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Object detection using deep learning with OpenCV and Python
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
pip install numpy opencv-python
Note: Compatability with Python 2.x is not officially tested.
YOLO (You Only Look Once)
Download the pre-trained YOLO v3 weights file from this link 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
Provided all the files are in the current directory, below command will apply object detection on the input image dog.jpg.
$ python yolo_opencv.py --image dog.jpg --config yolov3.cfg --weights yolov3.weights --classes yolov3.txt
Command format
$ python yolo_opencv.py --image /path/to/input/image --config /path/to/config/file --weights /path/to/weights/file --classes /path/to/classes/file
Checkout the blog post to learn more.
sample output :
Checkout the object detection implementation available in cvlib which enables detecting common objects in the context through a single function call detect_common_objects().
(SSD and Faster R-CNN examples will be added soon)