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Merge pull request #22754 from mshabunin:c-cleanup
C-API cleanup for OpenCV 5.x (imgproc, highgui) * imgproc: C-API cleanup * imgproc: increase cvtColor test diff threshold * imgproc: C-API cleanup pt.2 * imgproc: C-API cleanup pt.3 * imgproc: C-API cleanup pt.4 * imgproc: C-API cleanup pt.5 * imgproc: C-API cleanup pt.5 * imgproc: C-API cleanup pt.6 * highgui: C-API cleanup * highgui: C-API cleanup pt.2 * highgui: C-API cleanup pt.3 * highgui: C-API cleanup pt.3 * imgproc: C-API cleanup pt.7 * fixup! highgui: C-API cleanup pt.3 * fixup! imgproc: C-API cleanup pt.6 * imgproc: C-API cleanup pt.8 * imgproc: C-API cleanup pt.9 * fixup! imgproc: C-API cleanup pt.9 * fixup! imgproc: C-API cleanup pt.9 * fixup! imgproc: C-API cleanup pt.9 * fixup! imgproc: C-API cleanup pt.9 * fixup! imgproc: C-API cleanup pt.9 * fixup! imgproc: C-API cleanup pt.9
This commit is contained in:
Maksim Shabunin
GitHub
@@ -44,372 +44,6 @@
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namespace opencv_test { namespace {
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class CV_FindContourTest : public cvtest::BaseTest
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{
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public:
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enum { NUM_IMG = 4 };
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CV_FindContourTest();
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~CV_FindContourTest();
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void clear();
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protected:
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int read_params( const cv::FileStorage& fs );
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int prepare_test_case( int test_case_idx );
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int validate_test_results( int test_case_idx );
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void run_func();
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int min_blob_size, max_blob_size;
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int blob_count, max_log_blob_count;
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int retr_mode, approx_method;
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int min_log_img_width, max_log_img_width;
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int min_log_img_height, max_log_img_height;
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Size img_size;
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int count, count2;
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IplImage* img[NUM_IMG];
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CvMemStorage* storage;
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CvSeq *contours, *contours2, *chain;
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static const bool useVeryWideImages =
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#if SIZE_MAX <= 0xffffffff
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// 32-bit: don't even try the very wide images
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false
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#else
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// 64-bit: test with very wide images
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true
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#endif
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;
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};
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CV_FindContourTest::CV_FindContourTest()
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{
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int i;
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test_case_count = useVeryWideImages ? 10 : 300;
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min_blob_size = 1;
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max_blob_size = 50;
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max_log_blob_count = 10;
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min_log_img_width = useVeryWideImages ? 17 : 3;
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max_log_img_width = useVeryWideImages ? 17 : 10;
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min_log_img_height = 3;
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max_log_img_height = 10;
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for( i = 0; i < NUM_IMG; i++ )
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img[i] = 0;
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storage = 0;
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}
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CV_FindContourTest::~CV_FindContourTest()
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{
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clear();
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}
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void CV_FindContourTest::clear()
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{
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int i;
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cvtest::BaseTest::clear();
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for( i = 0; i < NUM_IMG; i++ )
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cvReleaseImage( &img[i] );
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cvReleaseMemStorage( &storage );
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}
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int CV_FindContourTest::read_params( const cv::FileStorage& fs )
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{
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int t;
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int code = cvtest::BaseTest::read_params( fs );
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if( code < 0 )
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return code;
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read( find_param( fs, "min_blob_size" ), min_blob_size, min_blob_size );
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read( find_param( fs, "max_blob_size" ), max_blob_size, max_blob_size );
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read( find_param( fs, "max_log_blob_count" ), max_log_blob_count, max_log_blob_count );
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read( find_param( fs, "min_log_img_width" ), min_log_img_width, min_log_img_width );
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read( find_param( fs, "max_log_img_width" ), max_log_img_width, max_log_img_width );
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read( find_param( fs, "min_log_img_height"), min_log_img_height, min_log_img_height );
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read( find_param( fs, "max_log_img_height"), max_log_img_height, max_log_img_height );
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min_blob_size = cvtest::clipInt( min_blob_size, 1, 100 );
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max_blob_size = cvtest::clipInt( max_blob_size, 1, 100 );
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if( min_blob_size > max_blob_size )
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CV_SWAP( min_blob_size, max_blob_size, t );
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max_log_blob_count = cvtest::clipInt( max_log_blob_count, 1, 10 );
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min_log_img_width = cvtest::clipInt( min_log_img_width, 1, useVeryWideImages ? 17 : 10 );
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min_log_img_width = cvtest::clipInt( max_log_img_width, 1, useVeryWideImages ? 17 : 10 );
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min_log_img_height = cvtest::clipInt( min_log_img_height, 1, 10 );
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min_log_img_height = cvtest::clipInt( max_log_img_height, 1, 10 );
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if( min_log_img_width > max_log_img_width )
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std::swap( min_log_img_width, max_log_img_width );
