Function main

dependency/densecrf/examples/my_dense_inference.cpp:106–207

}

int main( int argc, char* argv[]){
    if (argc<5){
        printf("Usage: %s param image probability output\n", argv[0] );
        return 1;
    }
    float w1, alpha, beta, w2, gamma;
    int iter;
    // read parameters from file
    ifstream paramfile(argv[1]);
    if(paramfile.is_open())
    {
        paramfile>>w1>>alpha>>beta>>w2>>gamma>>iter;
        paramfile.close();
    }
    else{
        cout<<"unable to open param file: "<<argv[1]<<endl;
        return 0;
    }
    cout<<"params"<<endl;
    cout<<"w1    "<<w1   <<endl;
    cout<<"alpha "<<alpha<<endl;
    cout<<"beta  "<<beta <<endl;
    cout<<"w2    "<<w2   <<endl;
    cout<<"gamma "<<gamma<<endl;
    cout<<"iter  "<<iter<<endl;
    
    // Number of labels
    const int M = 2;
    // Load the color image and some crude annotations (which are used in a simple classifier)
    int W, H, GW, GH;
    unsigned char * im = readPNG(argv[2], W, H, LCT_RGB);
    if (!im){
        printf("Failed to load image!\n");
        return 1;
    }
    unsigned char * anno = readPNG( argv[3], GW, GH, LCT_GREY );
    if (!anno){
        printf("Failed to load annotations!\n");
        return 1;
    }
    if (W!=GW || H!=GH){
        printf("Annotation size doesn't match image!\n");
        return 1;
    }

    /////////// Put your own unary classifier here! ///////////
//    MatrixXf unary = computeUnary( getLabeling( anno, W*H, M ), M );
    MatrixXf unary = computeUnary(anno,W, H);
    ///////////////////////////////////////////////////////////
    
    // Setup the CRF model
    DenseCRF2D crf(W, H, M);
    // Specify the unary potential as an array of size W*H*(#classes)
    // packing order: x0y0l0 x0y0l1 x0y0l2 .. x1y0l0 x1y0l1 ...
    crf.setUnaryEnergy( unary );

    // add a color dependent term (feature = xyrgb)
    // x_stddev = 60