Skip to content

O
opencv
Commit 89889ae8 authored by ManuelFreudenreich's avatar ManuelFreudenreich
Browse files
Options

changed hog to work with variable parameters and changed the hog sample to test… 

changed hog to work with variable parameters and changed the hog sample to test it with more options

added comments and tests
parent 424c2bdd
Expand all Show whitespace changes
Inline Side-by-side
Showing with 333 additions and 32 deletions
modules/cudaobjdetect/src/cuda/hog.cu
View file @ 89889ae8
This diff is collapsed.
modules/cudaobjdetect/src/hog.cpp
View file @ 89889ae8
......@@ -51,34 +51,45 @@ Ptr<cuda::HOG> cv::cuda::HOG::create(Size, Size, Size, Size, int) { throw_no_cud
#else
/****************************************************************************************\
The code below is implementation of HOG (Histogram-of-Oriented Gradients)
descriptor and object detection, introduced by Navneet Dalal and Bill Triggs.
The computed feature vectors are compatible with the
INRIA Object Detection and Localization Toolkit
(http://pascal.inrialpes.fr/soft/olt/)
\****************************************************************************************/
namespace cv { namespace cuda { namespace device
{
namespace hog
{
void set_up_constants(int nbins, int block_stride_x, int block_stride_y,
int nblocks_win_x, int nblocks_win_y);
int nblocks_win_x, int nblocks_win_y,
int ncells_block_x, int ncells_block_y);
void compute_hists(int nbins, int block_stride_x, int blovck_stride_y,
int height, int width, const cv::cuda::PtrStepSzf& grad,
const cv::cuda::PtrStepSzb& qangle, float sigma, float* block_hists);
void compute_hists(int nbins, int block_stride_x, int block_stride_y,
int height, int width, const PtrStepSzf& grad,
const PtrStepSzb& qangle, float sigma, float* block_hists,
int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y);
void normalize_hists(int nbins, int block_stride_x, int block_stride_y,
int height, int width, float* block_hists, float threshold);
int height, int width, float* block_hists, float threshold, int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y);
void classify_hists(int win_height, int win_width, int block_stride_y,
int block_stride_x, int win_stride_y, int win_stride_x, int height,
int width, float* block_hists, float* coefs, float free_coef,
float threshold, unsigned char* labels);
float threshold, int cell_size_x, int ncells_block_x, unsigned char* labels);
void compute_confidence_hists(int win_height, int win_width, int block_stride_y, int block_stride_x,
int win_stride_y, int win_stride_x, int height, int width, float* block_hists,
float* coefs, float free_coef, float threshold, float *confidences);
float* coefs, float free_coef, float threshold, int cell_size_x, int ncells_block_x, float *confidences);
void extract_descrs_by_rows(int win_height, int win_width, int block_stride_y, int block_stride_x,
int win_stride_y, int win_stride_x, int height, int width, float* block_hists,
int win_stride_y, int win_stride_x, int height, int width, float* block_hists, int cell_size_x, int ncells_block_x,
cv::cuda::PtrStepSzf descriptors);
void extract_descrs_by_cols(int win_height, int win_width, int block_stride_y, int block_stride_x,
int win_stride_y, int win_stride_x, int height, int width, float* block_hists,
int win_stride_y, int win_stride_x, int height, int width, float* block_hists, int cell_size_x, int ncells_block_x,
cv::cuda::PtrStepSzf descriptors);
void compute_gradients_8UC1(int nbins, int height, int width, const cv::cuda::PtrStepSzb& img,
