Opencv 4 DNN, People detection CPU performance with yolo 2 tiny model I am still working on tutorial how to simply run yolo and others model in opencv 4. I try to do as simple as possible. Not like a general sample in Opencv. Differently, simply, and described as much as possible. Some technical specifications video Testing opencv 4.0 DNN with yolo tiny 2 model on people detection in a mall. Pure CPU, I7 (4 cores), running by the following command under windows 10. Performance for CPU without 2 much optimization effort is 500 ms per image approximately on my configuration. Let me know if there is some problem with the parameters. testOpecv . exe --config= C :\darknet-master\cfg\yolov2- tiny . cfg --model= C :\darknet-master\weights\yolov2- tiny . weights --classes= C :\opencv32\darknet-master\data\ coco . names --width= 616 --height= 616 --scale= 0.00192 --rgb Sources: Thank you for a great job https://opencv.org/ https:
The blog is full of OpenCV source code, tutorials, tips, tricks, and from basics to advanced streaming and video processing. The code examples are from C++. Most of the tutorials are dedicated to basics C++ OpenCV image processing, people detection from LBP haar cascades to modern deep learning. The tutorials are as well dealing with GStreamer OpenCV integration to be able to stream OpenCV output as a video stream to the web.