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299 lines
11 KiB
C++
299 lines
11 KiB
C++
// 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 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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// 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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