......@@ -167,6 +178,7 @@ namespace
double scale0_;
int group_threshold_;
int descr_format_;
Size cells_per_block_;
private:
int getTotalHistSize(Size img_size) const;
......@@ -197,7 +209,8 @@ namespace
win_stride_(block_stride),
scale0_(1.05),
group_threshold_(2),
descr_format_(DESCR_FORMAT_COL_BY_COL)
descr_format_(DESCR_FORMAT_COL_BY_COL),
cells_per_block_(block_size.width / cell_size.width, block_size.height / cell_size.height)
{
CV_Assert((win_size.width - block_size.width ) % block_stride.width == 0 &&
(win_size.height - block_size.height) % block_stride.height == 0);
......@@ -205,12 +218,13 @@ namespace
CV_Assert(block_size.width % cell_size.width == 0 &&
block_size.height % cell_size.height == 0);
CV_Assert(block_stride == cell_size);
CV_Assert(cell_size == Size(8, 8));
// Navneet Dalal and Bill Triggs. Histograms of oriented gradients for
// human detection. In International Conference on Computer Vision and
// Pattern Recognition, volume 2, pages 886–893, June 2005
// http://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf (28.07.2015) [Figure 5]
CV_Assert(block_stride == (block_size / 2));
Size cells_per_block(block_size.width / cell_size.width, block_size.height / cell_size.height);
CV_Assert(cells_per_block == Size(2, 2));
CV_Assert(cell_size.width == cell_size.height);
}
static int numPartsWithin(int size, int part_size, int stride)
......@@ -231,8 +245,7 @@ namespace
size_t HOG_Impl::getBlockHistogramSize() const
{
Size cells_per_block(block_size_.width / cell_size_.width, block_size_.height / cell_size_.height);
return nbins_ * cells_per_block.area();
return nbins_ * cells_per_block_.area();
}
double HOG_Impl::getWinSigma() const
......@@ -313,6 +326,7 @@ namespace
detector_.ptr<float>(),
(float)free_coef_,
(float)hit_threshold_,
cell_size_.width, cells_per_block_.width,
labels.ptr());
Mat labels_host;
......@@ -339,6 +353,7 @@ namespace
detector_.ptr<float>(),
(float)free_coef_,
(float)hit_threshold_,
cell_size_.width, cells_per_block_.width,
labels.ptr<float>());
Mat labels_host;
......@@ -465,6 +480,7 @@ namespace
win_stride_.height, win_stride_.width,
img.rows, img.cols,
block_hists.ptr<float>(),
cell_size_.width, cells_per_block_.width,
descriptors);
break;
case DESCR_FORMAT_COL_BY_COL:
......@@ -473,6 +489,7 @@ namespace
win_stride_.height, win_stride_.width,
img.rows, img.cols,
block_hists.ptr<float>(),
cell_size_.width, cells_per_block_.width,
descriptors);
break;
default:
......@@ -490,7 +507,7 @@ namespace
void HOG_Impl::computeBlockHistograms(const GpuMat& img, GpuMat& block_hists)
{
cv::Size blocks_per_win = numPartsWithin(win_size_, block_size_, block_stride_);
hog::set_up_constants(nbins_, block_stride_.width, block_stride_.height, blocks_per_win.width, blocks_per_win.height);
hog::set_up_constants(nbins_, block_stride_.width, block_stride_.height, blocks_per_win.width, blocks_per_win.height, cells_per_block_.width, cells_per_block_.height);
BufferPool pool(Stream::Null());
......@@ -505,13 +522,17 @@ namespace
img.rows, img.cols,
grad, qangle,
(float)getWinSigma(),
block_hists.ptr<float>());
block_hists.ptr<float>(),
cell_size_.width, cell_size_.height,
cells_per_block_.width, cells_per_block_.height);
hog::normalize_hists(nbins_,
block_stride_.width, block_stride_.height,
img.rows, img.cols,
block_hists.ptr<float>(),
(float)threshold_L2hys_);
(float)threshold_L2hys_,
cell_size_.width, cell_size_.height,
cells_per_block_.width, cells_per_block_.height);
}
void HOG_Impl::computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle)
......
