g im = im*255 # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(sess, net, im) #scores_max = scores[:,1:-1].max(axis=1) #scores_max_idx = np.argmax(scores_max) #scores = scores[scores_max_idx:scores_max_idx+1,:] #boxes = boxes[scores_max_idx:scores_max_idx+1, :] timer.toc() print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) #fig, ax = plt.subplots(figsize=(12, 12)) #uncommand this when writing files into disk # Visualize detections for each class CONF_THRESH = 0.00005 NMS_THRESH = 0.3 top_score = 0; top_cls = 0; angle_compensated = 0.0 top_boxes = np.zeros(2) for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] # stack all det > threshold dets_th = np.delete(dets, np.where(dets[:, -1] < CONF_THRESH)[0], axis=0) bbs_array = np.vstack((bbs_array, dets_th)) #vis_detections(ax, im, cls, dets, thresh=CONF_THRESH) #uncommand if you want to visualize if ( max(cls_scores) > top_score): #print (max(cls_scores)) #print(np.amax(cls_scores)) #print (np.argmax(cls_scores)) #print (cls_boxes[np.argmax(cls_scores),:]) tmp_top_boxes = cls_boxes[np.argmax(cls_scores),:] top_boxes[0] = (tmp_top_boxes[0] + tmp_top_boxes[2]) / 2 top_boxes[1] = (tmp_top_boxes[1] + tmp_top_boxes[3]) / 2 top_score = max(cls_scores) top_cls = cls_ind x_table = 0 y_table = 0 if bbs_array.shape[0] != 0: print('array got~') bbs_array_cnt = np.transpose(np.vstack(( bbs_array[:,0]/2+bbs_array[:,2]/2, bbs_array[:,1]/2+bbs_array[:,3]/2))) bbs_array_cnt_mean = bbs_array_cnt.mean(axis=0, keepdims=True) bbs_array_dist = np.sum(np.square(bbs_array_cnt - bbs_array_cnt_mean), axis=1) bbs_array_ins = np.argmin(bbs_array_dist) circle2 = plt.Circle((bbs_array[bbs_array_ins,0]/2+bbs_array[bbs_array_ins,2]/2, bbs_array[bbs_array_ins,1]/2+bbs_array[bbs_array_ins,3]/2), 2, color='g') ax.add_artist(circle2) # if need top 10, use this: x_table, y_table, _ = coordinate_img2table(frame_current, bbs_array[bbs_array_ins,0]/2+bbs_array[bbs_array_ins,2]/2, bbs_array[bbs_array_ins,1]/2+bbs_array[bbs_array_ins,3]/2, top_cls) # if need top 1, use this x_table, y_table, _ = coordinate_img2table(frame_current, top_boxes[0], top_boxes[1], top_cls) angle_compensated = compute_imgRot(frame_current) else: print('no array!!') print("top class is: " + str(top_cls)) print("top class angle is: " + str(ANGLES[top_cls] + angle_compensated)) print("top class location is: " + str(top_boxes[0]) + " " + str(top_boxes[1])) #coordinate_img2table(frame_current, top_boxes[0], top_boxes[1], top_cls) circle1 = plt.Circle((top_boxes[0], top_boxes[1]), 2, color='y') ax.add_artist(circle1) plt.axis('off') plt.tight_layout() #save result count = count +1 #savepath = './data/demo/results_all_cls/' + str(count) + '.png' #plt.savefig(savepath) coordinate_drawerTop2table(frame_current) file = open('/home/fujenchu/projects/robotArm/toy-opencv-mat-socket-server-master_pcl/bbs/rotation.txt', "w") file.write(str(x_table) + '\n') file.write(str(y_table) + '\n') file.write(str(ANGLES[top_cls] + angle_compensated) + '\n') file.close() #plt.draw() #plt.show() #cv2.imshow('deepGrasp_top_score', frame_current) #choice = cv2.waitKey(20) #if choice == 27: # break def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', choices=NETS.keys(), default='res101') parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', choices=DATASETS.keys(), default='pascal_voc_0712') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() # model path demonet = args.demo_net dataset = args.dataset tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0]) if not os.path.isfile(tfmodel + '.meta'): raise IOError(('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(tfmodel + '.meta')) # set config tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth=True #tfconfig = tf.ConfigProto(device_count={'GPU': 0}) # init session sess = tf.Session(config=tfconfig) # load network if demonet == 'vgg16': net = vgg16(batch_size=1) elif demonet == 'res101': net = resnetv1(batch_size=1, num_layers=101) elif demonet == 'res50': net = resnetv1(batch_size=1, num_layers=50) else: raise NotImplementedError net.create_architecture(sess, "TEST", 20, tag='default', anchor_scales=[8, 16, 32]) saver = tf.train.Saver() saver.restore(sess, tfmodel) print('Loaded network {:s}'.format(tfmodel)) demo_process = threading.Thread(target=demo_process, args=(sess,net)) demo_process.start() # TCP/IP HOST='' PORT=2330 #PORT = 2325 s=socket.socket(socket.AF_INET,socket.SOCK_STREAM) print('Socket created') s.bind((HOST,PORT)) print('Socket bind complete') s.listen(10) print('Socket now listening') client,addr=s.accept() #count = 0 frame = np.zeros((480, 640, 3)) while True: start = time.time() frame = np.zeros((480, 640, 3)) show_frame = np.zeros((480, 640, 3)) frame = np.reshape(frame, (480*640*3)) i=0 while i < 480*640*3: data = client.recv(640*480*3 - i) datalen = len(data) #if len(data) != 1024: datalen_str = str(len(data)) datalen_str = datalen_str + 'B' data_up = struct.unpack(datalen_str, data) data_up_np = np.asarray(data_up) frame[i:i + len(data)] = data_up_np i += len(data) # frame_depth = np.zeros((240, 320, 1)) # show_frame_depth = np.zeros((240, 320, 1)) # frame_depth = np.reshape(frame_depth, (320*240*1)) # i=0 # while i < 240*640*1: # data = client.recv(240*640*1 - i) # # datalen = len(data) # # print(datalen) # # #if len(data) != 1024: # #datalen_str = str(len(data)/2) # #datalen_str = datalen_str + 'H' # #data_up = struct.unpack(datalen_str, data) # #data_up_np = np.asarray(data_up) # data_up_np = np.fromstring(data, dtype='>H') # frame_depth[i:i + 76800] = data_up_np # # i += len(data) ########## save images for calibration ####################### # if cv2.waitKey(10) == ord('s'): # count = count +1 # savepath = './data/demo/live/' + str(count) + '.png' # cv2.imwrite(savepath, np.reshape(frame, (480, 640, 3))) # # savepath = './data/demo/live/' + str(count) + '_d.png' # cv2.imwrite(savepath, np.reshape(frame_depth, (240, 320, 1))/ np.max(frame_depth)) ############################################################## frame_current = np.reshape(frame, (480, 640, 3))/255.0 #frame_current_depth = 1-(np.reshape(frame_depth, (240, 320, 1)) / np.max(frame_depth)) duration = time.time()-start #print("processed time main =" + str(duration)) cv2.imshow('frame',frame_current) #cv2.imshow('frame_depth', frame_current_depth) cv2.waitKey(1) #im_names = ['rgd_0076Cropped320.png','rgd_0095.png','pcd0122r_rgd_preprocessed_1.png','pcd0875r_rgd_preprocessed_1.png','resized_0875_2.png'] #im_names = ['pcd0875r_rgd_preprocessed_1.png','pic_0010.png'] #for im_name in im_names: # print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') # print('Demo for data/demo/{}'.format(im_name)) # demo(sess, net, im_name) #plt.show() ================================================ FILE: tools/demo_graspRGD_socket_save_to_rgbd.py~ ================================================ #!/usr/bin/env python # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- """ Demo script showing detections in sample images. See README.md for installation instructions before running. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.config import cfg from model.test import im_detect from model.nms_wrapper import nms from utils.timer import Timer import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os, cv2 import argparse import threading import socket import struct import time import cv2.aruco as aruco from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 import scipy from shapely.geometry import Polygon pi = scipy.pi dot = scipy.dot sin = scipy.sin cos = scipy.cos ar = scipy.array frame_current=[] CLASSES = ('__background__', 'angle_01', 'angle_02', 'angle_03', 'angle_04', 'angle_05', 'angle_06', 'angle_07', 'angle_08', 'angle_09', 'angle_10', 'angle_11', 'angle_12', 'angle_13', 'angle_14', 'angle_15', 'angle_16', 'angle_17', 'angle_18', 'angle_19') NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',),'res50': ('res50_faster_rcnn_iter_240000.ckpt',)} DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',),'grasp': ('train',)} ANGLES = (-1000, 0, -10, -20, -30, -40, -50, -60, -70, -80, -90, 80, 70, 60, 50, 40, 30, 20, 10, 0) def Rotate2D(pts,cnt,ang=scipy.pi/4): '''pts = {} Rotates points(nx2) about center cnt(2) by angle ang(1) in radian''' return dot(pts-cnt,ar([[cos(ang),sin(ang)],[-sin(ang),cos(ang)]]))+cnt def vis_detections(ax, im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: return im = im[:, :, (2, 1, 0)] #fig, ax = plt.subplots(figsize=(12, 12)) ax.imshow(im, aspect='equal') for i in inds: bbox = dets[i, :4] score = dets[i, -1] #ax.add_patch( # plt.Rectangle((bbox[0], bbox[1]), # bbox[2] - bbox[0], # bbox[3] - bbox[1], fill=False, # edgecolor='red', linewidth=3.5) # ) # plot rotated rectangles pts = ar([[bbox[0],bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]]) cnt = ar([(bbox[0] + bbox[2])/2, (bbox[1] + bbox[3])/2]) angle = int(class_name[6:]) r_bbox = Rotate2D(pts, cnt, -pi/2-pi/20*(angle-1)) pred_label_polygon = Polygon([(r_bbox[0,0],r_bbox[0,1]), (r_bbox[1,0], r_bbox[1,1]), (r_bbox[2,0], r_bbox[2,1]), (r_bbox[3,0], r_bbox[3,1])]) pred_x, pred_y = pred_label_polygon.exterior.xy plt.plot(pred_x[0:2],pred_y[0:2], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[1:3],pred_y[1:3], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) plt.plot(pred_x[2:4],pred_y[2:4], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[3:5],pred_y[3:5], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) #ax.text(bbox[0], bbox[1] - 2, # '{:s} {:.3f}'.format(class_name, score), # bbox=dict(facecolor='blue', alpha=0.5), # fontsize=14, color='white') #ax.set_title(('{} detections with ' # 'p({} | box) >= {:.1f}').format(class_name, class_name, # thresh), # fontsize=14) #plt.axis('off') #plt.tight_layout() #save result #savepath = './data/demo/results/' + str(image_name) + str(class_name) + '.png' #plt.savefig(savepath) #plt.draw() def compute_imgRot(frame): # aim to find a ARUCO marker and compute the camera rotation on XY plane # camera matrix markerLength = 0.06 # old camera matrix used by Yufeng # cameraMatrix = np.array([[297.47608, 0.0, 320], [0.0, 297.14815, 240], [0.0, 0.0, 1.0]]) # camera frame from socket # distCoeffs = np.array([0.15190073, -0.8267655, 0.00985276, -0.00435892, 1.58437205]) # fake value # new camera matrix obtained at 01/2018 cameraMatrix = np.array( [[592.90077, 0.0, 327.06503], [0.0, 591.07515, 239.40367], [0.0, 0.0, 1.0]]) # camera frame from socket distCoeffs = np.array([-0.02067, 0.06351, -0.00285, 0.00083, 0.00000]) # fake value # find ARUCO marker gray = frame * 255 # print(gray) gray = gray.astype(np.uint8) gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) # print (gray) # gray = gray.astype(np.uint8) aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) parameters = aruco.DetectorParameters_create() corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters) print(ids) gray = aruco.drawDetectedMarkers(gray, corners) dst = 0 tvec = 0 T = 0 angle = 0.0 if ids is not None: point0 = corners[0][0][0] point3 = corners[0][0][3] #if point0[1] >= point3[1]: # rotate counter-clock wise angle = np.arctan((point0[1] - point3[1]) / (point3[0] - point0[0])) print('counter-clock rotate angle in degree: (just added to detected degree to compensate the table)') print(angle/3.14*180) return angle/3.14*180 # else: # rotate clock wise # angle = np.arctan((point3[1] - point0[1]) / (point3[0] - point0[0])) # print('clock') # print(angle/3.14*180) else: return angle def coordinate_img2table(frame, u, v, rot): """project found u v coordinate on image to x y coordinate on table with ARUCO marker.""" # camera matrix markerLength = 0.06 # old camera matrix used by Yufeng #cameraMatrix = np.array([[297.47608, 0.0, 320], [0.0, 297.14815, 240], [0.0, 0.0, 1.0]]) # camera frame from socket #distCoeffs = np.array([0.15190073, -0.8267655, 0.00985276, -0.00435892, 1.58437205]) # fake value # new camera matrix obtained at 01/2018 cameraMatrix = np.array([[592.90077, 0.0, 327.06503], [0.0, 591.07515, 239.40367], [0.0, 0.0, 1.0]]) # camera frame from socket distCoeffs = np.array([-0.02067, 0.06351, -0.00285, 0.00083, 0.00000]) # fake value # find ARUCO marker gray = frame*255 #print(gray) gray = gray.astype(np.uint8) gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) #print (gray) #gray = gray.astype(np.uint8) aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) parameters = aruco.DetectorParameters_create() corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters) print(ids) gray = aruco.drawDetectedMarkers(gray, corners) dst = 0 tvec = 0 T = 0 point_new = np.zeros((3, 1)) if ids is not None: rvec, tvec, _ = aruco.estimatePoseSingleMarkers(corners[0], markerLength, cameraMatrix, distCoeffs) dst, jacobian = cv2.Rodrigues(rvec) T = np.zeros((4, 4)) T[:3, :3] = dst T[:3, 3] = tvec T[3, :] = np.array([0, 0, 0, 1]) # projection imagePts = np.array([u, v, 1]) normal_old = np.array([0, 0, 1]) ray_center = np.array([0, 0, 0]) distance_old = 0 normal_new = np.dot(dst, normal_old) #(3,) normal_new = np.expand_dims(normal_new, 1) #(3,1) translation_old = tvec #(1,1,3) translation_old = np.squeeze(translation_old, 0) #(1,3) distance_new = -(distance_old + np.dot(translation_old, normal_new)) ray = np.dot(np.linalg.inv(cameraMatrix), imagePts) #(3,) t = -(np.dot(normal_new.transpose(), ray_center) + distance_new) / np.dot(normal_new.transpose(), ray) # (1,1) intersection = np.multiply(ray, t) #(1,3) intersection_homo = np.array([intersection[0,0], intersection[0,1], intersection[0,2], 1]) point_new = np.dot(np.linalg.inv(T), intersection_homo) #print(point_new[0] + 0.36)#0.3 #print(point_new[1] + 0.35)#0.4# print(point_new[0] + 0.40)#0.3 print(point_new[1] + 0.30)#0.4# return point_new[0] + 0.40, point_new[1] + 0.30, rot def demo_process(sess, net): """Detect object classes in an image using pre-computed object proposals.""" count = 0 bbs_array = np.array([], dtype=np.float32).reshape(0, 5) im = [] tmp_g = [] scores= [] boxes = [] fig, ax = plt.subplots(figsize=(12, 12)) while True: if frame_current != []: print('flag') bbs_array = np.array([], dtype=np.float32).reshape(0, 5) # Load the demo image #im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) #im = cv2.imread(im_file) #print (frame_current) im = frame_current tmp_g = im[:,:,1] im[:,:,1] = im[:,:,2] im[:,:,2] = tmp_g im = im*255 # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(sess, net, im) #scores_max = scores[:,1:-1].max(axis=1) #scores_max_idx = np.argmax(scores_max) #scores = scores[scores_max_idx:scores_max_idx+1,:] #boxes = boxes[scores_max_idx:scores_max_idx+1, :] timer.toc() print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) #fig, ax = plt.subplots(figsize=(12, 12)) #uncommand this when writing files into disk # Visualize detections for each class CONF_THRESH = 0.5 NMS_THRESH = 0.3 top_score = 0; top_cls = 0; angle_compensated = 0.0 top_boxes = np.zeros(2) for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] # stack all det > threshold dets_th = np.delete(dets, np.where(dets[:, -1] < CONF_THRESH)[0], axis=0) bbs_array = np.vstack((bbs_array, dets_th)) vis_detections(ax, im, cls, dets, thresh=CONF_THRESH) #uncommand if you want to visualize if ( max(cls_scores) > top_score): #print (max(cls_scores)) #print(np.amax(cls_scores)) #print (np.argmax(cls_scores)) #print (cls_boxes[np.argmax(cls_scores),:]) tmp_top_boxes = cls_boxes[np.argmax(cls_scores),:] top_boxes[0] = (tmp_top_boxes[0] + tmp_top_boxes[2]) / 2 top_boxes[1] = (tmp_top_boxes[1] + tmp_top_boxes[3]) / 2 top_score = max(cls_scores) top_cls = cls_ind x_table = 0 y_table = 0 if bbs_array.shape[0] != 0: bbs_array_cnt = np.transpose(np.vstack(( bbs_array[:,0]/2+bbs_array[:,2]/2, bbs_array[:,1]/2+bbs_array[:,3]/2))) bbs_array_cnt_mean = bbs_array_cnt.mean(axis=0, keepdims=True) bbs_array_dist = np.sum(np.square(bbs_array_cnt - bbs_array_cnt_mean), axis=1) bbs_array_ins = np.argmin(bbs_array_dist) circle2 = plt.Circle((bbs_array[bbs_array_ins,0]/2+bbs_array[bbs_array_ins,2]/2, bbs_array[bbs_array_ins,1]/2+bbs_array[bbs_array_ins,3]/2), 2, color='g') ax.add_artist(circle2) x_table, y_table, _ = coordinate_img2table(frame_current, bbs_array[bbs_array_ins,0]/2+bbs_array[bbs_array_ins,2]/2, bbs_array[bbs_array_ins,1]/2+bbs_array[bbs_array_ins,3]/2, top_cls) angle_compensated = compute_imgRot(frame_current) print("top class is: " + str(top_cls)) print("top class angle is: " + str(ANGLES[top_cls] + angle_compensated)) print("top class location is: " + str(top_boxes[0]) + " " + str(top_boxes[1])) #coordinate_img2table(frame_current, top_boxes[0], top_boxes[1], top_cls) circle1 = plt.Circle((top_boxes[0], top_boxes[1]), 2, color='y') ax.add_artist(circle1) plt.axis('off') plt.tight_layout() #save result count = count +1 savepath = './data/demo/results_all_cls/' + str(count) + '.png' plt.savefig(savepath) file = open('/home/fujenchu/projects/robotArm/toy-opencv-mat-socket-server-master_pcl/bbs/rotation.txt', "w") file.write(str(x_table) + '\n') file.write(str(y_table) + '\n') file.write(str(ANGLES[top_cls] + angle_compensated) + '\n') file.close() #plt.draw() #plt.show() #cv2.imshow('deepGrasp_top_score', frame_current) #choice = cv2.waitKey(20) #if choice == 27: # break def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', choices=NETS.keys(), default='res101') parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', choices=DATASETS.keys(), default='pascal_voc_0712') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() # model path demonet = args.demo_net dataset = args.dataset tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0]) if not os.path.isfile(tfmodel + '.meta'): raise IOError(('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(tfmodel + '.meta')) # set config tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth=True tfconfig = tf.ConfigProto(device_count={'GPU': 0}) # init session sess = tf.Session(config=tfconfig) # load network if demonet == 'vgg16': net = vgg16(batch_size=1) elif demonet == 'res101': net = resnetv1(batch_size=1, num_layers=101) elif demonet == 'res50': net = resnetv1(batch_size=1, num_layers=50) else: raise NotImplementedError net.create_architecture(sess, "TEST", 20, tag='default', anchor_scales=[8, 16, 32]) saver = tf.train.Saver() saver.restore(sess, tfmodel) print('Loaded network {:s}'.format(tfmodel)) demo_process = threading.Thread(target=demo_process, args=(sess,net)) demo_process.start() # TCP/IP HOST='' #PORT=2330 PORT = 2325 s=socket.socket(socket.AF_INET,socket.SOCK_STREAM) print('Socket created') s.bind((HOST,PORT)) print('Socket bind complete') s.listen(10) print('Socket now listening') client,addr=s.accept() count = 100 frame = np.zeros((480, 640, 3)) while True: start = time.time() frame = np.zeros((480, 640, 3)) show_frame = np.zeros((480, 640, 3)) frame = np.reshape(frame, (480*640*3)) i=0 while i < 480*640*3: data = client.recv(640*480*3 - i) datalen = len(data) #if len(data) != 1024: datalen_str = str(len(data)) datalen_str = datalen_str + 'B' data_up = struct.unpack(datalen_str, data) data_up_np = np.asarray(data_up) frame[i:i + len(data)] = data_up_np i += len(data) frame_depth = np.zeros((240, 320, 1)) show_frame_depth = np.zeros((240, 320, 1)) frame_depth = np.reshape(frame_depth, (320*240*1)) i=0 while i < 240*640*1: data = client.recv(240*640*1 - i) # datalen = len(data) # print(datalen) # #if len(data) != 1024: #datalen_str = str(len(data)/2) #datalen_str = datalen_str + 'H' #data_up = struct.unpack(datalen_str, data) #data_up_np = np.asarray(data_up) data_up_np = np.fromstring(data, dtype='>H') frame_depth[i:i + 76800] = data_up_np i += len(data) ########## save images for calibration ####################### if cv2.waitKey(10) == ord('s'): count = count +1 savepath = './data/demo/live/' + str(count) + '.png' cv2.imwrite(savepath, np.reshape(frame, (480, 640, 3))) savepath = './data/demo/live/' + str(count) + '_d.png' cv2.imwrite(savepath, np.reshape(frame_depth, (240, 320, 1))/ np.max(frame_depth)) ############################################################## frame_current = np.reshape(frame, (480, 640, 3))/255.0 #frame_current_depth = 1-(np.reshape(frame_depth, (240, 320, 1)) / np.max(frame_depth)) duration = time.time()-start #print("processed time main =" + str(duration)) cv2.imshow('frame',frame_current) #cv2.imshow('frame_depth', frame_current_depth) cv2.waitKey(1) #im_names = ['rgd_0076Cropped320.png','rgd_0095.png','pcd0122r_rgd_preprocessed_1.png','pcd0875r_rgd_preprocessed_1.png','resized_0875_2.png'] #im_names = ['pcd0875r_rgd_preprocessed_1.png','pic_0010.png'] #for im_name in im_names: # print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') # print('Demo for data/demo/{}'.format(im_name)) # demo(sess, net, im_name) #plt.show() ================================================ FILE: tools/demo_graspRGD_vis_mask.py ================================================ #!/usr/bin/env python # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- """ Demo script showing detections in sample images. See README.md for installation instructions before running. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.config import cfg from model.test import im_detect from model.nms_wrapper import nms from utils.timer import Timer import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os, cv2 import argparse from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 import scipy from shapely.geometry import Polygon pi = scipy.pi dot = scipy.dot sin = scipy.sin cos = scipy.cos ar = scipy.array CLASSES = ('__background__', 'angle_01', 'angle_02', 'angle_03', 'angle_04', 'angle_05', 'angle_06', 'angle_07', 'angle_08', 'angle_09', 'angle_10', 'angle_11', 'angle_12', 'angle_13', 'angle_14', 'angle_15', 'angle_16', 'angle_17', 'angle_18', 'angle_19') NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',),'res50': ('res50_faster_rcnn_iter_240000.ckpt',)} DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',),'grasp': ('train',)} def Rotate2D(pts,cnt,ang=scipy.pi/4): '''pts = {} Rotates points(nx2) about center cnt(2) by angle ang(1) in radian''' return dot(pts-cnt,ar([[cos(ang),sin(ang)],[-sin(ang),cos(ang)]]))+cnt def vis_detections(ax, image_name, im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: return im = im[:, :, (2, 1, 0)] #fig, ax = plt.subplots(figsize=(12, 12)) ax.imshow(im, aspect='equal') for i in inds: bbox = dets[i, :4] score = dets[i, -1] #ax.add_patch( # plt.Rectangle((bbox[0], bbox[1]), # bbox[2] - bbox[0], # bbox[3] - bbox[1], fill=False, # edgecolor='red', linewidth=3.5) # ) # plot rotated rectangles pts = ar([[bbox[0],bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]]) cnt = ar([(bbox[0] + bbox[2])/2, (bbox[1] + bbox[3])/2]) angle = int(class_name[6:]) r_bbox = Rotate2D(pts, cnt, -pi/2-pi/20*(angle-1)) pred_label_polygon = Polygon([(r_bbox[0,0],r_bbox[0,1]), (r_bbox[1,0], r_bbox[1,1]), (r_bbox[2,0], r_bbox[2,1]), (r_bbox[3,0], r_bbox[3,1])]) pred_x, pred_y = pred_label_polygon.exterior.xy plt.plot(pred_x[0:2],pred_y[0:2], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[1:3],pred_y[1:3], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) plt.plot(pred_x[2:4],pred_y[2:4], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[3:5],pred_y[3:5], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) #ax.text(bbox[0], bbox[1] - 2, # '{:s} {:.3f}'.format(class_name, score), # bbox=dict(facecolor='blue', alpha=0.5), # fontsize=14, color='white') #ax.set_title(('{} detections with ' # 'p({} | box) >= {:.1f}').format(class_name, class_name, # thresh), # fontsize=14) #plt.axis('off') #plt.tight_layout() #save result #savepath = './data/demo/results/' + str(image_name) + str(class_name) + '.png' #plt.savefig(savepath) #plt.draw() def demo(sess, net, image_name, mask_name): """Detect object classes in an image using pre-computed object proposals.""" # Load the demo image im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) mask_file = os.path.join(cfg.DATA_DIR, 'demo', mask_name) im = cv2.imread(im_file) mask = cv2.imread(mask_file,0) #im = im*np.stack([mask,mask,mask], axis=2) im = cv2.bitwise_and(im,im,mask = mask) # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(sess, net, im) #scores_max = scores[:,1:-1].max(axis=1) #scores_max_idx = np.argmax(scores_max) #scores = scores[scores_max_idx:scores_max_idx+1,:] #boxes = boxes[scores_max_idx:scores_max_idx+1, :] #im = cv2.imread('/home/fujenchu/projects/deepLearning/tensorflow-finetune-flickr-style-master/data/grasps_ivalab/rgb_cropped320/rgb_0076Cropped320.png') timer.toc() print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) fig, ax = plt.subplots(figsize=(12, 12)) # Visualize detections for each class CONF_THRESH = 0.1 NMS_THRESH = 0.3 for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] vis_detections(ax, image_name, im, cls, dets, thresh=CONF_THRESH) #tmp = max(cls_scores) plt.axis('off') plt.tight_layout() #cv2.imshow('deepGrasp_top_score', im) #choice = cv2.waitKey(100) #save result savepath = './data/demo/results_all_cls/' + str(image_name) + '.png' plt.savefig(savepath) plt.draw() def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', choices=NETS.keys(), default='res101') parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', choices=DATASETS.keys(), default='pascal_voc_0712') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() # model path demonet = args.demo_net dataset = args.dataset tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0]) if not os.path.isfile(tfmodel + '.meta'): raise IOError(('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(tfmodel + '.meta')) # set config tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth=True # init session sess = tf.Session(config=tfconfig) # load network if demonet == 'vgg16': net = vgg16(batch_size=1) elif demonet == 'res101': net = resnetv1(batch_size=1, num_layers=101) elif demonet == 'res50': net = resnetv1(batch_size=1, num_layers=50) else: raise NotImplementedError net.create_architecture(sess, "TEST", 20, tag='default', anchor_scales=[8, 16, 32]) saver = tf.train.Saver() saver.restore(sess, tfmodel) print('Loaded network {:s}'.format(tfmodel)) #im_names = ['rgd_0076Cropped320.png','rgd_0095.png','pcd0122r_rgd_preprocessed_1.png','pcd0875r_rgd_preprocessed_1.png','resized_0875_2.png'] im_names = ['rgd_0000Cropped320.png'] mask_name = 'mask.jpg' for im_name in im_names: print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') print('Demo for data/demo/{}'.format(im_name)) demo(sess, net, im_name, mask_name) plt.show() ================================================ FILE: tools/demo_graspRGD_vis_select.py ================================================ #!/usr/bin/env python # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- """ Demo script showing detections in sample images. See README.md for installation instructions before running. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.config import cfg from model.test import im_detect from model.nms_wrapper import nms from utils.timer import Timer import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os, cv2 import argparse from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 import scipy from shapely.geometry import Polygon pi = scipy.pi dot = scipy.dot sin = scipy.sin cos = scipy.cos ar = scipy.array CLASSES = ('__background__', 'angle_01', 'angle_02', 'angle_03', 'angle_04', 'angle_05', 'angle_06', 'angle_07', 'angle_08', 'angle_09', 'angle_10', 'angle_11', 'angle_12', 'angle_13', 'angle_14', 'angle_15', 'angle_16', 'angle_17', 'angle_18', 'angle_19') global SELECTION_COUNT SELECTION_COUNT = 0 NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',),'res50': ('res50_faster_rcnn_iter_240000.ckpt',)} DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',),'grasp': ('train',)} def Rotate2D(pts,cnt,ang=scipy.pi/4): '''pts = {} Rotates points(nx2) about center cnt(2) by angle ang(1) in radian''' return dot(pts-cnt,ar([[cos(ang),sin(ang)],[-sin(ang),cos(ang)]]))+cnt def vis_detections(ax, image_name, im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: return im = im[:, :, (2, 1, 0)] #fig, ax = plt.subplots(figsize=(12, 12)) ax.imshow(im, aspect='equal') for i in inds: bbox = dets[i, :4] score = dets[i, -1] #ax.add_patch( # plt.Rectangle((bbox[0], bbox[1]), # bbox[2] - bbox[0], # bbox[3] - bbox[1], fill=False, # edgecolor='red', linewidth=3.5) # ) # plot