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 structered edge 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 "opencv2/core/utility.hpp"
#include <iostream>

using namespace cv;
using namespace cv::ximgproc;

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

int main( int argc, const char** argv )
{
    bool printHelp = ( argc == 1 );
    printHelp = printHelp || ( argc == 2 && String(argv[1]) == "--help" );
    printHelp = printHelp || ( argc == 2 && String(argv[1]) == "-h" );

    if ( printHelp )
    {
        std::cout << "\nThis sample demonstrates structured forests for fast edge detection\n"
               "Call:\n"
               "    structured_edge_detection -i=in_image_name -m=model_name [-o=out_image_name]\n\n";
        return 0;
    }

    CommandLineParser parser(argc, argv, keys);
    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, 1);
    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);

    Mat edges(image.size(), image.type());

    Ptr<StructuredEdgeDetection> pDollar =
        createStructuredEdgeDetection(modelFilename);
    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);

    if ( outFilename.size() == 0 )
    {
        namedWindow("edges", 1);
        imshow("edges", edges);
        namedWindow("edges nms", 1);
        imshow("edges nms", edge_nms);
        waitKey(0);
    }
    else
        imwrite(outFilename, 255*edges);

    return 0;
}

Explanation

1. Load source color image

   cv::Mat image = cv::imread(inFilename, 1);
   if ( image.empty() )
   {
       printf("Cannot read image file: %s\n", inFilename.c_str());
       return -1;
   }

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

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

3. Run main algorithm

   cv::Mat edges(image.size(), image.type());
   
   cv::Ptr<StructuredEdgeDetection> pDollar =
       cv::createStructuredEdgeDetection(modelFilename);
   pDollar->detectEdges(image, edges);

4. Show results

   if ( outFilename == "" )
   {
       cv::namedWindow("edges", 1);
       cv::imshow("edges", edges);
   
       cv::waitKey(0);
   }
   else
       cv::imwrite(outFilename, 255*edges);

Literature

For more information, refer to the following papers : [46] [121]