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Repository: zhwhong/lidc_nodule_detection
Branch: master
Commit: f673fa1284e6
Files: 114
Total size: 29.5 MB
Directory structure:
gitextract_o0fgwltz/
├── CNN_LSTM/
│ ├── README.md
│ ├── analysis_results.py
│ ├── convert_txt2json.py
│ ├── data/
│ │ └── lung_data/
│ │ ├── lung_data/
│ │ │ └── 说明.txt
│ │ ├── lung_test.json
│ │ ├── lung_train.json
│ │ ├── lung_val.json
│ │ ├── test.json
│ │ └── test_small.json
│ ├── download_data.sh
│ ├── dump.txt
│ ├── evaluate.ipynb
│ ├── evaluate.ipynb.bak
│ ├── evaluate.py
│ ├── evaluate.py.bak
│ ├── hypes/
│ │ ├── lstm.json
│ │ ├── lstm_resnet_rezoom.json
│ │ ├── lstm_resnet_rezoom_lung.json
│ │ ├── lstm_rezoom.json
│ │ ├── lstm_rezoom_lung.json
│ │ ├── overfeat.json
│ │ ├── overfeat_resnet_rezoom.json
│ │ └── overfeat_rezoom.json
│ ├── lung_test.txt
│ ├── lung_train.txt
│ ├── lung_val.txt
│ ├── make_json.py
│ ├── nohup.out
│ ├── nohup.out.old
│ ├── nohup_resnet.log
│ ├── requirements.txt
│ ├── run.sh
│ ├── run_convert_txt2json.sh
│ ├── run_lstm_resnet.sh
│ ├── test.sh
│ ├── test_inception.sh
│ ├── test_resnet.sh
│ ├── train.py
│ ├── utils/
│ │ ├── Makefile
│ │ ├── __init__.py
│ │ ├── annolist/
│ │ │ ├── AnnoList_pb2.py
│ │ │ ├── AnnotationLib.py
│ │ │ ├── LICENSE_FOR_THIS_FOLDER
│ │ │ ├── MatPlotter.py
│ │ │ ├── PalLib.py
│ │ │ ├── __init__.py
│ │ │ ├── doRPC.py
│ │ │ ├── ma_utils.py
│ │ │ └── plotSimple.py
│ │ ├── data_utils.py
│ │ ├── googlenet_load.py
│ │ ├── hungarian/
│ │ │ ├── hungarian.cc
│ │ │ ├── hungarian.cpp
│ │ │ └── hungarian.hpp
│ │ ├── rect.py
│ │ ├── slim_nets/
│ │ │ ├── __init__.py
│ │ │ ├── inception_v1.py
│ │ │ ├── resnet_utils.py
│ │ │ └── resnet_v1.py
│ │ ├── stitch_rects.cpp
│ │ ├── stitch_rects.hpp
│ │ ├── stitch_wrapper.py
│ │ ├── stitch_wrapper.pyx
│ │ └── train_utils.py
│ └── view_weights.py
├── README.docx
├── README.md
├── api_lidc/
│ ├── annotation2.py
│ ├── company_GE.txt
│ ├── get_dat.sh
│ ├── get_list.sh
│ ├── get_txt.sh
│ ├── information.txt
│ ├── list.py
│ ├── list2.py
│ ├── nodule2.py
│ ├── nodule_structs.py
│ ├── nohup2.log
│ ├── read_file.py
│ ├── te.py
│ ├── test17.py
│ └── write_bin_file.py
├── cnn_3d/
│ ├── 3d_cnn_lung_detect.py
│ ├── 3d_cnn_lung_test.py
│ └── detect2.py
├── inception_vgg_table.docx
├── lidc-idri nodule counts (6-23-2015).xlsx
├── pylung/
│ ├── __init__.py
│ ├── annotation.py
│ ├── base.py
│ ├── detector.py
│ ├── filters/
│ │ ├── __init__.py
│ │ ├── filter.py
│ │ ├── parenchyma.py
│ │ └── vgg.py
│ ├── libinfo.py
│ ├── models/
│ │ ├── __init__.py
│ │ ├── chest_detect.py
│ │ ├── test.py
│ │ ├── train.py
│ │ └── vgg_classifier.py
│ ├── nodule_structs.py
│ ├── tests/
│ │ ├── a.csv
│ │ ├── clean.sh
│ │ ├── demo.py
│ │ ├── main.py
│ │ ├── predict.py
│ │ ├── run.sh
│ │ ├── test.csv
│ │ ├── test_detect.py
│ │ ├── tmp.py
│ │ └── train.py
│ └── utils.py
└── tcia-diagnosis-data-2012-04-20.xls
================================================
FILE CONTENTS
================================================
================================================
FILE: CNN_LSTM/README.md
================================================
<img src=http://russellsstewart.com/s/tensorbox/tensorbox_output.jpg></img>
<img src="https://travis-ci.org/TensorBox/TensorBox.svg?branch=master"></img>
TensorBox is a simple framework for training neural networks to detect objects in images.
Training requires a json file (e.g. [here](http://russellsstewart.com/s/tensorbox/test_boxes.json))
containing a list of images and the bounding boxes in each image.
The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the
[ReInspect](https://github.com/Russell91/ReInspect/)
algorithm, reproducing state-of-the-art detection results on the highly occluded TUD crossing and brainwash datasets.
## OverFeat Installation & Training
First, [install TensorFlow from source or pip](https://www.tensorflow.org/versions/r0.11/get_started/os_setup.html#pip-installation) (NB: source installs currently break threading on 0.11)
$ git clone http://github.com/russell91/tensorbox
$ cd tensorbox
$ ./download_data.sh
$ cd /path/to/tensorbox/utils && make && cd ..
$ python train.py --hypes hypes/overfeat_rezoom.json --gpu 0 --logdir output
$ #see evaluation instructions below
Note that running on your own dataset should only require modifying the `hypes/overfeat_rezoom.json` file.
## ReInspect Installation & Training
ReInspect, [initially implemented](https://github.com/Russell91/ReInspect) in Caffe,
is a neural network extension to Overfeat-GoogLeNet in Tensorflow.
It is designed for high performance object detection in images with heavily overlapping instances.
See <a href="http://arxiv.org/abs/1506.04878" target="_blank">the paper</a> for details or the <a href="https://www.youtube.com/watch?v=QeWl0h3kQ24" target="_blank">video</a> for a demonstration.
# REQUIRES TENSORFLOW VERSION >= 0.11
$ git clone http://github.com/russell91/tensorbox
$ cd tensorbox
$ ./download_data.sh
$ # Download the cudnn version used by your tensorflow verion and
$ # put the libcudnn*.so files on your LD_LIBRARY_PATH e.g.
$ cp /path/to/appropriate/cudnn/lib64/* /usr/local/cuda/lib64
$ cd /path/to/tensorbox/utils && make && make hungarian && cd ..
$ python train.py --hypes hypes/lstm_rezoom.json --gpu 0 --logdir output
$ #see evaluation instructions below
## Evaluation
There are two options for evaluation, an ipython notebook and a python script.
### IPython Notebook
The [ipython notebook](https://github.com/Russell91/tensorbox/blob/master/evaluate.ipynb)
allows you to interactively modify the inference algorithm, and can be run concurrently
with training (assuming you have 2 gpus). You can evaluate on new data by modifying paths
and pointing to new weights.
### Python script
For those who would prefer to evaluate using a script, you can alternately use evaluate.py.
The following instructions demonstrate how evaluate.py wase used after one of my experiments -
you will need to change paths as appropriate:
$ # kill training script if you don't have a spare GPU
$ cd /path/to/tensorbox
$ python evaluate.py --weights output/overfeat_rezoom_2017_01_17_15.20/save.ckpt-130000 --test_boxes data/brainwash/val_boxes.json
$ # val_boxes should contain the list of images you want to output boxes on, and
$ # the annotated boxes for each image if you want to generate a precision recall curve
$ cd ./output/overfeat_rezoom_2017_01_17_15.20/images_val_boxes_130000/
$ ls # ... notice the images with predicted boxes painted on, and the results saved in results.png
$ python -m SimpleHTTPServer 8080 # set up a image server to view the images from your browser
$ ssh myserver -N -L localhost:8080:localhost:8080 # set up an ssh tunnel to your server (skip if running locally)
$ # open firefox and visit localhost:8080 to view images
## Finetuning
If you get some decent results and want to improve your performance, there are many things you can try.
For hyperparameter optimization, the Learning rate, dropout ratios, and parameter initializations are a great place to start. You may want to
read this <a href="http://russellsstewart.com/blog/0">blog post</a> for a more generic tutorial on debugging neural nets.
We have recently added a resnet version as well, which should work slightly better on larger boxes (this repo has historically done poorly
on these, as they weren't port of the original research goal). I would recommend using the overfeat version over the lstm as well
if you have a large variation in box sizes.
## Tensorboard
You can visualize the progress of your experiments during training using Tensorboard.
$ cd /path/to/tensorbox
$ tensorboard --logdir output
$ # (optional, start an ssh tunnel if not experimenting locally)
$ ssh myserver -N -L localhost:6006:localhost:6006
$ # open localhost:6006 in your browser
For example, the following is a screenshot of a Tensorboard comparing two different experiments with learning rate decays that kick in at different points. The learning rate drops in half at 60k iterations for the green experiment and 300k iterations for red experiment.
<img src=http://russellsstewart.com/s/tensorbox/tensorboard_loss.png></img>
## Community
If you're new to object detection, and want to chat with other people that are working on similar problems, check out the community chat at https://gitter.im/Russell91/TensorBox, especially on Saturdays.
