173 lines
		
	
	
		
			6.1 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
			
		
		
	
	
			173 lines
		
	
	
		
			6.1 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
| /*M///////////////////////////////////////////////////////////////////////////////////////
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| //
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| //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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| //
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| //  By downloading, copying, installing or using the software you agree to this license.
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| //  If you do not agree to this license, do not download, install,
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| //  copy or use the software.
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| //
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| //
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| //                           License Agreement
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| //                For Open Source Computer Vision Library
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| //
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| // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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| // Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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| // Third party copyrights are property of their respective owners.
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| //
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| // Redistribution and use in source and binary forms, with or without modification,
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| // are permitted provided that the following conditions are met:
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| //
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| //   * Redistribution's of source code must retain the above copyright notice,
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| //     this list of conditions and the following disclaimer.
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| //
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| //   * Redistribution's in binary form must reproduce the above copyright notice,
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| //     this list of conditions and the following disclaimer in the documentation
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| //     and/or other materials provided with the distribution.
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| //
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| //   * The name of the copyright holders may not be used to endorse or promote products
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| //     derived from this software without specific prior written permission.
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| //
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| // This software is provided by the copyright holders and contributors "as is" and
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| // any express or implied warranties, including, but not limited to, the implied
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| // warranties of merchantability and fitness for a particular purpose are disclaimed.
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| // In no event shall the Intel Corporation or contributors be liable for any direct,
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| // indirect, incidental, special, exemplary, or consequential damages
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| // (including, but not limited to, procurement of substitute goods or services;
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| // loss of use, data, or profits; or business interruption) however caused
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| // and on any theory of liability, whether in contract, strict liability,
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| // or tort (including negligence or otherwise) arising in any way out of
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| // the use of this software, even if advised of the possibility of such damage.
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| //
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| //M*/
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| 
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| #include "test_precomp.hpp"
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| 
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| #include <string>
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| #include <iostream>
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| 
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| using namespace std;
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| using namespace cv;
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| 
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| class CV_GrabcutTest : public cvtest::BaseTest
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| {
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| public:
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|     CV_GrabcutTest();
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|     ~CV_GrabcutTest();
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| protected:
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|     bool verify(const Mat& mask, const Mat& exp);
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|     void run(int);
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| };
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| 
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| CV_GrabcutTest::CV_GrabcutTest() {}
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| CV_GrabcutTest::~CV_GrabcutTest() {}
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| 
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| bool CV_GrabcutTest::verify(const Mat& mask, const Mat& exp)
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| {
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|     const float maxDiffRatio = 0.005f;
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|     int expArea = countNonZero( exp );
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|     int nonIntersectArea = countNonZero( mask != exp );
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| 
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|     float curRatio = (float)nonIntersectArea / (float)expArea;
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|     ts->printf( cvtest::TS::LOG, "nonIntersectArea/expArea = %f\n", curRatio );
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|     return curRatio < maxDiffRatio;
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| }
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| 
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| void CV_GrabcutTest::run( int /* start_from */)
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| {
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|     cvtest::DefaultRngAuto defRng;
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| 
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|     Mat img = imread(string(ts->get_data_path()) + "shared/airplane.png");
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|     Mat mask_prob = imread(string(ts->get_data_path()) + "grabcut/mask_prob.png", 0);
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|     Mat exp_mask1 = imread(string(ts->get_data_path()) + "grabcut/exp_mask1.png", 0);
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|     Mat exp_mask2 = imread(string(ts->get_data_path()) + "grabcut/exp_mask2.png", 0);
