44 lines
1.6 KiB
Markdown
44 lines
1.6 KiB
Markdown
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# Object detection using deep learning with OpenCV and Python
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OpenCV `dnn` module supports running inference on pre-trained deep learning models from popular frameworks like Caffe, Torch and TensorFlow.
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When it comes to object detection, popular detection frameworks are
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* YOLO
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* SSD
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* Faster R-CNN
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Support for running YOLO/DarkNet has been added to OpenCV dnn module recently.
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## Dependencies
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* opencv
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* numpy
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`pip install numpy opencv-python`
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**Note: Compatability with Python 2.x is not officially tested.**
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## YOLO (You Only Look Once)
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Download the pre-trained YOLO v3 weights file from this [link](https://pjreddie.com/media/files/yolov3.weights) and place it in the current directory or you can directly download to the current directory in terminal using
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`$ wget https://pjreddie.com/media/files/yolov3.weights`
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Provided all the files are in the current directory, below command will apply object detection on the input image `dog.jpg`.
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`$ python yolo_opencv.py --image dog.jpg --config yolov3.cfg --weights yolov3.weights --classes yolov3.txt`
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**Command format**
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_$ python yolo_opencv.py --image /path/to/input/image --config /path/to/config/file --weights /path/to/weights/file --classes /path/to/classes/file_
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Checkout the [blog post](http://www.arunponnusamy.com/yolo-object-detection-opencv-python.html) to learn more.
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### sample output :
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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()`.
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(_SSD and Faster R-CNN examples will be added soon_)
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