#include "precomp.hpp"\r
\r
#if !defined (HAVE_CUDA)\r
- \r
+\r
cv::gpu::HOGDescriptor::HOGDescriptor(Size, Size, Size, Size, int, double, double, bool, int) { throw_nogpu(); }\r
size_t cv::gpu::HOGDescriptor::getDescriptorSize() const { throw_nogpu(); return 0; }\r
size_t cv::gpu::HOGDescriptor::getBlockHistogramSize() const { throw_nogpu(); return 0; }\r
\r
#else\r
\r
-namespace cv { namespace gpu { namespace device \r
+namespace cv { namespace gpu { namespace device\r
{\r
- namespace hog \r
+ namespace hog\r
{\r
- void set_up_constants(int nbins, int block_stride_x, int block_stride_y, \r
+ void set_up_constants(int nbins, int block_stride_x, int block_stride_y,\r
int nblocks_win_x, int nblocks_win_y);\r
\r
void compute_hists(int nbins, int block_stride_x, int blovck_stride_y,\r
- int height, int width, const cv::gpu::DevMem2Df& grad, \r
+ int height, int width, const cv::gpu::DevMem2Df& grad,\r
const cv::gpu::DevMem2Db& qangle, float sigma, float* block_hists);\r
\r
- void normalize_hists(int nbins, int block_stride_x, int block_stride_y, \r
+ void normalize_hists(int nbins, int block_stride_x, int block_stride_y,\r
int height, int width, float* block_hists, float threshold);\r
\r
- void classify_hists(int win_height, int win_width, int block_stride_y, \r
- int block_stride_x, int win_stride_y, int win_stride_x, int height, \r
- int width, float* block_hists, float* coefs, float free_coef, \r
+ void classify_hists(int win_height, int win_width, int block_stride_y,\r
+ int block_stride_x, int win_stride_y, int win_stride_x, int height,\r
+ int width, float* block_hists, float* coefs, float free_coef,\r
float threshold, unsigned char* labels);\r
\r
- void extract_descrs_by_rows(int win_height, int win_width, int block_stride_y, int block_stride_x, \r
- int win_stride_y, int win_stride_x, int height, int width, float* block_hists, \r
+ void extract_descrs_by_rows(int win_height, int win_width, int block_stride_y, int block_stride_x,\r
+ int win_stride_y, int win_stride_x, int height, int width, float* block_hists,\r
cv::gpu::DevMem2Df descriptors);\r
- void extract_descrs_by_cols(int win_height, int win_width, int block_stride_y, int block_stride_x, \r
- int win_stride_y, int win_stride_x, int height, int width, float* block_hists, \r
+ void extract_descrs_by_cols(int win_height, int win_width, int block_stride_y, int block_stride_x,\r
+ int win_stride_y, int win_stride_x, int height, int width, float* block_hists,\r
cv::gpu::DevMem2Df descriptors);\r
\r
- void compute_gradients_8UC1(int nbins, int height, int width, const cv::gpu::DevMem2Db& img, \r
+ void compute_gradients_8UC1(int nbins, int height, int width, const cv::gpu::DevMem2Db& img,\r
float angle_scale, cv::gpu::DevMem2Df grad, cv::gpu::DevMem2Db qangle, bool correct_gamma);\r
- void compute_gradients_8UC4(int nbins, int height, int width, const cv::gpu::DevMem2Db& img, \r
+ void compute_gradients_8UC4(int nbins, int height, int width, const cv::gpu::DevMem2Db& img,\r
float angle_scale, cv::gpu::DevMem2Df grad, cv::gpu::DevMem2Db qangle, bool correct_gamma);\r
\r
void resize_8UC1(const cv::gpu::DevMem2Db& src, cv::gpu::DevMem2Db dst);\r
}}}\r
\r
using namespace ::cv::gpu::device;\r
- \r
+\r
