yolo-python-rtsp:通过实时流协议 (RTSP) 使用 Yolo、OpenCV 和 Python 的深度学习进行对...

# Object Detection with Yolo, OpenCV and Python via Real Time Streaming Protocol (RTSP) Object detection using deep learning with Yolo, OpenCV and Python via Real Time Streaming Protocol (`RTSP`) Recognized objects are stored in date seperated in folders per class for further training or face recognition. OpenCV `dnn` module supports running inference on pre-trained deep learning models from popular frameworks like Caffe, Torch and TensorFlow. When it comes to object detection, popular detection frameworks are * YOLO * SSD * Faster R-CNN Support for running YOLO/DarkNet has been added to OpenCV dnn module recently. ## Dependencies * opencv * numpy * imageio-ffmpeg `pip install numpy opencv-python imageio-ffmpeg` **Note: Python 2.x is not supported** ## YOLO (You Only Look Once) Download the pre-trained YOLO v3 weights file from this [link](https://pjreddie.com/media/files/yolov3.weights) or for tiny weights for slower machines [link](https://pjreddie.com/media/files/yolov3-tiny.weights) and place it in the current directory or you can directly download to the current directory in terminal using `$ wget https://pjreddie.com/media/files/yolov3.weights` `$ wget https://pjreddie.com/media/files/yolov3-tiny.weights` Provided all the files are in the current directory, below command will apply object detection on the input video `commuters.mp4`. `$ python yolo_opencv.py --input sampledata/commuters.mp4 --config cfg/yolov3-tiny.cfg --weights yolov3-tiny.weights --classes cfg/yolov3.txt` For RTSP simply put the RTSP URL as --input `$ python yolo_opencv.py --input rtsp://xxxxx:1935/live/sys.stream --framestart 100 --framelimit 100 --config cfg/yolov3-tiny.cfg --weights yolov3-tiny.weights --classes cfg/yolov3.txt` **Arguments** | parameter | type | description | | --------- | ------- | ------------------------------------------------ | | `input` | String | /path/to/input/stream | | `outputfile` | String | /path/to/outputfile | | `outputdir` | String | /path/to/outputdir | | `framestart` | Int | start detecting at frame x (int) | | `framelimit` | Int | stop after x (int) frames and save the video in case of streams. 0 no limit | | `config` | String | /path/to/config/file | | `weights` | String | /path/to/weights/file | | `classes` | String | /path/to/classes/file | | `invertcolor` | Boolean | in case of BGR streams | | `fpsthrottle` | Int | in case of slower machines to keep up with a stream | ### sample output :  Checkout the object detection implementation available in [cvlib](http:cvlib.net) which enables detecting common objects in the context through a single function call `detect_common_objects()`. ## Credits This project is based on [Arun Ponnusamy's Object Detection OpenCV](https://github.com/arunponnusamy/object-detection-opencv) Sample video footage from [Videvo - Free Stock Video Footage](https://www.videvo.net/video/people-crossing-road-in-hong-kong-cbd/8162/)