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Repository: ShapeNet/RenderForCNN
Branch: master
Commit: c0bee04aad3d
Files: 172
Total size: 2.2 MB
Directory structure:
gitextract_kjgmib7a/
├── .gitignore
├── LICENSE
├── README.md
├── caffe_models/
│ ├── deploy.prototxt
│ ├── fetch_model.sh
│ └── imagenet_mean.npy
├── data/
│ └── detection_results/
│ └── bbox_mat_filelist.txt
├── datasets/
│ ├── get_pascal3d.sh
│ ├── get_shapenet.sh
│ └── get_sun2012pascalformat.sh
├── demo_render/
│ ├── .gitignore
│ ├── README.md
│ ├── render_class_view.py
│ ├── run_demo.py
│ ├── sample_bkg_images/
│ │ └── filelist.txt
│ ├── sample_model/
│ │ ├── model.mtl
│ │ ├── model.obj
│ │ └── model_source.txt
│ ├── sample_model2/
│ │ ├── model.mtl
│ │ ├── model.obj
│ │ └── model_source.txt
│ ├── sample_truncations.txt
│ └── sample_viewpoints.txt
├── demo_view/
│ ├── .gitignore
│ ├── README.md
│ ├── run_demo.py
│ ├── run_demo_topk.py
│ ├── run_visualize_3dview.py
│ └── run_visualize_3dview_topk.py
├── global_variables.py.example
├── render_pipeline/
│ ├── README.md
│ ├── blank.blend
│ ├── crop_gray.m
│ ├── crop_images.m
│ ├── kde/
│ │ ├── get_voc12train_truncation_stats.m
│ │ ├── get_voc12train_view_stats.m
│ │ ├── matlab_kde_package/
│ │ │ ├── Contents.m
│ │ │ ├── README.txt
│ │ │ ├── adjustBW.m
│ │ │ ├── adjustPoints.m
│ │ │ ├── adjustWeights.m
│ │ │ ├── condition.m
│ │ │ ├── covar.m
│ │ │ ├── display.m
│ │ │ ├── double_kde.m
│ │ │ ├── encode.m
│ │ │ ├── entropy.m
│ │ │ ├── entropyGrad.m
│ │ │ ├── evalAvgLogL.m
│ │ │ ├── evalFGT.m
│ │ │ ├── evalIFGT.m
│ │ │ ├── evaluate.m
│ │ │ ├── examples/
│ │ │ │ ├── demo_kde_1.m
│ │ │ │ ├── demo_kde_2.m
│ │ │ │ ├── demo_kde_3.m
│ │ │ │ └── demo_regress.m
│ │ │ ├── findBWCrit.m
│ │ │ ├── getBW.m
│ │ │ ├── getDim.m
│ │ │ ├── getNeff.m
│ │ │ ├── getNpts.m
│ │ │ ├── getPoints.m
│ │ │ ├── getType.m
│ │ │ ├── getWeights.m
│ │ │ ├── gram.m
│ │ │ ├── hist.m
│ │ │ ├── ise.m
│ │ │ ├── joinTrees.m
│ │ │ ├── kde.m
│ │ │ ├── klGrad.m
│ │ │ ├── kld.m
│ │ │ ├── knn.m
│ │ │ ├── ksize.m
│ │ │ ├── license.gpl
│ │ │ ├── llGrad.m
│ │ │ ├── llHess.m
│ │ │ ├── marginal.m
│ │ │ ├── max.m
│ │ │ ├── maxlogerr.m
│ │ │ ├── mean.m
│ │ │ ├── mex/
│ │ │ │ ├── BallTree.cpp
│ │ │ │ ├── BallTreeDensity.cpp
│ │ │ │ ├── DualTree.cpp
│ │ │ │ ├── adjustBW.cpp
│ │ │ │ ├── adjustPoints.cpp
│ │ │ │ ├── adjustWeights.cpp
│ │ │ │ ├── cpp/
│ │ │ │ │ ├── BallTree.h
│ │ │ │ │ ├── BallTreeClass.cc
│ │ │ │ │ ├── BallTreeDensity.h
│ │ │ │ │ ├── BallTreeDensityClass.cc
│ │ │ │ │ ├── README.txt
│ │ │ │ │ └── kernels.h
│ │ │ │ ├── entropyGradISE.cpp
│ │ │ │ ├── entropyGradRS.cpp
│ │ │ │ ├── iseEpsilon.cpp
│ │ │ │ ├── klGradRS.cpp
│ │ │ │ ├── knn.cpp
│ │ │ │ ├── llGrad.cpp
│ │ │ │ ├── makemex.m
│ │ │ │ ├── maketmp.m
│ │ │ │ ├── prodSampleEpsilon.cpp
│ │ │ │ ├── prodSampleExact.cpp
│ │ │ │ ├── prodSampleGibbs.cpp
│ │ │ │ ├── prodSampleGibbs1.cpp
│ │ │ │ ├── prodSampleGibbs2.cpp
│ │ │ │ ├── prodSampleGibbsMS.cpp
│ │ │ │ ├── prodSampleGibbsMS1.cpp
│ │ │ │ ├── prodSampleGibbsMS2.cpp
│ │ │ │ └── reduceSolve.cpp
│ │ │ ├── miGrad.m
│ │ │ ├── modes.m
│ │ │ ├── plot.m
│ │ │ ├── private/
│ │ │ │ ├── DualTree.m
│ │ │ │ ├── entropyDist.m
│ │ │ │ ├── entropyGradDist.m
│ │ │ │ ├── golden.m
│ │ │ │ ├── iqr.m
│ │ │ │ ├── ksizeCalcUseful.m
│ │ │ │ ├── ksizeHall.m
│ │ │ │ ├── ksizeLSCV.m
│ │ │ │ ├── ksizeMSP.m
│ │ │ │ ├── ksizeROT.m
│ │ │ │ ├── prodSampleImportGauss.m
│ │ │ │ ├── prodSampleImportMix.m
│ │ │ │ ├── prodSampleImportPair.m
│ │ │ │ ├── randKernel.m
│ │ │ │ ├── reduceKD.m
│ │ │ │ ├── reduceKD2.m
│ │ │ │ └── reduceSolveM.m
│ │ │ ├── productApprox.m
│ │ │ ├── productExact.m
│ │ │ ├── quantize.m
│ │ │ ├── reduce.m
│ │ │ ├── resample.m
│ │ │ ├── rescale.m
│ │ │ └── sample.m
│ │ ├── run_sampling.m
│ │ ├── sample_truncations.m
│ │ ├── sample_viewpoints.m
│ │ └── setup_path.m
│ ├── overlay_background.m
│ ├── rdir.m
│ ├── render_helper.py
│ ├── render_model_views.py
│ ├── run_crop.py
│ ├── run_overlay.py
│ └── run_render.py
├── setup.py
├── train/
│ ├── imagenet_mean.binaryproto
│ ├── solver.prototxt.example
│ └── train_val.prototxt.example
└── view_estimation/
├── .gitignore
├── README.md
├── box_overlap.m
├── caffe_utils.py
├── combine_bbox_view.m
├── compute_recall_precision_accuracy_3dview.m
├── compute_recall_precision_accuracy_azimuth.m
├── compute_vp_acc_mederror.m
├── data_prep_helper.py
├── evaluation_helper.py
├── extract_records.m
├── jitter_imcrop.m
├── prepare_testing_data.py
├── prepare_training_data.py
├── prepare_voc12_imgs.m
├── prepare_voc12val_det_imgs.m
├── ref_evaluation_results/
│ ├── acc_mederr_results.txt
│ └── avp_nv_results.txt
├── run_evaluation.py
├── test_det.m
└── test_gt.m
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
*.pyc
*.m~
*.mat
*.mex*
*.dll
*.caffemodel
*.solverstate
global_variables.m
global_variables.py
data/syn_images*
data/view_*
data/truncation_*
data/real_images
data/tmp*
data/real_lmdbs
data/syn_lmdbs
datasets/pascal3d
datasets/shapenetcore
datasets/sun2012pascalformat
caffe_models/*.zip
*deformed*
experiments
================================================
FILE: LICENSE
================================================
Render for CNN
Copyright (c) 2015, Geometric Computation Group of Stanford University
All rights reserved.
MIT License
Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.
================================================
FILE: README.md
================================================
## Render for CNN: *Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views*
Created by <a href="http://ai.stanford.edu/~haosu/" target="_blank">Hao Su</a>, <a href="http://web.stanford.edu/~rqi/" target="_blank">Charles R. Qi</a>, <a href="http://web.stanford.edu/~yangyan/" target="_blank">Yangyan Li</a>, <a href="http://geometry.stanford.edu/member/guibas/" target="_blank">Leonidas J. Guibas</a> from Stanford University.
### Introduction
Our work was initially described in an [arXiv tech report](http://arxiv.org/abs/1505.05641) and will appear as an ICCV 2015 paper. Render for CNN is a scalable image synthesis pipeline for generating millions of training images for high-capacity models such as deep CNNs. We demonstrated how to use this pipeline, together with specially designed network architecture, to train CNNs to learn viewpoints of objects from millions of synthetic images and real images. In this repository, we provide both the rendering pipeline codes and off-the-shelf viewpoint estimator for PASCAL3D+ objects.
### License
Render for CNN is released under the MIT License (refer to the LICENSE file for details).
### Citing Render for CNN
If you find Render for CNN useful in your research, please consider citing:
@InProceedings{Su_2015_ICCV,
Title={Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views},
Author={Su, Hao and Qi, Charles R. and Li, Yangyan and Guibas, Leonidas J.},
Booktitle={The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
Year= {2015}
}
### Contents
1. [Render for CNN Image Synthesis Pipeline](#render-for-cnn-image-synthesis-pipeline)
2. [Off-the-shelf Viewpoint Estimator](#off-the-shelf-viewpoint-estimator)
3. [Testing on VOC12 val](#testing-on-voc12-val)
4. [Training your Own Models](#training-your-own-models)
### Render for CNN Image Synthesis Pipeline
**Prerequisites**
0. Blender (tested with Blender 2.71 on 64-bit Linux). You can get it from <a href="http://www.blender.org/features/past-releases/2-71/" target="_blank">Blender website</a> for free.
1. MATLAB (tested with 2014b on 64-bit Linux). You also need to compile the external kde package in `render_pipeline/kde/matlab_kde_package` by following the `README.txt` file in that folder.
