Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Multi Camera Live Object Tracking | 562 | a year ago | 22 | gpl-3.0 | Python | |||||
Multi-camera live traffic and object counting with YOLO v4, Deep SORT, and Flask. | ||||||||||
Gather Deployment | 345 | a year ago | mit | Jupyter Notebook | ||||||
Gathers scalable Tensorflow, Python infrastructure deployment and practices, 100% Docker. | ||||||||||
Ctai | 236 | 16 days ago | 2 | Python | ||||||
基于深度学习的肿瘤辅助诊断系统,以图像分割为核心,利用人工智能完成肿瘤区域的识别勾画并提供肿瘤区域的特征来辅助医生进行诊断。有完整的模型构建、后端架设和前端访问功能。 | ||||||||||
Hiitpi | 92 | 3 years ago | mit | Python | ||||||
A workout trainer Dash/Flask app that helps track your HIIT workouts by analyzing real-time video streaming from your sweet Pi using machine learning and Edge TPU.. | ||||||||||
Phormatics | 79 | 2 years ago | 6 | Jupyter Notebook | ||||||
Using A.I. and computer vision to build a virtual personal fitness trainer. (Most Startup-Viable Hack - HackNYU2018) | ||||||||||
Automatic_number_plate_recognition_yolo_ocr | 76 | a month ago | 4 | mit | Python | |||||
Automatic number plate recognition using tech: Yolo, OCR, Scene text detection, scene text recognation, flask, torch | ||||||||||
Live Stream Face Detection | 75 | a month ago | 2 | mit | Python | |||||
Live Streaming and Face Detection with Flask in Browser | ||||||||||
Ocr Tesseract Docker | 69 | a year ago | 5 | HTML | ||||||
OCR using Python, Tesseract and OpenCV in a Docker container | ||||||||||
Opencv Rest Api | 65 | a year ago | 1 | Python | ||||||
Learn to create a REST API microservice for extracting faces from images using OpenCV, OpenCV-python, Flask, Docker, and Heroku | ||||||||||
Flask_face_detection | 64 | a year ago | mit | Python | ||||||
Face detection example in Python 3 based on OpenCV and Flask |
Web application for real-time object detection on video streaming via web browser.
Watch the demo video.
Create and activate an virtual environment, as follows:
$ cd cloned/directory/
$ python -m venv env
$ env/Scripts/activate
After have installed and activated the environment, install all the dependencies:
$ pip install -r requirements.txt
After that, you can run the following command and access the application at 127.0.0.1:5000 on your browser.
$ python application.py
obs.: This application was tested only on Google Chrome.
To download the yolov3.weights
, just run:
$ cd models/
$ python dl-weights.py