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

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added comments and tests
This commit is contained in:
ManuelFreudenreich
2015-06-11 13:29:23 +02:00
parent 424c2bddb3
commit 89889ae8ea
4 changed files with 467 additions and 113 deletions
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61
modules/cudaobjdetect/src/hog.cpp
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@@ -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);
// 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));
CV_Assert(cell_size == Size(8, 8));
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)