2. Datasets (ShapeNet, PASCAL3D+, SUN2012) [**not required for the demo**]. If you already have the same datasets (as in urls specified in the shell scripts) downloaded, you can build soft links to the datasets with the same pathname as specified in the shell scripts. Otherwise, just do the following steps under project root folder:
<pre>
bash dataset/get_shapenet.sh
bash dataset/get_sun2012pascalformat.sh
bash dataset/get_pascal3d.sh
</pre>
**Set up paths**
All data and code paths should be set in `global_variables.py`. We have provided you an example version `global_variables.py.example`. You only need to copy or rename the example file and **modify the Blender and MATLAB path** in it (in default the paths are set to `blend` and `matlab`). All other paths are relative to the project root folder and should be fine.
cp global_variables.py.example global_variables.py
After setting Blender and MATLAB paths in `global_variables.py`, run script to set up MATLAB global variable file.
python setup.py
#### Demo of synthesis pipeline
This small demo at `demo_render` shows how we get cropped, background overlaid images of objects from a 3D model. It also helps verity that you have all enviroment set up. To run the demo, cd into project root folder and follow steps below.
cd demo_render
python run_demo.py
#### Running large scale synthesis
0. **Estimate viewpoint and truncation distributions** with KDE (kernal density estimation). If you haven't compiled the kde package, go to `render_pipeline/kde/matlab_kde_package/mex`, open MATLAB and run `makemex` in MATLAB to generate mex files.
<pre>
cd render_pipeline/kde
</pre>
Open MATLAB and run the following command (expect to see plots popping up). Viewpoint and truncation statistics will be saved to `data/view_statistics` and `data/truncation_statistics`. Samples generated from estimated distrubtion will be saved to `data/view_distribution` and `truncation_distribution`.
<pre>
run_sampling;
</pre>
1. **Render images with Blender** This step is computationally heavy and may take a long time depending how powerful your computers are. It takes us around 8 hours to render 2.4M images on 6 multi-core servers. If you have multiple servers with shared filesystem, you can set `g_hostname_synset_idx_map` in `global_variables.py` accordingly. Note that currently models are directly from ShapeNet, deformed models will be released separately later.
<pre>
python render_pipeline/run_render.py
</pre>
You can stop rendering at any time and execute following commands to crop and overlay background on images that have already been rendered. In default, rendered images will be saved at `data/syn_images`.
2. **Crop images** This step is IO heavy and it takes around 1~2 hours on a multi-core server. SSD or high-end HDD disk could help a lot. In default, cropped images are saved to `data/syn_images_cropped`.
<pre>
python render_pipeline/run_crop.py
</pre>
3. **Overlay backgrounds** Time consumption is similar to cropping step above. In default, background overlaid (also cropped from step above) images are saved to `data/syn_images_cropped_bkg_overlaid`.
<pre>
python render_pipeline/run_overlay.py
</pre>
If you'd like to get a file containing all synthesized image filenames and their labels (class, azimuth, elevation, tilt angles), we have some helper functions for that - just go look at `get_one_category_image_label_file` and `combine_files` in `view_estimation/data_prep_helper.py`, also refer to `view_estimation/prepare_training_data.py` for usage examples.
### Off-the-shelf Viewpoint Estimator
**Prerequisites**
0. <a href="https://github.com/BVLC/caffe" target="_blank">Caffe</a> (with pycaffe compiled). For testing we support the new caffe interface and prototxt files (which uses "layer" instead of "layers" in prototxt file). You can follow <a href="http://caffe.berkeleyvision.org/installation.html" target="_blank">this webpage</a> for installation details.
1. Download our pre-trained caffe model (~390MB). The model was trained on rendered images and VOC12 train set real images.
<pre>
cd caffe_models
sh fetch_model.sh
</pre>
**Set up paths**
The steps are the same as above in Render for CNN Image Synthesis Pipeline.
#### Demo of 3D viewpoint estimator
This demo at `demo_view` shows how one can use our off-the-shelf viewpoint estimator. To estimate viewpoint of an example image of airplane, do the following.
cd demo_view
python run_demo.py
To visualize the estimated 3D viewpoint, run and see a rendered image of the viewpoint.
python run_visualize_3dview.py
### Testing on VOC12 val
**Prerequisites**
0. Caffe with python interface and pretrained caffe mdoel - as in requirement in [Off-the-shelf Viewpoint Estimator](#off-the-shelf-viewpoint-estimator).
1. PASCAL3D+ dataset - if you haven't downloaded it. It will be used for preparing test images and evaluation.
<pre>
bash dataset/get_pascal3d.sh
</pre>
2. MATLAB for preparing test images.
**Set up paths**
The steps are the same as above in Render for CNN Image Synthesis Pipeline.
#### Evaluation of AVP-NV, Acc-pi/6 and MedErr
For AVP-NV (Average Viewpoint Precision), both localization (from R-CNN) and viewpoint estimation (azimuth) are evaluated. For Acc-\pi/6 and MedErr, we evaluate on VOC12 val images without truncations and occlusions. For more details on definition of the metrics, please refer to the paper.
Firstly, we need to prepare the testing images from VOC12, by running:
python view_estimation/prepare_testing_data.py
Then, do evaluation by running:
python view_estimation/run_evaluation.py
Results are displayed on screen and saved to `view_estimation/avp_test_results/avp_nv_results.txt` and `view_estimation/vp_test_results/acc_mederr_results.txt`
### Training your Own Models
For Caffe, go to <a href='https://github.com/charlesq34/caffe-render-for-cnn' target='_blank'>caffe-render-for-cnn</a> for the version with special loss layers (in view_prediction branch).
To prepare training data, run the script below. This will genereate LMDB in the data folder.
python view_estimation/prepare_training_data.py
To do the training, modify prototxt files in the `train` folder.
You can directly download synthetic images rendered by us at https://shapenet.cs.stanford.edu/media/syn_images_cropped_bkg_overlaid.tar
We name images in this package by their viewpoints. An example is the following:
synsetid_modelmd5_a010_e007_t016_d002.png
where azimuth angle is 10 degree, elevation angle is 7 degree.
NOTE: The tilt angle should be flipped as to what is written in the filename. Here the tilt angle should be -16 (344) degree to make it consistent with PASCAL3D annotation.
================================================
FILE: caffe_models/deploy.prototxt
================================================
name: "RCNN_Fine_Tune"
input: "data"
input_shape {
dim: 1
dim: 3
dim: 227
dim: 227
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm1"
type: "LRN"
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "norm1"
top: "conv2"
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm2"
type: "LRN"
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
top: "conv3"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc-azimuth"
type: "InnerProduct"
bottom: "fc7"
top: "fc-azimuth"
inner_product_param {
num_output: 4320
}
}
layer {
name: "fc-elevation"
type: "InnerProduct"
bottom: "fc7"
top: "fc-elevation"
inner_product_param {
num_output: 4320
}
}
layer {
name: "fc-tilt"
type: "InnerProduct"
bottom: "fc7"
top: "fc-tilt"
inner_product_param {
num_output: 4320
}
}
================================================
FILE: caffe_models/fetch_model.sh
================================================
#!/usr/bin/env sh
# This script downloads pretrained caffe model
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd $DIR
FILE=render4cnn-release1-caffe-model.zip
MODEL_FILE=render4cnn_3dview.caffemodel
CHECKSUM=6d690d19f13795bf6f197131895e95bc
if [ -f $MODEL_FILE ]; then
echo "File already exists. Checking md5..."
os=`uname -s`
if [ "$os" = "Linux" ]; then
checksum=`md5sum $MODEL_FILE | awk '{ print $1 }'`
elif [ "$os" = "Darwin" ]; then
checksum=`cat $MODEL_FILE | md5`
fi
if [ "$checksum" = "$CHECKSUM" ]; then
echo "Model checksum is correct. No need to download."
exit 0
else
echo "Model checksum is incorrect. Need to download again."
fi
fi
echo "Downloading precomputed viewpoint estimation caffe model (390MB)..."
wget https://shapenet.cs.stanford.edu/media/$FILE
echo "Unzipping..."
unzip $FILE
rm $FILE
echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
================================================
FILE: data/detection_results/bbox_mat_filelist.txt
================================================
rcnn_bbox_reg_pruned/aeroplane_pruned_boxes_voc_2012_val_bbox_reg.mat
rcnn_bbox_reg_pruned/bicycle_pruned_boxes_voc_2012_val_bbox_reg.mat
rcnn_bbox_reg_pruned/boat_pruned_boxes_voc_2012_val_bbox_reg.mat
rcnn_bbox_reg_pruned/bottle_pruned_boxes_voc_2012_val_bbox_reg.mat
rcnn_bbox_reg_pruned/bus_pruned_boxes_voc_2012_val_bbox_reg.mat
rcnn_bbox_reg_pruned/car_pruned_boxes_voc_2012_val_bbox_reg.mat
rcnn_bbox_reg_pruned/chair_pruned_boxes_voc_2012_val_bbox_reg.mat
rcnn_bbox_reg_pruned/diningtable_pruned_boxes_voc_2012_val_bbox_reg.mat
rcnn_bbox_reg_pruned/motorbike_pruned_boxes_voc_2012_val_bbox_reg.mat
rcnn_bbox_reg_pruned/sofa_pruned_boxes_voc_2012_val_bbox_reg.mat
rcnn_bbox_reg_pruned/train_pruned_boxes_voc_2012_val_bbox_reg.mat
rcnn_bbox_reg_pruned/tvmonitor_pruned_boxes_voc_2012_val_bbox_reg.mat
================================================
FILE: datasets/get_pascal3d.sh
================================================
#!/usr/bin/env sh
# This script downloads the PASCAL3D+ (release 1.1) data and unzips it.
# do not change this name
dataset_dir="pascal3d"
# if you have already had the same version of dataset, you can
# create soft link like this:
# >> ln -s <path/to/PASCAL3D+/> pascal3d
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd $DIR
echo "Downloading..."
wget ftp://cs.stanford.edu/cs/cvgl/PASCAL3D+_release1.1.zip
echo "Unzipping..."
mkdir $dataset_dir
unzip PASCAL3D+_release1.1.zip && rm -f PASCAL3D+_release1.1.zip
mv PASCAL3D+_release1.1/* $dataset_dir && rm -rf PASCAL3D+_release1.1
echo "Done."
