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#include "test_precomp.hpp"
#ifdef HAVE_CUDA
using namespace cvtest;
//#define DUMP
struct HOG : testing::TestWithParam<cv::cuda::DeviceInfo>
{
cv::cuda::DeviceInfo devInfo;
cv::Ptr<cv::cuda::HOG> hog;
#ifdef DUMP
std::ofstream f;
#else
std::ifstream f;
#endif
int wins_per_img_x;
int wins_per_img_y;
int blocks_per_win_x;
int blocks_per_win_y;
int block_hist_size;
virtual void SetUp()
{
devInfo = GetParam();
cv::cuda::setDevice(devInfo.deviceID());
hog = cv::cuda::HOG::create();
}
#ifdef DUMP
void dump(const std::vector<cv::Point>& locations)
{
int nlocations = locations.size();
f.write((char*)&nlocations, sizeof(nlocations));
for (int i = 0; i < locations.size(); ++i)
f.write((char*)&locations[i], sizeof(locations[i]));
}
#else
void compare(const std::vector<cv::Point>& locations)
{
// skip block_hists check
int rows, cols;
f.read((char*)&rows, sizeof(rows));
f.read((char*)&cols, sizeof(cols));
for (int i = 0; i < rows; ++i)
{
for (int j = 0; j < cols; ++j)
{
float val;
f.read((char*)&val, sizeof(val));
}
}
int nlocations;
f.read((char*)&nlocations, sizeof(nlocations));
ASSERT_EQ(nlocations, static_cast<int>(locations.size()));
for (int i = 0; i < nlocations; ++i)
{
cv::Point location;
f.read((char*)&location, sizeof(location));
ASSERT_EQ(location, locations[i]);
}
}
#endif
void testDetect(const cv::Mat& img)
{
hog->setGammaCorrection(false);
hog->setSVMDetector(hog->getDefaultPeopleDetector());
std::vector<cv::Point> locations;
// Test detect
hog->detect(loadMat(img), locations);
#ifdef DUMP
dump(locations);
#else
compare(locations);
#endif
// Test detect on smaller image
cv::Mat img2;
cv::resize(img, img2, cv::Size(img.cols / 2, img.rows / 2));
hog->detect(loadMat(img2), locations);
#ifdef DUMP
dump(locations);
#else
compare(locations);
#endif
// Test detect on greater image
cv::resize(img, img2, cv::Size(img.cols * 2, img.rows * 2));
hog->detect(loadMat(img2), locations);
#ifdef DUMP
dump(locations);
#else
compare(locations);
#endif
}
};
// desabled while resize does not fixed
CUDA_TEST_P(HOG, DISABLED_Detect)
{
cv::Mat img_rgb = readImage("hog/road.png");
ASSERT_FALSE(img_rgb.empty());
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());
// Test on color image
cv::Mat img;
cv::cvtColor(img_rgb, img, cv::COLOR_BGR2BGRA);
testDetect(img);
// Test on gray image
cv::cvtColor(img_rgb, img, cv::COLOR_BGR2GRAY);
testDetect(img);
}
CUDA_TEST_P(HOG, GetDescriptors)
{
// Load image (e.g. train data, composed from windows)
cv::Mat img_rgb = readImage("hog/train_data.png");
ASSERT_FALSE(img_rgb.empty());
// Convert to C4
cv::Mat img;
cv::cvtColor(img_rgb, img, cv::COLOR_BGR2BGRA);
cv::cuda::GpuMat d_img(img);
// Convert train images into feature vectors (train table)
cv::cuda::GpuMat descriptors, descriptors_by_cols;
hog->setWinStride(Size(64, 128));
hog->setDescriptorFormat(cv::cuda::HOG::DESCR_FORMAT_ROW_BY_ROW);
hog->compute(d_img, descriptors);
hog->setDescriptorFormat(cv::cuda::HOG::DESCR_FORMAT_COL_BY_COL);
hog->compute(d_img, descriptors_by_cols);
// 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);
ASSERT_EQ(descr_size_expected, descriptors.size());
// 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)
ASSERT_EQ(l[(y * blocks_per_win_x + x) * block_hist_size + k],
r[(x * blocks_per_win_y + y) * block_hist_size + k]);
}
}
INSTANTIATE_TEST_CASE_P(CUDA_ObjDetect, HOG, ALL_DEVICES);
//============== caltech hog tests =====================//
