/*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. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 { class CV_ThreshTest : public cvtest::ArrayTest { public: CV_ThreshTest(int test_type = 0); protected: void get_test_array_types_and_sizes( int test_case_idx, vector >& sizes, vector >& types ); double get_success_error_level( int test_case_idx, int i, int j ); void run_func(); void prepare_to_validation( int ); int thresh_type; double thresh_val; double max_val; int extra_type; }; CV_ThreshTest::CV_ThreshTest(int test_type) { CV_Assert( (test_type & CV_THRESH_MASK) == 0 ); test_array[INPUT].push_back(NULL); test_array[OUTPUT].push_back(NULL); test_array[REF_OUTPUT].push_back(NULL); optional_mask = false; element_wise_relative_error = true; extra_type = test_type; // Reduce number of test with automated thresholding if (extra_type != 0) test_case_count = 250; } void CV_ThreshTest::get_test_array_types_and_sizes( int test_case_idx, vector >& sizes, vector >& types ) { RNG& rng = ts->get_rng(); int depth = cvtest::randInt(rng) % 5, cn = cvtest::randInt(rng) % 4 + 1; cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types ); depth = depth == 0 ? CV_8U : depth == 1 ? CV_16S : depth == 2 ? CV_16U : depth == 3 ? CV_32F : CV_64F; if ( extra_type == CV_THRESH_OTSU ) { depth = cvtest::randInt(rng) % 2 == 0 ? CV_8U : CV_16U; cn = 1; } types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth,cn); thresh_type = cvtest::randInt(rng) % 5; if( depth == CV_8U ) { thresh_val = (cvtest::randReal(rng)*350. - 50.); max_val = (cvtest::randReal(rng)*350. - 50.); if( cvtest::randInt(rng)%4 == 0 ) max_val = 255.f; } else if( depth == CV_16S ) { double min_val = SHRT_MIN-100.f; max_val = SHRT_MAX+100.f; thresh_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val); max_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val); if( cvtest::randInt(rng)%4 == 0 ) max_val = (double)SHRT_MAX; } else if( depth == CV_16U ) { double min_val = -100.f; max_val = USHRT_MAX+100.f; thresh_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val); max_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val); if( cvtest::randInt(rng)%4 == 0 ) max_val = (double)USHRT_MAX; } else { thresh_val = (cvtest::randReal(rng)*1000. - 500.); max_val = (cvtest::randReal(rng)*1000. - 500.); } } double CV_ThreshTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ ) { return FLT_EPSILON*10; } void CV_ThreshTest::run_func() { cvThreshold( test_array[INPUT][0], test_array[OUTPUT][0], thresh_val, max_val, thresh_type | extra_type); } static double compute_otsu_thresh(const Mat& _src) { int depth = _src.depth(); int width = _src.cols, height = _src.rows; const int N = 65536; std::vector h(N, 0); int i, j; double mu = 0, scale = 1./(width*height); for(i = 0; i < height; ++i) { for(j = 0; j < width; ++j) { const int val = depth == CV_16UC1 ? (int)_src.at(i, j) : (int)_src.at(i,j); h[val]++; } } for( i = 0; i < N; i++ ) { mu += i*(double)h[i]; } mu *= scale; double mu1 = 0, q1 = 0; double max_sigma = 0, max_val = 0; for( i = 0; i < N; i++ ) { double p_i, q2, mu2, sigma; p_i = h[i]*scale; mu1 *= q1; q1 += p_i; q2 = 1. - q1; if( std::min(q1,q2) < FLT_EPSILON || std::max(q1,q2) > 1. - FLT_EPSILON ) continue; mu1 = (mu1 + i*p_i)/q1; mu2 = (mu - q1*mu1)/q2; sigma = q1*q2*(mu1 - mu2)*(mu1 - mu2); if( sigma > max_sigma ) { max_sigma = sigma; max_val = i; } } return max_val; } static void test_threshold( const Mat& _src, Mat& _dst, double thresh, double maxval, int thresh_type, int extra_type ) { int i, j; int depth = _src.depth(), cn = _src.channels(); int width_n = _src.cols*cn, height = _src.rows; int ithresh = cvFloor(thresh); int imaxval, ithresh2; if (extra_type == CV_THRESH_OTSU) { thresh = compute_otsu_thresh(_src); ithresh = cvFloor(thresh); } if( depth == CV_8U ) { ithresh2 = saturate_cast(ithresh); imaxval = saturate_cast(maxval); } else if( depth == CV_16S ) { ithresh2 = saturate_cast(ithresh); imaxval = saturate_cast(maxval); } else if( depth == CV_16U ) { ithresh2 = saturate_cast(ithresh); imaxval = saturate_cast(maxval); } else { ithresh2 = cvRound(ithresh); imaxval = cvRound(maxval); } CV_Assert( depth == CV_8U || depth == CV_16S || depth == CV_16U || depth == CV_32F || depth == CV_64F ); switch( thresh_type ) { case CV_THRESH_BINARY: for( i = 0; i < height; i++ ) { if( depth == CV_8U ) { const uchar* src = _src.ptr(i); uchar* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) dst[j] = (uchar)(src[j] > ithresh ? imaxval : 0); } else if( depth == CV_16S ) { const short* src = _src.ptr(i); short* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) dst[j] = (short)(src[j] > ithresh ? imaxval : 0); } else if( depth == CV_16U ) { const ushort* src = _src.ptr(i); ushort* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) dst[j] = (ushort)(src[j] > ithresh ? imaxval : 0); } else if( depth == CV_32F ) { const float* src = _src.ptr(i); float* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) dst[j] = (float)(src[j] > thresh ? maxval : 0.f); } else { const double* src = _src.ptr(i); double* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) dst[j] = src[j] > thresh ? maxval : 0.0; } } break; case CV_THRESH_BINARY_INV: for( i = 0; i < height; i++ ) { if( depth == CV_8U ) { const uchar* src = _src.ptr(i); uchar* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) dst[j] = (uchar)(src[j] > ithresh ? 0 : imaxval); } else if( depth == CV_16S ) { const short* src = _src.ptr(i); short* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) dst[j] = (short)(src[j] > ithresh ? 0 : imaxval); } else if( depth == CV_16U ) { const ushort* src = _src.ptr(i); ushort* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) dst[j] = (ushort)(src[j] > ithresh ? 0 : imaxval); } else if( depth == CV_32F ) { const float* src = _src.ptr(i); float* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) dst[j] = (float)(src[j] > thresh ? 0.f : maxval); } else { const double* src = _src.ptr(i); double* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) dst[j] = src[j] > thresh ? 0.0 : maxval; } } break; case CV_THRESH_TRUNC: for( i = 0; i < height; i++ ) { if( depth == CV_8U ) { const uchar* src = _src.ptr(i); uchar* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { int s = src[j]; dst[j] = (uchar)(s > ithresh ? ithresh2 : s); } } else if( depth == CV_16S ) { const short* src = _src.ptr(i); short* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { int s = src[j]; dst[j] = (short)(s > ithresh ? ithresh2 : s); } } else if( depth == CV_16U ) { const ushort* src = _src.ptr(i); ushort* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { int s = src[j]; dst[j] = (ushort)(s > ithresh ? ithresh2 : s); } } else if( depth == CV_32F ) { const float* src = _src.ptr(i); float* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { float s = src[j]; dst[j] = (float)(s > thresh ? thresh : s); } } else { const double* src = _src.ptr(i); double* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { double s = src[j]; dst[j] = s > thresh ? thresh : s; } } } break; case CV_THRESH_TOZERO: for( i = 0; i < height; i++ ) { if( depth == CV_8U ) { const uchar* src = _src.ptr(i); uchar* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { int s = src[j]; dst[j] = (uchar)(s > ithresh ? s : 0); } } else if( depth == CV_16S ) { const short* src = _src.ptr(i); short* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { int s = src[j]; dst[j] = (short)(s > ithresh ? s : 0); } } else if( depth == CV_16U ) { const ushort* src = _src.ptr(i); ushort* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { int s = src[j]; dst[j] = (ushort)(s > ithresh ? s : 0); } } else if( depth == CV_32F ) { const float* src = _src.ptr(i); float* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { float s = src[j]; dst[j] = s > thresh ? s : 0.f; } } else { const double* src = _src.ptr(i); double* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { double s = src[j]; dst[j] = s > thresh ? s : 0.0; } } } break; case CV_THRESH_TOZERO_INV: for( i = 0; i < height; i++ ) { if( depth == CV_8U ) { const uchar* src = _src.ptr(i); uchar* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { int s = src[j]; dst[j] = (uchar)(s > ithresh ? 0 : s); } } else if( depth == CV_16S ) { const short* src = _src.ptr(i); short* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { int s = src[j]; dst[j] = (short)(s > ithresh ? 0 : s); } } else if( depth == CV_16U ) { const ushort* src = _src.ptr(i); ushort* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { int s = src[j]; dst[j] = (ushort)(s > ithresh ? 0 : s); } } else if (depth == CV_32F) { const float* src = _src.ptr(i); float* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { float s = src[j]; dst[j] = s > thresh ? 0.f : s; } } else { const double* src = _src.ptr(i); double* dst = _dst.ptr(i); for( j = 0; j < width_n; j++ ) { double s = src[j]; dst[j] = s > thresh ? 0.0 : s; } } } break; default: CV_Assert(0); } } void CV_ThreshTest::prepare_to_validation( int /*test_case_idx*/ ) { test_threshold( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], thresh_val, max_val, thresh_type, extra_type ); } TEST(Imgproc_Threshold, accuracy) { CV_ThreshTest test; test.safe_run(); } TEST(Imgproc_Threshold, accuracyOtsu) { CV_ThreshTest test(CV_THRESH_OTSU); test.safe_run(); } BIGDATA_TEST(Imgproc_Threshold, huge) { Mat m(65000, 40000, CV_8U); ASSERT_FALSE(m.isContinuous()); uint64 i, n = (uint64)m.rows*m.cols; for( i = 0; i < n; i++ ) m.data[i] = (uchar)(i & 255); cv::threshold(m, m, 127, 255, cv::THRESH_BINARY); int nz = cv::countNonZero(m); // FIXIT 'int' is not enough here (overflow is possible with other inputs) ASSERT_EQ((uint64)nz, n / 2); } TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_16085) { Size sz(16, 16); Mat input(sz, CV_32F, Scalar::all(2)); Mat result; cv::threshold(input, result, 2.0, 0.0, THRESH_TOZERO); EXPECT_EQ(0, cv::norm(result, NORM_INF)); } TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258) { Size sz(16, 16); float val = nextafterf(16.0f, 0.0f); // 0x417fffff, all bits in mantissa are 1 Mat input(sz, CV_32F, Scalar::all(val)); Mat result; cv::threshold(input, result, val, 0.0, THRESH_TOZERO); EXPECT_EQ(0, cv::norm(result, NORM_INF)); } TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258_Min) { Size sz(16, 16); float min_val = -std::numeric_limits::max(); Mat input(sz, CV_32F, Scalar::all(min_val)); Mat result; cv::threshold(input, result, min_val, 0.0, THRESH_TOZERO); EXPECT_EQ(0, cv::norm(result, NORM_INF)); } TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258_Max) { Size sz(16, 16); float max_val = std::numeric_limits::max(); Mat input(sz, CV_32F, Scalar::all(max_val)); Mat result; cv::threshold(input, result, max_val, 0.0, THRESH_TOZERO); EXPECT_EQ(0, cv::norm(result, NORM_INF)); } }} // namespace