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Open Source Computer Vision Library
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345 lines
13 KiB
345 lines
13 KiB
// 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 directory |
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// of this distribution and at http://opencv.org/license.html |
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namespace opencv_test { namespace { |
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/****************************************************************************************\ |
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* Regression tests for descriptor extractors. * |
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\****************************************************************************************/ |
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static void double_image(Mat& src, Mat& dst) { |
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dst.create(Size(src.cols*2, src.rows*2), src.type()); |
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Mat H = Mat::zeros(2, 3, CV_32F); |
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H.at<float>(0, 0) = 0.5f; |
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H.at<float>(1, 1) = 0.5f; |
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cv::warpAffine(src, dst, H, dst.size(), INTER_LINEAR | WARP_INVERSE_MAP, BORDER_REFLECT); |
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} |
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static Mat prepare_img(bool rows_indexed) { |
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int rows = 5; |
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int columns = 5; |
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Mat img(rows, columns, CV_32F); |
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for (int i = 0; i < rows; i++) { |
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for (int j = 0; j < columns; j++) { |
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if (rows_indexed) { |
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img.at<float>(i, j) = (float)i; |
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} else { |
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img.at<float>(i, j) = (float)j; |
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} |
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} |
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} |
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return img; |
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} |
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static void writeMatInBin( const Mat& mat, const string& filename ) |
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{ |
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FILE* f = fopen( filename.c_str(), "wb"); |
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if( f ) |
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{ |
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CV_Assert(4 == sizeof(int)); |
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int type = mat.type(); |
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fwrite( (void*)&mat.rows, sizeof(int), 1, f ); |
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fwrite( (void*)&mat.cols, sizeof(int), 1, f ); |
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fwrite( (void*)&type, sizeof(int), 1, f ); |
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int dataSize = (int)(mat.step * mat.rows); |
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fwrite( (void*)&dataSize, sizeof(int), 1, f ); |
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fwrite( (void*)mat.ptr(), 1, dataSize, f ); |
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fclose(f); |
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} |
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} |
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static Mat readMatFromBin( const string& filename ) |
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{ |
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FILE* f = fopen( filename.c_str(), "rb" ); |
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if( f ) |
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{ |
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CV_Assert(4 == sizeof(int)); |
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int rows, cols, type, dataSize; |
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size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f ); |
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size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f ); |
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size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f ); |
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size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f ); |
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CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1); |
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int step = dataSize / rows / CV_ELEM_SIZE(type); |
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CV_Assert(step >= cols); |
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Mat returnMat = Mat(rows, step, type).colRange(0, cols); |
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size_t elements_read = fread( returnMat.ptr(), 1, dataSize, f ); |
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CV_Assert(elements_read == (size_t)(dataSize)); |
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fclose(f); |
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return returnMat; |
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} |
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return Mat(); |
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} |
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template<class Distance> |
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class CV_DescriptorExtractorTest : public cvtest::BaseTest |
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{ |
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public: |
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typedef typename Distance::ValueType ValueType; |
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typedef typename Distance::ResultType DistanceType; |
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CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor, |
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Distance d = Distance(), Ptr<FeatureDetector> _detector = Ptr<FeatureDetector>()): |
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name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) , detector(_detector) {} |
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~CV_DescriptorExtractorTest() |
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{ |
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} |
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protected: |
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virtual void createDescriptorExtractor() {} |
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void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors ) |
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{ |
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if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
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} |
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CV_Assert( DataType<ValueType>::type == validDescriptors.type() ); |
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int dimension = validDescriptors.cols; |
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DistanceType curMaxDist = 0; |
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size_t exact_count = 0, failed_count = 0; |
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for( int y = 0; y < validDescriptors.rows; y++ ) |
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{ |
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DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension ); |
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if (dist == 0) |
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exact_count++; |
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if( dist > curMaxDist ) |
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{ |
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if (dist > maxDist) |
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failed_count++; |
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curMaxDist = dist; |
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} |
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#if 0 |
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if (dist > 0) |
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{ |
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std::cout << "i=" << y << " fail_count=" << failed_count << " dist=" << dist << std::endl; |
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std::cout << "valid: " << validDescriptors.row(y) << std::endl; |
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std::cout << " calc: " << calcDescriptors.row(y) << std::endl; |
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} |
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#endif |
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} |
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float exact_percents = (100 * (float)exact_count / validDescriptors.rows); |
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float failed_percents = (100 * (float)failed_count / validDescriptors.rows); |
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std::stringstream ss; |
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ss << "Exact count (dist == 0): " << exact_count << " (" << (int)exact_percents << "%)" << std::endl |
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<< "Failed count (dist > " << maxDist << "): " << failed_count << " (" << (int)failed_percents << "%)" << std::endl |
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<< "Max distance between valid and computed descriptors (" << validDescriptors.size() << "): " << curMaxDist; |
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EXPECT_LE(failed_percents, 20.0f); |
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std::cout << ss.str() << std::endl; |
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} |
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void emptyDataTest() |
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{ |
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assert( dextractor ); |
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// One image. |
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Mat image; |
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vector<KeyPoint> keypoints; |
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Mat descriptors; |
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try |
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{ |
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dextractor->compute( image, keypoints, descriptors ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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} |
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RNG rng; |
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image = cvtest::randomMat(rng, Size(50, 50), CV_8UC3, 0, 255, false); |
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try |
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{ |