struct CalTech : public ::testing::TestWithParam<std::tr1::tuple<cv::cuda::DeviceInfo, std::string> >
{
cv::cuda::DeviceInfo devInfo;
cv::Mat img;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
img = readImage(GET_PARAM(1), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
}
};
CUDA_TEST_P(CalTech, HOG)
{
cv::cuda::GpuMat d_img(img);
cv::Mat markedImage(img.clone());
cv::Ptr<cv::cuda::HOG> d_hog = cv::cuda::HOG::create();
d_hog->setSVMDetector(d_hog->getDefaultPeopleDetector());
d_hog->setNumLevels(d_hog->getNumLevels() + 32);
std::vector<cv::Rect> found_locations;
d_hog->detectMultiScale(d_img, found_locations);
#if defined (LOG_CASCADE_STATISTIC)
for (int i = 0; i < (int)found_locations.size(); i++)
{
cv::Rect r = found_locations[i];
std::cout << r.x << " " << r.y << " " << r.width << " " << r.height << std::endl;
cv::rectangle(markedImage, r , CV_RGB(255, 0, 0));
}
cv::imshow("Res", markedImage);
cv::waitKey();
#endif
}
INSTANTIATE_TEST_CASE_P(detect, CalTech, testing::Combine(ALL_DEVICES,
::testing::Values<std::string>("caltech/image_00000009_0.png", "caltech/image_00000032_0.png",
"caltech/image_00000165_0.png", "caltech/image_00000261_0.png", "caltech/image_00000469_0.png",
"caltech/image_00000527_0.png", "caltech/image_00000574_0.png")));
//////////////////////////////////////////////////////////////////////////////////////////
/// LBP classifier
PARAM_TEST_CASE(LBP_Read_classifier, cv::cuda::DeviceInfo, int)
{
cv::cuda::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(LBP_Read_classifier, Accuracy)
{
std::string classifierXmlPath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/lbpcascade_frontalface.xml";
cv::Ptr<cv::cuda::CascadeClassifier> d_cascade;
ASSERT_NO_THROW(
d_cascade = cv::cuda::CascadeClassifier::create(classifierXmlPath);
);
ASSERT_FALSE(d_cascade.empty());
}
INSTANTIATE_TEST_CASE_P(CUDA_ObjDetect, LBP_Read_classifier,
testing::Combine(ALL_DEVICES, testing::Values<int>(0)));
PARAM_TEST_CASE(LBP_classify, cv::cuda::DeviceInfo, int)
{
cv::cuda::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(LBP_classify, Accuracy)
{
std::string classifierXmlPath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/lbpcascade_frontalface.xml";
std::string imagePath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/er.png";
cv::CascadeClassifier cpuClassifier(classifierXmlPath);
ASSERT_FALSE(cpuClassifier.empty());
cv::Mat image = cv::imread(imagePath);
image = image.colRange(0, image.cols/2);
cv::Mat grey;
cvtColor(image, grey, cv::COLOR_BGR2GRAY);
ASSERT_FALSE(image.empty());
std::vector<cv::Rect> rects;
cpuClassifier.detectMultiScale(grey, rects);
cv::Mat markedImage = image.clone();
std::vector<cv::Rect>::iterator it = rects.begin();
for (; it != rects.end(); ++it)
cv::rectangle(markedImage, *it, cv::Scalar(255, 0, 0));
cv::Ptr<cv::cuda::CascadeClassifier> gpuClassifier =
cv::cuda::CascadeClassifier::create(classifierXmlPath);
cv::cuda::GpuMat tested(grey);
cv::cuda::GpuMat gpu_rects_buf;
gpuClassifier->detectMultiScale(tested, gpu_rects_buf);
std::vector<cv::Rect> gpu_rects;
gpuClassifier->convert(gpu_rects_buf, gpu_rects);
#if defined (LOG_CASCADE_STATISTIC)
for (size_t i = 0; i < gpu_rects.size(); i++)
{
cv::Rect r = gpu_rects[i];
std::cout << r.x << " " << r.y << " " << r.width << " " << r.height << std::endl;
cv::rectangle(markedImage, r , CV_RGB(255, 0, 0));
}
cv::imshow("Res", markedImage);
cv::waitKey();
#endif
}
INSTANTIATE_TEST_CASE_P(CUDA_ObjDetect, LBP_classify,
testing::Combine(ALL_DEVICES, testing::Values<int>(0)));
#endif // HAVE_CUDA