================================================
FILE: CNN_LSTM/analysis_results.py
================================================
import numpy as N
import os
import cv2
import sys
import struct
import cv2
import gc
import array
try:
import cPickle as pickle
except ImportError:
import pickle
def get_volume(gs):
return (gs[1]-gs[0] + 1)*(gs[4]-gs[2] + 1)*(gs[5]-gs[3] + 1)
def get_area(gs):
return (gs[4]-gs[2] + 1)*(gs[5]-gs[3] + 1)
def combined_sample(gs1, gs2):
if gs1 == [] or gs2 == []:
return 0, []
z1 = max(gs1[0], gs2[0])
z2 = min(gs1[1], gs2[1])
x1 = max(gs1[2], gs2[2])
y1 = max(gs1[3], gs2[3])
x2 = min(gs1[4], gs2[4])
y2 = min(gs1[5], gs2[5])
gs = []
flag = 0
if(z1 <= z2 and x1 <= x2 and y1 <= y2):
v0 = get_volume([z1, z2, x1, y1, x2, y2])
v1 = get_volume(gs1)
v2 = get_volume(gs2)
if(v0 > 0.5 * min(v1, v2)):
z1 = min(gs1[0], gs2[0])
z2 = max(gs1[1], gs2[1])
x1 = min(gs1[2], gs2[2])
y1 = min(gs1[3], gs2[3])
x2 = max(gs1[4], gs2[4])
y2 = max(gs1[5], gs2[5])
gs = [z1, z2, x1, y1, x2, y2]
flag = 1
return flag, gs
def check_in2d(gs1, gs2):
z1 = max(gs1[0], gs2[0])
z2 = min(gs1[1], gs2[1])
x1 = max(gs1[2], gs2[2])
y1 = max(gs1[3], gs2[3])
x2 = min(gs1[4], gs2[4])
y2 = min(gs1[5], gs2[5])
gs = []
flag = 0
if(z1 <= z2 and x1 <= x2 and y1 <= y2):
v0 = get_area([z1, z2, x1, y1, x2, y2])
v1 = get_area(gs1)
v2 = get_area(gs2)
if(v0 > 0.5 * min(v1, v2)):
z1 = min(gs1[0], gs2[0])
z2 = max(gs1[1], gs2[1])
x1 = min(gs1[2], gs2[2])
y1 = min(gs1[3], gs2[3])
x2 = max(gs1[4], gs2[4])
y2 = max(gs1[5], gs2[5])
gs = [z1, z2, x1, y1, x2, y2]
flag = 1
return flag, gs
def get_single_matched_list(org_sample, det_sample):
flag, _ = check_in2d(org_sample, det_sample)
res_list = []
if(flag == 1):
for index in range(det_sample[0], det_sample[1] + 1):
if index in range(org_sample[0], org_sample[1] + 1):
res_list.append(det_sample[6][index - det_sample[0]])
return res_list
def commpare_sample(org, det):
matched_scores_list = list()
gd_num = len(org)
dt_num = len(det)
for org_sample in org:
for det_sample in det:
l_s = get_single_matched_list(org_sample, det_sample)
if(l_s != []):
matched_scores_list.append(min(l_s))
return matched_scores_list, gd_num, dt_num
def modify_rc(sample6):
dis_x = sample6[4] - sample6[2]
dis_y = sample6[5] - sample6[3]
if dis_x < 32:
dis_x = (32 - dis_x) / 2
sample6[4] + dis_x
sample6[2] - dis_x
if dis_y < 32:
dis_y = (32 - dis_y) / 2
sample6[5] + dis_y
sample6[3] - dis_y
return sample6
def merge_gd(gd_sample_set):
# for i1 in range(len(gd_sample_set)):
# gd_sample_set[i1] = modify_rc(gd_sample_set[i1])
for i1 in range(len(gd_sample_set)):
for i2 in range(i1 + 1, len(gd_sample_set)):
f, merge_sample = combined_sample(gd_sample_set[i1], gd_sample_set[i2])
if(f == 1):
gd_sample_set[i1] = merge_sample
gd_sample_set[i2] = []
while [] in gd_sample_set:
gd_sample_set.remove([])
return gd_sample_set
def get_dt_scores(sample_dt):
sc_list = list()
for i in range(len(sample_dt)):
sc = list()
for j in range(len(sample_dt[i][6])):
sc.append(sample_dt[i][6][j])
sc_list.append(min(sc))
return sc_list
def main():
scores_set = set([])
# f = open('dump_dt.txt', 'rb')
# compare_info_dt= pickle.load(f)
# f.close()
#
# f = open('dump_gd.txt', 'rb')
# compare_info_gd= pickle.load(f)
# f.close()
#
# compare_info = []
# for dt in compare_info_dt:
# for gd in compare_info_gd:
# if (dt[0] == gd[0]):
# compare_info.append([dt[0], gd[1], dt[2]])
# f = open('dump.txt', 'wb')
# pickle.dump(compare_info, f)
# f.close()
f = open('dump.txt', 'rb')
compare_info = pickle.load(f)
f.close()
list()
for sample in compare_info:
sample[1] = merge_gd(sample[1])
for dets in sample[2]:
for score in dets[6]:
scores_set.add(score)
scores_all_list = list(scores_set)
scores_match_list = list(scores_set)
scores_all_list.sort(cmp=lambda x, y: cmp(x, y))
scores_match_list.sort(cmp=lambda x, y: cmp(x, y))
score2index = dict()
for n in range(len(scores_match_list)):
score2index[scores_match_list[n]] = n
scores_match_list[n] = [scores_match_list[n], 0]
scores_all_list[n] = [scores_all_list[n], 0]
all_gd_num = 0
all_dt_num = 0
for sample in compare_info:
matched_score_list, gd_num, dt_num = commpare_sample(sample[1], sample[2])
all_gd_num = all_gd_num + gd_num
all_dt_num = all_dt_num + dt_num
for s in matched_score_list:
scores_match_list[score2index[s]][1] = scores_match_list[score2index[s]][1] + 1
single_dt_sc = get_dt_scores(sample[2])
for s in single_dt_sc:
scores_all_list[score2index[s]][1] = scores_all_list[score2index[s]][1] + 1
#scores_all_list[score2index[single_dt_sc]][1] = scores_all_list[score2index[single_dt_sc]][1] + 1
nnn = []
nnn.extend(matched_score_list)
nnn.extend(single_dt_sc)
#if(scores_all_list[3927][0] in nnn):
# print scores_all_list[3927], scores_match_list[3927]
c1 = []
c2 = []
l = (range(len(scores_match_list) - 1))
l.reverse()
for i in l:
scores_match_list[i][1] = scores_match_list[i][1] + scores_match_list[i + 1][1]
l = (range(len(scores_all_list) - 1))
l.reverse()
for i in l:
scores_all_list[i][1] = scores_all_list[i][1] + scores_all_list[i + 1][1]
score = list()
dt_num_list = list()
match_num_list = list()
for index in range(len(scores_match_list)):
score.append(scores_match_list[index][0])
dt_num_list.append(scores_all_list[index][1])
match_num_list.append(scores_match_list[index][1])
import matplotlib.pyplot as plt
plt.figure(1)
plt.figure(2)
score = N.array(score)
dt_num_list = N.array(dt_num_list)
#dt_num_list = 1 - dt_num_list / (1.* all_dt_num)
match_num_list = N.array(match_num_list)
#match_num_list = 1.* match_num_list / all_gd_num
index = 0
for i in range(len(dt_num_list)):
if(dt_num_list[i] == 0):
index = i
break
dt_num_list = dt_num_list[:index]
match_num_list = match_num_list[:index]
score = score[:index]
for i in range(len(dt_num_list)):
if (dt_num_list[i] < match_num_list[i]):
dt_num_list[i] = match_num_list[i]
pres_l = 1.*match_num_list/dt_num_list
recall_l = 1.* match_num_list/all_gd_num
plt.figure(1)
plt.plot(score, pres_l)
plt.plot(score, recall_l)
plt.figure(2)
plt.plot(pres_l, recall_l)
plt.show()
plt.waitforbuttonpress()
#--gpu=0
if __name__ == '__main__':
main()
================================================
FILE: CNN_LSTM/convert_txt2json.py
================================================
import sys
import json
import string
import random
if len(sys.argv) != 4:
print ": python convert_txt2json.py lung_train.txt lung_train.json 2"
exit()
json_images = []
path = sys.argv[1]
outfile_name = sys.argv[2]
rate = float(sys.argv[3])
pos_num = 0
neg_num = 0
with open(path, 'r') as f:
done = 0
while not done:
aLine = f.readline()
if (aLine != ''):
filename = aLine
filename = filename.strip('\n')
rects = []
num = f.readline()
num = string.atoi(num)
for i in range(0, num, 1):
aLine = f.readline()
if(num > 0) :
pos_num = pos_num + 1
else:
neg_num = neg_num + 1
else:
done = 1
outfile = open(outfile_name, 'w')
with open(path, 'r') as f:
done = 0
while not done:
aLine = f.readline()
if (aLine != ''):
filename = aLine
filename = filename.strip('\n')
rects = []
num = f.readline()
num = string.atoi(num)
for i in range(0, num, 1):
aLine = f.readline()
splite_str = aLine.split()
splite_int = [int(s) for s in splite_str]
kind = splite_int[0]
pad_x = 0
if(splite_int[3]<32):
pad_x = (32 - splite_int[3])/2
pad_y = 0
if(splite_int[4]<32):
pad_y = (32 - splite_int[4])/2
l = max(0, splite_int[1] - pad_x)
r = min(351, splite_int[1] + splite_int[3] + pad_x)
t = max(0, splite_int[2] - pad_y)
b = min(255, splite_int[2] + splite_int[4] + pad_y)
#bbox = dict([("x1", splite_int[1]), ("y1", splite_int[2]), ("x2", splite_int[1] + splite_int[3]), ("y2", splite_int[2] + splite_int[4])])
bbox = dict([("x1", l), ("y1", t), ("x2", r), ("y2", b)])
if(kind == 1):
rects.append(bbox)
if(num > 0 & len(rects) > 0):
json_image = dict([("image_path", filename), ("rects", rects)])
json_images.append(json_image)
elif(random.uniform(0, 1) <= rate*pos_num/neg_num):
json_image = dict([("image_path", filename), ("rects", rects)])
json_images.append(json_image)
else:
done = 1
outfile.write(json.dumps(json_images, indent = 1))
# outfile.write(json.dumps(json_images, indent=2, sort_keys=True))
outfile.close()
================================================
FILE: CNN_LSTM/data/lung_data/lung_data/说明.txt
================================================
ļдеCTͼѵ֤õͼΪBMPʽͨɫͼ
================================================
FILE: CNN_LSTM/data/lung_data/lung_test.json
================================================
[
{
"rects": [],
"image_path": "./lung_data/LIDC-IDRI-0103.dat_40_.bmp"
},
{
"rects": [
{
"y1": 168,
"x2": 320,
"x1": 289,
"y2": 199
}
],
"image_path": "./lung_data/LIDC-IDRI-0103.dat_53_.bmp"
},
{
"rects": [
{
"y1": 168,
"x2": 321,
"x1": 289,
"y2": 199
}
],
"image_path": "./lung_data/LIDC-IDRI-0103.dat_54_.bmp"
},
{
"rects": [],
"image_path": "./lung_data/LIDC-IDRI-0103.dat_79_.bmp"
},
{
"rects": [],
"image_path": "./lung_data/LIDC-IDRI-0103.dat_88_.bmp"
},
{
"rects": [
{
"y1": 229,
"x2": 261,
"x1": 230,
"y2": 255
}
],
"image_path": "./lung_data/LIDC-IDRI-0559.dat_14_.bmp"
},
{
"rects": [
{
"y1": 229,
"x2": 261,
"x1": 230,
"y2": 255
}
],
"image_path": "./lung_data/LIDC-IDRI-0559.dat_15_.bmp"
},
{
"rects": [
{
"y1": 228,
"x2": 261,
"x1": 229,
"y2": 255
}
],
"image_path": "./lung_data/LIDC-IDRI-0559.dat_16_.bmp"
},
{
"rects": [],
"image_path": "./lung_data/LIDC-IDRI-0559.dat_19_.bmp"
},
{
"rects": [],
"image_path": "./lung_data/LIDC-IDRI-0559.dat_35_.bmp"
},
{
"rects": [
{
"y1": 222,
"x2": 288,
"x1": 256,
"y2": 253
}
],
"image_path": "./lung_data/LIDC-IDRI-0559.dat_41_.bmp"
},
{
"rects": [],
"image_path": "./lung_data/LIDC-IDRI-0559.dat_75_.bmp"
},
{
"rects": [],
"image_path": "./lung_data/LIDC-IDRI-0937.dat_16_.bmp"
},
{
"rects": [],
"image_path": "./lung_data/LIDC-IDRI-0937.dat_24_.bmp"
},
{
"rects": [],
"image_path": "./lung_data/LIDC-IDRI-0937.dat_34_.bmp"