cv::gpu::HOGDescriptor::HOGDescriptor(Size win_size_, Size block_size_, Size block_stride_, Size cell_size_,\r
int nbins_, double win_sigma_, double threshold_L2hys_, bool gamma_correction_, int nlevels_)\r
: win_size(win_size_),\r
gamma_correction(gamma_correction_),\r
nlevels(nlevels_)\r
{\r
- CV_Assert((win_size.width - block_size.width ) % block_stride.width == 0 && \r
+ CV_Assert((win_size.width - block_size.width ) % block_stride.width == 0 &&\r
(win_size.height - block_size.height) % block_stride.height == 0);\r
\r
CV_Assert(block_size.width % cell_size.width == 0 && block_size.height % cell_size.height == 0);\r
\r
cv::Size blocks_per_win = numPartsWithin(win_size, block_size, block_stride);\r
hog::set_up_constants(nbins, block_stride.width, block_stride.height, blocks_per_win.width, blocks_per_win.height);\r
-} \r
+}\r
\r
size_t cv::gpu::HOGDescriptor::getDescriptorSize() const\r
{\r
return numPartsWithin(win_size, block_size, block_stride).area() * getBlockHistogramSize();\r
}\r
\r
-size_t cv::gpu::HOGDescriptor::getBlockHistogramSize() const \r
+size_t cv::gpu::HOGDescriptor::getBlockHistogramSize() const\r
{\r
Size cells_per_block = Size(block_size.width / cell_size.width, block_size.height / cell_size.height);\r
return (size_t)(nbins * cells_per_block.area());\r
\r
this->detector.upload(Mat(detector_reordered).reshape(1, 1));\r
\r
- size_t descriptor_size = getDescriptorSize(); \r
+ size_t descriptor_size = getDescriptorSize();\r
free_coef = _detector.size() > descriptor_size ? _detector[descriptor_size] : 0;\r
\r
CV_Assert(checkDetectorSize());\r
\r
cv::gpu::GpuMat cv::gpu::HOGDescriptor::getBuffer(const Size& sz, int type, GpuMat& buf)\r
{\r
- if (buf.empty() || buf.type() != type)\r
- buf.create(sz, type);\r
- else\r
- if (buf.cols < sz.width || buf.rows < sz.height)\r
- buf.create(std::max(buf.rows, sz.height), std::max(buf.cols, sz.width), type); \r
+ if (buf.empty() || buf.type() != type)\r
+ buf.create(sz, type);\r
+ else\r
+ if (buf.cols < sz.width || buf.rows < sz.height)\r
+ buf.create(std::max(buf.rows, sz.height), std::max(buf.cols, sz.width), type);\r
\r
- return buf(Rect(Point(0,0), sz));\r
+ return buf(Rect(Point(0,0), sz));\r
}\r
\r
cv::gpu::GpuMat cv::gpu::HOGDescriptor::getBuffer(int rows, int cols, int type, GpuMat& buf)\r
-{ \r
- return getBuffer(Size(cols, rows), type, buf); \r
+{\r
+ return getBuffer(Size(cols, rows), type, buf);\r
}\r
\r
\r
void cv::gpu::HOGDescriptor::computeGradient(const GpuMat& img, GpuMat& _grad, GpuMat& _qangle)\r
{\r
CV_Assert(img.type() == CV_8UC1 || img.type() == CV_8UC4);\r
- \r
+\r
// grad.create(img.size(), CV_32FC2);\r
_grad = getBuffer(img.size(), CV_32FC2, grad_buf);\r
\r
_qangle = getBuffer(img.size(), CV_8UC2, qangle_buf);\r
\r
float angleScale = (float)(nbins / CV_PI);\r
- switch (img.type()) \r
- {\r
+ switch (img.type())\r
+ {\r
case CV_8UC1:\r
hog::compute_gradients_8UC1(nbins, img.rows, img.cols, img, angleScale, _grad, _qangle, gamma_correction);\r
break;\r
size_t block_hist_size = getBlockHistogramSize();\r
Size blocks_per_img = numPartsWithin(img.size(), block_size, block_stride);\r
\r
- // block_hists.create(1, block_hist_size * blocks_per_img.area(), CV_32F);\r
