89 lines
1.8 KiB
Markdown
89 lines
1.8 KiB
Markdown
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Structured forests for fast edge detection {#tutorial_ximgproc_prediction}
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==========================================
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Introduction
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In this tutorial you will learn how to use structured forests for the purpose of edge detection in
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an image.
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Examples
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--------
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@note binarization techniques like Canny edge detector are applicable to edges produced by both
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algorithms (Sobel and StructuredEdgeDetection::detectEdges).
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Source Code
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-----------
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@includelineno ximgproc/samples/structured_edge_detection.cpp
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Explanation
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-----------
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-# **Load source color image**
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@code{.cpp}
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cv::Mat image = cv::imread(inFilename, 1);
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if ( image.empty() )
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{
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printf("Cannot read image file: %s\n", inFilename.c_str());
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return -1;
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}
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@endcode
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-# **Convert source image to [0;1] range**
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@code{.cpp}
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image.convertTo(image, cv::DataType<float>::type, 1/255.0);
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@endcode
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-# **Run main algorithm**
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@code{.cpp}
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cv::Mat edges(image.size(), image.type());
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cv::Ptr<StructuredEdgeDetection> pDollar =
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cv::createStructuredEdgeDetection(modelFilename);
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pDollar->detectEdges(image, edges);
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@endcode
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-# **Show results**
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@code{.cpp}
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if ( outFilename == "" )
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{
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cv::namedWindow("edges", 1);
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cv::imshow("edges", edges);
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cv::waitKey(0);
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}
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else
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cv::imwrite(outFilename, 255*edges);
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@endcode
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Literature
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----------
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For more information, refer to the following papers : @cite Dollar2013 @cite Lim2013
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