edge_detection.cpp

#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>

std::vector<Rect> boxes; std::vector<float> confidences; std::vector<int> classIds;


#include <opencv2/dnn/layer.details.hpp>

#include <iostream>

using namespace cv;
using namespace cv::dnn;


class CentralCrop : public Layer
{
public:
    static Ptr<Layer> create(LayerParams& params)
    {
        return Ptr<Layer>(new CentralCrop());
    }

    virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
                                 const int requiredOutputs,
                                 std::vector<std::vector<int> > &outputs,
                                 std::vector<std::vector<int> > &internals) const
    {
        CV_Assert(inputs.size() == 2);
        int batchSize   = inputs[0][0];
        int numChannels = inputs[0][1];
        int outH        = inputs[1][2];
        int outW        = inputs[1][3];
        outputs.resize(1, {batchSize, numChannels, outH, outW});
        return false;  // For not inplace layers
    }

    virtual void forward(InputArrayOfArrays inputs_vec,
                         OutputArrayOfArrays outputs_vec,
                         OutputArrayOfArrays internals)
    {
        std::vector<Mat> inputs, outputs;
        inputs_vec.getMatVector(inputs);
        outputs_vec.getMatVector(outputs);

        int outH = outputs[0].size[2];
        int outW = outputs[0].size[3];

        // Do central crop.
        int y = (inputs[0].size[2] - outH) / 2;
        int x = (inputs[0].size[3] - outW) / 2;

        Range ranges[] = {Range::all(), Range::all(),
                          Range(y, y + outH), Range(x, x + outW)};
        inputs[0](ranges).copyTo(outputs[0]);
    }

private:
    int xstart, xend, ystart, yend;
};

int main(int argc, char** argv)
{
    Net net = readNet("/home/dkurt/Downloads/hed_pretrained_bsds.ptorotxt",
                      "/home/dkurt/Downloads/hed_pretrained_bsds.caffemodel");
    net.setPreferableBackend(DNN_BACKEND_OPENCV);

    Mat img = imread("/home/dkurt/opencv_extra/testdata/viz/lena.png");
    Mat blob = blobFromImage(img, 1.0, Size(500, 500), Scalar(104.00698793, 116.66876762, 122.67891434));
    net.setInput(blob);

    CV_DNN_REGISTER_LAYER_CLASS(Crop, CentralCrop);

    Mat out = net.forward().reshape(1, 500);

    imshow("out", out);
    out.convertTo(img, CV_8UC1, 255);
    imwrite("central.jpg", img);
    waitKey();

    return 0;
}