rotated rectangles pts = ar([[bbox[0],bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]]) cnt = ar([(bbox[0] + bbox[2])/2, (bbox[1] + bbox[3])/2]) angle = int(class_name[6:]) r_bbox = Rotate2D(pts, cnt, -pi/2-pi/20*(angle-1)) pred_label_polygon = Polygon([(r_bbox[0,0],r_bbox[0,1]), (r_bbox[1,0], r_bbox[1,1]), (r_bbox[2,0], r_bbox[2,1]), (r_bbox[3,0], r_bbox[3,1])]) pred_x, pred_y = pred_label_polygon.exterior.xy plt.plot(pred_x[0:2],pred_y[0:2], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[1:3],pred_y[1:3], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) plt.plot(pred_x[2:4],pred_y[2:4], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[3:5],pred_y[3:5], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) #ax.text(bbox[0], bbox[1] - 2, # '{:s} {:.3f}'.format(class_name, score), # bbox=dict(facecolor='blue', alpha=0.5), # fontsize=14, color='white') global SELECTION_COUNT ax.text(bbox[0], bbox[1] - 2, '{:s} {:.3f}'.format(str(SELECTION_COUNT), score), bbox=dict(facecolor='blue', alpha=0.5), fontsize=14, color='white') SELECTION_COUNT = SELECTION_COUNT + 1 #ax.set_title(('{} detections with ' # 'p({} | box) >= {:.1f}').format(class_name, class_name, # thresh), # fontsize=14) #plt.axis('off') #plt.tight_layout() #save result #savepath = './data/demo/results/' + str(image_name) + str(class_name) + '.png' #plt.savefig(savepath) #plt.draw() def demo(sess, net, image_name): """Detect object classes in an image using pre-computed object proposals.""" # Load the demo image im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) im = cv2.imread(im_file) #print(im) # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(sess, net, im) #scores_max = scores[:,1:-1].max(axis=1) #scores_max_idx = np.argmax(scores_max) #scores = scores[scores_max_idx:scores_max_idx+1,:] #boxes = boxes[scores_max_idx:scores_max_idx+1, :] #im = cv2.imread('/home/fujenchu/projects/deepLearning/tensorflow-finetune-flickr-style-master/data/grasps_ivalab/rgb_cropped320/rgb_0076Cropped320.png') timer.toc() print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0])) fig, ax = plt.subplots(figsize=(12, 12)) # Visualize detections for each class CONF_THRESH = 0.1 NMS_THRESH = 0.3 global SELECTION_COUNT SELECTION_COUNT = 0 for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] vis_detections(ax, image_name, im, cls, dets, thresh=CONF_THRESH) #tmp = max(cls_scores) plt.axis('off') plt.tight_layout() #cv2.imshow('deepGrasp_top_score', im) #choice = cv2.waitKey(100) #save result savepath = './data/demo/results_all_cls/' + str(image_name) + '.png' plt.savefig(savepath) plt.draw() def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', choices=NETS.keys(), default='res101') parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', choices=DATASETS.keys(), default='pascal_voc_0712') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() # model path demonet = args.demo_net dataset = args.dataset tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0]) if not os.path.isfile(tfmodel + '.meta'): raise IOError(('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(tfmodel + '.meta')) # set config tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth=True # init session sess = tf.Session(config=tfconfig) # load network if demonet == 'vgg16': net = vgg16(batch_size=1) elif demonet == 'res101': net = resnetv1(batch_size=1, num_layers=101) elif demonet == 'res50': net = resnetv1(batch_size=1, num_layers=50) else: raise NotImplementedError net.create_architecture(sess, "TEST", 20, tag='default', anchor_scales=[8, 16, 32]) saver = tf.train.Saver() saver.restore(sess, tfmodel) print('Loaded network {:s}'.format(tfmodel)) #im_names = ['rgd_0076Cropped320.png','rgd_0095.png','pcd0122r_rgd_preprocessed_1.png','pcd0875r_rgd_preprocessed_1.png','resized_0875_2.png'] im_names = ['pcd0100r_rgd_preprocessed_1.png','pcd0266r_rgd_preprocessed_1.png','pcd0882r_rgd_preprocessed_1.png','rgd_0000Cropped320.png'] for im_name in im_names: print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') print('Demo for data/demo/{}'.format(im_name)) demo(sess, net, im_name) plt.show() ================================================ FILE: tools/eval_graspRGD.py~ ================================================ #!/usr/bin/env python # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- """ Demo script showing detections in sample images. See README.md for installation instructions before running. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.config import cfg from model.test import im_detect from model.nms_wrapper import nms from utils.timer import Timer import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os, cv2 import argparse from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 import scipy from shapely.geometry import Polygon from math import acos pi = scipy.pi dot = scipy.dot sin = scipy.sin cos = scipy.cos ar = scipy.array total_found = 0 total_count = 0 total_predicted = 0 total_predicted_and_matched = 0 CLASSES = ('__background__', 'angle_01', 'angle_02', 'angle_03', 'angle_04', 'angle_05', 'angle_06', 'angle_07', 'angle_08', 'angle_09', 'angle_10', 'angle_11', 'angle_12', 'angle_13', 'angle_14', 'angle_15', 'angle_16', 'angle_17', 'angle_18', 'angle_19') NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',),'res50': ('res50_faster_rcnn_iter_159000.ckpt',)} DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',),'grasp': ('train',)} def dotproduct(a,b): return sum([a[i]*b[i] for i in range(len(a))]) #Calculates the size of a vector def veclength(a): return sum([a[i]**2 for i in range(len(a))])**.5 #Calculates the angle between two vector def