dnn multiple images

mulim.cpp

#include <iostream>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;


using namespace cv;
using namespace std;
int main(int argc, char** argv) {
    string folder = "c:/data/dnn/tcnn/";
    // small 5 pt facial keypoint model from gil levy
    dnn::Net net = dnn::readNet(folder + "vanilla_deplay.prototxt", folder + "vanillaCNN.caffemodel");

    string imp = "C:\\data\\faces\\lfw40_crop\\Abdullah_Gul_0002.jpg";
    Mat img = imread(imp);
    cout << img.size() << endl;

    auto run = [&](const Mat &blob) {
        net.setInput(blob);
        Mat out = net.forward();
        cout << out.size << endl;
        cout << out << endl;
    };

    run(dnn::blobFromImage(img,1,Size(40,40)));

    vector<Mat> vec(10,img);
    run(dnn::blobFromImages(vec,1,Size(40,40)));

    return 0;
}

/*
[96 x 96]
1 x 10
[-0.18263823, -0.2103174, 0.12870482, -0.15074131, -0.055463284, 0.042840961, -0.13968539, 0.22529338, 0.11189944, 0.26449415]
10 x 10
[-0.18263823, -0.2103174, 0.12870482, -0.15074131, -0.055463284, 0.042840961, -0.13968539, 0.22529338, 0.11189944, 0.26449415;
 -0.18263823, -0.2103174, 0.12870482, -0.15074131, -0.055463284, 0.042840961, -0.13968539, 0.22529338, 0.11189944, 0.26449415;
 -0.18263823, -0.2103174, 0.12870482, -0.15074131, -0.055463284, 0.042840961, -0.13968539, 0.22529338, 0.11189944, 0.26449415;
 -0.18263823, -0.2103174, 0.12870482, -0.15074131, -0.055463284, 0.042840961, -0.13968539, 0.22529338, 0.11189944, 0.26449415;
 -0.18263823, -0.2103174, 0.12870482, -0.15074131, -0.055463284, 0.042840961, -0.13968539, 0.22529338, 0.11189944, 0.26449415;
 -0.18263823, -0.2103174, 0.12870482, -0.15074131, -0.055463284, 0.042840961, -0.13968539, 0.22529338, 0.11189944, 0.26449415;
 -0.18263823, -0.2103174, 0.12870482, -0.15074131, -0.055463284, 0.042840961, -0.13968539, 0.22529338, 0.11189944, 0.26449415;
 -0.18263823, -0.2103174, 0.12870482, -0.15074131, -0.055463284, 0.042840961, -0.13968539, 0.22529338, 0.11189944, 0.26449415;
 -0.18263823, -0.2103174, 0.12870482, -0.15074131, -0.055463284, 0.042840961, -0.13968539, 0.22529338, 0.11189944, 0.26449415;
 -0.18263823, -0.2103174, 0.12870482, -0.15074131, -0.055463284, 0.042840961, -0.13968539, 0.22529338, 0.11189944, 0.26449415]

*/