},
{
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gitextract_o0fgwltz/ ├── CNN_LSTM/ │ ├── README.md │ ├── analysis_results.py │ ├── convert_txt2json.py │ ├── data/ │ │ └── lung_data/ │ │ ├── lung_data/ │ │ │ └── 说明.txt │ │ ├── lung_test.json │ │ ├── lung_train.json │ │ ├── lung_val.json │ │ ├── test.json │ │ └── test_small.json │ ├── download_data.sh │ ├── dump.txt │ ├── evaluate.ipynb │ ├── evaluate.ipynb.bak │ ├── evaluate.py │ ├── evaluate.py.bak │ ├── hypes/ │ │ ├── lstm.json │ │ ├── lstm_resnet_rezoom.json │ │ ├── lstm_resnet_rezoom_lung.json │ │ ├── lstm_rezoom.json │ │ ├── lstm_rezoom_lung.json │ │ ├── overfeat.json │ │ ├── overfeat_resnet_rezoom.json │ │ └── overfeat_rezoom.json │ ├── lung_test.txt │ ├── lung_train.txt │ ├── lung_val.txt │ ├── make_json.py │ ├── nohup.out │ ├── nohup.out.old │ ├── nohup_resnet.log │ ├── requirements.txt │ ├── run.sh │ ├── run_convert_txt2json.sh │ ├── run_lstm_resnet.sh │ ├── test.sh │ ├── test_inception.sh │ ├── test_resnet.sh │ ├── train.py │ ├── utils/ │ │ ├── Makefile │ │ ├── __init__.py │ │ ├── annolist/ │ │ │ ├── AnnoList_pb2.py │ │ │ ├── AnnotationLib.py │ │ │ ├── LICENSE_FOR_THIS_FOLDER │ │ │ ├── MatPlotter.py │ │ │ ├── PalLib.py │ │ │ ├── __init__.py │ │ │ ├── doRPC.py │ │ │ ├── ma_utils.py │ │ │ └── plotSimple.py │ │ ├── data_utils.py │ │ ├── googlenet_load.py │ │ ├── hungarian/ │ │ │ ├── hungarian.cc │ │ │ ├── hungarian.cpp │ │ │ └── hungarian.hpp │ │ ├── rect.py │ │ ├── slim_nets/ │ │ │ ├── __init__.py │ │ │ ├── inception_v1.py │ │ │ ├── resnet_utils.py │ │ │ └── resnet_v1.py │ │ ├── stitch_rects.cpp │ │ ├── stitch_rects.hpp │ │ ├── stitch_wrapper.py │ │ ├── stitch_wrapper.pyx │ │ └── train_utils.py │ └── view_weights.py ├── README.docx ├── README.md ├── api_lidc/ │ ├── annotation2.py │ ├── company_GE.txt │ ├── get_dat.sh │ ├── get_list.sh │ ├── get_txt.sh │ ├── information.txt │ ├── list.py │ ├── list2.py │ ├── nodule2.py │ ├── nodule_structs.py │ ├── nohup2.log │ ├── read_file.py │ ├── te.py │ ├── test17.py │ └── write_bin_file.py ├── cnn_3d/ │ ├── 3d_cnn_lung_detect.py │ ├── 3d_cnn_lung_test.py │ └── detect2.py ├── inception_vgg_table.docx ├── lidc-idri nodule counts (6-23-2015).xlsx ├── pylung/ │ ├── __init__.py │ ├── annotation.py │ ├── base.py │ ├── detector.py │ ├── filters/ │ │ ├── __init__.py │ │ ├── filter.py │ │ ├── parenchyma.py │ │ └── vgg.py │ ├── libinfo.py │ ├── models/ │ │ ├── __init__.py │ │ ├── chest_detect.py │ │ ├── test.py │ │ ├── train.py │ │ └── vgg_classifier.py │ ├── nodule_structs.py │ ├── tests/ │ │ ├── a.csv │ │ ├── clean.sh │ │ ├── demo.py │ │ ├── main.py │ │ ├── predict.py │ │ ├── run.sh │ │ ├── test.csv │ │ ├── test_detect.py │ │ ├── tmp.py │ │ └── train.py │ └── utils.py └── tcia-diagnosis-data-2012-04-20.xls
SYMBOL INDEX (492 symbols across 54 files)
FILE: CNN_LSTM/analysis_results.py
function get_volume (line 20) | def get_volume(gs):
function get_area (line 23) | def get_area(gs):
function combined_sample (line 26) | def combined_sample(gs1, gs2):
function check_in2d (line 56) | def check_in2d(gs1, gs2):
function get_single_matched_list (line 83) | def get_single_matched_list(org_sample, det_sample):
function commpare_sample (line 93) | def commpare_sample(org, det):
function modify_rc (line 106) | def modify_rc(sample6):
function merge_gd (line 123) | def merge_gd(gd_sample_set):
function get_dt_scores (line 137) | def get_dt_scores(sample_dt):
function main (line 146) | def main():
FILE: CNN_LSTM/evaluate.py
function get_image_dir (line 15) | def get_image_dir(args):
function get_results (line 22) | def get_results(args, H):
function main (line 69) | def main():
FILE: CNN_LSTM/make_json.py
function removeAllPatches (line 33) | def removeAllPatches():
function skip (line 38) | def skip(event): #called when the skip button is hit
function next (line 51) | def next(event): #called when the next button is hit
function clear (line 92) | def clear(event): #called when the clear button is hit
function onclick (line 97) | def onclick(event): #called when anywhere inside the window is clicked
function undo (line 109) | def undo(event): #called when the undo button is hit
FILE: CNN_LSTM/train.py
function _hungarian_grad (line 32) | def _hungarian_grad(op, *args):
function build_lstm_inner (line 35) | def build_lstm_inner(H, lstm_input):
function build_overfeat_inner (line 61) | def build_overfeat_inner(H, lstm_input):
function deconv (line 74) | def deconv(x, output_shape, channels):
function rezoom (line 82) | def rezoom(H, pred_boxes, early_feat, early_feat_channels, w_offsets, h_...
function build_forward (line 125) | def build_forward(H, x, phase, reuse):
function build_forward_backward (line 237) | def build_forward_backward(H, x, phase, boxes, flags):
function build (line 310) | def build(H, q):
function train (line 417) | def train(H, test_images):
function main (line 526) | def main():
FILE: CNN_LSTM/utils/__init__.py
function tf_concat (line 7) | def tf_concat(axis, values, **kwargs):
FILE: CNN_LSTM/utils/annolist/AnnotationLib.py
function cmpAnnoRectsByScore (line 33) | def cmpAnnoRectsByScore(r1, r2):
function cmpAnnoRectsByScoreDescending (line 36) | def cmpAnnoRectsByScoreDescending(r1, r2):
function cmpDetAnnoRectsByScore (line 39) | def cmpDetAnnoRectsByScore(r1, r2):
function suffixMatch (line 43) | def suffixMatch(fn1, fn2):
class AnnoList (line 55) | class AnnoList(MutableSequence):
method __init__ (line 61) | def __init__(self, data=None):
method add_attribute (line 72) | def add_attribute(self, name, dtype):
method add_attribute_val (line 93) | def add_attribute_val(self, aname, vname, val):
method attribute_get_value_str (line 111) | def attribute_get_value_str(self, aname, val):
method save (line 117) | def save(self, fname):
method __len__ (line 121) | def __len__(self):
method __getitem__ (line 124) | def __getitem__(self, ii):
method __delitem__ (line 133) | def __delitem__(self, ii):
method __setitem__ (line 136) | def __setitem__(self, ii, val):
method __str__ (line 140) | def __str__(self):
method __repr__ (line 143) | def __repr__(self):
method insert (line 146) | def insert(self, ii, val):
method append (line 149) | def append(self, val):
function is_compatible_attr_type (line 154) | def is_compatible_attr_type(protobuf_type, attr_type):
function protobuf_type_to_python (line 165) | def protobuf_type_to_python(protobuf_type):
class AnnoPoint (line 176) | class AnnoPoint(object):
method __init__ (line 177) | def __init__(self, x=None, y=None, id=None):
class AnnoRect (line 182) | class AnnoRect(object):
method __init__ (line 183) | def __init__(self, x1=-1, y1=-1, x2=-1, y2=-1):
method width (line 203) | def width(self):
method height (line 206) | def height(self):
method centerX (line 209) | def centerX(self):
method centerY (line 212) | def centerY(self):
method left (line 215) | def left(self):
method right (line 218) | def right(self):
method top (line 221) | def top(self):
method bottom (line 224) | def bottom(self):
method forceAspectRatio (line 227) | def forceAspectRatio(self, ratio, KeepHeight = False, KeepWidth = False):
method clipToImage (line 240) | def clipToImage(self, min_x, max_x, min_y, max_y):
method printContent (line 250) | def printContent(self):
method ascii (line 257) | def ascii(self):
method writeIDL (line 265) | def writeIDL(self, file):
method writeJSON (line 272) | def writeJSON(self):
method sortCoords (line 279) | def sortCoords(self):
method rescale (line 285) | def rescale(self, factor):
method resize (line 291) | def resize(self, factor, factor_y = None):
method intersection (line 304) | def intersection(self, other):
method cover (line 330) | def cover(self, other):
method overlap_pascal (line 336) | def overlap_pascal(self, other):
method isMatchingPascal (line 350) | def isMatchingPascal(self, other, minOverlap):
method distance (line 357) | def distance(self, other, aspectRatio=-1, fixWH='fixheight'):
method isMatchingStd (line 374) | def isMatchingStd(self, other, coverThresh, overlapThresh, distThresh,...
method isMatching (line 389) | def isMatching(self, other, style, coverThresh, overlapThresh, distThr...
method addToXML (line 397) | def addToXML(self, node, doc): # no Silhouette yet
class Annotation (line 475) | class Annotation(object):
method __init__ (line 477) | def __init__(self):
method clone_empty (line 483) | def clone_empty(self):
method filename (line 491) | def filename(self):
method printContent (line 494) | def printContent(self):
method writeIDL (line 499) | def writeIDL(self, file):
method writeJSON (line 514) | def writeJSON(self):
method addToXML (line 522) | def addToXML(self, node, doc): # no frame# yet
method sortByScore (line 542) | def sortByScore(self, dir="ascending"):
method __getitem__ (line 548) | def __getitem__(self, index):
class detAnnoRect (line 551) | class detAnnoRect:
method __init (line 552) | def __init(self):
function parseTii (line 562) | def parseTii(filename):
function parseXML (line 613) | def parseXML(filename):
function parseJSON (line 718) | def parseJSON(filename):
function parse (line 749) | def parse(filename, abs_path=False):
function parseIDL (line 775) | def parseIDL(filename):
function save (line 874) | def save(filename, annotations):
function saveIDL (line 899) | def saveIDL(filename, annotations):
function saveJSON (line 922) | def saveJSON(filename, annotations):
function idlBase (line 933) | def idlBase(filename):
function saveXML (line 958) | def saveXML(filename, annotations):
function getStats (line 993) | def getStats(annotations):
function mergeIDL (line 1072) | def mergeIDL(detIDL, det2IDL, detectionFuse= True, minOverlap = 0.5):
function forceAspectRatio (line 1114) | def forceAspectRatio(annotations, ratio, KeepHeight = False, KeepWidth =...
