Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "test_precomp.hpp"
#include <time.h>
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
const int FLOAT_TYPE [2] = {CV_32F, CV_64F};
const int INT_TYPE [5] = {CV_8U, CV_8S, CV_16U, CV_16S, CV_32S};
#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(1e-8f), eps_64(1e-16f), 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<uchar>(i, j) = cv::randu<uchar>(); break; }
case CV_8S: { src.at<char>(i, j) = cv::randu<uchar>() - 128; break; }
case CV_16U: { src.at<ushort>(i, j) = cv::randu<ushort>(); break; }
case CV_16S: { src.at<short>(i, j) = cv::randu<short>(); break; }
case CV_32S: { src.at<int>(i, j) = cv::randu<int>(); break; }
case CV_32F: { src.at<float>(i, j) = cv::randu<float>(); break; }
case CV_64F: { src.at<double>(i, j) = cv::randu<double>(); 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<uchar>(i, j)) {src.at<uchar>(i, j) = cv::randu<uchar>(); n += (src.at<uchar>(i, j) > 0);} break; }
case CV_8S: { if (!src.at<char>(i, j)) {src.at<char>(i, j) = cv::randu<uchar>() - 128; n += abs(sign(src.at<char>(i, j)));} break; }
case CV_16U: { if (!src.at<ushort>(i, j)) {src.at<ushort>(i, j) = cv::randu<ushort>(); n += (src.at<ushort>(i, j) > 0);} break; }
case CV_16S: { if (!src.at<short>(i, j)) {src.at<short>(i, j) = cv::randu<short>(); n += abs(sign(src.at<short>(i, j)));} break; }
case CV_32S: { if (!src.at<int>(i, j)) {src.at<int>(i, j) = cv::randu<int>(); n += abs(sign(src.at<int>(i, j)));} break; }
case CV_32F: { if (fabs(src.at<float>(i, j)) <= eps_32) {src.at<float>(i, j) = cv::randu<float>(); n += (fabs(src.at<float>(i, j)) > eps_32);} break; }
case CV_64F: { if (fabs(src.at<double>(i, j)) <= eps_64) {src.at<double>(i, j) = cv::randu<double>(); n += (fabs(src.at<double>(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<uchar>(i, j) > 0);
else if (current_type == CV_8S) result += abs(sign(src.at<char>(i, j)));
else if (current_type == CV_16U) result += (src.at<ushort>(i, j) > 0);
else if (current_type == CV_16S) result += abs(sign(src.at<short>(i, j)));
else if (current_type == CV_32S) result += abs(sign(src.at<int>(i, j)));
else if (current_type == CV_32F) result += (fabs(src.at<float>(i, j)) > eps_32);
else result += (fabs(src.at<double>(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<uchar>()%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(); }