opencv/modules/core/test/test_countnonzero.cpp

342 lines
12 KiB
C++

/*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"
namespace opencv_test { namespace {
#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<float>::min()), eps_64(std::numeric_limits<double>::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<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(cv::Error::StsError, 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(cv::Error::StsError, 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(cv::Error::StsError, MESSAGE_ERROR_COUNT);
return;
}
break;
}
default: break;
}
}
}
TEST (Core_CountNonZero, accuracy) { CV_CountNonZeroTest test; test.safe_run(); }
typedef testing::TestWithParam<tuple<int, int> > CountNonZeroND;
TEST_P (CountNonZeroND, ndim)
{
const int dims = get<0>(GetParam());
const int type = get<1>(GetParam());
const int ONE_SIZE = 5;
vector<int> sizes(dims);
std::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<int>(pow(static_cast<float>(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)
)
);
typedef testing::TestWithParam<tuple<int, cv::Size> > CountNonZeroBig;
TEST_P(CountNonZeroBig, /**/)
{
const int type = get<0>(GetParam());
const Size sz = get<1>(GetParam());
EXPECT_EQ(0, cv::countNonZero(cv::Mat::zeros(sz, type)));
EXPECT_EQ(sz.area(), cv::countNonZero(cv::Mat::ones(sz, type)));
}
INSTANTIATE_TEST_CASE_P(Core, CountNonZeroBig,
testing::Combine(
testing::Values(CV_8UC1, CV_32FC1),
testing::Values(Size(1, 524190), Size(524190, 1), Size(3840, 2160))
)
);
typedef testing::TestWithParam<int> CountNonZero1D;
TEST_P(CountNonZero1D, /**/)
{
const int depth = GetParam();
int i, M = 112 + depth, N = 3 + depth;
std::vector<uint8_t> v(M);
int nz_ref = 0;
for (i = 0; i < M; i++) {
v[i] = (uint8_t)(rand() % 7 == 0);
nz_ref += v[i] != 0;
}
Mat mv;
Mat(v).convertTo(mv, depth);
EXPECT_EQ(mv.dims, 1);
size_t esz = mv.elemSize();
// check countNonZero on a vector transformed to Mat inplace, e.g. on 1xM matrix
int nz0 = countNonZero(mv);
EXPECT_EQ(nz0, nz_ref);
// another method to get 1xM matrix, this time 2D matrix
int nz0_ = countNonZero(Mat(Size(M, 1), depth, mv.data));
EXPECT_EQ(nz0_, nz_ref);
// let's now transpose it and get Mx1
Mat m1 = mv.t();
int nz1 = countNonZero(m1);
EXPECT_EQ(nz1, nz_ref);
Mat mwide(M, N, mv.type());
randu(mwide, 0, 3);
int colidx = rand()%N;
Mat mcol = mwide.col(colidx);
EXPECT_EQ(mcol.data, mwide.data + colidx*esz);
// let's now embed this column into a wider matrix
// make sure it's copied inside, not reallocated.
m1.copyTo(mcol);
EXPECT_EQ(mcol.data, mwide.data + colidx*esz);
// now it's not continuous
EXPECT_EQ(mcol.isContinuous(), false);
int nz2 = countNonZero(mcol);
EXPECT_EQ(nz2, nz_ref);
}
INSTANTIATE_TEST_CASE_P(Core, CountNonZero1D,
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32U, CV_32S, CV_64U, CV_64S, CV_32F, CV_64F, CV_16F, CV_16BF, CV_Bool)
);
}} // namespace