modules/cudaobjdetect/test/test_objdetect.cpp
View file @ 89889ae8
......@@ -217,9 +217,9 @@ CUDA_TEST_P(HOG, GetDescriptors)
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> >
......@@ -269,8 +269,204 @@ INSTANTIATE_TEST_CASE_P(detect, CalTech, testing::Combine(ALL_DEVICES,
"caltech/image_00000527_0.png", "caltech/image_00000574_0.png")));
//------------------------variable GPU HOG Tests------------------------//
struct Hog_var : public ::testing::TestWithParam<std::tr1::tuple<cv::cuda::DeviceInfo, std::string> >
{
cv::cuda::DeviceInfo devInfo;
cv::Mat img, c_img;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
cv::Rect roi(0, 0, 16, 32);
img = readImage(GET_PARAM(1), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
c_img = img(roi);
}
};
CUDA_TEST_P(Hog_var, HOG)
{
cv::cuda::GpuMat _img(c_img);
cv::cuda::GpuMat d_img;
int win_stride_width = 8;int win_stride_height = 8;
int win_width = 16;
int block_width = 8;
int block_stride_width = 4;int block_stride_height = 4;
int cell_width = 4;
int nbins = 9;
Size win_stride(win_stride_width, win_stride_height);
Size win_size(win_width, win_width * 2);
Size block_size(block_width, block_width);
Size block_stride(block_stride_width, block_stride_height);
Size cell_size(cell_width, cell_width);
cv::Ptr<cv::cuda::HOG> gpu_hog = cv::cuda::HOG::create(win_size, block_size, block_stride, cell_size, nbins);
gpu_hog->setNumLevels(13);
gpu_hog->setHitThreshold(0);
gpu_hog->setWinStride(win_stride);
gpu_hog->setScaleFactor(1.05);
gpu_hog->setGroupThreshold(8);
gpu_hog->compute(_img, d_img);
vector<float> gpu_desc_vec;
ASSERT_TRUE(gpu_desc_vec.empty());
cv::Mat R(d_img);
cv::HOGDescriptor cpu_hog(win_size, block_size, block_stride, cell_size, nbins);
cpu_hog.nlevels = 13;
vector<float> cpu_desc_vec;
ASSERT_TRUE(cpu_desc_vec.empty());
cpu_hog.compute(c_img, cpu_desc_vec, win_stride, Size(0,0));
}
INSTANTIATE_TEST_CASE_P(detect, Hog_var, testing::Combine(ALL_DEVICES,
::testing::Values<std::string>("/hog/road.png")));
struct Hog_var_cell : public ::testing::TestWithParam<std::tr1::tuple<cv::cuda::DeviceInfo, std::string> >
{
cv::cuda::DeviceInfo devInfo;
cv::Mat img, c_img, c_img2, c_img3, c_img4;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
cv::Rect roi(0, 0, 48, 96);
img = readImage(GET_PARAM(1), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
c_img = img(roi);
cv::Rect roi2(0, 0, 54, 108);
c_img2 = img(roi2);
cv::Rect roi3(0, 0, 64, 128);
c_img3 = img(roi3);
cv::Rect roi4(0, 0, 32, 64);
c_img4 = img(roi4);
}
};
CUDA_TEST_P(Hog_var_cell, HOG)
{
cv::cuda::GpuMat _img(c_img);
cv::cuda::GpuMat _img2(c_img2);
cv::cuda::GpuMat _img3(c_img3);
cv::cuda::GpuMat _img4(c_img4);
cv::cuda::GpuMat d_img;
ASSERT_FALSE(_img.empty());
ASSERT_TRUE(d_img.empty());
int win_stride_width = 8;int win_stride_height = 8;
int win_width = 48;
int block_width = 16;
int block_stride_width = 8;int block_stride_height = 8;
int cell_width = 8;
int nbins = 9;
Size win_stride(win_stride_width, win_stride_height);
Size win_size(win_width, win_width * 2);
Size block_size(block_width, block_width);
Size block_stride(block_stride_width, block_stride_height);
Size cell_size(cell_width, cell_width);
cv::Ptr<cv::cuda::HOG> gpu_hog = cv::cuda::HOG::create(win_size, block_size, block_stride, cell_size, nbins);
gpu_hog->setNumLevels(13);
gpu_hog->setHitThreshold(0);
gpu_hog->setWinStride(win_stride);
gpu_hog->setScaleFactor(1.05);