================================================
FILE: datasets/get_shapenet.sh
================================================
#!/usr/bin/env sh
# This script downloads the ShapeNet data and unzips it.
# do not change this name
dataset_dir="shapenetcore"
zip_file="ShapeNetCore.v1.zip"
# if you have already had the same version of dataset, you can
# create soft link like this:
# >> ln -s <path/to/ShapeNetCore/> shapenetcore
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd $DIR
if [ -f $zip_file ];
then
echo "Good. you already have the zip file downloaded."
else
echo "Please visit http://shapenet.cs.stanford.edu to request ShapeNet data and then put the zip file in this folder and then run this script again.."
fi
echo "Unzipping..."
mkdir $dataset_dir
unzip ShapeNetCore.v1.zip && rm -f ShapeNetCore.v1.zip
mv ShapeNetCore.v1/* $dataset_dir && rm -rf ShapeNetCore.v1
cd $dataset_dir
for zipfile in `ls *.zip`; do unzip $zipfile; done
cd ..
echo "Done."
================================================
FILE: datasets/get_sun2012pascalformat.sh
================================================
#!/usr/bin/env sh
# This script downloads the SUN2012 data and unzips it.
# do not change this name
dataset_dir="sun2012pascalformat"
# if you have already had the same version of dataset, you can
# create soft link like this:
# >> ln -s <path/to/SUN2012pascalformat/> sun2012pascalformat
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd $DIR
echo "Downloading..."
wget http://groups.csail.mit.edu/vision/SUN/releases/SUN2012pascalformat.tar.gz
echo "Unzipping..."
mkdir $dataset_dir
tar -zxvf SUN2012pascalformat.tar.gz && rm -f SUN2012pascalformat.tar.gz
mv SUN2012pascalformat/* $dataset_dir && rm -rf SUN2012pascalformat
ls JPEGImages > filelist.txt
echo "Done."
================================================
FILE: demo_render/.gitignore
================================================
demo_images*
================================================
FILE: demo_render/README.md
================================================
Render for CNN: image synthesis pipeline demo
python run_demo.py
reference output images are in `ref_output`.
================================================
FILE: demo_render/render_class_view.py
================================================
#!/usr/bin/python
import os.path as osp
import sys
import argparse
import os, tempfile, glob, shutil
BASE_DIR = osp.dirname(__file__)
sys.path.append(osp.join(BASE_DIR,'../'))
from global_variables import *
parser = argparse.ArgumentParser(description='Render Model Images of a certain class and view')
parser.add_argument('-m', '--model_file', help='CAD Model obj filename', default=osp.join(BASE_DIR,'sample_model/model.obj'))
parser.add_argument('-a', '--azimuth', default='45')
parser.add_argument('-e', '--elevation', default='20')
parser.add_argument('-t', '--tilt', default='0')
parser.add_argument('-d', '--distance', default='2.0')
parser.add_argument('-o', '--output_img', help='Output img filename.', default=osp.join(BASE_DIR, 'demo_img.png'))
args = parser.parse_args()
blank_file = osp.join(g_blank_blend_file_path)
render_code = osp.join(g_render4cnn_root_folder, 'render_pipeline/render_model_views.py')
# MK TEMP DIR
temp_dirname = tempfile.mkdtemp()
view_file = osp.join(temp_dirname, 'view.txt')
view_fout = open(view_file,'w')
view_fout.write(' '.join([args.azimuth, args.elevation, args.tilt, args.distance]))
view_fout.close()
try:
render_cmd = '%s %s --background --python %s -- %s %s %s %s %s' % (g_blender_executable_path, blank_file, render_code, args.model_file, 'xxx', 'xxx', view_file, temp_dirname)
print render_cmd
os.system(render_cmd)
imgs = glob.glob(temp_dirname+'/*.png')
shutil.move(imgs[0], args.output_img)
except:
print('render failed. render_cmd: %s' % (render_cmd))
# CLEAN UP
shutil.rmtree(temp_dirname)
================================================
FILE: demo_render/run_demo.py
================================================
#!/usr/bin/python
# -*- coding: utf-8 -*-
'''
RENDERING PIPELINE DEMO
run it several times to see random images with different lighting conditions,
viewpoints, truncations and backgrounds.
'''
import os
import sys
from PIL import Image
import random
from datetime import datetime
random.seed(datetime.now())
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR,'../'))
from global_variables import *
# set debug mode
debug_mode = 1
if debug_mode:
io_redirect = ''
else:
io_redirect = ' > /dev/null 2>&1'
# -------------------------------------------
# RENDER
# -------------------------------------------
# set filepath
syn_images_folder = os.path.join(BASE_DIR, 'demo_images')
model_name = 'chair001'
image_name = 'demo_img.png'
if not os.path.exists(syn_images_folder):
os.mkdir(syn_images_folder)
os.mkdir(os.path.join(syn_images_folder, model_name))
viewpoint_samples_file = os.path.join(BASE_DIR, 'sample_viewpoints.txt')
viewpoint_samples = [[float(x) for x in line.rstrip().split(' ')] for line in open(viewpoint_samples_file,'r')]
# run code
v = random.choice(viewpoint_samples)
print ">> Selected view: ", v
python_cmd = 'python %s -a %s -e %s -t %s -d %s -o %s' % (os.path.join(BASE_DIR, 'render_class_view.py'),
str(v[0]), str(v[1]), str(v[2]), str(v[3]), os.path.join(syn_images_folder, model_name, image_name))
print ">> Running rendering command: \n \t %s" % (python_cmd)
os.system('%s %s' % (python_cmd, io_redirect))
# show result
print(">> Displaying rendered image ...")
im = Image.open(os.path.join(syn_images_folder, model_name, image_name))
im.show()
# -------------------------------------------
# CROP
# -------------------------------------------
# set filepath
bkg_folder = os.path.join(BASE_DIR, 'sample_bkg_images')
bkg_filelist = os.path.join(bkg_folder, 'filelist.txt')
syn_images_cropped_folder = os.path.join(BASE_DIR, 'demo_images_cropped')
truncation_samples_file = os.path.join(BASE_DIR, 'sample_truncations.txt')
# run matlab code
matlab_cmd = "addpath('%s'); crop_images('%s','%s','%s',1);" % (os.path.join(g_render4cnn_root_folder, 'render_pipeline'), syn_images_folder, syn_images_cropped_folder, truncation_samples_file)
print ">> Starting MATLAB ... to run cropping command: \n \t %s" % matlab_cmd
os.system('%s -nodisplay -r "try %s ; catch; end; quit;" %s' % (g_matlab_executable_path, matlab_cmd, io_redirect))
# show result
print(">> Displaying cropped image ...")
im = Image.open(os.path.join(syn_images_cropped_folder, model_name, image_name))
im.show()
# -------------------------------------------
# OVERLAY BACKGROUND
# -------------------------------------------
# set filepath
syn_images_cropped_bkg_overlaid_folder = os.path.join(BASE_DIR, 'demo_images_cropped_bkg_overlaid')
# run code
matlab_cmd = "addpath('%s'); overlay_background('%s','%s','%s', '%s', %f, 1);" % (os.path.join(g_render4cnn_root_folder, 'render_pipeline'), syn_images_cropped_folder, syn_images_cropped_bkg_overlaid_folder, bkg_filelist, bkg_folder, 1.0)
print ">> Starting MATLAB ... to run background overlaying command: \n \t %s" % matlab_cmd
os.system('%s -nodisplay -r "try %s ; catch; end; quit;" %s' % (g_matlab_executable_path, matlab_cmd, io_redirect))
# show result
print("Displaying background overlaid image ...")