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if (min_log_img_height > max_log_img_height)
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std::swap(min_log_img_height, max_log_img_height);
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return 0;
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}
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static void
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cvTsGenerateBlobImage( IplImage* img, int min_blob_size, int max_blob_size,
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int blob_count, int min_brightness, int max_brightness,
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RNG& rng )
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{
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int i;
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Size size;
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CV_Assert(img->depth == IPL_DEPTH_8U && img->nChannels == 1);
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cvZero( img );
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// keep the border clear
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cvSetImageROI( img, cvRect(1,1,img->width-2,img->height-2) );
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size = cvGetSize( img );
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for( i = 0; i < blob_count; i++ )
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{
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Point center;
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Size axes;
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int angle = cvtest::randInt(rng) % 180;
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int brightness = cvtest::randInt(rng) %
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(max_brightness - min_brightness) + min_brightness;
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center.x = cvtest::randInt(rng) % size.width;
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center.y = cvtest::randInt(rng) % size.height;
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axes.width = (cvtest::randInt(rng) %
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(max_blob_size - min_blob_size) + min_blob_size + 1)/2;
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axes.height = (cvtest::randInt(rng) %
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(max_blob_size - min_blob_size) + min_blob_size + 1)/2;
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cvEllipse( img, cvPoint(center), cvSize(axes), angle, 0, 360, cvScalar(brightness), CV_FILLED );
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}
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cvResetImageROI( img );
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}
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static void
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cvTsMarkContours( IplImage* img, int val )
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{
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int i, j;
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int step = img->widthStep;
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CV_Assert( img->depth == IPL_DEPTH_8U && img->nChannels == 1 && (val&1) != 0);
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for( i = 1; i < img->height - 1; i++ )
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for( j = 1; j < img->width - 1; j++ )
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{
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uchar* t = (uchar*)(img->imageData + img->widthStep*i + j);
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if( *t == 1 && (t[-step] == 0 || t[-1] == 0 || t[1] == 0 || t[step] == 0))
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*t = (uchar)val;
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}
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cvThreshold( img, img, val - 2, val, CV_THRESH_BINARY );
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}
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int CV_FindContourTest::prepare_test_case( int test_case_idx )
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{
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RNG& rng = ts->get_rng();
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const int min_brightness = 0, max_brightness = 2;
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int i, code = cvtest::BaseTest::prepare_test_case( test_case_idx );
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if( code < 0 )
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return code;
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clear();
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blob_count = cvRound(exp(cvtest::randReal(rng)*max_log_blob_count*CV_LOG2));
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img_size.width = cvRound(exp((cvtest::randReal(rng)*
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(max_log_img_width - min_log_img_width) + min_log_img_width)*CV_LOG2));
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img_size.height = cvRound(exp((cvtest::randReal(rng)*
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(max_log_img_height - min_log_img_height) + min_log_img_height)*CV_LOG2));
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approx_method = cvtest::randInt( rng ) % 4 + 1;
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retr_mode = cvtest::randInt( rng ) % 4;
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storage = cvCreateMemStorage( 1 << 10 );
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for( i = 0; i < NUM_IMG; i++ )
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img[i] = cvCreateImage( cvSize(img_size), 8, 1 );
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cvTsGenerateBlobImage( img[0], min_blob_size, max_blob_size,
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blob_count, min_brightness, max_brightness, rng );
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cvCopy( img[0], img[1] );
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cvCopy( img[0], img[2] );
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cvTsMarkContours( img[1], 255 );
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return 1;
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}
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void CV_FindContourTest::run_func()
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{
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contours = contours2 = chain = 0;
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count = cvFindContours( img[2], storage, &contours, sizeof(CvContour), retr_mode, approx_method );
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cvZero( img[3] );
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if( contours && retr_mode != CV_RETR_EXTERNAL && approx_method < CV_CHAIN_APPROX_TC89_L1 )
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cvDrawContours( img[3], contours, cvScalar(255), cvScalar(255), INT_MAX, -1 );
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cvCopy( img[0], img[2] );
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count2 = cvFindContours( img[2], storage, &chain, sizeof(CvChain), retr_mode, CV_CHAIN_CODE );
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if( chain )
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contours2 = cvApproxChains( chain, storage, approx_method, 0, 0, 1 );
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cvZero( img[2] );
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if( contours && retr_mode != CV_RETR_EXTERNAL && approx_method < CV_CHAIN_APPROX_TC89_L1 )
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cvDrawContours( img[2], contours2, cvScalar(255), cvScalar(255), INT_MAX );
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}
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// the whole testing is done here, run_func() is not utilized in this test
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int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ )
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{
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int code = cvtest::TS::OK;