gpu_hog->setGroupThreshold(8);
gpu_hog->compute(_img, d_img);
//------------------------------------------------------------------------------
cv::cuda::GpuMat d_img2;
ASSERT_TRUE(d_img2.empty());
int win_stride_width2 = 8;int win_stride_height2 = 8;
int win_width2 = 48;
int block_width2 = 16;
int block_stride_width2 = 8;int block_stride_height2 = 8;
int cell_width2 = 4;
Size win_stride2(win_stride_width2, win_stride_height2);
Size win_size2(win_width2, win_width2 * 2);
Size block_size2(block_width2, block_width2);
Size block_stride2(block_stride_width2, block_stride_height2);
Size cell_size2(cell_width2, cell_width2);
cv::Ptr<cv::cuda::HOG> gpu_hog2 = cv::cuda::HOG::create(win_size2, block_size2, block_stride2, cell_size2, nbins);
gpu_hog2->setWinStride(win_stride2);
gpu_hog2->compute(_img, d_img2);
//------------------------------------------------------------------------------
cv::cuda::GpuMat d_img3;
ASSERT_TRUE(d_img3.empty());
int win_stride_width3 = 9;int win_stride_height3 = 9;
int win_width3 = 54;
int block_width3 = 18;
int block_stride_width3 = 9;int block_stride_height3 = 9;
int cell_width3 = 6;
Size win_stride3(win_stride_width3, win_stride_height3);
Size win_size3(win_width3, win_width3 * 2);
Size block_size3(block_width3, block_width3);
Size block_stride3(block_stride_width3, block_stride_height3);
Size cell_size3(cell_width3, cell_width3);
cv::Ptr<cv::cuda::HOG> gpu_hog3 = cv::cuda::HOG::create(win_size3, block_size3, block_stride3, cell_size3, nbins);
gpu_hog3->setWinStride(win_stride3);
gpu_hog3->compute(_img2, d_img3);
//------------------------------------------------------------------------------
cv::cuda::GpuMat d_img4;
ASSERT_TRUE(d_img4.empty());
int win_stride_width4 = 16;int win_stride_height4 = 16;
int win_width4 = 64;
int block_width4 = 32;
int block_stride_width4 = 16;int block_stride_height4 = 16;
int cell_width4 = 8;
Size win_stride4(win_stride_width4, win_stride_height4);
Size win_size4(win_width4, win_width4 * 2);
Size block_size4(block_width4, block_width4);
Size block_stride4(block_stride_width4, block_stride_height4);
Size cell_size4(cell_width4, cell_width4);
cv::Ptr<cv::cuda::HOG> gpu_hog4 = cv::cuda::HOG::create(win_size4, block_size4, block_stride4, cell_size4, nbins);
gpu_hog4->setWinStride(win_stride4);
gpu_hog4->compute(_img3, d_img4);
//------------------------------------------------------------------------------
cv::cuda::GpuMat d_img5;
ASSERT_TRUE(d_img5.empty());
int win_stride_width5 = 16;int win_stride_height5 = 16;
int win_width5 = 64;
int block_width5 = 32;
int block_stride_width5 = 16;int block_stride_height5 = 16;
int cell_width5 = 16;
Size win_stride5(win_stride_width5, win_stride_height5);
Size win_size5(win_width5, win_width5 * 2);
Size block_size5(block_width5, block_width5);
Size block_stride5(block_stride_width5, block_stride_height5);
Size cell_size5(cell_width5, cell_width5);
cv::Ptr<cv::cuda::HOG> gpu_hog5 = cv::cuda::HOG::create(win_size5, block_size5, block_stride5, cell_size5, nbins);
gpu_hog5->setWinStride(win_stride5);
gpu_hog5->compute(_img3, d_img5);
//------------------------------------------------------------------------------
}
INSTANTIATE_TEST_CASE_P(detect, Hog_var_cell, testing::Combine(ALL_DEVICES,
::testing::Values<std::string>("/hog/road.png")));
//////////////////////////////////////////////////////////////////////////////////////////
/// LBP classifier
......
