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EdgeDrawing Improvements 1
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‎modules/ximgproc/include/opencv2/ximgproc.hpp

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@defgroup ximgproc_edge_drawing EdgeDrawing
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EDGE DRAWING LIBRARY FOR GEOMETRIC FEATURE EXTRACTION AND VALIDATION
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Edge Drawing (ED) algorithm is an proactive approach on edge detection problem. In contrast to many other existing edge detection algorithms which follow a subtractive
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approach (i.e. after applying gradient filters onto an image eliminating pixels w.r.t. several rules, e.g. non-maximal suppression and hysteresis in Canny), ED algorithm
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works via an additive strategy, i.e. it picks edge pixels one by one, hence the name Edge Drawing. Then we process those random shaped edge segments to extract higher level
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edge features, i.e. lines, circles, ellipses, etc. The popular method of extraction edge pixels from the thresholded gradient magnitudes is non-maximal supressiun that tests
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every pixel whether it has the maximum gradient response along its gradient direction and eliminates if it does not. However, this method does not check status of the
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neighboring pixels, and therefore might result low quality (in terms of edge continuity, smoothness, thinness, localization) edge segments. Instead of non-maximal supression,
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ED points a set of edge pixels and join them by maximizing the total gradient response of edge segments. Therefore it can extract high quality edge segments without need for
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an additional hysteresis step.
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@defgroup ximgproc_fourier Fourier descriptors
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@}
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*/

‎modules/ximgproc/include/opencv2/ximgproc/edge_drawing.hpp

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//! @{
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/** @brief Class implementing the ED (EdgeDrawing) @cite topal2012edge, EDLines @cite akinlar2011edlines, EDPF @cite akinlar2012edpf and EDCircles @cite akinlar2013edcircles algorithms
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EDGE DRAWING LIBRARY FOR GEOMETRIC FEATURE EXTRACTION AND VALIDATION
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Edge Drawing (ED) algorithm is an proactive approach on edge detection problem. In contrast to many other existing edge detection algorithms which follow a subtractive
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approach (i.e. after applying gradient filters onto an image eliminating pixels w.r.t. several rules, e.g. non-maximal suppression and hysteresis in Canny), ED algorithm
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works via an additive strategy, i.e. it picks edge pixels one by one, hence the name Edge Drawing. Then we process those random shaped edge segments to extract higher level
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edge features, i.e. lines, circles, ellipses, etc. The popular method of extraction edge pixels from the thresholded gradient magnitudes is non-maximal supressiun that tests
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every pixel whether it has the maximum gradient response along its gradient direction and eliminates if it does not. However, this method does not check status of the
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neighboring pixels, and therefore might result low quality (in terms of edge continuity, smoothness, thinness, localization) edge segments. Instead of non-maximal supression,
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ED points a set of edge pixels and join them by maximizing the total gradient response of edge segments. Therefore it can extract high quality edge segments without need for
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an additional hysteresis step.
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*/
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class CV_EXPORTS_W EdgeDrawing : public Algorithm

‎modules/ximgproc/perf/perf_edge_drawing.cpp

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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level
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// directory of this distribution and at http://opencv.org/license.html.
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#include "perf_precomp.hpp"
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namespace opencv_test
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{
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namespace
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{
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/* 1. Define parameter type and test fixture */
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typedef tuple<int, int> EDTestParam;
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typedef TestBaseWithParam<EDTestParam> EdgeDrawing_Detect_Edges;
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typedef TestBaseWithParam<EDTestParam> EdgeDrawing_Detect_Lines;
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typedef TestBaseWithParam<EDTestParam> EdgeDrawing_Detect_Ellipses;
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/* 2. Declare the testsuite */
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PERF_TEST_P(EdgeDrawing_Detect_Edges, perf,
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Combine(Values(0, 1, 2, 3), Values(10, 20, 30, 50, 80, 100, 120, 150)))
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{
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/* 3. Get actual test parameters */
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EDTestParam params = GetParam();
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int EdgeDetectionOperator = get<0>(params);
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int GradientThresholdValue = get<1>(params);
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/* 4. Allocate and initialize arguments for tested function */
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std::string filename = getDataPath("perf/1680x1050.png");
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Mat src = imread(filename, 0);
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/* 5. Manifest your expectations about this test */
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declare.in(src);
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/* 6. Collect the samples! */
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PERF_SAMPLE_BEGIN();
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Ptr<ximgproc::EdgeDrawing> ed = ximgproc::createEdgeDrawing();
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ed->params.EdgeDetectionOperator = EdgeDetectionOperator;
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ed->params.GradientThresholdValue = GradientThresholdValue;
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ed->detectEdges(src);
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PERF_SAMPLE_END();
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/* 7. Do not check anything */
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SANITY_CHECK_NOTHING();
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}
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/* 2. Declare the testsuite */
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PERF_TEST_P(EdgeDrawing_Detect_Lines, perf,
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Combine(Values(0, 1, 2, 3), Values(10, 20, 30, 50, 80, 100, 120, 150)))
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{
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/* 3. Get actual test parameters */
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EDTestParam params = GetParam();
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int EdgeDetectionOperator = get<0>(params);
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int GradientThresholdValue = get<1>(params);
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/* 4. Allocate and initialize arguments for tested function */
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std::string filename = getDataPath("perf/1680x1050.png");
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Mat src = imread(filename, 0);
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/* 5. Manifest your expectations about this test */
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declare.in(src);
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/* 6. Collect the samples! */
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PERF_SAMPLE_BEGIN();
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Ptr<ximgproc::EdgeDrawing> ed = ximgproc::createEdgeDrawing();
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ed->params.EdgeDetectionOperator = EdgeDetectionOperator;
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ed->params.GradientThresholdValue = GradientThresholdValue;
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ed->detectEdges(src);
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std::vector<Vec4f> lines;
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ed->detectLines(lines);
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PERF_SAMPLE_END();
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/* 7. Do not check anything */
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SANITY_CHECK_NOTHING();
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}
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/* 2. Declare the testsuite */
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PERF_TEST_P(EdgeDrawing_Detect_Ellipses, perf,
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Combine(Values(0, 1, 2, 3), Values(10, 20, 30, 50, 80, 100, 120, 150)))
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{
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/* 3. Get actual test parameters */
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EDTestParam params = GetParam();
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int EdgeDetectionOperator = get<0>(params);
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int GradientThresholdValue = get<1>(params);
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/* 4. Allocate and initialize arguments for tested function */
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std::string filename = getDataPath("perf/1680x1050.png");
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Mat src = imread(filename, 0);
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/* 5. Manifest your expectations about this test */
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declare.in(src);
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/* 6. Collect the samples! */
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PERF_SAMPLE_BEGIN();
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Ptr<ximgproc::EdgeDrawing> ed = ximgproc::createEdgeDrawing();
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ed->params.EdgeDetectionOperator = EdgeDetectionOperator;
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ed->params.GradientThresholdValue = GradientThresholdValue;
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ed->detectEdges(src);
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vector<Vec6d> ellipses;
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ed->detectEllipses(ellipses);
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PERF_SAMPLE_END();
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/* 7. Do not check anything */
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SANITY_CHECK_NOTHING();
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}
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} // namespace
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} // namespace opencv_test

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