This is the third demo of the OpenCV Deep Neural Network (dnn) module in Processing with my latest CVImage library. In this version, I used the Darknet YOLO v3 pre-trained model for object detection. It is based on the object_detection sample from the latest OpenCV distribution. The configuration and weights model files for the COCO datasets are also available in the Darknet website. In the data folder of the Processing sketch, you will have the following 3 files:
This is the 2nd test of the OpenCV dnn module in Processing through my CVImage library. It used the OpenPose pre-trained Caffe model.
Since the OpenCV dnn module can read the Caffe model through the readNetFromCaffe() function, the demo sends the real time webcam image to the model for human pose detection. It made use of the configuration file openpose_pose_coco.prototxt and the saved model pose_iter_440000.caffemodel. The original reference of the demo is from the openpose.cpp official OpenCV sample and the Java implementation from the GitHub of berak. You can download the model details below
This is my first demo run of the dnn (deep neural network) module in OpenCV 3.4.2 with Processing, using my CVImage library. The module can input pre-trained models from Caffe, Tensorflow, Darknet, and Torch. In this example, I used the Tensorflow model Inception v2 SSD COCO from here. I also obtained the label map file from the Tensorflow GitHub. The following 3 files are in the data folder of the Processing sketch.
The source code is in my GitHub repository of this website here.