- block_hists = getBuffer(1, static_cast<int>(block_hist_size * blocks_per_img.area()), CV_32F, block_hists_buf);\r
- \r
- hog::compute_hists(nbins, block_stride.width, block_stride.height, img.rows, img.cols, \r
- grad, qangle, (float)getWinSigma(), block_hists.ptr<float>());\r
+ // block_hists.create(1, block_hist_size * blocks_per_img.area(), CV_32F);\r
+ block_hists = getBuffer(1, static_cast<int>(block_hist_size * blocks_per_img.area()), CV_32F, block_hists_buf);\r
+\r
+ hog::compute_hists(nbins, block_stride.width, block_stride.height, img.rows, img.cols,\r
+ grad, qangle, (float)getWinSigma(), block_hists.ptr<float>());\r
\r
- hog::normalize_hists(nbins, block_stride.width, block_stride.height, img.rows, img.cols, \r
+ hog::normalize_hists(nbins, block_stride.width, block_stride.height, img.rows, img.cols,\r
block_hists.ptr<float>(), (float)threshold_L2hys);\r
}\r
\r
switch (descr_format)\r
{\r
case DESCR_FORMAT_ROW_BY_ROW:\r
- hog::extract_descrs_by_rows(win_size.height, win_size.width, block_stride.height, block_stride.width, \r
+ hog::extract_descrs_by_rows(win_size.height, win_size.width, block_stride.height, block_stride.width,\r
win_stride.height, win_stride.width, img.rows, img.cols, block_hists.ptr<float>(), descriptors);\r
break;\r
case DESCR_FORMAT_COL_BY_COL:\r
- hog::extract_descrs_by_cols(win_size.height, win_size.width, block_stride.height, block_stride.width, \r
+ hog::extract_descrs_by_cols(win_size.height, win_size.width, block_stride.height, block_stride.width,\r
win_stride.height, win_stride.width, img.rows, img.cols, block_hists.ptr<float>(), descriptors);\r
break;\r
default:\r
\r
Size wins_per_img = numPartsWithin(img.size(), win_size, win_stride);\r
// labels.create(1, wins_per_img.area(), CV_8U);\r
- labels = getBuffer(1, wins_per_img.area(), CV_8U, labels_buf);\r
+ labels = getBuffer(1, wins_per_img.area(), CV_8U, labels_buf);\r
\r
- hog::classify_hists(win_size.height, win_size.width, block_stride.height, block_stride.width, \r
- win_stride.height, win_stride.width, img.rows, img.cols, block_hists.ptr<float>(), \r
+ hog::classify_hists(win_size.height, win_size.width, block_stride.height, block_stride.width,\r
+ win_stride.height, win_stride.width, img.rows, img.cols, block_hists.ptr<float>(),\r
detector.ptr<float>(), (float)free_coef, (float)hit_threshold, labels.ptr());\r
\r
labels.download(labels_host);\r
{\r
int y = i / wins_per_img.width;\r
int x = i - wins_per_img.width * y;\r
- if (vec[i]) \r
+ if (vec[i])\r
hits.push_back(Point(x * win_stride.width, y * win_stride.height));\r
}\r
}\r
\r
\r
\r
-void cv::gpu::HOGDescriptor::detectMultiScale(const GpuMat& img, vector<Rect>& found_locations, double hit_threshold, \r
- Size win_stride, Size padding, double scale0, int group_threshold)\r
+void cv::gpu::HOGDescriptor::detectMultiScale(const GpuMat& img, vector<Rect>& found_locations, double hit_threshold,\r
+ Size win_stride, Size padding, double scale0, int group_threshold)\r
{\r
\r
- CV_Assert(img.type() == CV_8UC1 || img.type() == CV_8UC4);\r
+ CV_Assert(img.type() == CV_8UC1 || img.type() == CV_8UC4);\r
\r
vector<double> level_scale;\r
double scale = 1.;\r
for (levels = 0; levels < nlevels; levels++)\r
{\r
level_scale.push_back(scale);\r