function extractSubSet (line 1144) | def extractSubSet(gtIDL, detIDL):
function filterMinScore (line 1216) | def filterMinScore(detections, minScore):
function suffixIdlFileName (line 1233) | def suffixIdlFileName(filename, suffix):
function annoAnalyze (line 1248) | def annoAnalyze(detIDL):
FILE: CNN_LSTM/utils/annolist/MatPlotter.py
class MatPlotter (line 13) | class MatPlotter:
method __init__ (line 27) | def __init__(self, fontsize=15):
method formatLegend (line 33) | def formatLegend(self, newFontSize = 14, newPlace = 'lower right', bor...
method newFigure (line 39) | def newFigure(self, plotTitle="", fsize=rcParams['figure.figsize']):
method newRPCFigure (line 42) | def newRPCFigure(self, plotTitle="", fsize=rcParams['figure.figsize']):
method newFPPIFigure (line 61) | def newFPPIFigure(self, plotTitle="", fsize=rcParams['figure.figsize']):
method newFreqFigure (line 80) | def newFreqFigure(self, plotTitle="", maxX = 10, maxY = 10,fsize=rcPar...
method newFPPWFigure (line 98) | def newFPPWFigure(self, plotTitle="", fsize=rcParams['figure.figsize']):
method newLogFPPIFigure (line 111) | def newLogFPPIFigure(self, plotTitle="", fsize=rcParams['figure.figsiz...
method loadRPCData (line 125) | def loadRPCData(self, fname):
method loadFreqData (line 241) | def loadFreqData(self, fname):
method loadFPPWData (line 257) | def loadFPPWData(self, fname):
method finishPlot (line 260) | def finishPlot(self, axlimits = [0,1.0,0,1.0]):
method finishFreqPlot (line 270) | def finishFreqPlot(self):
method show (line 276) | def show(self, plotEER = True, axlimits = [0,1.0,0,1.0]):
method saveCurrentFigure (line 285) | def saveCurrentFigure(self, plotEER, filename, axlimits = [0,1.0,0,1.0]):
method plotRFP (line 295) | def plotRFP(self, numImages, fname, line="r-"):
method plotRPC (line 298) | def plotRPC(self, fname, descr="line", style="-1", axlimits = [0,1.0,0...
method plotFPPI (line 323) | def plotFPPI(self, fname, descr="line", style="-1", axlimits = [0,2,0,...
method plotFreq (line 345) | def plotFreq(self, fname, descr="line", style="-1", linewidth = 2, das...
method plotFPPW (line 364) | def plotFPPW(self, fname, descr="line", style="-1", axlimits = [5e-6, ...
method plotLogFPPI (line 392) | def plotLogFPPI(self, fname, descr="line", style="-1", axlimits = [5e-...
FILE: CNN_LSTM/utils/annolist/PalLib.py
function loadPal (line 7) | def loadPal(filename):
function savePal (line 16) | def savePal(filename, _annolist):
function al2pal (line 21) | def al2pal(annotations):
function pal2al (line 89) | def pal2al(_annolist):
FILE: CNN_LSTM/utils/annolist/doRPC.py
function edmonds_karp (line 17) | def edmonds_karp(n, C, s, t, sumC):
class AnnoGraph (line 50) | class AnnoGraph:
method __init__ (line 52) | def __init__(self, anno, det, ignore, style, minCover, minOverlap, max...
method match (line 116) | def match(self, style, minCover, minOverlap, maxDistance):
method decreaseScore (line 133) | def decreaseScore(self, score):
method addBB (line 149) | def addBB(self, rect):
method insertIntoC (line 161) | def insertIntoC(self, i):
method maxflow (line 173) | def maxflow(self):
method consideredDets (line 176) | def consideredDets(self):
method ignoredFlow (line 179) | def ignoredFlow(self):
method getTruePositives (line 182) | def getTruePositives(self):
method getIgnoredTruePositives (line 196) | def getIgnoredTruePositives(self):
method getMissingRecall (line 210) | def getMissingRecall(self):
method getFalsePositives (line 218) | def getFalsePositives(self):
function asort (line 226) | def asort(idlGT, idlDet, minWidth, minHeight, style, minCover, minOverla...
function comp_prec_recall (line 310) | def comp_prec_recall(annoIDL, detIDL, minOverlap):
function comp_prec_recall_graphs (line 318) | def comp_prec_recall_graphs(annoIDL, detIDL, minOverlap):
function comp_prec_recall_all_params (line 328) | def comp_prec_recall_all_params(annoIDL, detIDL, ignoreIDL, minWidth=0, ...
function main (line 462) | def main():
FILE: CNN_LSTM/utils/annolist/ma_utils.py
function is_number (line 1) | def is_number(s):
FILE: CNN_LSTM/utils/annolist/plotSimple.py
function main (line 14) | def main(argv):
FILE: CNN_LSTM/utils/data_utils.py
function annotation_to_h5 (line 10) | def annotation_to_h5(H, a, cell_width, cell_height, max_len):
function get_cell_grid (line 52) | def get_cell_grid(cell_width, cell_height, region_size):
function annotation_jitter (line 70) | def annotation_jitter(I, a_in, min_box_width=20, jitter_scale_min=0.9, j...
FILE: CNN_LSTM/utils/googlenet_load.py
function model (line 5) | def model(x, H, reuse, is_training=True):
FILE: CNN_LSTM/utils/hungarian/hungarian.cc
class Rect (line 26) | class Rect {
method Rect (line 35) | explicit Rect(int cx, int cy, int width, int height, float confidence) {
method Rect (line 44) | Rect(const Rect& other) {
method overlaps (line 53) | bool overlaps(const Rect& other) const {
method distance (line 63) | int distance(const Rect& other) const {
method intersection (line 68) | float intersection(const Rect& other) const {
method area (line 79) | int area() const {
method union_area (line 83) | float union_area(const Rect& other) const {
method iou (line 87) | float iou(const Rect& other) const {
function hungarian_print_matrix (line 187) | void hungarian_print_matrix(int** C, int rows, int cols) {
function hungarian_print_assignment (line 200) | void hungarian_print_assignment(hungarian_problem_t* p) {
function hungarian_print_costmatrix (line 204) | void hungarian_print_costmatrix(hungarian_problem_t* p) {
function hungarian_print_status (line 208) | void hungarian_print_status(hungarian_problem_t* p) {
function hungarian_imax (line 218) | int hungarian_imax(int a, int b) {
function hungarian_init (line 222) | int hungarian_init(hungarian_problem_t* p, int** cost_matrix, int rows, ...
function hungarian_free (line 278) | void hungarian_free(hungarian_problem_t* p) {
function hungarian_solve (line 292) | void hungarian_solve(hungarian_problem_t* p)
class HungarianOp (line 582) | class HungarianOp : public OpKernel {
method HungarianOp (line 584) | explicit HungarianOp(OpKernelConstruction* context) : OpKernel(context...
method Compute (line 586) | void Compute(OpKernelContext* context) override {
method get_assignment (line 699) | vector<int> get_assignment(vector<float> match_cost, vector<float> los...
FILE: CNN_LSTM/utils/hungarian/hungarian.cpp
function hungarian_print_matrix (line 49) | void hungarian_print_matrix(int** C, int rows, int cols) {
function hungarian_print_assignment (line 62) | void hungarian_print_assignment(hungarian_problem_t* p) {
function hungarian_print_costmatrix (line 66) | void hungarian_print_costmatrix(hungarian_problem_t* p) {
function hungarian_print_status (line 70) | void hungarian_print_status(hungarian_problem_t* p) {
function hungarian_imax (line 80) | int hungarian_imax(int a, int b) {
function hungarian_init (line 84) | int hungarian_init(hungarian_problem_t* p, int** cost_matrix, int rows, ...
function hungarian_free (line 140) | void hungarian_free(hungarian_problem_t* p) {
function hungarian_solve (line 154) | void hungarian_solve(hungarian_problem_t* p)
FILE: CNN_LSTM/utils/rect.py
class Rect (line 1) | class Rect(object):
method __init__ (line 2) | def __init__(self, cx, cy, width, height, confidence):
method overlaps (line 9) | def overlaps(self, other):
method distance (line 16) | def distance(self, other):
method intersection (line 19) | def intersection(self, other):
method area (line 27) | def area(self):
method union (line 29) | def union(self, other):
method iou (line 31) | def iou(self, other):
method __eq__ (line 33) | def __eq__(self, other):
FILE: CNN_LSTM/utils/slim_nets/inception_v1.py
function inception_v1_base (line 29) | def inception_v1_base(inputs,
function inception_v1 (line 248) | def inception_v1(inputs,
function inception_v1_arg_scope (line 306) | def inception_v1_arg_scope(weight_decay=0.00004,
FILE: CNN_LSTM/utils/slim_nets/resnet_utils.py
class Block (line 56) | class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
function subsample (line 69) | def subsample(inputs, factor, scope=None):
function conv2d_same (line 87) | def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=...
function stack_blocks_dense (line 150) | def stack_blocks_dense(net,
function resnet_arg_scope (line 237) | def resnet_arg_scope(is_training=True,
FILE: CNN_LSTM/utils/slim_nets/resnet_v1.py
function bottleneck (line 69) | def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
function resnet_v1 (line 115) | def resnet_v1(inputs,
function resnet_v1_50 (line 208) | def resnet_v1_50(inputs,
function resnet_v1_101 (line 231) | def resnet_v1_101(inputs,
function resnet_v1_152 (line 254) | def resnet_v1_152(inputs,
function resnet_v1_200 (line 276) | def resnet_v1_200(inputs,
FILE: CNN_LSTM/utils/stitch_rects.cpp
function filter_rects (line 13) | void filter_rects(const vector<vector<vector<Rect> > >& all_rects,
FILE: CNN_LSTM/utils/stitch_rects.hpp
class Rect (line 15) | class Rect {
method Rect (line 24) | explicit Rect(int cx, int cy, int width, int height, float confidence) {
method Rect (line 33) | Rect(const Rect& other) {
method overlaps (line 42) | bool overlaps(const Rect& other, float tau) const {
method distance (line 52) | int distance(const Rect& other) const {
method intersection (line 57) | float intersection(const Rect& other) const {
method area (line 68) | int area() const {
method union_area (line 72) | float union_area(const Rect& other) const {
method iou (line 76) | float iou(const Rect& other) const {
FILE: CNN_LSTM/utils/train_utils.py
function rescale_boxes (line 15) | def rescale_boxes(current_shape, anno, target_height, target_width):
function load_idl_tf (line 27) | def load_idl_tf(idlfile, H, jitter):
function make_sparse (line 73) | def make_sparse(n, d):
function load_data_gen (line 78) | def load_data_gen(H, phase, jitter):
function add_rectangles (line 99) | def add_rectangles(H, orig_image, confidences, boxes, use_stitching=Fals...
function to_x1y1x2y2 (line 159) | def to_x1y1x2y2(box):
function intersection (line 168) | def intersection(box1, box2):
function area (line 179) | def area(box):
function union (line 184) | def union(box1, box2):
function iou (line 187) | def iou(box1, box2):
function to_idx (line 190) | def to_idx(vec, w_shape):
function interp (line 197) | def interp(w, i, channel_dim):
function bilinear_select (line 240) | def bilinear_select(H, pred_boxes, early_feat, early_feat_channels, w_of...
