基于 YOLOv5 模型的 AI 解决方案，用于检测戴口罩或不戴口罩的人

# Face-Mask-Detector [](https://github.com/rohandubey/Face-Mask-Detector/blob/master/LICENSE)   [](https://pypi.python.org/pypi/trains-jupyter-plugin/) <br></br> In the amidst of COVID-19, as there is no proper channel to effectively monitor people wearing mask and not. So to tackle this problem, I made an AI solution based on YOLOv5 model to detect those who are **not wearing mask**. This software is tested by Rajasthan Police (Govt. of India), and is designed to be installed alongside their CCTVs for efficient working. This software can **multi-stream** many sources together and collects data contionuously from those streams. Based on **YOLOv5** model and **RetinaNet Architecture**, having <br> | | | |---------- |------ | | Accuracy | 98% | | Mean Average Precision (val) | 54.98 | | FPS (max) | 300+ | _Model runs on on a variety of sources like images, videos, directory, glob, http/rtsp/rtmp streams._ ## Requirements Python 3.8 or later with all [requirements.txt](https://github.com/rohandubey/Face-Mask-Detector/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run: ```bash $ pip install -r requirements.txt ``` ## Inference **_Linux :_** execute : ```./run.sh```<br> **_Windows :_** execute : c```run.bat```<br> For more smoother inference visit [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) to verify for correct versions ## Working - When the program is initialized by running `./run.sh` or `run.bat`, we see the following GUI having 3 inputs namely, **Camera, Web Links, Videos/Images/Folders.** * * <img src="source/gui.png" width="700"> * **1. Camera**: Submit the Camera ID for any local cameras connected to your device. ( 0 for default camera)<br> * * <img src="source/camera.png" width="700"> * **2. Web Links**: Choose the link type : http/rtmp/rtsp and enter the IP address, if IP is password protected, then enter Username and password(if required)<br> * * <img src="source/links.png" width="700"> * **3. Images/Videos/Folders**: for any Videos/Images/Folder that contains Videos/Images, choose the path in Browse button and after choosing we can see progress of detection and time left.<br> * * <img src="source/video.png" width="700"> * * Progress : * * <img src="source/progress.png" width="700"> ## Detection Results For Videos/Images the output videos/images is stored in `imference/output` Post-Detection.<br> For Live streams from Webcam or WebStream (ex. `http://ip`), all images without mask are stored in `inference/data/<DATE>/<TIME>.png` format, so all the images detectedin a day are stored in one folder time-wise like : ``` $ tree --dirsfirst ├── inference\data\ │ ├── 21_Dec_20 (folder name = date) ├── 21_Dec_20 15_28_54.png (time is 15:28:54) └── 21_Dec_20 12_20_05.png ... │ ... │ ├── 22_Dec_20 ├── 22_Dec_20 05_08_04.png └── 22_Dec_20 02_00_55.png ``` ***Demo Pic form Live Stream*** <br> <img src="source/result_pic.png" width="700"> <br> [***Demo Video from YouTube*** <br>](https://drive.google.com/file/d/1DfX1nSEcgMyTUz4v5BD5aRJU7cykBWzn/view?usp=sharing) Click to open! ## Built With * YOLOv5 - Object Detection Algorithm * OpenCV - Video/Image Streaming * Tkinter - GUI toolkit for Python. * PyTorch - Model training/detection Framework. * Batch/Bash - Easy execution Programming languages for Windows/Linux respectively. * onnx - An ecosystem for interchanging different ML Model Frameworks. ## Training Results - Model is trained os NVIDA 940MX 2GB(used TensorFlow-GPU with CUDA). - I created and labelled my own dataset using [Label data Helper](https://github.com/rohandubey/Label-Data-Helper). - Added multi-threading to make detection smoother and faster. - Added GUI for easy support and analysis. - Converted raw pyton files to **batch** and **bash** script for direct usage.  ## Authors Made with ❤️ by Rohan Dubey ## References - [OpenCV](https://github.com/opencv/opencv) - [YOLOv5](https://github.com/ultralytics/yolov5) - [PyTorch](https://github.com/pytorch/pytorch)