im = Image.open(os.path.join(syn_images_cropped_bkg_overlaid_folder, model_name, image_name.replace('.png', '.jpg')))
im.show()
================================================
FILE: demo_render/sample_bkg_images/filelist.txt
================================================
sun_admehzzcyrwqeagf.jpg
sun_amxqyvcaerikvqep.jpg
sun_amyacsvlywqnpmum.jpg
sun_amyamgmyudixgkeu.jpg
sun_amyaojhhpfkypfki.jpg
sun_amydkgquihpzayht.jpg
sun_amyhazfgaoclbgqs.jpg
sun_amymtdfzotooyham.jpg
sun_amynimbbnwqwejeg.jpg
sun_amyqdfkizefupfta.jpg
sun_amyqrurobufixjqr.jpg
sun_amyrciusrihbhswi.jpg
sun_amyuvmazodqhkrin.jpg
sun_amyvdsxkokkyxnft.jpg
sun_amywfwnlavdpohzn.jpg
sun_amywncoshhwkcvek.jpg
sun_amyxaanrovbdsxnk.jpg
sun_aruegippndskcujm.jpg
================================================
FILE: demo_render/sample_model/model.mtl
================================================
# Blender MTL File: 'blank.blend'
# Material Count: 3
newmtl material_0_1_8
Ns 96.078431
Ka 0.000000 0.000000 0.000000
Kd 0.640000 0.640000 0.640000
Ks 0.500000 0.500000 0.500000
Ni -1.000000
d 1.000000
illum 2
map_Kd ./images/texture0.jpg
newmtl material_1_24
Ns 96.078431
Ka 0.000000 0.000000 0.000000
Kd 0.800000 0.800000 0.800000
Ks 0.500000 0.500000 0.500000
Ni -1.000000
d 1.000000
illum 2
newmtl material_2_2_8
Ns 96.078431
Ka 0.000000 0.000000 0.000000
Kd 0.640000 0.640000 0.640000
Ks 0.500000 0.500000 0.500000
Ni -1.000000
d 1.000000
illum 2
map_Kd ./images/texture1.jpg
================================================
FILE: demo_render/sample_model/model.obj
================================================
# Blender v2.71 (sub 0) OBJ File: 'blank.blend'
# www.blender.org
mtllib model.mtl
o mesh7_mesh7-geometry
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usemtl material_2_2_8
s off
f 35/2 47/3 68/4
f 96/5 68/6 78/7
s 1
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usemtl material_0_1_8
s 1
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usemtl material_1_24
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gitextract_kjgmib7a/
├── .gitignore
├── LICENSE
├── README.md
├── caffe_models/
│ ├── deploy.prototxt
│ ├── fetch_model.sh
│ └── imagenet_mean.npy
├── data/
│ └── detection_results/
│ └── bbox_mat_filelist.txt
├── datasets/
│ ├── get_pascal3d.sh
│ ├── get_shapenet.sh
│ └── get_sun2012pascalformat.sh
├── demo_render/
│ ├── .gitignore
│ ├── README.md
│ ├── render_class_view.py
│ ├── run_demo.py
│ ├── sample_bkg_images/
│ │ └── filelist.txt
│ ├── sample_model/
│ │ ├── model.mtl
│ │ ├── model.obj
│ │ └── model_source.txt
│ ├── sample_model2/
│ │ ├── model.mtl
│ │ ├── model.obj
│ │ └── model_source.txt
│ ├── sample_truncations.txt
│ └── sample_viewpoints.txt
├── demo_view/
│ ├── .gitignore
│ ├── README.md
│ ├── run_demo.py
│ ├── run_demo_topk.py
│ ├── run_visualize_3dview.py
│ └── run_visualize_3dview_topk.py
├── global_variables.py.example
├── render_pipeline/
│ ├── README.md
│ ├── blank.blend
│ ├── crop_gray.m
│ ├── crop_images.m
│ ├── kde/
│ │ ├── get_voc12train_truncation_stats.m
│ │ ├── get_voc12train_view_stats.m
│ │ ├── matlab_kde_package/
│ │ │ ├── Contents.m
│ │ │ ├── README.txt
│ │ │ ├── adjustBW.m
│ │ │ ├── adjustPoints.m
│ │ │ ├── adjustWeights.m
│ │ │ ├── condition.m
│ │ │ ├── covar.m
│ │ │ ├── display.m
│ │ │ ├── double_kde.m
│ │ │ ├── encode.m
│ │ │ ├── entropy.m
│ │ │ ├── entropyGrad.m
│ │ │ ├── evalAvgLogL.m
│ │ │ ├── evalFGT.m
│ │ │ ├── evalIFGT.m
│ │ │ ├── evaluate.m
│ │ │ ├── examples/
│ │ │ │ ├── demo_kde_1.m
│ │ │ │ ├── demo_kde_2.m
│ │ │ │ ├── demo_kde_3.m
│ │ │ │ └── demo_regress.m
│ │ │ ├── findBWCrit.m
│ │ │ ├── getBW.m
│ │ │ ├── getDim.m
│ │ │ ├── getNeff.m
│ │ │ ├── getNpts.m
│ │ │ ├── getPoints.m
│ │ │ ├── getType.m
│ │ │ ├── getWeights.m
│ │ │ ├── gram.m
│ │ │ ├── hist.m
│ │ │ ├── ise.m
│ │ │ ├── joinTrees.m
│ │ │ ├── kde.m
│ │ │ ├── klGrad.m
│ │ │ ├── kld.m
│ │ │ ├── knn.m
│ │ │ ├── ksize.m
│ │ │ ├── license.gpl
│ │ │ ├── llGrad.m
│ │ │ ├── llHess.m
│ │ │ ├── marginal.m
│ │ │ ├── max.m
│ │ │ ├── maxlogerr.m
│ │ │ ├── mean.m
│ │ │ ├── mex/
│ │ │ │ ├── BallTree.cpp
│ │ │ │ ├── BallTreeDensity.cpp
│ │ │ │ ├── DualTree.cpp
│ │ │ │ ├── adjustBW.cpp
│ │ │ │ ├── adjustPoints.cpp
│ │ │ │ ├── adjustWeights.cpp
│ │ │ │ ├── cpp/
│ │ │ │ │ ├── BallTree.h
│ │ │ │ │ ├── BallTreeClass.cc
│ │ │ │ │ ├── BallTreeDensity.h
│ │ │ │ │ ├── BallTreeDensityClass.cc
│ │ │ │ │ ├── README.txt
│ │ │ │ │ └── kernels.h
│ │ │ │ ├── entropyGradISE.cpp
│ │ │ │ ├── entropyGradRS.cpp
│ │ │ │ ├── iseEpsilon.cpp
│ │ │ │ ├── klGradRS.cpp
│ │ │ │ ├── knn.cpp
│ │ │ │ ├── llGrad.cpp
│ │ │ │ ├── makemex.m
│ │ │ │ ├── maketmp.m
│ │ │ │ ├── prodSampleEpsilon.cpp
│ │ │ │ ├── prodSampleExact.cpp
│ │ │ │ ├── prodSampleGibbs.cpp
│ │ │ │ ├── prodSampleGibbs1.cpp
│ │ │ │ ├── prodSampleGibbs2.cpp
│ │ │ │ ├── prodSampleGibbsMS.cpp
│ │ │ │ ├── prodSampleGibbsMS1.cpp
│ │ │ │ ├── prodSampleGibbsMS2.cpp
│ │ │ │ └── reduceSolve.cpp
│ │ │ ├── miGrad.m
│ │ │ ├── modes.m
│ │ │ ├── plot.m
│ │ │ ├── private/
│ │ │ │ ├── DualTree.m
│ │ │ │ ├── entropyDist.m
│ │ │ │ ├── entropyGradDist.m
│ │ │ │ ├── golden.m
│ │ │ │ ├── iqr.m
│ │ │ │ ├── ksizeCalcUseful.m
│ │ │ │ ├── ksizeHall.m
│ │ │ │ ├── ksizeLSCV.m
│ │ │ │ ├── ksizeMSP.m
│ │ │ │ ├── ksizeROT.m
│ │ │ │ ├── prodSampleImportGauss.m
│ │ │ │ ├── prodSampleImportMix.m
│ │ │ │ ├── prodSampleImportPair.m
│ │ │ │ ├── randKernel.m
│ │ │ │ ├── reduceKD.m
│ │ │ │ ├── reduceKD2.m
│ │ │ │ └── reduceSolveM.m
│ │ │ ├── productApprox.m
│ │ │ ├── productExact.m
│ │ │ ├── quantize.m
│ │ │ ├── reduce.m
│ │ │ ├── resample.m
│ │ │ ├── rescale.m
│ │ │ └── sample.m
│ │ ├── run_sampling.m
│ │ ├── sample_truncations.m
│ │ ├── sample_viewpoints.m
│ │ └── setup_path.m
│ ├── overlay_background.m
│ ├── rdir.m
│ ├── render_helper.py
│ ├── render_model_views.py
│ ├── run_crop.py
│ ├── run_overlay.py
│ └── run_render.py
├── setup.py
├── train/
│ ├── imagenet_mean.binaryproto
│ ├── solver.prototxt.example
│ └── train_val.prototxt.example
└── view_estimation/
├── .gitignore
├── README.md
├── box_overlap.m
├── caffe_utils.py
├── combine_bbox_view.m
├── compute_recall_precision_accuracy_3dview.m
├── compute_recall_precision_accuracy_azimuth.m
├── compute_vp_acc_mederror.m
├── data_prep_helper.py
├── evaluation_helper.py
├── extract_records.m
├── jitter_imcrop.m
├── prepare_testing_data.py
├── prepare_training_data.py
├── prepare_voc12_imgs.m
├── prepare_voc12val_det_imgs.m
├── ref_evaluation_results/
│ ├── acc_mederr_results.txt
│ └── avp_nv_results.txt
├── run_evaluation.py
├── test_det.m
└── test_gt.m
SYMBOL INDEX (103 symbols across 31 files)
FILE: render_pipeline/kde/matlab_kde_package/mex/BallTree.cpp
function mexFunction (line 12) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
FILE: render_pipeline/kde/matlab_kde_package/mex/BallTreeDensity.cpp
function mexFunction (line 12) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
FILE: render_pipeline/kde/matlab_kde_package/mex/DualTree.cpp
function mexFunction (line 13) | void mexFunction(int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[])
FILE: render_pipeline/kde/matlab_kde_package/mex/adjustBW.cpp
function mexFunction (line 13) | void mexFunction(int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[])
FILE: render_pipeline/kde/matlab_kde_package/mex/adjustPoints.cpp
function mexFunction (line 13) | void mexFunction(int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[])
FILE: render_pipeline/kde/matlab_kde_package/mex/adjustWeights.cpp
function mexFunction (line 13) | void mexFunction(int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[])
FILE: render_pipeline/kde/matlab_kde_package/mex/cpp/BallTree.h
function class (line 33) | class BallTree {
FILE: render_pipeline/kde/matlab_kde_package/mex/cpp/BallTreeClass.cc
function mxArray (line 432) | mxArray* BallTree::createInMatlab(const mxArray* _pointsMatrix, const mx...
function mxArray (line 443) | mxArray* BallTree::matlabMakeStruct(const mxArray* _pointsMatrix, const ...
FILE: render_pipeline/kde/matlab_kde_package/mex/cpp/BallTreeDensity.h
function class (line 20) | class BallTreeDensity : public BallTree {
FILE: render_pipeline/kde/matlab_kde_package/mex/cpp/BallTreeDensityClass.cc
function pushDownLocal (line 30) | void pushDownLocal(const BallTree& atTree, const BallTree::index aRoot)
function pushDownAll (line 41) | void pushDownAll(const BallTree& locations)
function recurseMinMax (line 55) | void recurseMinMax(const BallTree& atTree, const BallTree::index aRoot)
function mxArray (line 484) | mxArray* BallTreeDensity::createInMatlab(const mxArray* _pointsMatrix, c...
function mxArray (line 495) | mxArray* BallTreeDensity::matlabMakeStruct(const mxArray* _pointsMatrix,...
