/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "test_precomp.hpp" namespace opencv_test { namespace { #undef RENDER_MSERS #define RENDER_MSERS 0 #if defined RENDER_MSERS && RENDER_MSERS static void renderMSERs(const Mat& gray, Mat& img, const vector >& msers) { cvtColor(gray, img, COLOR_GRAY2BGR); RNG rng((uint64)1749583); for( int i = 0; i < (int)msers.size(); i++ ) { uchar b = rng.uniform(0, 256); uchar g = rng.uniform(0, 256); uchar r = rng.uniform(0, 256); Vec3b color(b, g, r); const Point* pt = &msers[i][0]; size_t j, n = msers[i].size(); for( j = 0; j < n; j++ ) img.at(pt[j]) = color; } } #endif TEST(Features2d_MSER, cases) { uchar buf[] = { 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255 }; Mat big_image = imread(cvtest::TS::ptr()->get_data_path() + "mser/puzzle.png", 0); Mat small_image(14, 26, CV_8U, buf); static const int thresharr[] = { 0, 70, 120, 180, 255 }; const int kDelta = 5; Ptr mserExtractor = MSER::create( kDelta ); vector > msers; vector boxes; RNG rng((uint64)123456); for( int i = 0; i < 100; i++ ) { bool use_big_image = rng.uniform(0, 7) != 0; bool invert = rng.uniform(0, 2) != 0; bool binarize = use_big_image ? rng.uniform(0, 5) != 0 : false; bool blur = rng.uniform(0, 2) != 0; int thresh = thresharr[rng.uniform(0, 5)]; /*if( i == 0 ) { use_big_image = true; invert = binarize = blur = false; }*/ const Mat& src0 = use_big_image ? big_image : small_image; Mat src = src0.clone(); int kMinArea = use_big_image ? 256 : 10; int kMaxArea = (int)src.total()/4; mserExtractor->setMinArea(kMinArea); mserExtractor->setMaxArea(kMaxArea); mserExtractor->setMinDiversity(0); if( invert ) bitwise_not(src, src); if( binarize ) cv::threshold(src, src, thresh, 255, THRESH_BINARY); if( blur ) GaussianBlur(src, src, Size(5, 5), 1.5, 1.5); int minRegs = use_big_image ? 7 : 2; int maxRegs = use_big_image ? 1000 : 20; if( binarize && (thresh == 0 || thresh == 255) ) minRegs = maxRegs = 0; mserExtractor->detectRegions( src, msers, boxes ); int nmsers = (int)msers.size(); ASSERT_EQ(nmsers, (int)boxes.size()); if( maxRegs < nmsers || minRegs > nmsers ) { printf("%d. minArea=%d, maxArea=%d, nmsers=%d, minRegs=%d, maxRegs=%d, " "image=%s, invert=%d, binarize=%d, thresh=%d, blur=%d\n", i, kMinArea, kMaxArea, nmsers, minRegs, maxRegs, use_big_image ? "big" : "small", (int)invert, (int)binarize, thresh, (int)blur); #if defined RENDER_MSERS && RENDER_MSERS Mat image; imshow("source", src); renderMSERs(src, image, msers); imshow("result", image); waitKey(); #endif } ASSERT_LE(minRegs, nmsers); ASSERT_GE(maxRegs, nmsers); } } TEST(Features2d_MSER, history_update_regression) { String dataPath = cvtest::TS::ptr()->get_data_path() + "mser/"; vector tstImages; tstImages.push_back(imread(dataPath + "mser_test.png", IMREAD_GRAYSCALE)); tstImages.push_back(imread(dataPath + "mser_test2.png", IMREAD_GRAYSCALE)); for(size_t j = 0; j < tstImages.size(); j++) { size_t previous_size = 0; for(int minArea = 100; minArea > 10; minArea--) { Ptr mser = MSER::create(1, minArea, (int)(tstImages[j].cols * tstImages[j].rows * 0.2)); mser->setPass2Only(true); mser->setMinDiversity(0); vector > mserContours; vector boxRects; mser->detectRegions(tstImages[j], mserContours, boxRects); ASSERT_LE(previous_size, mserContours.size()); previous_size = mserContours.size(); } } } TEST(Features2d_MSER, bug_5630) { String dataPath = cvtest::TS::ptr()->get_data_path() + "mser/"; Mat img = imread(dataPath + "mser_test.png", IMREAD_GRAYSCALE); Ptr mser = MSER::create(1, 1); vector > mserContours; vector boxRects; // set min diversity and run detection mser->setMinDiversity(0.1); mser->detectRegions(img, mserContours, boxRects); size_t originalNumberOfContours = mserContours.size(); // increase min diversity and run detection again mser->setMinDiversity(0.2); mser->detectRegions(img, mserContours, boxRects); size_t newNumberOfContours = mserContours.size(); // there should be fewer regions detected with a higher min diversity ASSERT_LT(newNumberOfContours, originalNumberOfContours); } }} // namespace