samples/gpu/hog.cpp
View file @ 89889ae8
......@@ -22,10 +22,14 @@ public:
static Args read(int argc, char** argv);
string src;
bool src_is_folder;
bool src_is_video;
bool src_is_camera;
int camera_id;
bool svm_load;
string svm;
bool write_video;
string dst_video;
double dst_video_fps;
......@@ -44,6 +48,10 @@ public:
int win_width;
int win_stride_width, win_stride_height;
int block_width;
int block_stride_width, block_stride_height;
int cell_width;
int nbins;
bool gamma_corr;
};
......@@ -93,6 +101,9 @@ static void printHelp()
cout << "Histogram of Oriented Gradients descriptor and detector sample.\n"
<< "\nUsage: hog_gpu\n"
<< " (<image>|--video <vide>|--camera <camera_id>) # frames source\n"
<< " or"
<< " (--folder <folder_path>) # load images from folder\n"
<< " [--svm <file> # load svm file"
<< " [--make_gray <true/false>] # convert image to gray one or not\n"
<< " [--resize_src <true/false>] # do resize of the source image or not\n"
<< " [--width <int>] # resized image width\n"
......@@ -100,9 +111,14 @@ static void printHelp()
<< " [--hit_threshold <double>] # classifying plane distance threshold (0.0 usually)\n"
<< " [--scale <double>] # HOG window scale factor\n"
<< " [--nlevels <int>] # max number of HOG window scales\n"
<< " [--win_width <int>] # width of the window (48 or 64)\n"
<< " [--win_width <int>] # width of the window\n"
<< " [--win_stride_width <int>] # distance by OX axis between neighbour wins\n"
<< " [--win_stride_height <int>] # distance by OY axis between neighbour wins\n"
<< " [--block_width <int>] # width of the block\n"
<< " [--block_stride_width <int>] # distance by 0X axis between neighbour blocks\n"
<< " [--block_stride_height <int>] # distance by 0Y axis between neighbour blocks\n"
<< " [--cell_width <int>] # width of the cell\n"
<< " [--nbins <int>] # number of bins\n"
<< " [--gr_threshold <int>] # merging similar rects constant\n"
<< " [--gamma_correct <int>] # do gamma correction or not\n"
<< " [--write_video <bool>] # write video or not\n"
......@@ -142,6 +158,8 @@ Args::Args()
{
src_is_video = false;
src_is_camera = false;
src_is_folder = false;
svm_load = false;
camera_id = 0;
write_video = false;
......@@ -162,6 +180,11 @@ Args::Args()
win_width = 48;
win_stride_width = 8;
win_stride_height = 8;
block_width = 16;
block_stride_width = 8;
block_stride_height = 8;
cell_width = 8;
nbins = 9;
gamma_corr = true;
}
......@@ -186,6 +209,11 @@ Args Args::read(int argc, char** argv)
else if (string(argv[i]) == "--win_width") args.win_width = atoi(argv[++i]);
else if (string(argv[i]) == "--win_stride_width") args.win_stride_width = atoi(argv[++i]);
else if (string(argv[i]) == "--win_stride_height") args.win_stride_height = atoi(argv[++i]);
else if (string(argv[i]) == "--block_width") args.block_width = atoi(argv[++i]);
else if (string(argv[i]) == "--block_stride_width") args.block_stride_width = atoi(argv[++i]);
else if (string(argv[i]) == "--block_stride_height") args.block_stride_height = atoi(argv[++i]);
else if (string(argv[i]) == "--cell_width") args.cell_width = atoi(argv[++i]);
else if (string(argv[i]) == "--nbins") args.nbins = atoi(argv[++i]);
else if (string(argv[i]) == "--gr_threshold") args.gr_threshold = atoi(argv[++i]);
else if (string(argv[i]) == "--gamma_correct") args.gamma_corr = (string(argv[++i]) == "true");
else if (string(argv[i]) == "--write_video") args.write_video = (string(argv[++i]) == "true");
......@@ -194,6 +222,8 @@ Args Args::read(int argc, char** argv)
else if (string(argv[i]) == "--help") printHelp();
else if (string(argv[i]) == "--video") { args.src = argv[++i]; args.src_is_video = true; }
else if (string(argv[i]) == "--camera") { args.camera_id = atoi(argv[++i]); args.src_is_camera = true; }
else if (string(argv[i]) == "--folder") { args.src = argv[++i]; args.src_is_folder = true;}
else if (string(argv[i]) == "--svm") { args.svm = argv[++i]; args.svm_load = true;}
else if (args.src.empty()) args.src = argv[i];
else throw runtime_error((string("unknown key: ") + argv[i]));
}
......@@ -228,16 +258,17 @@ App::App(const Args& s)
gamma_corr = args.gamma_corr;
if (args.win_width != 64 && args.win_width != 48)
args.win_width = 64;