- if (cvRound(img.cols/scale) < win_size.width || \r
+ if (cvRound(img.cols/scale) < win_size.width ||\r
cvRound(img.rows/scale) < win_size.height || scale0 <= 1)\r
break;\r
scale *= scale0;\r
}\r
levels = std::max(levels, 1);\r
level_scale.resize(levels);\r
- image_scales.resize(levels);\r
+ image_scales.resize(levels);\r
\r
- std::vector<Rect> all_candidates; \r
+ std::vector<Rect> all_candidates;\r
vector<Point> locations;\r
\r
for (size_t i = 0; i < level_scale.size(); i++)\r
{\r
- double _scale = level_scale[i];\r
- Size sz(cvRound(img.cols / _scale), cvRound(img.rows / _scale));\r
+ scale = level_scale[i];\r
+ Size sz(cvRound(img.cols / scale), cvRound(img.rows / scale));\r
GpuMat smaller_img;\r
\r
if (sz == img.size())\r
smaller_img = img;\r
else\r
- { \r
+ {\r
image_scales[i].create(sz, img.type());\r
- switch (img.type()) \r
- {\r
+ switch (img.type())\r
+ {\r
case CV_8UC1: hog::resize_8UC1(img, image_scales[i]); break;\r
case CV_8UC4: hog::resize_8UC4(img, image_scales[i]); break;\r
}\r
- smaller_img = image_scales[i];\r
+ smaller_img = image_scales[i];\r
}\r
\r
detect(smaller_img, locations, hit_threshold, win_stride, padding);\r
all_candidates.push_back(Rect(Point2d((CvPoint)locations[j]) * scale, scaled_win_size));\r
}\r
\r
- found_locations.assign(all_candidates.begin(), all_candidates.end()); \r
+ found_locations.assign(all_candidates.begin(), all_candidates.end());\r
groupRectangles(found_locations, group_threshold, 0.2/*magic number copied from CPU version*/);\r
}\r
\r
-int cv::gpu::HOGDescriptor::numPartsWithin(int size, int part_size, int stride) \r
+int cv::gpu::HOGDescriptor::numPartsWithin(int size, int part_size, int stride)\r
{\r
return (size - part_size + stride) / stride;\r
}\r
\r
-cv::Size cv::gpu::HOGDescriptor::numPartsWithin(cv::Size size, cv::Size part_size, cv::Size stride) \r
+cv::Size cv::gpu::HOGDescriptor::numPartsWithin(cv::Size size, cv::Size part_size, cv::Size stride)\r
{\r
return Size(numPartsWithin(size.width, part_size.width, stride.width), numPartsWithin(size.height, part_size.height, stride.height));\r
}\r
std::vector<float> cv::gpu::HOGDescriptor::getPeopleDetector48x96()\r
{\r
static const float detector[] = {\r
- 0.294350f, -0.098796f, -0.129522f, 0.078753f, 0.387527f, 0.261529f, \r
- 0.145939f, 0.061520f, 0.328699f, 0.227148f, -0.066467f, -0.086723f, \r
- 0.047559f, 0.106714f, 0.037897f, 0.111461f, -0.024406f, 0.304769f, \r
- 0.254676f, -0.069235f, 0.082566f, 0.147260f, 0.326969f, 0.148888f, \r
- 0.055270f, -0.087985f, 0.261720f, 0.143442f, 0.026812f, 0.238212f, \r
- 0.194020f, 0.056341f, -0.025854f, -0.034444f, -0.156631f, 0.205174f, \r
- 0.089008f, -0.139811f, -0.100147f, -0.037830f, -0.029230f, -0.055641f, \r
- 0.033248f, -0.016512f, 0.155244f, 0.247315f, -0.124694f, -0.048414f, \r
- -0.062219f, 0.193683f, 0.004574f, 0.055089f, 0.093565f, 0.167712f, \r
- 0.167581f, 0.018895f, 0.215258f, 0.122609f, 0.090520f, -0.067219f, \r
- -0.049029f, -0.099615f, 0.241804f, -0.094893f, -0.176248f, 0.001727f, \r
- -0.134473f, 0.104442f, 0.050942f, 0.081165f, 0.072156f, 0.121646f, \r
- 0.002656f, -0.297974f, -0.133587f, -0.060121f, -0.092515f, -0.048974f, \r
- -0.084754f, -0.180111f, -0.038590f, 0.086283f, -0.134636f, -0.107249f, \r
