/*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" #include #include using namespace cv; using namespace std; #define CORE_COUNTNONZERO_ERROR_COUNT 1 #define MESSAGE_ERROR_COUNT "Count non zero elements returned by OpenCV function is incorrect." #define sign(a) a > 0 ? 1 : a == 0 ? 0 : -1 #define MAX_WIDTH 100 #define MAX_HEIGHT 100 class CV_CountNonZeroTest: public cvtest::BaseTest { public: CV_CountNonZeroTest(); ~CV_CountNonZeroTest(); protected: void run (int); private: float eps_32; double eps_64; Mat src; int current_type; void generate_src_data(cv::Size size, int type); void generate_src_data(cv::Size size, int type, int count_non_zero); void generate_src_stat_data(cv::Size size, int type, int distribution); int get_count_non_zero(); void print_information(int right, int result); }; CV_CountNonZeroTest::CV_CountNonZeroTest(): eps_32(std::numeric_limits::min()), eps_64(std::numeric_limits::min()), src(Mat()), current_type(-1) {} CV_CountNonZeroTest::~CV_CountNonZeroTest() {} void CV_CountNonZeroTest::generate_src_data(cv::Size size, int type) { src.create(size, CV_MAKETYPE(type, 1)); for (int j = 0; j < size.width; ++j) for (int i = 0; i < size.height; ++i) switch (type) { case CV_8U: { src.at(i, j) = cv::randu(); break; } case CV_8S: { src.at(i, j) = cv::randu() - 128; break; } case CV_16U: { src.at(i, j) = cv::randu(); break; } case CV_16S: { src.at(i, j) = cv::randu(); break; } case CV_32S: { src.at(i, j) = cv::randu(); break; } case CV_32F: { src.at(i, j) = cv::randu(); break; } case CV_64F: { src.at(i, j) = cv::randu(); break; } default: break; } } void CV_CountNonZeroTest::generate_src_data(cv::Size size, int type, int count_non_zero) { src = Mat::zeros(size, CV_MAKETYPE(type, 1)); int n = 0; RNG& rng = ts->get_rng(); while (n < count_non_zero) { int i = rng.next()%size.height, j = rng.next()%size.width; switch (type) { case CV_8U: { if (!src.at(i, j)) {src.at(i, j) = cv::randu(); n += (src.at(i, j) > 0);} break; } case CV_8S: { if (!src.at(i, j)) {src.at(i, j) = cv::randu() - 128; n += abs(sign(src.at(i, j)));} break; } case CV_16U: { if (!src.at(i, j)) {src.at(i, j) = cv::randu(); n += (src.at(i, j) > 0);} break; } case CV_16S: { if (!src.at(i, j)) {src.at(i, j) = cv::randu(); n += abs(sign(src.at(i, j)));} break; } case CV_32S: { if (!src.at(i, j)) {src.at(i, j) = cv::randu(); n += abs(sign(src.at(i, j)));} break; } case CV_32F: { if (fabs(src.at(i, j)) <= eps_32) {src.at(i, j) = cv::randu(); n += (fabs(src.at(i, j)) > eps_32);} break; } case CV_64F: { if (fabs(src.at(i, j)) <= eps_64) {src.at(i, j) = cv::randu(); n += (fabs(src.at(i, j)) > eps_64);} break; } default: break; } } } void CV_CountNonZeroTest::generate_src_stat_data(cv::Size size, int type, int distribution) { src.create(size, CV_MAKETYPE(type, 1)); double mean = 0.0, sigma = 1.0; double left = -1.0, right = 1.0; RNG& rng = ts->get_rng(); if (distribution == RNG::NORMAL) rng.fill(src, RNG::NORMAL, Scalar::all(mean), Scalar::all(sigma)); else if (distribution == RNG::UNIFORM) rng.fill(src, RNG::UNIFORM, Scalar::all(left), Scalar::all(right)); } int CV_CountNonZeroTest::get_count_non_zero() { int result = 0; for (int i = 0; i < src.rows; ++i) for (int j = 0; j < src.cols; ++j) { if (current_type == CV_8U) result += (src.at(i, j) > 0); else if (current_type == CV_8S) result += abs(sign(src.at(i, j))); else if (current_type == CV_16U) result += (src.at(i, j) > 0); else if (current_type == CV_16S) result += abs(sign(src.at(i, j))); else if (current_type == CV_32S) result += abs(sign(src.at(i, j))); else if (current_type == CV_32F) result += (fabs(src.at(i, j)) > eps_32); else result += (fabs(src.at(i, j)) > eps_64); } return result; } void CV_CountNonZeroTest::print_information(int right, int result) { cout << endl; cout << "Checking for the work of countNonZero function..." << endl; cout << endl; cout << "Type of Mat: "; switch (current_type) { case 0: {cout << "CV_8U"; break;} case 1: {cout << "CV_8S"; break;} case 2: {cout << "CV_16U"; break;} case 3: {cout << "CV_16S"; break;} case 4: {cout << "CV_32S"; break;} case 5: {cout << "CV_32F"; break;} case 6: {cout << "CV_64F"; break;} default: break; } cout << endl; cout << "Number of rows: " << src.rows << " Number of cols: " << src.cols << endl; cout << "True count non zero elements: " << right << " Result: " << result << endl; cout << endl; } void CV_CountNonZeroTest::run(int) { const size_t N = 1500; for (int k = 1; k <= 3; ++k) for (size_t i = 0; i < N; ++i) { RNG& rng = ts->get_rng(); int w = rng.next()%MAX_WIDTH + 1, h = rng.next()%MAX_HEIGHT + 1; current_type = rng.next()%7; switch (k) { case 1: { generate_src_data(Size(w, h), current_type); int right = get_count_non_zero(), result = countNonZero(src); if (result != right) { cout << "Number of experiment: " << i << endl; cout << "Method of data generation: RANDOM" << endl; print_information(right, result); CV_Error(CORE_COUNTNONZERO_ERROR_COUNT, MESSAGE_ERROR_COUNT); return; } break; } case 2: { int count_non_zero = rng.next()%(w*h); generate_src_data(Size(w, h), current_type, count_non_zero); int result = countNonZero(src); if (result != count_non_zero) { cout << "Number of experiment: " << i << endl; cout << "Method of data generation: HALF-RANDOM" << endl; print_information(count_non_zero, result); CV_Error(CORE_COUNTNONZERO_ERROR_COUNT, MESSAGE_ERROR_COUNT); return; } break; } case 3: { int distribution = cv::randu()%2; generate_src_stat_data(Size(w, h), current_type, distribution); int right = get_count_non_zero(), result = countNonZero(src); if (right != result) { cout << "Number of experiment: " << i << endl; cout << "Method of data generation: STATISTIC" << endl; print_information(right, result); CV_Error(CORE_COUNTNONZERO_ERROR_COUNT, MESSAGE_ERROR_COUNT); return; } break; } default: break; } } } TEST (Core_CountNonZero, accuracy) { CV_CountNonZeroTest test; test.safe_run(); } typedef testing::TestWithParam > CountNonZeroND; TEST_P (CountNonZeroND, ndim) { const int dims = std::tr1::get<0>(GetParam()); const int type = std::tr1::get<1>(GetParam()); const int ONE_SIZE = 5; vector sizes(dims); fill(sizes.begin(), sizes.end(), ONE_SIZE); Mat data(sizes, CV_MAKETYPE(type, 1)); data = 0; EXPECT_EQ(0, cv::countNonZero(data)); data = Scalar::all(1); int expected = static_cast(pow(static_cast(ONE_SIZE), dims)); EXPECT_EQ(expected, cv::countNonZero(data)); } INSTANTIATE_TEST_CASE_P(Core, CountNonZeroND, testing::Combine( testing::Range(2, 9), testing::Values(CV_8U, CV_8S, CV_32F) ) );