FILE: CNN_LSTM/view_weights.py
function find_all_files (line 12) | def find_all_files(root, suffix=None):
function main (line 22) | def main():
FILE: api_lidc/annotation2.py
function find_all_files (line 13) | def find_all_files(root, suffix=None):
function parse_dir (line 23) | def parse_dir(dirname, outdir, flatten=True, pickle=True):
function parse_original_xmls (line 45) | def parse_original_xmls(dirname, outdir, pickle=True):
function parse (line 65) | def parse(xml_filename):
function parse_header (line 100) | def parse_header(root):
function parse_nodule (line 120) | def parse_nodule(xml_node): # xml_node is one unblindedReadNodule
function parse_non_nodule (line 175) | def parse_non_nodule(xml_node): # xml_node is one nonNodule
function flatten_annotation (line 190) | def flatten_annotation(annotation_dict):
function flatten_nodule (line 204) | def flatten_nodule(nodules, type, result):
FILE: api_lidc/list.py
function build_parser (line 10) | def build_parser():
function analyse (line 17) | def analyse(srcpath, dstpath):
function main (line 28) | def main():
FILE: api_lidc/list2.py
function build_parser (line 10) | def build_parser():
function analyse (line 17) | def analyse(srcpath, dstpath):
function main (line 39) | def main():
FILE: api_lidc/nodule2.py
function build_parser (line 12) | def build_parser():
function annotation (line 19) | def annotation(srcpath, dstpath):
function main (line 121) | def main():
FILE: api_lidc/nodule_structs.py
class NoduleCharstics (line 6) | class NoduleCharstics:
method __init__ (line 7) | def __init__(self):
method __str__ (line 19) | def __str__(self):
method set_values (line 28) | def set_values(self, sub, inter, calc, spher, lob, spic, tex, malig):
class NoduleRoi (line 40) | class NoduleRoi: # is common for nodule and non-nodule
method __init__ (line 41) | def __init__(self, z_pos=0., sop_uid=''):
method __str__ (line 51) | def __str__(self):
class Nodule (line 72) | class Nodule: # is base class for all nodule types (NormalNodule,
method __init__ (line 74) | def __init__(self):
method __str__ (line 79) | def __str__(self):
method tostring (line 85) | def tostring(self):
class NoduleAnnotationCluster (line 89) | class NoduleAnnotationCluster(): # to be seen
method __init__ (line 90) | def __init__(self):
method compute_centroid (line 103) | def compute_centroid(self):
method set_convex_hull (line 112) | def set_convex_hull(self):
class NormalNodule (line 137) | class NormalNodule(Nodule):
method __init__ (line 138) | def __init__(self):
method tostring (line 143) | def tostring(self):
class SmallNodule (line 154) | class SmallNodule(Nodule):
method __init__ (line 155) | def __init__(self):
method tostring (line 159) | def tostring(self):
class NonNodule (line 166) | class NonNodule(Nodule):
method __init__ (line 167) | def __init__(self):
method tostring (line 171) | def tostring(self):
class RadAnnotation (line 178) | class RadAnnotation:
method __init__ (line 179) | def __init__(self, init=True):
method is_init (line 191) | def is_init(self):
method set_init (line 194) | def set_init(self, init):
method __str__ (line 198) | def __str__(self):
class AnnotationHeader (line 226) | class AnnotationHeader:
method __init__ (line 227) | def __init__(
method __str__ (line 240) | def __str__(self):
class IdriReadMessage (line 256) | class IdriReadMessage:
method __init__ (line 257) | def __init__(self):
FILE: api_lidc/read_file.py
function main (line 6) | def main():
FILE: api_lidc/te.py
function build_parser (line 10) | def build_parser():
function analyse (line 17) | def analyse(srcpath, dstpath):
function main (line 42) | def main():
FILE: api_lidc/test17.py
function build_parser (line 10) | def build_parser():
function analyse (line 17) | def analyse(srcpath, dstpath):
function main (line 44) | def main():
FILE: api_lidc/write_bin_file.py
function build_parser (line 10) | def build_parser():
function find_all_files (line 16) | def find_all_files(root, suffix=None):
function main (line 26) | def main():
FILE: cnn_3d/3d_cnn_lung_detect.py
class CT_Image (line 16) | class CT_Image(object):
method __init__ (line 24) | def __init__(self, input_folder, lable_file, zoomrate=1.0):
method get_valid_patients (line 36) | def get_valid_patients(self, patient_list):
method get_3d_data (line 44) | def get_3d_data(self, path):
method process_image (line 50) | def process_image(self, patient):
method padding_image (line 58) | def padding_image(self, patient):
method get_max_imageshape (line 62) | def get_max_imageshape(self):
method get_patient_lable (line 73) | def get_patient_lable(self, patient_list):
method next_batch (line 78) | def next_batch(self, num):
method validate_batch (line 86) | def validate_batch(self):
class CNN_3D (line 92) | class CNN_3D(object):
method __init__ (line 96) | def __init__(self, ctobj):
method run (line 99) | def run(self):
method weight_variable (line 176) | def weight_variable(self, shape):
method bias_variable (line 181) | def bias_variable(self, shape):
method conv3d (line 186) | def conv3d(self, x, W):
method max_pool_2x2x2 (line 191) | def max_pool_2x2x2(self, x):
method add_conv_layer (line 195) | def add_conv_layer(self, inputs, in_size, out_size, layer):
FILE: cnn_3d/3d_cnn_lung_test.py
class CT_Image (line 16) | class CT_Image(object):
method __init__ (line 24) | def __init__(self, input_folder, lable_file, zoomrate=1.0):
method get_valid_patients (line 36) | def get_valid_patients(self, patient_list):
method get_3d_data (line 44) | def get_3d_data(self, path):
method process_image (line 50) | def process_image(self, patient):
method padding_image (line 58) | def padding_image(self, patient):
method get_max_imageshape (line 62) | def get_max_imageshape(self):
method get_patient_lable (line 73) | def get_patient_lable(self, patient_list):
method next_batch (line 78) | def next_batch(self, num):
method validate_batch (line 86) | def validate_batch(self):
class CNN_3D (line 92) | class CNN_3D(object):
method __init__ (line 96) | def __init__(self, ctobj):
method run (line 99) | def run(self):
method weight_variable (line 179) | def weight_variable(self, shape):
method bias_variable (line 184) | def bias_variable(self, shape):
method conv3d (line 189) | def conv3d(self, x, W):
method max_pool_2x2x2 (line 194) | def max_pool_2x2x2(self, x):
method add_conv_layer (line 198) | def add_conv_layer(self, inputs, in_size, out_size, layer):
FILE: cnn_3d/detect2.py
class CT_Image (line 15) | class CT_Image(object):
method __init__ (line 23) | def __init__(self, input_folder, lable_file, zoomrate=1.0):
method get_valid_patients (line 35) | def get_valid_patients(self, patient_list):
method get_3d_data (line 43) | def get_3d_data(self, path):
method process_image (line 49) | def process_image(self, patient):
method padding_image (line 57) | def padding_image(self, patient):
method get_max_imageshape (line 62) | def get_max_imageshape(self):
method get_patient_lable (line 73) | def get_patient_lable(self, patient_list):
method next_batch (line 78) | def next_batch(self, num):
class CNN_3D (line 89) | class CNN_3D(object):
method __init__ (line 93) | def __init__(self, ctobj):
method run (line 96) | def run(self):
method weight_variable (line 148) | def weight_variable(self, shape):
method bias_variable (line 152) | def bias_variable(self, shape):
method conv3d (line 156) | def conv3d(self, x, W):
method max_pool_2x2x2 (line 161) | def max_pool_2x2x2(self, x):
method add_conv_layer (line 165) | def add_conv_layer(self, inputs, in_size, out_size, layer):
FILE: pylung/annotation.py
function parse_dir (line 14) | def parse_dir(dirname, flatten=True, pickle=True):
function parse_original_xmls (line 36) | def parse_original_xmls(dirname, pickle=True):
function parse (line 56) | def parse(xml_filename):
function parse_header (line 91) | def parse_header(root):
function parse_nodule (line 111) | def parse_nodule(xml_node): # xml_node is one unblindedReadNodule
function parse_non_nodule (line 166) | def parse_non_nodule(xml_node): # xml_node is one nonNodule
function flatten_annotation (line 181) | def flatten_annotation(annotation_dict):
function flatten_nodule (line 194) | def flatten_nodule(nodules, type, result):
FILE: pylung/base.py
class PyLungError (line 28) | class PyLungError(Exception):
function _load_lib (line 33) | def _load_lib():
function check_call (line 69) | def check_call(ret):
function c_str (line 85) | def c_str(string):
function c_str (line 99) | def c_str(string):
function c_array (line 114) | def c_array(ctype, values):
function ctypes2buffer (line 133) | def ctypes2buffer(cptr, length):
function ctypes2numpy_shared (line 157) | def ctypes2numpy_shared(cptr, shape):
function ctypes2docstring (line 184) | def ctypes2docstring(num_args, arg_names, arg_types, arg_descs, remove_d...
