opencv/modules/gpu/test/test_hog.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// For Open Source Computer Vision Library
//
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#include "test_precomp.hpp"
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#ifdef HAVE_CUDA
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//#define DUMP
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struct CV_GpuHogDetectTestRunner : cv::gpu::HOGDescriptor
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{
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void run()
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{
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cv::Mat img_rgb = readImage("hog/road.png");
ASSERT_TRUE(!img_rgb.empty());
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#ifdef DUMP
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f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
ASSERT_TRUE(f.is_open());
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#else
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f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
ASSERT_TRUE(f.is_open());
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#endif
// Test on color image
cv::Mat img;
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
test(img);
// Test on gray image
cv::cvtColor(img_rgb, img, CV_BGR2GRAY);
test(img);
f.close();
}
#ifdef DUMP
void dump(const cv::Mat& block_hists, const std::vector<cv::Point>& locations)
{
f.write((char*)&block_hists.rows, sizeof(block_hists.rows));
f.write((char*)&block_hists.cols, sizeof(block_hists.cols));
for (int i = 0; i < block_hists.rows; ++i)
{
for (int j = 0; j < block_hists.cols; ++j)
{
float val = block_hists.at<float>(i, j);
f.write((char*)&val, sizeof(val));
}
}
int nlocations = locations.size();
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f.write((char*)&nlocations, sizeof(nlocations));
for (int i = 0; i < locations.size(); ++i)
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f.write((char*)&locations[i], sizeof(locations[i]));
}
#else
void compare(const cv::Mat& block_hists, const std::vector<cv::Point>& locations)
{
int rows, cols;
int nlocations;
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f.read((char*)&rows, sizeof(rows));
f.read((char*)&cols, sizeof(cols));
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ASSERT_EQ(rows, block_hists.rows);
ASSERT_EQ(cols, block_hists.cols);
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for (int i = 0; i < block_hists.rows; ++i)
{
for (int j = 0; j < block_hists.cols; ++j)
{
float val;
f.read((char*)&val, sizeof(val));
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ASSERT_NEAR(val, block_hists.at<float>(i, j), 1e-3);
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}
}
f.read((char*)&nlocations, sizeof(nlocations));
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ASSERT_EQ(nlocations, static_cast<int>(locations.size()));
for (int i = 0; i < nlocations; ++i)
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{
cv::Point location;
f.read((char*)&location, sizeof(location));
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ASSERT_EQ(location, locations[i]);
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}
}
#endif
void test(const cv::Mat& img)
{
cv::gpu::GpuMat d_img(img);
gamma_correction = false;
setSVMDetector(cv::gpu::HOGDescriptor::getDefaultPeopleDetector());
//cpu detector may be updated soon
//hog.setSVMDetector(cv::HOGDescriptor::getDefaultPeopleDetector());
std::vector<cv::Point> locations;
// Test detect
detect(d_img, locations, 0);
#ifdef DUMP
dump(block_hists, locations);
#else
compare(block_hists, locations);
#endif
// Test detect on smaller image
cv::Mat img2;
cv::resize(img, img2, cv::Size(img.cols / 2, img.rows / 2));
detect(cv::gpu::GpuMat(img2), locations, 0);
#ifdef DUMP
dump(block_hists, locations);
#else
compare(block_hists, locations);
#endif
// Test detect on greater image
cv::resize(img, img2, cv::Size(img.cols * 2, img.rows * 2));
detect(cv::gpu::GpuMat(img2), locations, 0);
#ifdef DUMP
dump(block_hists, locations);
#else
compare(block_hists, locations);
#endif
}
#ifdef DUMP
std::ofstream f;
#else
std::ifstream f;
#endif
};
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struct HogDetect : testing::TestWithParam<cv::gpu::DeviceInfo>
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{
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cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
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{
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devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
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}
};
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TEST_P(HogDetect, Accuracy)
{
PRINT_PARAM(devInfo);
ASSERT_NO_THROW(
CV_GpuHogDetectTestRunner runner;
runner.run();
);
}
INSTANTIATE_TEST_CASE_P(HOG, HogDetect, testing::ValuesIn(devices()));
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struct CV_GpuHogGetDescriptorsTestRunner : cv::gpu::HOGDescriptor
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{