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dextractor->compute( image, keypoints, descriptors ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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} |
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image = prepare_img(false); |
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Mat dbl; |
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try |
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{ |
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double_image(image, dbl); |
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Mat downsized_back(dbl.rows/2, dbl.cols/2, CV_32F); |
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resize(dbl, downsized_back, Size(dbl.cols/2, dbl.rows/2), 0, 0, INTER_NEAREST); |
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cv::Mat diff = (image != downsized_back); |
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ASSERT_EQ(0, cv::norm(image, downsized_back, NORM_INF)); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "double_image() must not generate exception (1).\n"); |
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ts->printf( cvtest::TS::LOG, "double_image() when downsized back by NEAREST must generate the same original image (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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} |
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// Several images. |
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vector<Mat> images; |
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vector<vector<KeyPoint> > keypointsCollection; |
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vector<Mat> descriptorsCollection; |
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try |
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{ |
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dextractor->compute( images, keypointsCollection, descriptorsCollection ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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} |
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} |
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void regressionTest() |
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{ |
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assert( dextractor ); |
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// Read the test image. |
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string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME; |
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Mat img = imread( imgFilename ); |
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if( img.empty() ) |
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{ |
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ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
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} |
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const std::string keypoints_filename = string(ts->get_data_path()) + |
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(detector.empty() |
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? (FEATURES2D_DIR + "/" + std::string("keypoints.xml.gz")) |
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: (DESCRIPTOR_DIR + "/" + name + "_keypoints.xml.gz")); |
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FileStorage fs(keypoints_filename, FileStorage::READ); |
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vector<KeyPoint> keypoints; |
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EXPECT_TRUE(fs.isOpened()) << "Keypoint testdata is missing. Re-computing and re-writing keypoints testdata..."; |
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if (!fs.isOpened()) |
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{ |
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fs.open(keypoints_filename, FileStorage::WRITE); |
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ASSERT_TRUE(fs.isOpened()) << "File for writing keypoints can not be opened."; |
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if (detector.empty()) |
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{ |
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Ptr<ORB> fd = ORB::create(); |
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fd->detect(img, keypoints); |
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} |
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else |
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{ |
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detector->detect(img, keypoints); |
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} |
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write(fs, "keypoints", keypoints); |
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fs.release(); |
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} |
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else |
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{ |
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read(fs.getFirstTopLevelNode(), keypoints); |
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fs.release(); |
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} |
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if(!detector.empty()) |
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{ |
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vector<KeyPoint> calcKeypoints; |
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detector->detect(img, calcKeypoints); |
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// TODO validate received keypoints |
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int diff = abs((int)calcKeypoints.size() - (int)keypoints.size()); |
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if (diff > 0) |
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{ |
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std::cout << "Keypoints difference: " << diff << std::endl; |
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EXPECT_LE(diff, (int)(keypoints.size() * 0.03f)); |
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} |
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} |
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ASSERT_FALSE(keypoints.empty()); |
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{ |
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Mat calcDescriptors; |
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double t = (double)getTickCount(); |
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dextractor->compute(img, keypoints, calcDescriptors); |
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t = getTickCount() - t; |
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ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)getTickFrequency()*1000.)/calcDescriptors.rows); |
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if (calcDescriptors.rows != (int)keypoints.size()) |
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{ |
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ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" ); |
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ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() ); |
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ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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return; |
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} |
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if (calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType()) |
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{ |
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ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" ); |
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ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() ); |
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ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols ); |
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ts->printf( cvtest::TS::LOG, "Expected type is %d.\n", dextractor->descriptorType() ); |
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ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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return; |
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} |
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// TODO read and write descriptor extractor parameters and check them |
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Mat validDescriptors = readDescriptors(); |
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EXPECT_FALSE(validDescriptors.empty()) << "Descriptors testdata is missing. Re-writing descriptors testdata..."; |
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if (!validDescriptors.empty()) |
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{ |
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compareDescriptors(validDescriptors, calcDescriptors); |
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} |
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else |
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{ |
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ASSERT_TRUE(writeDescriptors(calcDescriptors)) << "Descriptors can not be written."; |
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} |
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} |
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} |
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void run(int) |
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{ |
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createDescriptorExtractor(); |
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if( !dextractor ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Descriptor extractor is empty.\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
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} |
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emptyDataTest(); |
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regressionTest(); |
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ts->set_failed_test_info( cvtest::TS::OK ); |
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} |
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virtual Mat readDescriptors() |
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{ |
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Mat res = readMatFromBin( string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) ); |
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return res; |
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} |
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virtual bool writeDescriptors( Mat& descs ) |
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{ |
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writeMatInBin( descs, string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) ); |
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return true; |
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} |
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string name; |
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const DistanceType maxDist; |
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Ptr<DescriptorExtractor> dextractor; |
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Distance distance; |
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Ptr<FeatureDetector> detector; |
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private: |
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CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; } |
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}; |
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}} // namespace
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