function _notify_shutdown (line 226) | def _notify_shutdown():
FILE: pylung/detector.py
class NoduleDetector (line 11) | class NoduleDetector(object):
method __init__ (line 12) | def __init__(self, dicom_image):
method candidates (line 26) | def candidates(self):
method _init_candidates (line 29) | def _init_candidates(self):
method detect (line 38) | def detect(self):
method get_parenchyma_block (line 46) | def get_parenchyma_block(self):
FILE: pylung/filters/filter.py
class CandidateFilter (line 1) | class CandidateFilter(object):
method __init__ (line 2) | def __init__(self, image):
method _filter (line 5) | def _filter(self, x, y, h, w):
method filter (line 8) | def filter(self, candidates):
FILE: pylung/filters/parenchyma.py
function _get_parenchyma_mask (line 10) | def _get_parenchyma_mask(image, threashold):
class ParenchymaFilter (line 31) | class ParenchymaFilter(CandidateFilter):
method __init__ (line 35) | def __init__(self, image):
method _filter (line 39) | def _filter(self, x, y, h, w):
FILE: pylung/filters/vgg.py
class VGGFilter (line 11) | class VGGFilter(CandidateFilter):
method __init__ (line 12) | def __init__(self, image):
method _filter (line 18) | def _filter(self, x, y, h, w):
FILE: pylung/libinfo.py
function find_lib_path (line 8) | def find_lib_path():
FILE: pylung/models/chest_detect.py
function read_ct_file (line 13) | def read_ct_file(dicom_file):
function custom_square (line 27) | def custom_square(n):
function custom_cross (line 33) | def custom_cross(n):
function process_matrix (line 41) | def process_matrix(mtx):
function printMatrix (line 123) | def printMatrix(p1, p2, p3, p4, nfls):
function get_LUT_value (line 172) | def get_LUT_value(data, window, level):
function get_dicom_PIL (line 181) | def get_dicom_PIL(dataset):
function plot_panels (line 236) | def plot_panels(panels):
FILE: pylung/models/test.py
function hello_world (line 63) | def hello_world():
function process_serve_chestct (line 68) | def process_serve_chestct():
function upload (line 116) | def upload():
function get_label_size (line 152) | def get_label_size(a):
function thresh (line 155) | def thresh(a, b, max_value, C):
function block_size (line 158) | def block_size(size):
function custom_square (line 164) | def custom_square(n):
function custom_cross (line 169) | def custom_cross(n):
function get_number_neighbours (line 176) | def get_number_neighbours(mask,block):
function masked_adaptive_threshold (line 182) | def masked_adaptive_threshold(image,mask,max_value,size,C):
function motsu (line 192) | def motsu(im,iz):
function bintoint (line 198) | def bintoint(im, thresh):
function lpf (line 211) | def lpf(im, s, o, d):
function lpf2 (line 222) | def lpf2(im, s, o, d):
function allowed_file (line 237) | def allowed_file(filename):
function isjpg (line 240) | def isjpg(filename):
function ispng (line 243) | def ispng(filename):
function istiff (line 246) | def istiff(filename):
function isdicom (line 249) | def isdicom(filename):
function readChestCTFile (line 252) | def readChestCTFile(f):
function processMatrix (line 266) | def processMatrix(mtx,dim):
function printMatrix (line 344) | def printMatrix(p1, p2, p3, p4, pfx, nfls):
function get_LUT_value (line 402) | def get_LUT_value(data, window, level):
function get_dicom_PIL (line 409) | def get_dicom_PIL(dataset):
FILE: pylung/models/train.py
function load_data (line 14) | def load_data(batch=None):
function train (line 28) | def train(batch=0, n_epoch=10, checkpoint=None):
function main (line 38) | def main():
FILE: pylung/models/vgg_classifier.py
function load_mat (line 11) | def load_mat():
function net (line 25) | def net(input_image):
function classifier (line 66) | def classifier():
FILE: pylung/nodule_structs.py
class NoduleCharstics (line 6) | class NoduleCharstics:
method __init__ (line 7) | def __init__(self):
method __str__ (line 19) | def __str__(self):
method set_values (line 28) | def set_values(self, sub, inter, calc, spher, lob, spic, tex, malig):
class NoduleRoi (line 40) | class NoduleRoi: # is common for nodule and non-nodule
method __init__ (line 41) | def __init__(self, z_pos=0., sop_uid=''):
method __str__ (line 51) | def __str__(self):
class Nodule (line 72) | class Nodule: # is base class for all nodule types (NormalNodule,
method __init__ (line 74) | def __init__(self):
method __str__ (line 79) | def __str__(self):
method tostring (line 85) | def tostring(self):
class NoduleAnnotationCluster (line 89) | class NoduleAnnotationCluster(): # to be seen
method __init__ (line 90) | def __init__(self):
method compute_centroid (line 103) | def compute_centroid(self):
method set_convex_hull (line 112) | def set_convex_hull(self):
class NormalNodule (line 137) | class NormalNodule(Nodule):
method __init__ (line 138) | def __init__(self):
method tostring (line 143) | def tostring(self):
class SmallNodule (line 154) | class SmallNodule(Nodule):
method __init__ (line 155) | def __init__(self):
method tostring (line 159) | def tostring(self):
class NonNodule (line 166) | class NonNodule(Nodule):
method __init__ (line 167) | def __init__(self):
method tostring (line 171) | def tostring(self):
class RadAnnotation (line 178) | class RadAnnotation:
method __init__ (line 179) | def __init__(self, init=True):
method is_init (line 191) | def is_init(self):
method set_init (line 194) | def set_init(self, init):
method __str__ (line 198) | def __str__(self):
class AnnotationHeader (line 226) | class AnnotationHeader:
method __init__ (line 227) | def __init__(
method __str__ (line 240) | def __str__(self):
class IdriReadMessage (line 256) | class IdriReadMessage:
method __init__ (line 257) | def __init__(self):
FILE: pylung/tests/demo.py
function build_parser (line 12) | def build_parser():
function selectAnno (line 18) | def selectAnno(srcpath):
function selectPa (line 39) | def selectPa(srcpath):
function combine (line 62) | def combine():
function select (line 73) | def select(length):
function main (line 89) | def main():
FILE: pylung/tests/main.py
function build_parser (line 29) | def build_parser():
function is_in (line 38) | def is_in(centroid, field):
function parenchyma_cut (line 43) | def parenchyma_cut(dcm_file_path, save_file_path):
function annotation_cut (line 89) | def annotation_cut(dcm_file_path, save_file_path):
function annotation_cut2 (line 133) | def annotation_cut2(dcm_file_path, save_file_path):
function original_view (line 189) | def original_view(srcpath, dstpath):
function original_view2 (line 205) | def original_view2(srcpath, dstpath):
function parenchyma_view (line 234) | def parenchyma_view(srcpath, dstpath):
function annotation_view (line 251) | def annotation_view(srcpath, dstpath):
function main (line 300) | def main():
FILE: pylung/tests/predict.py
function train (line 15) | def train():
FILE: pylung/tests/test_detect.py
class NoduleDetectorTest (line 17) | class NoduleDetectorTest(unittest.TestCase):
method test_1 (line 18) | def test_1(self):
method test_2 (line 103) | def test_2(self):
method test_3 (line 130) | def test_3(self):
method test_4 (line 158) | def test_4(self):
FILE: pylung/tests/tmp.py
function annotation_cut (line 1) | def annotation_cut(dcm_file_path, save_file_path):
FILE: pylung/tests/train.py
function train (line 15) | def train():
FILE: pylung/utils.py
function floodfill (line 7) | def floodfill(image, start_point, value):
function switch_pixels (line 27) | def switch_pixels(image, origin_value, value):
function morphology_open (line 35) | def morphology_open(image):
function morphology_close (line 42) | def morphology_close(image):
function gray_to_rgb (line 49) | def gray_to_rgb(image):
function imsave (line 55) | def imsave(fname, image):
function dcm_to_gray (line 62) | def dcm_to_gray(image):
function find_all_files (line 70) | def find_all_files(root, suffix=None):
function dense_to_one_hot (line 80) | def dense_to_one_hot(labels_dense, num_classes):
Copy disabled (too large)
Download .json
Condensed preview — 114 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (21,208K chars).
[
{
"path": "CNN_LSTM/README.md",
"chars": 5476,
"preview": "<img src=http://russellsstewart.com/s/tensorbox/tensorbox_output.jpg></img>\n\n<img src=\"https://travis-ci.org/TensorBox/T"
},
{
"path": "CNN_LSTM/analysis_results.py",
"chars": 7300,
"preview": "import numpy as N\nimport os\nimport cv2\n\nimport sys\nimport struct\nimport cv2\nimport gc\nimport array\n\ntry:\n import cPic"
},
{
"path": "CNN_LSTM/convert_txt2json.py",
"chars": 2634,
"preview": "import sys\nimport json\nimport string\nimport random\n\nif len(sys.argv) != 4:\n print \": python convert_txt2json.py lung"
},
{
"path": "CNN_LSTM/data/lung_data/lung_data/说明.txt",
"chars": 19,
"preview": "ļдеCTͼѵ֤õͼΪBMPʽͨɫͼ"
},
{
"path": "CNN_LSTM/data/lung_data/lung_test.json",
"chars": 363612,
"preview": "[\n {\n \"rects\": [], \n \"image_path\": \"./lung_data/LIDC-IDRI-0103.dat_40_.bmp\"\n }, \n {\n \"rects\": [\n {\n \"y1\": 168, \n"
},
{
"path": "CNN_LSTM/data/lung_data/lung_train.json",
"chars": 2087919,
"preview": "[\n {\n \"rects\": [], \n \"image_path\": \"./lung_data/LIDC-IDRI-1003.dat_21_.bmp\"\n }, \n {\n \"rects\": [], \n \"image_path\": \"."
},
{
"path": "CNN_LSTM/data/lung_data/lung_val.json",
"chars": 253428,
"preview": "[\n {\n \"rects\": [], \n \"image_path\": \"./lung_data/LIDC-IDRI-0275.dat_28_.bmp\"\n }, \n {\n \"rects\": [], \n \"image_path\": \"."
},
{
"path": "CNN_LSTM/data/lung_data/test.json",
"chars": 363612,
"preview": "[\n {\n \"rects\": [], \n \"image_path\": \"./lung_data/LIDC-IDRI-0103.dat_40_.bmp\"\n }, \n {\n \"rects\": [\n {\n \"y1\": 168, \n"
},
{
"path": "CNN_LSTM/data/lung_data/test_small.json",
"chars": 1089,
"preview": "[\n {\n \"rects\": [\n {\n \"y1\": 168, \n \"x2\": 320, \n \"x1\": 289, \n \"y2\": 199\n }\n ], \n \"image_path\": \"./lung_"
},
{
"path": "CNN_LSTM/download_data.sh",
"chars": 837,
"preview": "#!/bin/bash\n# This scripts downloads the ptb data and unzips it.\n\nDIR=\"$( cd \"$(dirname \"$0\")\" ; pwd -P )\"\ncd $DIR\n\necho"
},
{
"path": "CNN_LSTM/dump.txt",
"chars": 255393,
"preview": "(lp1\n(lp2\nS'LIDC-IDRI-0103'\np3\na(lp4\n(lp5\nI69\naI70\naI415\naI354\naI424\naI363\naa(lp6\nI69\naI70\naI414\naI354\naI424\naI363\naa(lp"
},
{
"path": "CNN_LSTM/evaluate.ipynb",
"chars": 2244434,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outp"
},
{
"path": "CNN_LSTM/evaluate.py",
"chars": 5414,
"preview": "import tensorflow as tf\nimport os\nimport json\nimport subprocess\nfrom scipy.misc import imread, imresize\nfrom scipy impor"
},
{
"path": "CNN_LSTM/evaluate.py.bak",
"chars": 4840,
"preview": "import tensorflow as tf\nimport os\nimport json\nimport subprocess\nfrom scipy.misc import imread, imresize\nfrom scipy impor"
},
{
"path": "CNN_LSTM/hypes/lstm.json",
"chars": 1200,
"preview": "{\n \"data\": {\n \"train_idl\": \"./data/brainwash/train_boxes.json\",\n \"test_idl\": \"./data/brainwash/val_boxe"
},
{
"path": "CNN_LSTM/hypes/lstm_resnet_rezoom.json",
"chars": 1301,
"preview": "{\n \"data\": {\n \"train_idl\": \"./data/brainwash/train_boxes.json\",\n \"test_idl\": \"./data/brainwash/val_boxe"
},
{
"path": "CNN_LSTM/hypes/lstm_resnet_rezoom_lung.json",
"chars": 1397,
"preview": "{\n \"data\": {\n \"train_idl\": \"./data/lung_data/lung_train.json\",\n \"test_idl\": \"./data/lung_data/lung_val."