FILE: render_pipeline/kde/matlab_kde_package/mex/cpp/kernels.h
function minDistGauss (line 14) | double BallTreeDensity::minDistGauss(BallTree::index dRoot,
function maxDistGauss (line 34) | double BallTreeDensity::maxDistGauss(BallTree::index dRoot,
function minDistLaplace (line 57) | double BallTreeDensity::minDistLaplace(BallTree::index dRoot,
function maxDistLaplace (line 77) | double BallTreeDensity::maxDistLaplace(BallTree::index dRoot,
function minDistEpanetch (line 103) | double BallTreeDensity::minDistEpanetch(BallTree::index dRoot,
function maxDistEpanetch (line 124) | double BallTreeDensity::maxDistEpanetch(BallTree::index dRoot,
function dKdX_p (line 154) | void BallTreeDensity::dKdX_p(BallTree::index dRoot,const BallTree& atTree,
FILE: render_pipeline/kde/matlab_kde_package/mex/entropyGradISE.cpp
function erf (line 13) | double erf(double c) { // disabled for lack of windows erf f'n
function mexFunction (line 23) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
function kprm (line 68) | void kprm(const BallTreeDensity& dens, const BallTree& loc, double *e)
function kasum (line 117) | void kasum(const BallTreeDensity& dens, const BallTree& loc, double *e)
function kukprmconv (line 158) | void kukprmconv(const BallTreeDensity& dens, double a, bool erfFlag, dou...
function entDirISE (line 206) | void entDirISE(const BallTreeDensity& dens, double* err)
FILE: render_pipeline/kde/matlab_kde_package/mex/entropyGradRS.cpp
function erf (line 6) | double erf(double c) { // disabled for lack of windows erf f'n
function mexFunction (line 16) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
function entGrad_Resub (line 43) | void entGrad_Resub(const BallTreeDensity& dens, double* err1) {
FILE: render_pipeline/kde/matlab_kde_package/mex/iseEpsilon.cpp
function mexFunction (line 47) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
function normConstant (line 102) | double normConstant(void) {
function minDistProd (line 131) | double minDistProd(const BallTreeDensity& bt1, BallTree::index i,
function maxDistProd (line 150) | double maxDistProd(const BallTreeDensity& bt1, BallTree::index i,
function computeSigVals (line 170) | void computeSigVals(void) {
function multiEvalRecursive (line 195) | void multiEvalRecursive(void) {
function multiEval (line 286) | void multiEval(void) {
FILE: render_pipeline/kde/matlab_kde_package/mex/klGradRS.cpp
function erf (line 6) | double erf(double c) { // disabled for lack of windows erf f'n
function mexFunction (line 16) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
function KLGrad_Resub (line 91) | void KLGrad_Resub(const BallTreeDensity& p1, const BallTreeDensity &p2, ...
FILE: render_pipeline/kde/matlab_kde_package/mex/knn.cpp
function mexFunction (line 12) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
FILE: render_pipeline/kde/matlab_kde_package/mex/llGrad.cpp
function mexFunction (line 17) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
FILE: render_pipeline/kde/matlab_kde_package/mex/prodSampleEpsilon.cpp
function mexFunction (line 45) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
function normConstant (line 115) | double normConstant(void) {
function minDistProd (line 144) | double minDistProd(const BallTreeDensity& bt1, BallTree::index i,
function maxDistProd (line 163) | double maxDistProd(const BallTreeDensity& bt1, BallTree::index i,
function computeSigVals (line 183) | void computeSigVals(void) {
function multiEvalRecursive (line 208) | void multiEvalRecursive(void) {
function multiEval (line 299) | void multiEval(void) {
FILE: render_pipeline/kde/matlab_kde_package/mex/prodSampleExact.cpp
function mexFunction (line 40) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
function normConstant (line 97) | double normConstant(void) {
function exactEval (line 121) | void exactEval(void) {
FILE: render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbs.cpp
function initIndices (line 44) | void initIndices(void)
function calcIndices (line 67) | void calcIndices(void)
function samplePoint (line 81) | void samplePoint(double* X)
function sampleIndices (line 98) | void sampleIndices(double* X) {
function sampleIndex (line 133) | void sampleIndex(unsigned int j)
function gibbs1 (line 181) | void gibbs1(unsigned int _Ndens, const BallTreeDensity* _trees,
function gibbs2 (line 231) | void gibbs2(unsigned int _Ndens, const BallTreeDensity* _trees,
FILE: render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbs1.cpp
function mexFunction (line 21) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
FILE: render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbs2.cpp
function mexFunction (line 21) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
FILE: render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbsMS.cpp
function initIndices (line 45) | void initIndices(void)
function levelInit (line 64) | void levelInit(void)
function levelDown (line 75) | void levelDown(void)
function calcIndices (line 100) | void calcIndices(void)
function samplePoint (line 114) | void samplePoint(double* X)
function sampleIndices (line 131) | void sampleIndices(double* X) {
function sampleIndex (line 165) | void sampleIndex(unsigned int j)
function gibbs1 (line 213) | void gibbs1(unsigned int _Ndens, const BallTreeDensity* _trees,
function gibbs2 (line 285) | void gibbs2(unsigned int _Ndens, const BallTreeDensity* _trees,
FILE: render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbsMS1.cpp
function mexFunction (line 21) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
FILE: render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbsMS2.cpp
function mexFunction (line 21) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
FILE: render_pipeline/kde/matlab_kde_package/mex/reduceSolve.cpp
function mexFunction (line 19) | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prh...
function SMO (line 48) | void SMO(double* Q, double* D, unsigned int N, double* weights)
function searchPoint (line 116) | bool searchPoint(unsigned int& I1, unsigned int I2,double* weights,doubl...
function updateWeight (line 139) | bool updateWeight(unsigned int I1,unsigned int I2,double* weights,
function multUpdate (line 168) | void multUpdate(double* Q, double* D, unsigned int N, double* weights)
FILE: render_pipeline/render_helper.py
function load_one_category_shape_list (line 27) | def load_one_category_shape_list(shape_synset):
function load_one_category_shape_views (line 41) | def load_one_category_shape_views(synset):
function render_one_category_model_views (line 57) | def render_one_category_model_views(shape_list, view_params):
FILE: render_pipeline/render_model_views.py
function camPosToQuaternion (line 37) | def camPosToQuaternion(cx, cy, cz):
function quaternionFromYawPitchRoll (line 57) | def quaternionFromYawPitchRoll(yaw, pitch, roll):
function camPosToQuaternion (line 71) | def camPosToQuaternion(cx, cy, cz):
function camRotQuaternion (line 100) | def camRotQuaternion(cx, cy, cz, theta):
function quaternionProduct (line 112) | def quaternionProduct(qx, qy):
function obj_centened_camera_pos (line 127) | def obj_centened_camera_pos(dist, azimuth_deg, elevation_deg):
FILE: view_estimation/caffe_utils.py
function imglabel2datum (line 29) | def imglabel2datum(img_label):
function write_image_lmdb (line 59) | def write_image_lmdb(image_file, output_lmdb):
function write_vector_lmdb (line 87) | def write_vector_lmdb(input_txt_file, output_lmdb):
function load_vector_from_lmdb (line 109) | def load_vector_from_lmdb(dbname, feat_dim, max_num=float('Inf')):
function batch_predict (line 146) | def batch_predict(model_deploy_file, model_params_file, BATCH_SIZE, resu...
FILE: view_estimation/data_prep_helper.py
function path2label (line 23) | def path2label(path):
function get_one_category_image_label_file (line 40) | def get_one_category_image_label_file(shape_synset, train_image_label_fi...
function combine_files (line 74) | def combine_files(input_file_list, output_file, shuffle=1):
function view2label (line 92) | def view2label(degree, class_index):
function generate_image_view_lmdb (line 107) | def generate_image_view_lmdb(image_label_file, output_lmdb):
FILE: view_estimation/evaluation_helper.py
function viewpoint (line 26) | def viewpoint(img_filenames, class_idxs, output_result_file):
function get_top_preds (line 77) | def get_top_preds(prob, num_bin=8, diff_threshold=10):
function get_topk_viewpoints (line 115) | def get_topk_viewpoints(probs_3dview, topk):
function viewpoint_topk (line 177) | def viewpoint_topk(img_filenames, class_idxs, topk=1, output_result_file...
function test_avp_nv (line 250) | def test_avp_nv(cls_names, img_name_file_list, det_bbox_mat_file_list, r...
function test_vp_acc (line 294) | def test_vp_acc(cls_names, img_name_file_list, result_folder, view_label...
Condensed preview — 172 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,450K chars).