- 0.132890f, 0.141556f, 0.249425f, 0.130273f, -0.030031f, 0.073212f, \r
- -0.008155f, 0.019931f, 0.071688f, 0.000300f, -0.019525f, -0.021725f, \r
- -0.040993f, -0.086841f, 0.070124f, 0.240033f, 0.265350f, 0.043208f, \r
- 0.166754f, 0.091453f, 0.060916f, -0.036972f, -0.091043f, 0.079873f, \r
- 0.219781f, 0.158102f, -0.140618f, -0.043016f, 0.124802f, 0.093668f, \r
- 0.103208f, 0.094872f, 0.080541f, 0.137711f, 0.160566f, -0.169231f, \r
- 0.013983f, 0.309508f, -0.004217f, -0.057200f, -0.064489f, 0.014066f, \r
- 0.361009f, 0.251328f, -0.080983f, -0.044183f, 0.061436f, -0.037381f, \r
- -0.078786f, 0.030993f, 0.066314f, 0.037683f, 0.152325f, -0.091683f, \r
- 0.070203f, 0.217856f, 0.036435f, -0.076462f, 0.006254f, -0.094431f, \r
- 0.154829f, -0.023038f, -0.196961f, -0.024594f, 0.178465f, -0.050139f, \r
- -0.045932f, -0.000965f, 0.109112f, 0.046165f, -0.159373f, -0.008713f, \r
- 0.041307f, 0.097129f, -0.057211f, -0.064599f, 0.077165f, 0.176167f, \r
- 0.138322f, 0.065753f, -0.104950f, 0.017933f, 0.136255f, -0.011598f, \r
- 0.047007f, 0.080550f, 0.068619f, 0.084661f, -0.035493f, -0.091314f, \r
- -0.041411f, 0.060971f, -0.101912f, -0.079870f, -0.085977f, -0.022686f, \r
- 0.079788f, -0.098064f, -0.054603f, 0.040383f, 0.300794f, 0.128603f, \r
- 0.094844f, 0.047407f, 0.101825f, 0.061832f, -0.162160f, -0.204553f, \r
- -0.035165f, 0.101450f, -0.016641f, -0.027140f, -0.134392f, -0.008743f, \r
- 0.102331f, 0.114853f, 0.009644f, 0.062823f, 0.237339f, 0.167843f, \r
- 0.053066f, -0.012592f, 0.043158f, 0.002305f, 0.065001f, -0.038929f, \r
- -0.020356f, 0.152343f, 0.043469f, -0.029967f, -0.042948f, 0.032481f, \r
- 0.068488f, -0.110840f, -0.111083f, 0.111980f, -0.002072f, -0.005562f, \r
- 0.082926f, 0.006635f, -0.108153f, 0.024242f, -0.086464f, -0.189884f, \r
- -0.017492f, 0.191456f, -0.007683f, -0.128769f, -0.038017f, -0.132380f, \r
- 0.091926f, 0.079696f, -0.106728f, -0.007656f, 0.172744f, 0.011576f, \r
- 0.009883f, 0.083258f, -0.026516f, 0.145534f, 0.153924f, -0.130290f, \r
- -0.108945f, 0.124490f, -0.003186f, -0.100485f, 0.015024f, -0.060512f, \r
- 0.026288f, -0.086713f, -0.169012f, 0.076517f, 0.215778f, 0.043701f, \r
- -0.131642f, -0.012585f, -0.045181f, -0.118183f, -0.241544f, -0.167293f, \r
- -0.020107f, -0.019917f, -0.101827f, -0.107096f, -0.010503f, 0.044938f, \r
- 0.189680f, 0.217119f, -0.046086f, 0.044508f, 0.199716f, -0.036004f, \r
- -0.148927f, 0.013355f, -0.078279f, 0.030451f, 0.056301f, -0.024609f, \r
- 0.083224f, 0.099533f, -0.039432f, -0.138880f, 0.005482f, -0.024120f, \r
- -0.140468f, -0.066381f, -0.017057f, 0.009260f, -0.058004f, -0.028486f, \r
- -0.061610f, 0.007483f, -0.158309f, -0.150687f, -0.044595f, -0.105121f, \r
- -0.045763f, -0.006618f, -0.024419f, -0.117713f, -0.119366f, -0.175941f, \r
- -0.071542f, 0.119027f, 0.111362f, 0.043080f, 0.034889f, 0.093003f, \r
- 0.007842f, 0.057368f, -0.108834f, -0.079968f, 0.230959f, 0.020205f, \r
- 0.011470f, 0.098877f, 0.101310f, -0.030215f, -0.018018f, -0.059552f, \r
- -0.106157f, 0.021866f, -0.036471f, 0.080051f, 0.041165f, -0.082101f, \r
- 0.117726f, 0.030961f, -0.054763f, -0.084102f, -0.185778f, -0.061305f, \r
- -0.038089f, -0.110728f, -0.264010f, 0.076675f, -0.077111f, -0.137644f, \r
