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

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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 : [Dollar2013] [Lim2013]