[
{
"path": ".gitignore",
"chars": 312,
"preview": "*.pyc\n*.m~\n*.mat\n*.mex*\n*.dll\n*.caffemodel\n*.solverstate\nglobal_variables.m\nglobal_variables.py\ndata/syn_images*\ndata/vi"
},
{
"path": "LICENSE",
"chars": 1145,
"preview": "Render for CNN\n\nCopyright (c) 2015, Geometric Computation Group of Stanford University\nAll rights reserved.\n\nMIT License"
},
{
"path": "README.md",
"chars": 9264,
"preview": "## Render for CNN: *Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views*\nCreated by <a href=\""
},
{
"path": "caffe_models/deploy.prototxt",
"chars": 3057,
"preview": "name: \"RCNN_Fine_Tune\"\ninput: \"data\"\ninput_shape {\n dim: 1\n dim: 3\n dim: 227\n dim: 227\n}\nlayer {\n name: \"conv1\"\n t"
},
{
"path": "caffe_models/fetch_model.sh",
"chars": 969,
"preview": "#!/usr/bin/env sh\n# This script downloads pretrained caffe model\n\nDIR=\"$( cd \"$(dirname \"$0\")\" ; pwd -P )\"\ncd $DIR\n\nFILE"
},
{
"path": "data/detection_results/bbox_mat_filelist.txt",
"chars": 807,
"preview": "rcnn_bbox_reg_pruned/aeroplane_pruned_boxes_voc_2012_val_bbox_reg.mat\nrcnn_bbox_reg_pruned/bicycle_pruned_boxes_voc_2012"
},
{
"path": "datasets/get_pascal3d.sh",
"chars": 600,
"preview": "#!/usr/bin/env sh\n# This script downloads the PASCAL3D+ (release 1.1) data and unzips it.\n\n# do not change this name\ndat"
},
{
"path": "datasets/get_shapenet.sh",
"chars": 841,
"preview": "#!/usr/bin/env sh\n# This script downloads the ShapeNet data and unzips it.\n\n# do not change this name\ndataset_dir=\"shape"
},
{
"path": "datasets/get_sun2012pascalformat.sh",
"chars": 672,
"preview": "#!/usr/bin/env sh\n# This script downloads the SUN2012 data and unzips it.\n\n# do not change this name\ndataset_dir=\"sun201"
},
{
"path": "demo_render/.gitignore",
"chars": 13,
"preview": "demo_images*\n"
},
{
"path": "demo_render/README.md",
"chars": 116,
"preview": "Render for CNN: image synthesis pipeline demo\n\n python run_demo.py\n\nreference output images are in `ref_output`.\n"
},
{
"path": "demo_render/render_class_view.py",
"chars": 1580,
"preview": "#!/usr/bin/python\n\nimport os.path as osp\nimport sys\nimport argparse\nimport os, tempfile, glob, shutil\n\nBASE_DIR = osp.di"
},
{
"path": "demo_render/run_demo.py",
"chars": 3482,
"preview": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n'''\nRENDERING PIPELINE DEMO\nrun it several times to see random images with dif"
},
{
"path": "demo_render/sample_bkg_images/filelist.txt",
"chars": 450,
"preview": "sun_admehzzcyrwqeagf.jpg\nsun_amxqyvcaerikvqep.jpg\nsun_amyacsvlywqnpmum.jpg\nsun_amyamgmyudixgkeu.jpg\nsun_amyaojhhpfkypfki"
},
{
"path": "demo_render/sample_model/model.mtl",
"chars": 585,
"preview": "# Blender MTL File: 'blank.blend'\n# Material Count: 3\n\nnewmtl material_0_1_8\nNs 96.078431\nKa 0.000000 0.000000 0.000000\n"
},
{
"path": "demo_render/sample_model/model.obj",
"chars": 343342,
"preview": "# Blender v2.71 (sub 0) OBJ File: 'blank.blend'\n# www.blender.org\nmtllib model.mtl\no mesh7_mesh7-geometry\nv 0.242547 0.0"
},
{
"path": "demo_render/sample_model/model_source.txt",
"chars": 55,
"preview": "shapenetcore/03001627/21bfb150cfc23accb01c58badc8bbc39\n"
},
{
"path": "demo_render/sample_model2/model.mtl",
"chars": 1625,
"preview": "# Blender MTL File: 'blank.blend'\n# Material Count: 10\n\nnewmtl material_0_24\nNs 96.078431\nKa 0.000000 0.000000 0.000000\n"
},
{
"path": "demo_render/sample_model2/model.obj",
"chars": 1487682,
"preview": "# Blender v2.71 (sub 0) OBJ File: 'blank.blend'\n# www.blender.org\nmtllib model.mtl\no mesh86.002_mesh86-geometry\no mesh85"
},
{
"path": "demo_render/sample_model2/model_source.txt",
"chars": 55,
"preview": "shapenetcore/02691156/1caa02b831cccff090baeef8ba5b93e5\n"
},
{
"path": "demo_render/sample_truncations.txt",
"chars": 41,
"preview": "0 0.2 0 0\n0.2 0 0 0\n0 0 0 -0.3\n0 0 0.2 0\n"
},
{
"path": "demo_render/sample_viewpoints.txt",
"chars": 47,
"preview": "45 20 0 2.0\n120 40 3 2.5\n0 10 0 3\n240 25 2 2.4\n"
},
{
"path": "demo_view/.gitignore",
"chars": 18,
"preview": "*.txt\n*.log\n*.png\n"
},
{
"path": "demo_view/README.md",
"chars": 739,
"preview": "Render for CNN: demo of usages of the off-the-shelf viewpoint estiamtor on images of objects in PASCAL3D+ classes.\n\n "
},
{
"path": "demo_view/run_demo.py",
"chars": 932,
"preview": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport argparse\n\nBASE_DIR = os.path.dirname(os.path.absp"
},
{
"path": "demo_view/run_demo_topk.py",
"chars": 1268,
"preview": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport argparse\n\nBASE_DIR = os.path.dirname(os.path.absp"
},
{
"path": "demo_view/run_visualize_3dview.py",
"chars": 1713,
"preview": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport argparse\nfrom PIL import Image\n\nBASE_DIR = os.pat"
},
{
"path": "demo_view/run_visualize_3dview_topk.py",
"chars": 2466,
"preview": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport argparse\nfrom PIL import Image\nfrom PIL import Im"
},
{
"path": "global_variables.py.example",
"chars": 5852,
"preview": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport socket\n\ng_render4cnn_root_folder = os.path.dirname(os.path.a"
},
{
"path": "render_pipeline/README.md",
"chars": 482,
"preview": "Render for CNN: the rendering pipeline\n\nThree stages:\n - Render synthetic images of objects through overfit-resistant re"
},
{
"path": "render_pipeline/crop_gray.m",
"chars": 1283,
"preview": "% crop according to truncationParam\nfunction [I, top, bottom, left, right] = crop_gray(I, bgColor, truncationParam)\n\n% g"
},
{
"path": "render_pipeline/crop_images.m",
"chars": 1718,
"preview": "function crop_images(src_folder, dst_folder, truncation_distr_file, single_thread)\n\nif nargin < 4\n single_thread = 0;"
},
{
"path": "render_pipeline/kde/get_voc12train_truncation_stats.m",
"chars": 5686,
"preview": "function get_voc12train_truncation_stats(cls, visualize, debug)\n\nif nargin < 2\n visualize = 0;\n debug = 0;\nend\nif "
},
{
"path": "render_pipeline/kde/get_voc12train_view_stats.m",
"chars": 1847,
"preview": "function [azimuths, elevations, distances] = get_voc12train_view_stats(cls, visualize)\n\nif nargin < 2\n visualize = 0;"
},
{
"path": "render_pipeline/kde/matlab_kde_package/Contents.m",
"chars": 5396,
"preview": "% ==============================================================================\r\n% KDE Matlab class definition\r\n% ====="
},
{
"path": "render_pipeline/kde/matlab_kde_package/README.txt",
"chars": 11067,
"preview": "==============================================================================\nMATLAB KDE Class Description & Specificat"
},
{
"path": "render_pipeline/kde/matlab_kde_package/adjustBW.m",
"chars": 1090,
"preview": "function adjustBWs(npd,s)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% adjustBW"
},
{
"path": "render_pipeline/kde/matlab_kde_package/adjustPoints.m",
"chars": 555,
"preview": "function adjustPoints(kde, delta)\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n%\r"
},
{
"path": "render_pipeline/kde/matlab_kde_package/adjustWeights.m",
"chars": 1052,
"preview": "function adjustWeights(npd,w)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% adju"
},
{
"path": "render_pipeline/kde/matlab_kde_package/condition.m",
"chars": 1190,
"preview": "function p = condition(dens,ind,A)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/covar.m",
"chars": 885,
"preview": "function cov = covar(dens,noBiasFlag)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"
},
{
"path": "render_pipeline/kde/matlab_kde_package/display.m",
"chars": 953,
"preview": "function display(kde)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% display(kde)"
},
{
"path": "render_pipeline/kde/matlab_kde_package/double_kde.m",
"chars": 425,
"preview": "function d = double(npd)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% double(kd"
},
{
"path": "render_pipeline/kde/matlab_kde_package/encode.m",
"chars": 2845,
"preview": "function C = encode(p,R)\n%\n% Compute vector of transmit costs for each stage of the KD-tree\n%\n\nN = getNpts(p); bw = get"
},
{
"path": "render_pipeline/kde/matlab_kde_package/entropy.m",
"chars": 1562,
"preview": "function H = entropy(x,type,varargin)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"
},
{
"path": "render_pipeline/kde/matlab_kde_package/entropyGrad.m",
"chars": 1006,
"preview": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n%\r\n% err = entropyGrad(npd,estType)\r\n"
},
{
"path": "render_pipeline/kde/matlab_kde_package/evalAvgLogL.m",
"chars": 862,
"preview": "function ll = evalAvgLogL(dens,at,varargin)\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/evalFGT.m",
"chars": 6928,
"preview": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n%\r\n% evalIFGT Evaluate the density e"
},
{
"path": "render_pipeline/kde/matlab_kde_package/evalIFGT.m",
"chars": 7222,
"preview": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% evalIFGT Evaluate the density est"
},
{
"path": "render_pipeline/kde/matlab_kde_package/evaluate.m",
"chars": 1512,
"preview": "function p = evaluate(dens,pos,varargin)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/examples/demo_kde_1.m",
"chars": 817,
"preview": "% Simple Example #1 -- construction, BW choice, plotting\r\n%\r\n%\r\nfprintf('KDE Example #1 : 1D density estimate with vario"
},
{
"path": "render_pipeline/kde/matlab_kde_package/examples/demo_kde_2.m",
"chars": 975,
"preview": "% Simple Example #2 -- MI estimates, joinTrees, etc\r\n%\r\n%\r\nfprintf('KDE Example #2 : MI estimates, etc\\n');\r\n\r\n%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/examples/demo_kde_3.m",
"chars": 1102,
"preview": "% Simple Example #3 -- products of gaussian mixtures\r\n%\r\n%\r\nfprintf('KDE Example #3 : Product sampling methods (single, "
},
{
"path": "render_pipeline/kde/matlab_kde_package/examples/demo_regress.m",
"chars": 643,
"preview": "% Simple Example #3 -- products of gaussian mixtures\r\n%\r\n%\r\nfprintf('Example: Kernel Regression with KDE toolbox\\n');\r\nr"
},
{
"path": "render_pipeline/kde/matlab_kde_package/findBWCrit.m",
"chars": 947,
"preview": "function BW=findBWCrit(p,Nmodes)\r\n% BW = findBWCrit(p,Nmodes)\r\n% find Silverman's \"Critical Bandwidth\" -- min BW s.t. th"
},
{
"path": "render_pipeline/kde/matlab_kde_package/getBW.m",
"chars": 651,
"preview": "function s = getBW(dens,ind)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% getBW(P"
},
{
"path": "render_pipeline/kde/matlab_kde_package/getDim.m",
"chars": 361,
"preview": "function D = getDim(npd)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% getDim(kde"
},
{
"path": "render_pipeline/kde/matlab_kde_package/getNeff.m",
"chars": 429,
"preview": "function Neff = getNeff(dens)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% getNef"