},
{
"path": "CNN_LSTM/hypes/lstm_rezoom.json",
"chars": 1274,
"preview": "{\n \"data\": {\n \"train_idl\": \"./data/VOC2005_1/train_boxes.json\",\n \"test_idl\": \"./data/VOC2005_1/val_boxe"
},
{
"path": "CNN_LSTM/hypes/lstm_rezoom_lung.json",
"chars": 1261,
"preview": "{\n \"data\": {\n \"train_idl\": \"./data/lung_data/lung_train.json\",\n \"test_idl\": \"./data/lung_data/lung_val."
},
{
"path": "CNN_LSTM/hypes/overfeat.json",
"chars": 1201,
"preview": "{\n \"data\": {\n \"train_idl\": \"./data/brainwash/train_boxes.json\",\n \"test_idl\": \"./data/brainwash/val_boxe"
},
{
"path": "CNN_LSTM/hypes/overfeat_resnet_rezoom.json",
"chars": 1302,
"preview": "{\n \"data\": {\n \"train_idl\": \"./data/brainwash/train_boxes.json\",\n \"test_idl\": \"./data/brainwash/val_boxe"
},
{
"path": "CNN_LSTM/hypes/overfeat_rezoom.json",
"chars": 1275,
"preview": "{\n \"data\": {\n \"train_idl\": \"./data/VOC2005_1/train_boxes.json\",\n \"test_idl\": \"./data/VOC2005_1/val_boxe"
},
{
"path": "CNN_LSTM/lung_test.txt",
"chars": 1111673,
"preview": "./lung_data/LIDC-IDRI-0103.dat_1_.bmp\n0\n./lung_data/LIDC-IDRI-0103.dat_2_.bmp\n0\n./lung_data/LIDC-IDRI-0103.dat_3_.bmp\n0\n"
},
{
"path": "CNN_LSTM/lung_train.txt",
"chars": 7519454,
"preview": "./lung_data/LIDC-IDRI-1003.dat_1_.bmp\n0\n./lung_data/LIDC-IDRI-1003.dat_2_.bmp\n0\n./lung_data/LIDC-IDRI-1003.dat_3_.bmp\n0\n"
},
{
"path": "CNN_LSTM/lung_val.txt",
"chars": 761742,
"preview": "./lung_data/LIDC-IDRI-0275.dat_1_.bmp\n0\n./lung_data/LIDC-IDRI-0275.dat_2_.bmp\n0\n./lung_data/LIDC-IDRI-0275.dat_3_.bmp\n0\n"
},
{
"path": "CNN_LSTM/make_json.py",
"chars": 4850,
"preview": "#A tool for hand labelling images\n#Generates an IDL file\n#pass in the directory where you store your images and a filena"
},
{
"path": "CNN_LSTM/nohup.out",
"chars": 1112544,
"preview": "2017-04-26 18:36:41.328348: I tensorflow/core/common_runtime/gpu/gpu_device.cc:900] Found device 0 with properties: \nnam"
},
{
"path": "CNN_LSTM/nohup.out.old",
"chars": 1975641,
"preview": "I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally\nI tensorflow/s"
},
{
"path": "CNN_LSTM/nohup_resnet.log",
"chars": 1001039,
"preview": "2017-04-26 10:53:58.483833: I tensorflow/core/common_runtime/gpu/gpu_device.cc:900] Found device 0 with properties: \nnam"
},
{
"path": "CNN_LSTM/requirements.txt",
"chars": 64,
"preview": "runcython>=0.2.5\nopencv-python>=3.2\nscipy>=0.15.1\nPillow==2.6.0\n"
},
{
"path": "CNN_LSTM/run.sh",
"chars": 105,
"preview": "python train.py --hypes hypes/lstm_rezoom_lung.json --max_iter 1000000 --gpu 0 --logdir output_inception\n"
},
{
"path": "CNN_LSTM/run_convert_txt2json.sh",
"chars": 174,
"preview": "python convert_txt2json.py lung_train.txt lung_train.json 2\npython convert_txt2json.py lung_test.txt lung_test.json 2\npy"
},
{
"path": "CNN_LSTM/run_lstm_resnet.sh",
"chars": 109,
"preview": "python train.py --hypes hypes/lstm_resnet_rezoom_lung.json --max_iter 1000000 --gpu 2 --logdir output_resnet\n"
},
{
"path": "CNN_LSTM/test.sh",
"chars": 137,
"preview": "python evaluate.py --weights output/lstm_resnet_rezoom_lung_2017_04_10_15.37/save.ckpt-999999 --test_boxes data/lung_dat"
},
{
"path": "CNN_LSTM/test_inception.sh",
"chars": 140,
"preview": "python evaluate.py --weights output_inception/lstm_rezoom_lung_2017_04_26_18.37/save.ckpt-999999 --test_boxes data/lung_"
},
{
"path": "CNN_LSTM/test_resnet.sh",
"chars": 144,
"preview": "python evaluate.py --weights output_resnet/lstm_resnet_rezoom_lung_2017_04_26_10.53/save.ckpt-999999 --test_boxes data/l"
},
{
"path": "CNN_LSTM/train.py",
"chars": 26068,
"preview": "#!/usr/bin/env python\nimport json\nimport cv2\nimport tensorflow.contrib.slim as slim\nimport datetime\nimport random\nimport"
},
{
"path": "CNN_LSTM/utils/Makefile",
"chars": 521,
"preview": "SHELL := /bin/bash\n\n.PHONY: all\nall:\n\tpip install runcython\n\tmakecython++ stitch_wrapper.pyx \"\" \"stitch_rects.cpp ./hung"
},
{
"path": "CNN_LSTM/utils/__init__.py",
"chars": 317,
"preview": "\nimport tensorflow as tf\nfrom distutils.version import LooseVersion\n\nTENSORFLOW_VERSION = LooseVersion(tf.__version__)\n\n"
},
{
"path": "CNN_LSTM/utils/annolist/AnnoList_pb2.py",
"chars": 16251,
"preview": "# Generated by the protocol buffer compiler. DO NOT EDIT!\n# source: AnnoList.proto\n\nimport sys\n_b=sys.version_info[0]<3"
},
{
"path": "CNN_LSTM/utils/annolist/AnnotationLib.py",
"chars": 41675,
"preview": "import os\r\n\r\nfrom math import sqrt\r\n\r\nimport gzip\r\nimport json\r\nimport bz2\r\nimport sys\r\nimport numpy as np;\r\n\r\nfrom coll"
},
{
"path": "CNN_LSTM/utils/annolist/LICENSE_FOR_THIS_FOLDER",
"chars": 131,
"preview": "MPII Human Pose Dataset, Version 1.0\nCopyright 2015 Max Planck Institute for Informatics\nLicensed under the Simplified B"
},
{
"path": "CNN_LSTM/utils/annolist/MatPlotter.py",
"chars": 15941,
"preview": "import os\nimport sys\nimport string\nimport matplotlib\nmatplotlib.use('Agg')\nfrom pylab import *\nimport numpy as np\nfrom m"
},
{
"path": "CNN_LSTM/utils/annolist/PalLib.py",
"chars": 5008,
"preview": "import sys\n#import AnnoList_pb2\nimport AnnotationLib;\n\nfrom ma_utils import is_number;\n\ndef loadPal(filename):\n _anno"
},
{
"path": "CNN_LSTM/utils/annolist/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "CNN_LSTM/utils/annolist/doRPC.py",
"chars": 25778,
"preview": "#!/usr/bin/env python\n\nimport os, sys\nfrom AnnotationLib import *\nfrom optparse import OptionParser\nimport copy\nimport m"
},
{
"path": "CNN_LSTM/utils/annolist/ma_utils.py",
"chars": 108,
"preview": "def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False\n"
},
{
"path": "CNN_LSTM/utils/annolist/plotSimple.py",
"chars": 3260,
"preview": "#!/usr/bin/env python\n\nimport sys\nimport os\nimport random\nimport re\nfrom AnnotationLib import *\nfrom MatPlotter import *"
},
{
"path": "CNN_LSTM/utils/data_utils.py",
"chars": 5643,
"preview": "import os\nimport cv2\nimport re\nimport sys\nimport argparse\nimport numpy as np\nimport copy\nimport annolist.AnnotationLib a"
},
{
"path": "CNN_LSTM/utils/googlenet_load.py",
"chars": 1331,
"preview": "from slim_nets import inception_v1 as inception\nfrom slim_nets import resnet_v1 as resnet\nimport tensorflow.contrib.slim"
},
{
"path": "CNN_LSTM/utils/hungarian/hungarian.cc",
"chars": 20359,
"preview": "#include <assert.h>\n#include \"tensorflow/core/framework/op.h\"\n#include \"tensorflow/core/framework/op_kernel.h\"\n\nusing na"
},
{
"path": "CNN_LSTM/utils/hungarian/hungarian.cpp",
"chars": 9888,
"preview": "/********************************************************************\n *************************************************"
},
{
"path": "CNN_LSTM/utils/hungarian/hungarian.hpp",
"chars": 2205,
"preview": "/********************************************************************\n *************************************************"
},
{
"path": "CNN_LSTM/utils/rect.py",
"chars": 1614,
"preview": "class Rect(object):\n def __init__(self, cx, cy, width, height, confidence):\n self.cx = cx\n self.cy = cy"
},
{
"path": "CNN_LSTM/utils/slim_nets/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "CNN_LSTM/utils/slim_nets/inception_v1.py",
"chars": 16872,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "CNN_LSTM/utils/slim_nets/resnet_utils.py",
"chars": 11490,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "CNN_LSTM/utils/slim_nets/resnet_v1.py",
"chars": 13243,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "CNN_LSTM/utils/stitch_rects.cpp",
"chars": 3926,
"preview": "#include <vector>\n#include <math.h>\n#include <assert.h>\n#include <stdlib.h>\n#include <algorithm>\n#include <iostream>\n\n#i"
},
{
"path": "CNN_LSTM/utils/stitch_rects.hpp",
"chars": 2489,
"preview": "#ifndef STITCH_RECTS_HPP\n#define STITCH_RECTS_HPP\n\n#include <vector>\n#include <math.h>\n#include <stdlib.h>\n\n#include \"./"
},
{
"path": "CNN_LSTM/utils/stitch_wrapper.py",
"chars": 98,
"preview": "print('ERROR: stitch_wrapper not yet compiled. Please run `cd /path/to/tensorbox/utils && make`')\n"
},
{
"path": "CNN_LSTM/utils/stitch_wrapper.pyx",
"chars": 2909,
"preview": "from libcpp.vector cimport vector\nfrom libcpp.set cimport set\nfrom rect import Rect as PyRect\ncdef extern from \"stitch_r"
},
{
"path": "CNN_LSTM/utils/train_utils.py",
"chars": 10927,
"preview": "import numpy as np\nimport random\nimport json\nimport os\nimport cv2\nimport itertools\nfrom scipy.misc import imread, imresi"
},
{
"path": "CNN_LSTM/view_weights.py",
"chars": 1495,
"preview": "#!/usr/bin/env python\n#-*-coding: utf-8 -*-\n\nimport os\nimport tensorflow as tf\nimport numpy as np\nfrom scipy.misc import"
},
{
"path": "README.md",
"chars": 9737,
"preview": "# Introduction\n\nThis is a simple framework for training neural networks to detect nodules in CT images. Training require"
},
{
"path": "api_lidc/annotation2.py",
"chars": 8662,
"preview": "import cPickle\nimport logging\nimport os\nimport xml.etree.ElementTree as etree\nimport numpy as np\n\nfrom nodule_structs im"
},
{
"path": "api_lidc/company_GE.txt",
"chars": 115240,
"preview": "/baina/sda1/data/lidc/DOI/LIDC-IDRI-0001/1.3.6.1.4.1.14519.5.2.1.6279.6001.298806137288633453246975630178/1.3.6.1.4.1.14"
},
{
"path": "api_lidc/get_dat.sh",
"chars": 321,
"preview": "#!/usr/bin/env bash\n\ndcm_dir=\"/baina/sda1/data/lidc/DOI/\"\nfor s in `ls $dcm_dir`; do\n\tif [ -d $dcm_dir$s ];\n\tthen\n\t\tfor "
},
{
"path": "api_lidc/get_list.sh",
"chars": 317,
"preview": "#!/usr/bin/env bash\n\ndcm_dir=\"/baina/sda1/data/lidc/DOI/\"\nfor s in `ls $dcm_dir`; do\n\tif [ -d $dcm_dir$s ];\n\tthen\n\t\tfor "
},
{
"path": "api_lidc/get_txt.sh",
"chars": 358,
"preview": "#!/usr/bin/env bash\n\ndcm_dir=\"/baina/sda1/data/lidc/DOI/\"\nout=\"/baina/sda1/data/lidc_matrix/TXT/*.pkl\"\nfor s in `ls $dcm"
},
{
"path": "api_lidc/information.txt",
"chars": 67546,
"preview": "LIDC-IDRI-0001 GE MEDICAL SYSTEMS 2.500000 0.703125 0.703125 -272.500000\nLIDC-IDRI-0002 GE MEDICAL SYSTEMS 1.250000 0.68"
},
{
"path": "api_lidc/list.py",
"chars": 1069,
"preview": "import os\nimport argparse\nimport dicom\nfrom annotation2 import find_all_files\n\nDATA_PATH = './LIDC-IDRI-0001/'\nOUT_PATH "
},
{
"path": "api_lidc/list2.py",
"chars": 1453,
"preview": "import os\nimport argparse\nimport dicom\nfrom annotation2 import find_all_files\n\nDATA_PATH = './LIDC-IDRI-0001/'\nOUT_PATH "
},
{
"path": "api_lidc/nodule2.py",
"chars": 3824,
"preview": "import os\nimport argparse\nimport dicom\nimport pickle\nfrom annotation2 import parse_dir\nfrom annotation2 import find_all_"
},
{
"path": "api_lidc/nodule_structs.py",
"chars": 8377,
"preview": "\"\"\"\nModified from https://github.com/tizita-nesibu/lidc-idri-visualization\n\"\"\"\n\n\nclass NoduleCharstics:\n def __init__"
},
{
"path": "api_lidc/nohup2.log",
"chars": 234540,
"preview": "nohup: ignoring input\nDATA_PATH: /baina/sda1/data/lidc/DOI/LIDC-IDRI-0001/1.3.6.1.4.1.14519.5.2.1.6279.6001.29880613728"
},
{
"path": "api_lidc/read_file.py",
"chars": 1293,
"preview": "import numpy as np\nfrom struct import *\nimport scipy.misc\n\n\ndef main():\n h, w, l = 133, 512, 512\n data = [[[0 for "
},
{
"path": "api_lidc/te.py",
"chars": 1681,
"preview": "import os\nimport argparse\nimport dicom\nfrom annotation2 import find_all_files\n\nDATA_PATH = './LIDC-IDRI-0001/'\nOUT_PATH "
},
{
"path": "api_lidc/test17.py",
"chars": 1751,
"preview": "import os\nimport argparse\nimport dicom\nfrom annotation2 import find_all_files\n\nDATA_PATH = './LIDC-IDRI-0001/'\nOUT_PATH "
},
{
"path": "api_lidc/write_bin_file.py",
"chars": 1943,
"preview": "import os\nimport argparse\nimport dicom\nimport numpy as np\nimport struct\n\nDATA_PATH = './LIDC-IDRI-0001/'\nOUT_PATH = './o"
},
{
"path": "cnn_3d/3d_cnn_lung_detect.py",
"chars": 8097,
"preview": "#! -*- coding:utf-8 -*-\n\n#%pylab --no-import-all\n#%matplotlib inline\nimport numpy as np\nimport pandas as pd\nimport dicom"
},
{
"path": "cnn_3d/3d_cnn_lung_test.py",
"chars": 8198,
"preview": "#! -*- coding:utf-8 -*-\n\n#%pylab --no-import-all\n#%matplotlib inline\nimport numpy as np\nimport pandas as pd\nimport dicom"
},
{
"path": "cnn_3d/detect2.py",
"chars": 6742,
"preview": "#! -*- coding:utf-8 -*-\n\n#%pylab --no-import-all\n#%matplotlib inline\nimport numpy as np\nimport pandas as pd\nimport dicom"
},
{
"path": "pylung/__init__.py",
"chars": 109,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n"
},
{
"path": "pylung/annotation.py",
"chars": 8608,
"preview": "import cPickle\nimport logging\nimport os\nimport xml.etree.ElementTree as etree\nimport numpy as np\n\nfrom nodule_structs im"
},
{
"path": "pylung/base.py",
"chars": 5872,
"preview": "# coding: utf-8\n# pylint: disable=invalid-name, no-member\n\"\"\" ctypes library of pylung and helper functions \"\"\"\nfrom __f"
},
{
"path": "pylung/detector.py",
"chars": 1911,
"preview": "import logging\n\nimport dicom\nimport numpy as np\nfrom pylung.filters.parenchyma import filter_by_parenchyma\nfrom pylung.f"
},
{
"path": "pylung/filters/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "pylung/filters/filter.py",
"chars": 466,
"preview": "class CandidateFilter(object):\n def __init__(self, image):\n pass\n\n def _filter(self, x, y, h, w):\n r"
},
{
"path": "pylung/filters/parenchyma.py",
"chars": 1445,
"preview": "import cv2\nimport numpy as np\n\nfrom pylung.filters.filter import CandidateFilter\nfrom pylung.utils import floodfill\nfrom"
},
{
"path": "pylung/filters/vgg.py",
"chars": 933,
"preview": "import numpy as np\n\nfrom pylung.filters.filter import CandidateFilter\nfrom pylung.utils import dcm_to_gray, gray_to_rgb\n"
},
{
"path": "pylung/libinfo.py",
"chars": 1663,
"preview": "# coding: utf-8\n\"\"\"Information about pylung.\"\"\"\nfrom __future__ import absolute_import\nimport os\nimport platform\n\n\ndef f"
},
{
"path": "pylung/models/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "pylung/models/chest_detect.py",
"chars": 7739,
"preview": "import cStringIO\nimport os\n\nimport cv2\nimport dicom\nimport numpy as np\nfrom PIL import Image\nfrom matplotlib import pypl"
},
{
"path": "pylung/models/test.py",
"chars": 14565,
"preview": "# Math (can still use '//' if want integer output; this library gives floats)\nfrom __future__ import division\n\n# General"
},
{
"path": "pylung/models/train.py",
"chars": 1068,
"preview": "import csv\nimport os\n\nimport easytf\nimport tensorflow as tf\nfrom pylung.models import vgg_classifier\nfrom pylung.utils i"
},
{
"path": "pylung/models/vgg_classifier.py",
"chars": 3168,
"preview": "import os\n\nimport numpy as np\nimport scipy.io\nimport tensorflow as tf\nimport tflearn\n\n_data = None\n\n\ndef load_mat():\n "
},
{
"path": "pylung/nodule_structs.py",
"chars": 8377,
"preview": "\"\"\"\nModified from https://github.com/tizita-nesibu/lidc-idri-visualization\n\"\"\"\n\n\nclass NoduleCharstics:\n def __init__"
},
{
"path": "pylung/tests/a.csv",
"chars": 19721,
"preview": "./else/LIDC-IDRI-0050/,dcm_8_split_149_p.jpg,0\r\n./output/LIDC-IDRI-0011/,dcm_25_split_4_a.jpg,1\r\n./else/LIDC-IDRI-0050/,"
},
{
"path": "pylung/tests/clean.sh",
"chars": 41,
"preview": "#!/usr/bin/env bash\n\nrm -r tmp\nmkdir tmp\n"
},
{
"path": "pylung/tests/demo.py",
"chars": 3017,
"preview": "#prepare test.csv: 1nodule 1nomark 1nodule……\n\nimport sys\nimport os\nimport argparse\nimport numpy as np\nimport csv\nimport "
},
{
"path": "pylung/tests/main.py",
"chars": 15091,
"preview": "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nimport os\nimport sys\nimport argparse\nimport pickle\nimport csv\nimport dicom"
},
{
"path": "pylung/tests/predict.py",
"chars": 2054,
"preview": "import sys\nimport os\nimport numpy as np\nimport csv\nfrom scipy.misc import imread,imsave\n\ncurdir = os.path.dirname(__file"
},
{
"path": "pylung/tests/run.sh",
"chars": 685,
"preview": "#!/bin/sh\npython demo.py -p \"./output/LIDC-IDRI-0050/\"\npython demo.py -p \"./output/LIDC-IDRI-0150/\"\npython demo.py -p \"."
},
{
"path": "pylung/tests/test.csv",
"chars": 96280,
"preview": "./else/LIDC-IDRI-0050/,dcm_5_split_154_p.jpg,0\r\n./output/LIDC-IDRI-0051/,dcm_7_split_0_a.jpg,1\r\n./else/LIDC-IDRI-0050/,d"
},
{
"path": "pylung/tests/test_detect.py",
"chars": 6254,
"preview": "import os\nimport sys\nimport numpy as np\nimport dicom\nimport scipy.misc\nimport matplotlib.pyplot as plt\n\ncurdir = os.path"
},
{
"path": "pylung/tests/tmp.py",
"chars": 2814,
"preview": "def annotation_cut(dcm_file_path, save_file_path):\n '''\n :param dcm_file_path:\n :param save_file_path:\n :ret"
},
{
"path": "pylung/tests/train.py",
"chars": 1758,
"preview": "import sys\nimport os\nimport numpy as np\nimport csv\nfrom scipy.misc import imread,imsave\n\ncurdir = os.path.dirname(__file"
},
{
"path": "pylung/utils.py",
"chars": 2609,
"preview": "import cv2\nimport numpy as np\nimport os\nfrom matplotlib import pyplot as plt\n\n\ndef floodfill(image, start_point, value):"
}
]
// ... and 5 more files (download for full content)
About this extraction
This page contains the full source code of the zhwhong/lidc_nodule_detection GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 114 files (29.5 MB), approximately 5.0M tokens, and a symbol index with 492 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.