Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/foschmitz/yolo-python-rtsp
Object detection using deep learning with Yolo, OpenCV and Python via Real Time Streaming Protocol (RTSP)
https://github.com/foschmitz/yolo-python-rtsp
Last synced: 12 days ago
JSON representation
Object detection using deep learning with Yolo, OpenCV and Python via Real Time Streaming Protocol (RTSP)
- Host: GitHub
- URL: https://github.com/foschmitz/yolo-python-rtsp
- Owner: foschmitz
- License: mit
- Created: 2018-12-10T09:27:56.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-01-08T10:02:41.000Z (over 3 years ago)
- Last Synced: 2024-02-29T11:31:08.818Z (4 months ago)
- Language: Python
- Homepage:
- Size: 55.2 MB
- Stars: 126
- Watchers: 5
- Forks: 40
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-yolo-object-detection - foschmitz/yolo-python-rtsp - python-rtsp?style=social"/> : Object detection using deep learning with Yolo, OpenCV and Python via Real Time Streaming Protocol (RTSP). (Applications)
README
# 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/)