},
{
"path": "render_pipeline/kde/matlab_kde_package/getNpts.m",
"chars": 358,
"preview": "function N = getNpts(npd)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% getNpts(kd"
},
{
"path": "render_pipeline/kde/matlab_kde_package/getPoints.m",
"chars": 587,
"preview": "function pts = getPoints(dens,ind)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% g"
},
{
"path": "render_pipeline/kde/matlab_kde_package/getType.m",
"chars": 529,
"preview": "function typeS = getType(dens)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% getTy"
},
{
"path": "render_pipeline/kde/matlab_kde_package/getWeights.m",
"chars": 577,
"preview": "function wts = getWeights(dens,ind)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% "
},
{
"path": "render_pipeline/kde/matlab_kde_package/gram.m",
"chars": 521,
"preview": "function G = gram(p)\n%\n% Compute the Gram matrix of the kernel centers in p, under the distance metric determined\n% by "
},
{
"path": "render_pipeline/kde/matlab_kde_package/hist.m",
"chars": 2068,
"preview": "function [h,varargout] = hist(dens,N,dims,range)\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/ise.m",
"chars": 1212,
"preview": "function v = ise(p,q,type)\r\n%\r\n% ise(p,q [,'type']) -- estimate the integrated squared error between \r\n% "
},
{
"path": "render_pipeline/kde/matlab_kde_package/joinTrees.m",
"chars": 6414,
"preview": "function t1 = joinTrees(t1, t2, alpha)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/kde.m",
"chars": 2439,
"preview": "function p = kde(points,ks,weights,typeS)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/klGrad.m",
"chars": 3382,
"preview": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n% [err1,err2]=klGrad(dens1,dens2,estT"
},
{
"path": "render_pipeline/kde/matlab_kde_package/kld.m",
"chars": 1903,
"preview": "function KLD = kld(p1,p2,type,varargin)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/knn.m",
"chars": 857,
"preview": "function [neighbors, distance] = knn(kde,points,k)\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/ksize.m",
"chars": 4999,
"preview": "function npd = ksize(npd,type,varargin)\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/license.gpl",
"chars": 24382,
"preview": "\t\t GNU LESSER GENERAL PUBLIC LICENSE\n\t\t Version 2.1, February 1999\n\n Copyright (C) 1991, 1999 Free Software Found"
},
{
"path": "render_pipeline/kde/matlab_kde_package/llGrad.m",
"chars": 2513,
"preview": "function [err1, err2] = llGrad(p1,p2,type,tolGrad,tolEval)\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/llHess.m",
"chars": 2103,
"preview": "function Hessians = llHess(p1,p2,tolGrad,tolEval)\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/marginal.m",
"chars": 658,
"preview": "function p = marginal(dens,ind)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% p "
},
{
"path": "render_pipeline/kde/matlab_kde_package/max.m",
"chars": 372,
"preview": "function X = max(kde)\r\n% X = max(p)\r\n% A simple estimate of the maximal peak location of p; returns the kernel locatio"
},
{
"path": "render_pipeline/kde/matlab_kde_package/maxlogerr.m",
"chars": 2415,
"preview": "function [maxVal,location] = logerr(p,q)\n%\n% modes = logerr(p,q) -- Find location of max. log-error between p&q\n% \n%\n"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mean.m",
"chars": 372,
"preview": "function m = mean(dens)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% mean(dens) -"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/BallTree.cpp",
"chars": 630,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/BallTreeDensity.cpp",
"chars": 926,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/DualTree.cpp",
"chars": 1223,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/adjustBW.cpp",
"chars": 1311,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/adjustPoints.cpp",
"chars": 688,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/adjustWeights.cpp",
"chars": 867,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/cpp/BallTree.h",
"chars": 5126,
"preview": "//////////////////////////////////////////////////////////////////////////////////////\r\n// BallTree.h -- class definit"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/cpp/BallTreeClass.cc",
"chars": 16187,
"preview": "//////////////////////////////////////////////////////////////////////////////////////\r\n// BallTreeClass -- class defi"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/cpp/BallTreeDensity.h",
"chars": 6092,
"preview": "//////////////////////////////////////////////////////////////////////////////////////\r\n// BallTreeDensity.h -- class "
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/cpp/BallTreeDensityClass.cc",
"chars": 27088,
"preview": "//////////////////////////////////////////////////////////////////////////////////////\r\n// KD-tree code extended for use"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/cpp/README.txt",
"chars": 1560,
"preview": "Very brief README on the BallTree and BallTreeDensity classes\n=========================================================="
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/cpp/kernels.h",
"chars": 8544,
"preview": "/////////////////////////////////////////////////////////////////////\r\n// Find the kernel values at the minimum or maxim"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/entropyGradISE.cpp",
"chars": 7248,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/entropyGradRS.cpp",
"chars": 2888,
"preview": "#include <math.h>\r\n#include \"mex.h\"\r\n#define MEX\r\n#include \"cpp/BallTreeDensity.h\"\r\n\r\ndouble erf(double c) { //"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/iseEpsilon.cpp",
"chars": 12761,
"preview": "/***********************************************************************\r\n** ISE evaluation MEX code (taken from multi-t"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/klGradRS.cpp",
"chars": 8284,
"preview": "#include <math.h>\r\n#include \"mex.h\"\r\n#define MEX\r\n#include \"cpp/BallTreeDensity.h\"\r\n\r\ndouble erf(double c) { //"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/knn.cpp",
"chars": 1280,
"preview": "//\n// Matlab MEX interface for KD-tree C++ functions\n//\n// Written by Alex Ihler and Mike Mandel\n// Copyright (C) 2003 A"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/llGrad.cpp",
"chars": 2186,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/makemex.m",
"chars": 3201,
"preview": "clear mex;\n\n%%%CONSTRUCTOR%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nif (exist('BallTree.cpp'))\n mex BallTree.cpp c"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/maketmp.m",
"chars": 858,
"preview": "clear mex;\r\n\r\nif (0)\r\n%%%CONSTRUCTOR%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\nif (exist('BallTree.cpp'))\r\n mex Ba"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/prodSampleEpsilon.cpp",
"chars": 13238,
"preview": "/***********************************************************************\r\n** multi-tree approximate sampling MEX code\r\n*"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/prodSampleExact.cpp",
"chars": 7318,
"preview": "/***********************************************************************\r\n** exact sampling MEX file\r\n**\r\n** Calculate "
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbs.cpp",
"chars": 9793,
"preview": "///////////////////////////////////////////////////////\r\n// Functions for single-scale gibbs samplers\r\n//\r\n/////////////"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbs1.cpp",
"chars": 3680,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbs2.cpp",
"chars": 3670,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbsMS.cpp",
"chars": 12020,
"preview": "///////////////////////////////////////////////////////\r\n// Functions for single-scale gibbs samplers\r\n//\r\n/////////////"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbsMS1.cpp",
"chars": 4032,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/prodSampleGibbsMS2.cpp",
"chars": 4017,
"preview": "//\r\n// Matlab MEX interface for KD-tree C++ functions\r\n//\r\n// Written by Alex Ihler and Mike Mandel\r\n// Copyright (C) 20"
},
{
"path": "render_pipeline/kde/matlab_kde_package/mex/reduceSolve.cpp",
"chars": 6943,
"preview": "//\r\n// Matlab MEX interface for \"reduce\" function QP solvers\r\n//\r\n// Written by Alex Ihler\r\n// Copyright (C) 2003 Alexan"
},
{
"path": "render_pipeline/kde/matlab_kde_package/miGrad.m",
"chars": 1614,
"preview": "function errI = miGrad(x,a_index,type,y,gamma)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/modes.m",
"chars": 3423,
"preview": "function [modeList,attr] = modes(dens,start)\n%\n% [modes,assoc] = modes(kde [,init]) -- Find modes of a KDE via fixed poi"
},
{
"path": "render_pipeline/kde/matlab_kde_package/plot.m",
"chars": 7653,
"preview": "function H=plot(x,varargin)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% KDE plotting functio"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/DualTree.m",
"chars": 1545,
"preview": "function [e,varargout] = DualTree(dens,pos,lvFlag)\n%\n% Crappy matlab implementation of kernel density estimate evaluatio"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/entropyDist.m",
"chars": 381,
"preview": "function h=entropyDist(npd)\r\n%\r\n% Compute entropy estimate using nearest neighbor estimate\r\n%\r\n\r\n% Copyright (C) 2003 Al"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/entropyGradDist.m",
"chars": 573,
"preview": "function Dvect=entropyGradDist(npd)\r\n%\r\n% Compute entropy estimate using nearest neighbor estimate\r\n%\r\n\r\n% Copyright (C)"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/golden.m",
"chars": 1389,
"preview": "function [xmin, fmin] = golden(npd, f, ax, bx, cx, tol, varargin)\n%\n%GOLDEN Minimize function of one variable using go"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/iqr.m",
"chars": 268,
"preview": "function i = iqr(x)\n%\n% Calculate interquartile range without the stats package\n%\n\n% Copyright (C) 2003 Alexander Ihler;"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/ksizeCalcUseful.m",
"chars": 2075,
"preview": "function ksizeCalcUseful\r\n%\r\n% ksizeCalcUseful\n% -- find some useful numbers for the various kernels\r\n%\r\n\r\n\r\n%\r\n% Kern"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/ksizeHall.m",
"chars": 3035,
"preview": "function h = ksizeHall(npd)\n%\n% Find kernel size according to \"plug-in\" method of \n% Hall, Marron, Sheather, Jones ("
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/ksizeLSCV.m",
"chars": 1124,
"preview": "function h = ksizeLSCV(npd)\n% \"Least-Squares Cross Validation\" estimate (Silverman)\n%\n\n% Copyright (C) 2005 Alexander Ih"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/ksizeMSP.m",
