I have a Cv::Mat calle Prob
prob = deepLearningModel.forward();
This mat have 4 dimensions , and the size of each dimensions are tipically 1-1-nDetections-detectionsParameter
So I want is a 2D Mat based on the last dimensions , I mean nDetections*detectionsPameteter How can I achieve it??
Thanks.
those 4d Mat's used in cv::dnn are: [nimages, nchannels, H, W]
if your blob is a 3 channel image (with seperate color planes), [1,3,24,24] you would access e.g. the 2nd channel like:
Mat green(24,24,CV_32F, blob.ptr<float>(0,1)); // image 0, channel 1
if your prob blob came from a prediction (1 of N classes), you can simply reshape it :
Mat flat = prob.reshape(1,1); //a single row, with nDetections columns.
Point maxLoc;
minMaxLoc(flat, 0,0,0,&maxLoc);
int predicted = maxLoc.x;
if it was from a detection (N detections a 7 numbers)
// from the ssd_mobilenet_object_detection sample
Mat detectionMat(prob.size[2], prob.size[3], CV_32F, prob.ptr<float>());
In my case is not a nDetections columns. my model is a object detection, so it classifies and also give the bounding box. When applying reshape for example this are the results.
prob.dims = 4
prob.size = [1 X 1 X 3 X 7]
ProbMat [0, 9, 0.99742222, 0.69545317, 0.42244324, 0.99069548, 0.98882437, 0, 9, 0.86294723, 0.82298559, 0.23074003, 0.98194772, 0.48593336, 0, 15, 0.99996269, 0.18963903, 0.15723693, 0.8624571, 0.9951545]
So I want to obtain a (3,7) Mat.
Asked: 2017-10-24 02:34:54 -0600
Seen: 4,303 times
Last updated: Oct 24 '17