- 0.036232f, 0.277995f, 0.019116f, 0.107738f, 0.144003f, 0.080304f, \r
- 0.215036f, 0.228897f, 0.072713f, 0.077773f, 0.120168f, 0.075324f, \r
- 0.062730f, 0.122478f, -0.049008f, 0.164912f, 0.162450f, 0.041246f, \r
- 0.009891f, -0.097827f, -0.038700f, -0.023027f, -0.120020f, 0.203364f, \r
- 0.248474f, 0.149810f, -0.036276f, -0.082814f, -0.090343f, -0.027143f, \r
- -0.075689f, -0.320310f, -0.000500f, -0.143334f, -0.065077f, -0.186936f, \r
- 0.129372f, 0.116431f, 0.181699f, 0.170436f, 0.418854f, 0.460045f, \r
- 0.333719f, 0.230515f, 0.047822f, -0.044954f, -0.068086f, 0.140179f, \r
- -0.044821f, 0.085550f, 0.092483f, -0.107296f, -0.130670f, -0.206629f, \r
- 0.114601f, -0.317869f, -0.076663f, 0.038680f, 0.212753f, -0.016059f, \r
- -0.126526f, -0.163602f, 0.210154f, 0.099887f, -0.126366f, 0.118453f, \r
- 0.019309f, -0.021611f, -0.096499f, -0.111809f, -0.200489f, 0.142854f, \r
- 0.228840f, -0.353346f, -0.179151f, 0.116834f, 0.252389f, -0.031728f, \r
- -0.188135f, -0.158998f, 0.386523f, 0.122315f, 0.209944f, 0.394023f, \r
- 0.359030f, 0.260717f, 0.170335f, 0.013683f, -0.142596f, -0.026138f, \r
- -0.011878f, -0.150519f, 0.047159f, -0.107062f, -0.147347f, -0.187689f, \r
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+ 0.006273f, 0.020865f, 0.338695f, 0.256244f, -0.043927f, 0.115617f,\r
+ 0.003296f, 0.173965f, 0.021318f, -0.040936f, -0.118932f, 0.182380f,\r
+ 0.235922f, -0.053233f, -0.015053f, -0.101057f, 0.095341f, 0.051111f,\r
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+ 0.192105f, -0.048931f, 0.003086f, -0.010681f, 0.074698f, -0.016263f,\r
+ 0.096063f, 0.060267f, -0.007277f, 0.139139f, -0.080635f, 0.036628f,\r
+ 0.086058f, 0.131979f, 0.085707f, 0.025301f, 0.226094f, 0.194759f,\r
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+ 0.007530f, 0.240268f, 0.073270f, 0.013263f, 0.175323f, 0.012082f,\r
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+ -0.046993f, 0.139459f, 0.020272f, -0.123161f, 0.175269f, 0.105217f,\r
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+ -0.066768f, -0.001090f, -0.070419f, -0.004839f, -0.001504f, 0.034483f,\r
+ -0.044954f, -0.050336f, -0.088638f, -0.174782f, -0.116082f, -0.205507f,\r
+ 0.015587f, -0.042839f, -0.096879f, -0.144097f, -0.050268f, -0.196796f,\r
+ 0.109639f, 0.271411f, 0.173732f, 0.108070f, 0.156437f, 0.124255f,\r
+ 0.097242f, 0.238693f, 0.083941f, 0.109105f, 0.223940f, 0.267188f,\r
+ 0.027385f, 0.025819f, 0.125070f, 0.093738f, 0.040353f, 0.038645f,\r
+ -0.012730f, 0.144063f, 0.052931f, -0.009138f, 0.084193f, 0.160272f,\r
+ -0.041366f, 0.011951f, -0.121446f, -0.106713f, -0.047566f, 0.047984f,\r
+ -0.255224f, -0.076116f, 0.098685f, -0.150845f, -0.171513f, -0.156590f,\r
+ 0.058331f, 0.187493f, 0.413018f, 0.554265f, 0.372242f, 0.237943f,\r
+ 0.124571f, 0.110829f, 0.010322f, -0.174477f, -0.067627f, -0.001979f,\r
+ 0.142913f, 0.040597f, 0.019907f, 0.025963f, -0.043585f, -0.120732f,\r
+ 0.099937f, 0.091059f, 0.247307f, 0.204226f, -0.042753f, -0.068580f,\r
+ -0.119002f, 0.026722f, 0.034853f, -0.060934f, -0.025054f, -0.093026f,\r
+ -0.035372f, -0.233209f, -0.049869f, -0.039151f, -0.022279f, -0.065380f,\r
-9.063785f };\r
return vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));\r
}\r
projects / profile / ivi / opencv.git / commitdiff