"chars": 1057,
"preview": "function h = ksizeMSP(npd,noIQR)\n% \"Maximal Smoothing Principle\" estimate (Terrel '90)\n% Modified similarly to ROT fo"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/ksizeROT.m",
"chars": 1427,
"preview": "function h = ksizeROT(npd,noIQR)\n% \"Rule of Thumb\" estimate (Silverman)\n% Estimate is based on assumptions of Gaussia"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/prodSampleImportGauss.m",
"chars": 1977,
"preview": "function [ptsS, wtsS] = prodSampleImportGaussian(npds,Npts,anFns,anParams,overSamp,type)\r\n%\r\n% Gaussian-approximation-ba"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/prodSampleImportMix.m",
"chars": 1646,
"preview": "function [ptsS, wtsS] = prodSampleImportMix(npds,Npts,anFns,anParams,overSamp,type)\r\n%\r\n% Message-based importance sampl"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/prodSampleImportPair.m",
"chars": 2018,
"preview": "function [ptsS, wtsS] = prodSampleImportPair(npds,Npts,anFns,anParams,overSamp,type)\r\n%\r\n% Message-based importance samp"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/randKernel.m",
"chars": 1689,
"preview": "function samples = randKernel(N,M,type)\r\n%\r\n% samples = randKernel(N,M,type) -- Draw samples from a kernel of the \r\n% "
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/reduceKD.m",
"chars": 3536,
"preview": "function [q,e,c]=reduceKD(p,varargin)\r\n% KD-tree based density reduction method of Ihler et al, 2004.\r\n%\r\ncostType = 'kl"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/reduceKD2.m",
"chars": 2015,
"preview": "function q=reduceKD(p,maxCost,costType)\r\nif (nargin < 3) costType = 1; end; % \"max\" cost\r\nif (nargin < 2) maxCost = roun"
},
{
"path": "render_pipeline/kde/matlab_kde_package/private/reduceSolveM.m",
"chars": 13387,
"preview": "%\r\n% Internal routines for solving the quadratic minimization problem\r\n% occurring for \"reduced set density estimation\" "
},
{
"path": "render_pipeline/kde/matlab_kde_package/productApprox.m",
"chars": 4212,
"preview": "function [dens,ind] = productApprox(npd0, npds , anFns,anParams , type, varargin)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/productExact.m",
"chars": 2977,
"preview": "function dens = productExact(npd_placeholder, npdensities , analyticFns, analyticParams)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/quantize.m",
"chars": 728,
"preview": "function p = quantize(p,R,minV,maxV,minS,maxS)\n%\n% p = quantize(p,R,type) -- \"quantize\" elements of KDE p to R bits\n%\n\n%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/reduce.m",
"chars": 8009,
"preview": "function [q,o2,o3] = reduce(p,type,varargin)\n%\n% q = reduce(p,'type',[options]) -- \"Reduce\" a KDE, so that it requires "
},
{
"path": "render_pipeline/kde/matlab_kde_package/resample.m",
"chars": 1004,
"preview": "function p2 = resample(p,Np,ksType)\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n"
},
{
"path": "render_pipeline/kde/matlab_kde_package/rescale.m",
"chars": 689,
"preview": "function npd = rescale(npd,factor)\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n%"
},
{
"path": "render_pipeline/kde/matlab_kde_package/sample.m",
"chars": 999,
"preview": "function [points,ind] = sample(npd,Npts,ind)\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
},
{
"path": "render_pipeline/kde/run_sampling.m",
"chars": 2684,
"preview": "% Generate view samples for PASCAL3D classes\nclose all; clear;\n\nsetup_path;\nvisualize = 0;\n\nclsList = {'aeroplane', 'bic"
},
{
"path": "render_pipeline/kde/sample_truncations.m",
"chars": 2047,
"preview": "function sample_truncations(cls, num_samples, outlier_ratio)\n\nsetup_path;\nload(fullfile(g_truncation_statistics_folder, "
},
{
"path": "render_pipeline/kde/sample_viewpoints.m",
"chars": 2954,
"preview": "function sample_viewpoints(cls, num_samples, outlier_ratio, outlier_elevation_avg, outlier_elevation_std, outlier_tilt_a"
},
{
"path": "render_pipeline/kde/setup_path.m",
"chars": 421,
"preview": "% add paths\nRENDER4CNN_ROOT = fullfile(mfilename('fullpath'),'../../../');\nPASCAL3D_DIR = fullfile(RENDER4CNN_ROOT, 'dat"
},
{
"path": "render_pipeline/overlay_background.m",
"chars": 2448,
"preview": "function overlay_background(src_folder, dst_folder, bkgFilelist, bkgFolder, clutteredBkgRatio, single_thread)\n\nif nargin"
},
{
"path": "render_pipeline/rdir.m",
"chars": 4597,
"preview": "function [varargout] = rdir(rootdir,varargin)\r\n% Lists the files in a directory and its sub directories. \r\n% \r\n% functio"
},
{
"path": "render_pipeline/render_helper.py",
"chars": 3822,
"preview": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport shutil\nimport random\nimport tempfile\nimport date"
},
{
"path": "render_pipeline/render_model_views.py",
"chars": 7656,
"preview": "#!/usr/bin/python3\r\n# -*- coding: utf-8 -*-\r\n'''\r\nRENDER_MODEL_VIEWS.py\r\nbrief:\r\n\trender projections of a 3D model from "
},
{
"path": "render_pipeline/run_crop.py",
"chars": 1028,
"preview": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n'''\nCROP_ALL_IMAGES\n@brief:\n crop all rendered images of PASCAL3D 12 rigid"
},
{
"path": "render_pipeline/run_overlay.py",
"chars": 1063,
"preview": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n'''\nBACKGROUND OVERLAY\n@brief:\n overlay backgrounds to images of PASCAL3D "
},
{
"path": "render_pipeline/run_render.py",
"chars": 825,
"preview": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n'''\nRENDER_ALL_SHAPES\n@brief:\n render all shapes of PASCAL3D 12 rigid obje"
},
{
"path": "setup.py",
"chars": 848,
"preview": "import os\nfrom global_variables import *\n\n# matlab global variable file\nmf = open(os.path.join(g_render4cnn_root_folder,"
},
{
"path": "train/solver.prototxt.example",
"chars": 245,
"preview": "net: \"train_val.prototxt\"\ntest_iter: 100\ntest_interval: 1000\nbase_lr: 0.001\nlr_policy: \"step\"\ngamma: 0.1\nstepsize: 20000"
},
{
"path": "train/train_val.prototxt.example",
"chars": 6551,
"preview": "name: \"RenderForCNN\"\nlayers {\n name: \"data\"\n type: DATA\n top: \"data\"\n data_param {\n source: \"\"\n backend: LMDB\n"
},
{
"path": "view_estimation/.gitignore",
"chars": 35,
"preview": "avp_test_results*\nvp_test_results*\n"
},
{
"path": "view_estimation/README.md",
"chars": 409,
"preview": "Render for CNN: viewpoint estimator\n\nTo prepare testing images, run:\n\n python prepare_testing_data.py\n\nTo do evaluati"
},
{
"path": "view_estimation/box_overlap.m",
"chars": 670,
"preview": "% The box [x1 y1 x2 y2] here is different for box in matlab [x y w h]\nfunction o = box_overlap(a, b)\n\n% Compute the symm"
},
{
"path": "view_estimation/caffe_utils.py",
"chars": 7332,
"preview": "import caffe\nfrom caffe.proto import caffe_pb2\nimport lmdb\nimport os\nimport sys\nimport math\nimport numpy as np\nimport ar"
},
{
"path": "view_estimation/combine_bbox_view.m",
"chars": 2237,
"preview": "% combine detection bbox result and viewestimation result\n% if ~is3d\n% input: bbox: N*5 (x1,y1,x2,y2,score), view: N*1 ("
},
{
"path": "view_estimation/compute_recall_precision_accuracy_3dview.m",
"chars": 6040,
"preview": "% compute recall and viewpoint accuracy\n% modified from PASCAL3D+ VDPM/compute_recall_precision_accuracy.m\n% added an in"
},
{
"path": "view_estimation/compute_recall_precision_accuracy_azimuth.m",
"chars": 5491,
"preview": "% compute recall and viewpoint accuracy\n% modified from PASCAL3D+ VDPM/compute_recall_precision_accuracy.m\n% added an in"
},
{
"path": "view_estimation/compute_vp_acc_mederror.m",
"chars": 705,
"preview": "function [acc, mederr] = compute_vp_acc_mederror(view_filename, img_label_filename)\n\nest_views = importdata(view_filenam"
},
{
"path": "view_estimation/data_prep_helper.py",
"chars": 4578,
"preview": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport random\nimport tempfile\n\nBASE_DIR = os.path.dirnam"
},
{
"path": "view_estimation/evaluation_helper.py",
"chars": 12788,
"preview": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport random\nimport tempfile\nimport math\n\nBASE_DIR = os"
},
{
"path": "view_estimation/extract_records.m",
"chars": 700,
"preview": "BASE_DIR = fullfile(mfilename('fullpath'),'../');\naddpath(fullfile(BASE_DIR, '../'));\nglobal_variables;\n\n% viewpoint ann"
},
{
"path": "view_estimation/jitter_imcrop.m",
"chars": 1292,
"preview": "function [cropped_im, ov] = jitter_imcrop(im, rect, IoU)\n% im: input whole image\n% rect: [horizontal pos of top-left poi"
},
{
"path": "view_estimation/prepare_testing_data.py",
"chars": 1226,
"preview": "#!/usr/bin/python\n\n'''\nPrepare Testing Data\n\nprepare filelist and LMDB for real images\nrunning this program will populat"
},
{
"path": "view_estimation/prepare_training_data.py",
"chars": 2752,
"preview": "#!/usr/bin/python\n\n'''\nPrepare Training Data\n\nRunning this program will populate following folders:\n g_syn_images_lmdb_"
},
{
"path": "view_estimation/prepare_voc12_imgs.m",
"chars": 4771,
"preview": "function prepare_voc12_imgs(img_set, output_img_dir, opts)\n% PREPARE_VOC12_IMGS\n% prepare voc12 images (cropped from g"
},
{
"path": "view_estimation/prepare_voc12val_det_imgs.m",
"chars": 2702,
"preview": "function prepare_voc12val_det_imgs(output_img_dir, bbox_mat_filelist, resize_dim) \n% PREPARE_VOC12VAL_DET\n% Prepare "
},
{
"path": "view_estimation/ref_evaluation_results/acc_mederr_results.txt",
"chars": 370,
"preview": "aeroplane bicycle boat bottle bus car chair diningtable motorbike sofa train tvmonitor \n0.803636 0.838983 0.620690 0.955"
},
{
"path": "view_estimation/ref_evaluation_results/avp_nv_results.txt",
"chars": 842,
"preview": "aeroplane bicycle boat bottle bus car chair diningtable motorbike sofa train tvmonitor \n0.621800 0.590500 0.176500 0.309"
},
{
"path": "view_estimation/run_evaluation.py",
"chars": 932,
"preview": "import os\nimport sys\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(BASE_DIR)\nsys.path.append(os"
},
{
"path": "view_estimation/test_det.m",
"chars": 2391,
"preview": "function test_det(prediction_folder, ARP, show_curve, write_result)\naddpath(fullfile(mfilename('fullpath'), '../../'));\n"
},
{
"path": "view_estimation/test_gt.m",
"chars": 1365,
"preview": "function test_gt(prediction_folder, view_label_folder, write_result)\n% prediction_folder contains <classname>_pred_view."
}
]
// ... and 3 more files (download for full content)
About this extraction
This page contains the full source code of the ShapeNet/RenderForCNN GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 172 files (2.2 MB), approximately 591.5k tokens, and a symbol index with 103 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.