Structured forests for fast edge detection

Introduction

In this tutorial you will learn how to use structured forests for the purpose of edge detection in an image.

Examples

image

image

image

image

image

image

image

image

image

image

image

image

Note

binarization techniques like Canny edge detector are applicable to edges produced by both algorithms (Sobel and StructuredEdgeDetection::detectEdges).

Source Code

/**************************************************************************************
The structured forests for fast edge detection demo requires you to provide a model.
This model can be found at the opencv_extra repository on Github on the following link:
https://github.com/opencv/opencv_extra/blob/master/testdata/cv/ximgproc/model.yml.gz
***************************************************************************************/

#include <opencv2/ximgproc.hpp>
#include "opencv2/highgui.hpp"
#include <iostream>

using namespace cv;
using namespace cv::ximgproc;

const char* keys =
{
    "{i || input image file name}"
    "{m || model file name}"
    "{o || output image file name}"
};

int main( int argc, const char** argv )
{
    CommandLineParser parser(argc, argv, keys);
    parser.about("This sample demonstrates usage of structured forests for fast edge detection");
    parser.printMessage();

    if ( !parser.check() )
    {
        parser.printErrors();
        return -1;
    }

    String modelFilename = parser.get<String>("m");
    String inFilename = parser.get<String>("i");
    String outFilename = parser.get<String>("o");

    Mat image = imread(inFilename, IMREAD_COLOR);
    if ( image.empty() )
        CV_Error(Error::StsError, String("Cannot read image file: ") + inFilename);

    if ( modelFilename.size() == 0)
        CV_Error(Error::StsError, String("Empty model name"));

    image.convertTo(image, DataType<float>::type, 1/255.0);

    TickMeter tm;
    tm.start();
    Ptr<StructuredEdgeDetection> pDollar =
        createStructuredEdgeDetection(modelFilename);

    tm.stop();
    std::cout << "createStructuredEdgeDetection() time : " << tm << std::endl;

    tm.reset();
    tm.start();
    Mat edges;
    pDollar->detectEdges(image, edges);
    tm.stop();
    std::cout << "detectEdges() time : " << tm << std::endl;

    tm.reset();
    tm.start();
    // computes orientation from edge map
    Mat orientation_map;
    pDollar->computeOrientation(edges, orientation_map);

    // suppress edges
    Mat edge_nms;
    pDollar->edgesNms(edges, orientation_map, edge_nms, 2, 0, 1, true);

    tm.stop();
    std::cout << "nms time : " << tm << std::endl;

    if ( outFilename.size() == 0 )
    {
        imshow("edges", edges);
        imshow("edges nms", edge_nms);
        waitKey(0);
    }
    else
        imwrite(outFilename, 255*edges);

    return 0;
}

Explanation

1. Load source color image

       Mat image = imread(inFilename, IMREAD_COLOR);
       if ( image.empty() )
           CV_Error(Error::StsError, String("Cannot read image file: ") + inFilename);

2. Convert source image to float [0;1] range

       image.convertTo(image, DataType<float>::type, 1/255.0);

3. Run main algorithm

       Ptr<StructuredEdgeDetection> pDollar =
           createStructuredEdgeDetection(modelFilename);

       Mat edges;
       pDollar->detectEdges(image, edges);

       // computes orientation from edge map
       Mat orientation_map;
       pDollar->computeOrientation(edges, orientation_map);
   
       // suppress edges
       Mat edge_nms;
       pDollar->edgesNms(edges, orientation_map, edge_nms, 2, 0, 1, true);

4. Show results

       if ( outFilename.size() == 0 )
       {
           imshow("edges", edges);
           imshow("edges nms", edge_nms);
           waitKey(0);
       }
       else
           imwrite(outFilename, 255*edges);

   Literature

For more information, refer to the following papers : [59] [153]