Background : It is always a daunting task with Tensorflow sessions and standard handling of a typical Tensorflow model when you want to run inference. However, if you are an experienced developer you may also quickly go through these steps because you are already aware about how to use Tensorflow to run inference on your model.
Object detection usin OPenCV’s dnn module a mobilenet model trianed on caffe.
Implementing simple facial recognition using Haarcascades and OpenCV
An implementation of optical flow tracker using lucas-k : calcOpticalFlowPyrLK() method in OpenCV.
implementing canny edge detection from OpenCV with noise reduction, intensity gradient of image, non-maxima suppression and hysterisis thresholding
Finding dominant color in an image with the help of quantization and eigne value.
Why the sudden need? I have mostly used the OpenCV library as available from pip or by installing wheel until I required some more essential features that are not available from those pre built binaries.
Compiling and executing C++ programs Browse to the directory of your cpp program and open a terminal in that folder.
Create a CMake file name CMakeLists.txt in the same directory as your project :