ange(a,b): dp=dotproduct(a,b) la=veclength(a) lb=veclength(b) costheta=dp/(la*lb) degree = acos(costheta)/pi*180 if degree > 90: degree = 180 - degree return degree def Rotate2D(pts,cnt,ang=scipy.pi/4): '''pts = {} Rotates points(nx2) about center cnt(2) by angle ang(1) in radian''' return dot(pts-cnt,ar([[cos(ang),sin(ang)],[-sin(ang),cos(ang)]]))+cnt def vis_detections(gt_file, ax, image_name, im, class_name, dets, thresh=0.1): """Draw detected bounding boxes.""" overlap_found = False inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: missed = True return overlap_found, 0, 0 gt_x_ar = [] gt_y_ar = [] with open(gt_file) as f: for line in f: data = line.split() gt_x_ar.append(float(data[0])) gt_y_ar.append(float(data[1])) gt_label_polygon_list = [] for i in range(0,len(gt_x_ar),4): gt_label_polygon = Polygon([(gt_x_ar[i], gt_y_ar[i]), (gt_x_ar[i+1], gt_y_ar[i+1]), (gt_x_ar[i+2], gt_y_ar[i+2]), (gt_x_ar[i+3], gt_y_ar[i+3])]) gt_label_polygon_list.append(gt_label_polygon) #gt_x, gt_y = gt_label_polygon.exterior.xy #plt.plot(gt_x,gt_y, color='r', alpha = 0.7, linewidth=5, solid_capstyle='round', zorder=2) im = im[:, :, (2, 1, 0)] #fig, ax = plt.subplots(figsize=(12, 12)) ax.imshow(im, aspect='equal') predicted_and_matched_num = 0 for i in inds: inds_found = False bbox = dets[i, :4] score = dets[i, -1] pts = ar([[bbox[0],bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]]) cnt = ar([(bbox[0] + bbox[2])/2, (bbox[1] + bbox[3])/2]) angle = int(class_name[6:]) r_bbox = Rotate2D(pts, cnt, -pi/2-pi/20*(angle-1)) pred_label_polygon = Polygon([(r_bbox[0,0],r_bbox[0,1]), (r_bbox[1,0], r_bbox[1,1]), (r_bbox[2,0], r_bbox[2,1]), (r_bbox[3,0], r_bbox[3,1])]) pred_x, pred_y = pred_label_polygon.exterior.xy plt.plot(pred_x[0:2],pred_y[0:2], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[1:3],pred_y[1:3], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) plt.plot(pred_x[2:4],pred_y[2:4], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) plt.plot(pred_x[3:5],pred_y[3:5], color='r', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) for gt_label_polygon_ind in gt_label_polygon_list: intersection = pred_label_polygon.intersection(gt_label_polygon_ind) union = pred_label_polygon.union(gt_label_polygon_ind) if intersection.area/union.area > 0.25: gt_x, gt_y = gt_label_polygon_ind.exterior.xy degree = ange(np.array([pred_x[0] - pred_x[1], pred_y[0] - pred_y[1]]), np.array([gt_x[0] - gt_x[1], gt_y[0] - gt_y[1]])) if degree < 10: overlap_found = True inds_found = True #plt.plot(gt_x,gt_y, color='b', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) #plt.plot(gt_x[0:2],gt_y[0:2], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) #plt.plot(gt_x[1:3],gt_y[1:3], color='g', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) #plt.plot(gt_x[2:4],gt_y[2:4], color='k', alpha = 0.7, linewidth=1, solid_capstyle='round', zorder=2) #plt.plot(gt_x[3:5],gt_y[3:5], color='g', alpha = 0.7, linewidth=3, solid_capstyle='round', zorder=2) if inds_found is True: predicted_and_matched_num = predicted_and_matched_num+1 #ax.text(bbox[0], bbox[1] - 2, # '{:s} {:.3f}'.format(class_name, score), # bbox=dict(facecolor='blue', alpha=0.5), # fontsize=14, color='white') ax.set_title(('{} detections with ' 'p({} | box) >= {:.1f}').format(class_name, class_name, thresh), fontsize=14) return overlap_found, len(inds), predicted_and_matched_num def demo(sess, net, image_name): """Detect object classes in an image using pre-computed object proposals.""" global total_found global total_count global total_missed global total_predicted global total_predicted_and_matched # Load the demo image im_file = image_name gt_file_base = image_name[:-36] im = cv2.imread(im_file) image_name = os.path.basename(im_file) image_name = os.path.splitext(image_name)[0] gt_file = gt_file_base + '/' + image_name[:7] + 'cposCropped320.txt' print ('GT file: ' + gt_file) # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(sess, net, im) # need rgb for visualization? im_rgb_file = gt_file_base + '/' + image_name[:7] + 'rCropped320.png' im = cv2.imread(im_rgb_file) # only consider one bbox with highest score? scores_max = scores[:,1:-1].max(axis=1) scores_max_idx = np.argmax(scores_max) scores = scores[scores_max_idx:scores_max_idx+1,:] boxes = boxes[scores_max_idx:scores_max_idx+1, :] scores[0,0]=-1 # make sure background wont be the max scores_max_idx = np.argmax(scores) timer.toc() print (('Detection took {:.3f}s for ' '{:d} object proposals').format(timer.total_time, boxes.shape[0])) fig, ax = plt.subplots(figsize=(12, 12)) # Visualize detections for each class CONF_THRESH = 0.0000001 NMS_THRESH = 0.7 overall_overlap_found = False overall_missed = True for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background if scores_max_idx != cls_ind: continue cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] tmp_overlap_found, tmp_predictedNum, tmp_predictedMatchedNum = vis_detections(gt_file, ax, image_name, im, cls, dets, thresh=CONF_THRESH) #print tmp_overlap_found if tmp_overlap_found == True: overall_overlap_found = True total_predicted = total_predicted + tmp_predictedNum total_predicted_and_matched = total_predicted_and_matched + tmp_predictedMatchedNum total_count = total_count + 1 if overall_overlap_found == True: total_found = total_found + 1 print ('recall = ' + str(total_found) + '/' + str(total_count) + ' (the rate to find one grasp in one image)') print ('precision = ' + str(total_predicted_and_matched) + '/' + str(total_predicted) + ' (the rate per predicted grasp passed Jaccard)') plt.axis('off') plt.tight_layout() #save result savepath = './data/demo/results_pure/results_pure_pred/' + str(image_name) + '.png' plt.savefig(savepath) #plt.draw() def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', choices=NETS.keys(), default='res101') parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', choices=DATASETS.keys(), default='pascal_voc_0712') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() # model path demonet = args.demo_net dataset = args.dataset tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0]) if not os.path.isfile(tfmodel + '.meta'): raise IOError(('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(tfmodel + '.meta')) # set config tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth=True # init session sess = tf.Session(config=tfconfig) # load network if demonet == 'vgg16': net = vgg16(batch_size=1) elif demonet == 'res101': net = resnetv1(batch_size=1, num_layers=101) elif demonet == 'res50': net = resnetv1(batch_size=1, num_layers=50) else: raise NotImplementedError net.create_architecture(sess, "TEST", 20, tag='default', anchor_scales=[8, 16, 32]) saver = tf.train.Saver() saver.restore(sess, tfmodel) print('Loaded network {:s}'.format(tfmodel)) print ('reading images..') im_names = [] #with open('/home/fujenchu/projects/deepLearning/faster-rcnn-resnet/data/testCrop320rgd.txt') as f: with open('/media/fujenchu/home3/fasterrcnn_grasp/rgd_multibbs_5_5_5_object_tf/data/ImageSets/testfull.txt') as f: for line in f: im_path = "" for char in line: if char == '\n': break im_path = im_path + str(char) im_names.append(im_path) print ('total images: ' + str(len(im_names))) for im_name in im_names: print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') print('evaluating {}'.format(im_name)) demo(sess, net, im_name) #plt.show() ================================================ FILE: tools/mask_gen.py ================================================ import numpy as np mask = np.zeros((227,227)) mask[70:120,90:150]=1 import scipy.miscscipy.misc.imsave('mask.jpg', mask) ================================================ FILE: tools/trainval_net.py ================================================ # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Zheqi He, Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.train_val import get_training_roidb, train_net from model.config import cfg, cfg_from_file, cfg_from_list, get_output_dir, get_output_tb_dir from datasets.factory import get_imdb import datasets.imdb import argparse import pprint import numpy as np import sys import tensorflow as tf from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 def parse_args(): """ Parse input arguments """ parser = argparse.ArgumentParser(description='Train a Fast R-CNN network') parser.add_argument('--cfg', dest='cfg_file', help='optional config file', default=None, type=str) parser.add_argument('--weight', dest='weight', help='initialize with pretrained model weights', type=str) parser.add_argument('--imdb', dest='imdb_name', help='dataset to train on', default='voc_2007_trainval', type=str) parser.add_argument('--imdbval', dest='imdbval_name', help='dataset to validate on', default='voc_2007_test', type=str) parser.add_argument('--iters', dest='max_iters', help='number of iterations to train', default=70000, type=int) parser.add_argument('--tag', dest='tag', help='tag of the model', default=None, type=str) parser.add_argument('--net', dest='net', help='vgg16, res50, res101, res152', default='res50', type=str) parser.add_argument('--set', dest='set_cfgs', help='set config keys', default=None, nargs=argparse.REMAINDER) if len(sys.argv) == 1: parser.print_help() sys.exit(1) args = parser.parse_args() return args def combined_roidb(imdb_names): """ Combine multiple roidbs """ def get_roidb(imdb_name): imdb = get_imdb(imdb_name) print('Loaded dataset `{:s}` for training'.format(imdb.name)) imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD) print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)) roidb = get_training_roidb(imdb) return roidb roidbs = [get_roidb(s) for s in imdb_names.split('+')] roidb = roidbs[0] if len(roidbs) > 1: for r in roidbs[1:]: roidb.extend(r) tmp = get_imdb(imdb_names.split('+')[1]) imdb = datasets.imdb.imdb(imdb_names, tmp.classes) else: imdb = get_imdb(imdb_names) return imdb, roidb if __name__ == '__main__': args = parse_args() print('Called with args:') print(args) if args.cfg_file is not None: cfg_from_file(args.cfg_file) if args.set_cfgs is not None: cfg_from_list(args.set_cfgs) print('Using config:') pprint.pprint(cfg) np.random.seed(cfg.RNG_SEED) # train set imdb, roidb = combined_roidb(args.imdb_name) print('{:d} roidb entries'.format(len(roidb))) # output directory where the models are saved output_dir = get_output_dir(imdb, args.tag) print('Output will be saved to `{:s}`'.format(output_dir)) # tensorboard directory where the summaries are saved during training tb_dir = get_output_tb_dir(imdb, args.tag) print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir)) # also add the validation set, but with no flipping images orgflip = cfg.TRAIN.USE_FLIPPED cfg.TRAIN.USE_FLIPPED = False _, valroidb = combined_roidb(args.imdbval_name) print('{:d} validation roidb entries'.format(len(valroidb))) cfg.TRAIN.USE_FLIPPED = orgflip # load network if args.net == 'vgg16': net = vgg16(batch_size=cfg.TRAIN.IMS_PER_BATCH) elif args.net == 'res50': net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=50) elif args.net == 'res101': net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101) elif args.net == 'res152': net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=152) else: raise NotImplementedError train_net(net, imdb, roidb, valroidb, output_dir, tb_dir, pretrained_model=args.weight, max_iters=args.max_iters)