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cvCmpS( img[0], 0, img[0], CV_CMP_GT );
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if( count != count2 )
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{
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ts->printf( cvtest::TS::LOG, "The number of contours retrieved with different "
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"approximation methods is not the same\n"
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"(%d contour(s) for method %d vs %d contour(s) for method %d)\n",
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count, approx_method, count2, CV_CHAIN_CODE );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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}
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if( retr_mode != CV_RETR_EXTERNAL && approx_method < CV_CHAIN_APPROX_TC89_L1 )
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{
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Mat _img[4];
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for( int i = 0; i < 4; i++ )
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_img[i] = cvarrToMat(img[i]);
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code = cvtest::cmpEps2(ts, _img[0], _img[3], 0, true, "Comparing original image with the map of filled contours" );
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if( code < 0 )
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goto _exit_;
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code = cvtest::cmpEps2( ts, _img[1], _img[2], 0, true,
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"Comparing contour outline vs manually produced edge map" );
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if( code < 0 )
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goto _exit_;
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}
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if( contours )
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{
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CvTreeNodeIterator iterator1;
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CvTreeNodeIterator iterator2;
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int count3;
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for(int i = 0; i < 2; i++ )
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{
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CvTreeNodeIterator iterator;
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cvInitTreeNodeIterator( &iterator, i == 0 ? contours : contours2, INT_MAX );
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for( count3 = 0; cvNextTreeNode( &iterator ) != 0; count3++ )
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;
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if( count3 != count )
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{
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ts->printf( cvtest::TS::LOG,
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"The returned number of retrieved contours (using the approx_method = %d) does not match\n"
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"to the actual number of contours in the tree/list (returned %d, actual %d)\n",
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i == 0 ? approx_method : 0, count, count3 );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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goto _exit_;
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}
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}
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cvInitTreeNodeIterator( &iterator1, contours, INT_MAX );
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cvInitTreeNodeIterator( &iterator2, contours2, INT_MAX );
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for( count3 = 0; count3 < count; count3++ )
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{
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CvSeq* seq1 = (CvSeq*)cvNextTreeNode( &iterator1 );
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CvSeq* seq2 = (CvSeq*)cvNextTreeNode( &iterator2 );
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CvSeqReader reader1;
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CvSeqReader reader2;
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if( !seq1 || !seq2 )
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{
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ts->printf( cvtest::TS::LOG,
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"There are NULL pointers in the original contour tree or the "
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"tree produced by cvApproxChains\n" );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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goto _exit_;
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}
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cvStartReadSeq( seq1, &reader1 );
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cvStartReadSeq( seq2, &reader2 );
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if( seq1->total != seq2->total )
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{
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ts->printf( cvtest::TS::LOG,
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"The original contour #%d has %d points, while the corresponding contour has %d point\n",
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count3, seq1->total, seq2->total );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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goto _exit_;
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}
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for(int i = 0; i < seq1->total; i++ )
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{
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CvPoint pt1 = {0, 0};
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CvPoint pt2 = {0, 0};
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CV_READ_SEQ_ELEM( pt1, reader1 );
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CV_READ_SEQ_ELEM( pt2, reader2 );
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if( pt1.x != pt2.x || pt1.y != pt2.y )
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{
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ts->printf( cvtest::TS::LOG,
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"The point #%d in the contour #%d is different from the corresponding point "
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"in the approximated chain ((%d,%d) vs (%d,%d)", count3, i, pt1.x, pt1.y, pt2.x, pt2.y );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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goto _exit_;
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}
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}
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}
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}
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_exit_:
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if( code < 0 )
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{
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#if 0
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cvNamedWindow( "test", 0 );
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cvShowImage( "test", img[0] );
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cvWaitKey();
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#endif
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ts->set_failed_test_info( code );
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}
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return code;
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}
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TEST(Imgproc_FindContours, accuracy) { CV_FindContourTest test; test.safe_run(); }
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//rotate/flip a quadrant appropriately
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static void rot(int n, int *x, int *y, int rx, int ry)
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{
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