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| 
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|     if (img.empty() || (!mask_prob.empty() && img.size() != mask_prob.size()) ||
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|                        (!exp_mask1.empty() && img.size() != exp_mask1.size()) ||
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|                        (!exp_mask2.empty() && img.size() != exp_mask2.size()) )
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|     {
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|          ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
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|          return;
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|     }
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| 
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|     Rect rect(Point(24, 126), Point(483, 294));
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|     Mat exp_bgdModel, exp_fgdModel;
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| 
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|     Mat mask;
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|     mask = Scalar(0);
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|     Mat bgdModel, fgdModel;
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|     grabCut( img, mask, rect, bgdModel, fgdModel, 0, GC_INIT_WITH_RECT );
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|     grabCut( img, mask, rect, bgdModel, fgdModel, 2, GC_EVAL );
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| 
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|     // Multiply images by 255 for more visuality of test data.
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|     if( mask_prob.empty() )
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|     {
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|         mask.copyTo( mask_prob );
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|         imwrite(string(ts->get_data_path()) + "grabcut/mask_prob.png", mask_prob);
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|     }
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|     if( exp_mask1.empty() )
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|     {
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|         exp_mask1 = (mask & 1) * 255;
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|         imwrite(string(ts->get_data_path()) + "grabcut/exp_mask1.png", exp_mask1);
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|     }
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| 
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|     if (!verify((mask & 1) * 255, exp_mask1))
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|     {
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|         ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
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|         return;
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|     }
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| 
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|     mask = mask_prob;
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|     bgdModel.release();
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|     fgdModel.release();
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|     rect = Rect();
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|     grabCut( img, mask, rect, bgdModel, fgdModel, 0, GC_INIT_WITH_MASK );
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|     grabCut( img, mask, rect, bgdModel, fgdModel, 1, GC_EVAL );
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| 
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|     if( exp_mask2.empty() )
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|     {
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|         exp_mask2 = (mask & 1) * 255;
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|         imwrite(string(ts->get_data_path()) + "grabcut/exp_mask2.png", exp_mask2);
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|     }
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| 
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|     if (!verify((mask & 1) * 255, exp_mask2))
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|     {
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|         ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
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|         return;
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|     }
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|     ts->set_failed_test_info(cvtest::TS::OK);
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| }
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| 
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| TEST(Imgproc_GrabCut, regression) { CV_GrabcutTest test; test.safe_run(); }
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| 
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| TEST(Imgproc_GrabCut, repeatability)
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| {
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|     cvtest::TS& ts = *cvtest::TS::ptr();
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| 
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|     Mat image_1 = imread(string(ts.get_data_path()) + "grabcut/image1652.ppm", IMREAD_COLOR);
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|     Mat mask_1 = imread(string(ts.get_data_path()) + "grabcut/mask1652.ppm", IMREAD_GRAYSCALE);
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|     Rect roi_1(0, 0, 150, 150);
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| 
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|     Mat image_2 = image_1.clone();
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|     Mat mask_2 = mask_1.clone();
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|     Rect roi_2 = roi_1;
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| 
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|     Mat image_3 = image_1.clone();
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|     Mat mask_3 = mask_1.clone();
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|     Rect roi_3 = roi_1;
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| 
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|     Mat bgdModel_1, fgdModel_1;
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|     Mat bgdModel_2, fgdModel_2;
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|     Mat bgdModel_3, fgdModel_3;
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| 
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|     theRNG().state = 12378213;
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|     grabCut(image_1, mask_1, roi_1, bgdModel_1, fgdModel_1, 1, GC_INIT_WITH_MASK);
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|     theRNG().state = 12378213;
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|     grabCut(image_2, mask_2, roi_2, bgdModel_2, fgdModel_2, 1, GC_INIT_WITH_MASK);
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|     theRNG().state = 12378213;
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|     grabCut(image_3, mask_3, roi_3, bgdModel_3, fgdModel_3, 1, GC_INIT_WITH_MASK);
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| 
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|     EXPECT_EQ(0, countNonZero(mask_1 != mask_2));
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|     EXPECT_EQ(0, countNonZero(mask_1 != mask_3));
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|     EXPECT_EQ(0, countNonZero(mask_2 != mask_3));
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| }
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