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(),
)

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

https://drive.google.com/drive/folders/1mlc03lYLv2p9oMHW28wiXSQe4lPKgQSo

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

mahdeslami11 / deep-learning-knee Goto Github PK

deep-learning-knee's People

Contributors

Stargazers

Watchers

deep-learning-knee's Issues

setup

Readme

About

analyse sistem

-- coding: utf-8 --

link to get files

Run deepknee

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

Recommend Org

Facebook

Microsoft

Google

Alibaba

D3

Tencent