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CV_GpuHogGetDescriptorsTestRunner(): cv::gpu::HOGDescriptor(cv::Size(64, 128)) {}
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void run()
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{
// Load image (e.g. train data, composed from windows)
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cv::Mat img_rgb = readImage("hog/train_data.png");
ASSERT_TRUE(!img_rgb.empty());
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// Convert to C4
cv::Mat img;
cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
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cv::gpu::GpuMat d_img(img);
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// Convert train images into feature vectors (train table)
cv::gpu::GpuMat descriptors, descriptors_by_cols;
getDescriptors(d_img, win_size, descriptors, DESCR_FORMAT_ROW_BY_ROW);
getDescriptors(d_img, win_size, descriptors_by_cols, DESCR_FORMAT_COL_BY_COL);
// Check size of the result train table
wins_per_img_x = 3;
wins_per_img_y = 2;
blocks_per_win_x = 7;
blocks_per_win_y = 15;
block_hist_size = 36;
cv::Size descr_size_expected = cv::Size(blocks_per_win_x * blocks_per_win_y * block_hist_size,
wins_per_img_x * wins_per_img_y);
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ASSERT_EQ(descr_size_expected, descriptors.size());
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// Check both formats of output descriptors are handled correctly
cv::Mat dr(descriptors);
cv::Mat dc(descriptors_by_cols);
for (int i = 0; i < wins_per_img_x * wins_per_img_y; ++i)
{
const float* l = dr.rowRange(i, i + 1).ptr<float>();
const float* r = dc.rowRange(i, i + 1).ptr<float>();
for (int y = 0; y < blocks_per_win_y; ++y)
for (int x = 0; x < blocks_per_win_x; ++x)
for (int k = 0; k < block_hist_size; ++k)
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ASSERT_EQ(l[(y * blocks_per_win_x + x) * block_hist_size + k],
r[(x * blocks_per_win_y + y) * block_hist_size + k]);
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}
/* Now we want to extract the same feature vectors, but from single images. NOTE: results will
be defferent, due to border values interpolation. Using of many small images is slower, however we
wont't call getDescriptors and will use computeBlockHistograms instead of. computeBlockHistograms
works good, it can be checked in the gpu_hog sample */
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img_rgb = readImage("hog/positive1.png");
ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
// Everything is fine with interpolation for left top subimage
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ASSERT_EQ(0.0, cv::norm((cv::Mat)block_hists, (cv::Mat)descriptors.rowRange(0, 1)));
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img_rgb = readImage("hog/positive2.png");
ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(block_hists, descriptors.rowRange(1, 2));
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img_rgb = readImage("hog/negative1.png");
ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(block_hists, descriptors.rowRange(2, 3));
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img_rgb = readImage("hog/negative2.png");
ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(block_hists, descriptors.rowRange(3, 4));
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img_rgb = readImage("hog/positive3.png");
ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(block_hists, descriptors.rowRange(4, 5));
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img_rgb = readImage("hog/negative3.png");
ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
computeBlockHistograms(cv::gpu::GpuMat(img));
compare_inner_parts(block_hists, descriptors.rowRange(5, 6));
}
// Does not compare border value, as interpolation leads to delta
void compare_inner_parts(cv::Mat d1, cv::Mat d2)
{
for (int i = 1; i < blocks_per_win_y - 1; ++i)
for (int j = 1; j < blocks_per_win_x - 1; ++j)
for (int k = 0; k < block_hist_size; ++k)
{
float a = d1.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
float b = d2.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
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ASSERT_FLOAT_EQ(a, b);
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}
}
int wins_per_img_x;
int wins_per_img_y;
int blocks_per_win_x;
int blocks_per_win_y;
int block_hist_size;
};
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struct HogGetDescriptors : testing::TestWithParam<cv::gpu::DeviceInfo>
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{
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cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
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{
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devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
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}
};
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TEST_P(HogGetDescriptors, Accuracy)
{
PRINT_PARAM(devInfo);
ASSERT_NO_THROW(
CV_GpuHogGetDescriptorsTestRunner runner;
runner.run();
);
}
INSTANTIATE_TEST_CASE_P(HOG, HogGetDescriptors, testing::ValuesIn(devices()));
#endif // HAVE_CUDA