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

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

Source Code

@includelineno ximgproc/samples/structured_edge_detection.cpp

Explanation

-# Load source color image @code{.cpp} cv::Mat image = cv::imread(inFilename, 1); if ( image.empty() ) { printf("Cannot read image file: %s\n", inFilename.c_str()); return -1; } @endcode

-# Convert source image to [0;1] range @code{.cpp} image.convertTo(image, cv::DataType::type, 1/255.0); @endcode

-# Run main algorithm @code{.cpp} cv::Mat edges(image.size(), image.type());

cv::Ptr<StructuredEdgeDetection> pDollar =
    cv::createStructuredEdgeDetection(modelFilename);
pDollar->detectEdges(image, edges);
@endcode

-# Show results @code{.cpp} if ( outFilename == "" ) { cv::namedWindow("edges", 1); cv::imshow("edges", edges);

    cv::waitKey(0);
}
else
    cv::imwrite(outFilename, 255*edges);
@endcode

Literature

For more information, refer to the following papers : @cite Dollar2013 @cite Lim2013

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