cout << "Scale: " << scale << endl;
if (args.resize_src)
cout << "Resized source: (" << args.width << ", " << args.height << ")\n";
cout << "Group threshold: " << gr_threshold << endl;
cout << "Levels number: " << nlevels << endl;
cout << "Win width: " << args.win_width << endl;
cout << "Win size: (" << args.win_width << ", " << args.win_width*2 << ")\n";
cout << "Win stride: (" << args.win_stride_width << ", " << args.win_stride_height << ")\n";
cout << "Block size: (" << args.block_width << ", " << args.block_width << ")\n";
cout << "Block stride: (" << args.block_stride_width << ", " << args.block_stride_height << ")\n";
cout << "Cell size: (" << args.cell_width << ", " << args.cell_width << ")\n";
cout << "Bins number: " << args.nbins << endl;
cout << "Hit threshold: " << hit_threshold << endl;
cout << "Gamma correction: " << gamma_corr << endl;
cout << endl;
......@@ -249,22 +280,58 @@ void App::run()
running = true;
cv::VideoWriter video_writer;
Size win_size(args.win_width, args.win_width * 2); //(64, 128) or (48, 96)
Size win_stride(args.win_stride_width, args.win_stride_height);
Size win_size(args.win_width, args.win_width * 2);
Size block_size(args.block_width, args.block_width);
Size block_stride(args.block_stride_width, args.block_stride_height);
Size cell_size(args.cell_width, args.cell_width);
cv::Ptr<cv::cuda::HOG> gpu_hog = cv::cuda::HOG::create(win_size, block_size, block_stride, cell_size, args.nbins);
cv::HOGDescriptor cpu_hog(win_size, block_size, block_stride, cell_size, args.nbins);
if(args.svm_load) {
std::vector<float> svm_model;
const std::string model_file_name = args.svm;
FileStorage ifs(model_file_name, FileStorage::READ);
if (ifs.isOpened()) {
ifs["svm_detector"] >> svm_model;
} else {
const std::string what =
"could not load model for hog classifier from file: "
+ model_file_name;
throw std::runtime_error(what);
}
cv::Ptr<cv::cuda::HOG> gpu_hog = cv::cuda::HOG::create(win_size);
cv::HOGDescriptor cpu_hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9);
// check if the variables are initialized
if (svm_model.empty()) {
const std::string what =
"HoG classifier: svm model could not be loaded from file"
+ model_file_name;
throw std::runtime_error(what);
}
gpu_hog->setSVMDetector(svm_model);
cpu_hog.setSVMDetector(svm_model);
} else {
// Create HOG descriptors and detectors here
Mat detector = gpu_hog->getDefaultPeopleDetector();
gpu_hog->setSVMDetector(detector);
cpu_hog.setSVMDetector(detector);
}
cout << "gpusvmDescriptorSize : " << gpu_hog->getDescriptorSize()
<< endl;
cout << "cpusvmDescriptorSize : " << cpu_hog.getDescriptorSize()
<< endl;
while (running)
{
VideoCapture vc;
Mat frame;
vector<String> filenames;
unsigned int count = 1;
if (args.src_is_video)
{
......@@ -273,6 +340,14 @@ void App::run()
throw runtime_error(string("can't open video file: " + args.src));
vc >> frame;
}
else if (args.src_is_folder) {
String folder = args.src;
cout << folder << endl;
glob(folder, filenames);
frame = imread(filenames[count]); // 0 --> .gitignore
if (!frame.data)
cerr << "Problem loading image from folder!!!" << endl;
}
else if (args.src_is_camera)
{
vc.open(args.camera_id);
......@@ -342,11 +417,20 @@ void App::run()
putText(img_to_show, "Mode: GPU", Point(5, 25), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
else
putText(img_to_show, "Mode: CPU", Point(5, 25), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
putText(img_to_show, "FPS (HOG only): " + hogWorkFps(), Point(5, 65), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
putText(img_to_show, "FPS (total): " + workFps(), Point(5, 105), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
putText(img_to_show, "FPS HOG: " + hogWorkFps(), Point(5, 65), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
putText(img_to_show, "FPS total: " + workFps(), Point(5, 105), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
imshow("opencv_gpu_hog", img_to_show);
if (args.src_is_video || args.src_is_camera) vc >> frame;
if (args.src_is_folder) {
count++;
if (count < filenames.size()) {
frame = imread(filenames[count]);
} else {
Mat empty;
frame = empty;
}
}
workEnd();
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment