deep-learning-knee's People
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setup
setup(
name='ouludeepknee',
version='0.1',
author=nima zarei
packages=find_packages(),
include_package_data=True,
license='LICENSE.txt',
long_description=open('README.md').read(),
)
Readme
About
The software in this branch implements an automatic pipeline for osteoarthritis severity assessment from plain radiographs.
It can be utilized via REST over HTTP or DICOM protocols. Furthermore, we have built a web UI that is easy to use.
analyse sistem
-- coding: utf-8 --
import glob
import argparse
import requests
import os
import base64
import cv2
import pandas as pd
import numpy as np
from tqdm import tqdm
def base64tonumpy(buffer):
binary = base64.b64decode(buffer)
img = cv2.imdecode(np.fromstring(binary, dtype=np.uint8), cv2.IMREAD_COLOR)
return img
if name == "main":
parser = argparse.ArgumentParser()
parser.add_argument('--deepknee_host', default='http://127.0.0.1', help='Host on which deepknee is running.')
parser.add_argument('--deepknee_port', type=int, default=5001, help='Port of deepknee.')
parser.add_argument('--img_dir', default='', help='Directory with images.')
parser.add_argument('--patient_level', type=bool, default=False,
help='Whether there image lies in a patient directory. '
'In this case, the script will know that the img_dir '
'contains folders that contain images.')
parser.add_argument('--save_results', default='/tmp/deepknee', help='Folder where to save the results.')
args = parser.parse_args()
url = f'{args.deepknee_host}:{args.deepknee_port}/deepknee/predict/bilateral'
os.makedirs(args.save_results, exist_ok=True)
output_csv = []
if not args.patient_level:
flist = glob.glob(os.path.join(args.img_dir, '*'))
else:
flist = glob.glob(os.path.join(args.img_dir, '*', '*'))
for idx, img_path in tqdm(enumerate(flist), total=len(flist)):
# Encoding the DICOM as base64 and sending the request to the server
with open(img_path, 'rb') as f:
data_base64 = base64.b64encode(f.read()).decode('ascii')
response = requests.post(url, json={'dicom': data_base64})
res = response.json()
# Parsing the response
result = {}
for knee in 'LR':
# You can also access the localized image, heatmaps and the probability maps
result[knee] = {'img': base64tonumpy(res[knee]['img']),
'hm': base64tonumpy(res[knee]['hm']),
'probs_bar': base64tonumpy(res[knee]['preds_bar']),
'kl': res[knee]['kl']}
output_csv.append({'File': img_path, 'Side': knee, 'KL': result[knee]['kl']})
cv2.imwrite(os.path.join(args.save_results, f'{idx}_{knee}_img.png'), result[knee]['img'])
cv2.imwrite(os.path.join(args.save_results, f'{idx}_{knee}_hm.png'), result[knee]['hm'])
cv2.imwrite(os.path.join(args.save_results, f'{idx}_{knee}_probs.png'), result[knee]['probs_bar'])
df = pd.DataFrame(data=output_csv)
df.to_csv(os.path.join(args.save_results, 'deepknee.csv'), index=None)
link to get files
Run deepknee
mkdir -p logs
KNEEL_ADDR=http://127.0.0.1:5000 python -m ouludeepknee.inference.app
--snapshots_path snapshots_knee_grading/
--device cpu --deploy True
--port 6001
--logs logs/deepknee-cpu.log
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