はじめに

このチュートリアルでは、画像中のエッジ検出を目的として、structured forests を使用する方法を学ぶ。

例

image

覚え書き: Canny エッジ検出器のような2値化手法は、両方のアルゴリズム(Sobel および StructuredEdgeDetection::detectEdges)が生成したエッジに適用できる。

ソースコード

/**************************************************************************************
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;
}

解説

ソースのカラー画像を読み込む

Mat image = imread(inFilename, IMREAD_COLOR);

if ( image.empty() )

CV_Error(Error::StsError, String("Cannot read image file: ") + inFilename);
ソース画像を float の [0;1] 範囲に変換する

image.convertTo(image, DataType<float>::type, 1/255.0);
メインアルゴリズムを実行する

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);
結果を表示する

if ( outFilename.size() == 0 )

{

imshow("edges", edges);

imshow("edges nms", edge_nms);

waitKey(0);

}

else

imwrite(outFilename, 255*edges);

参考文献

詳細は次の論文を参照のこと : [74] [174]