diff --git a/modules/objdetect/src/hog.cpp b/modules/objdetect/src/hog.cpp
index 977f5b030..676d6f4b8 100644
--- a/modules/objdetect/src/hog.cpp
+++ b/modules/objdetect/src/hog.cpp
@@ -39,12 +39,12 @@
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
-#include <stdio.h>
+
#include "precomp.hpp"
+
+#include <cstdio>
#include <iterator>
-#ifdef HAVE_IPP
-#include "ipp.h"
-#endif
+
/****************************************************************************************\
The code below is implementation of HOG (Histogram-of-Oriented Gradients)
descriptor and object detection, introduced by Navneet Dalal and Bill Triggs.
@@ -63,6 +63,7 @@ size_t HOGDescriptor::getDescriptorSize() const
blockSize.height % cellSize.height == 0);
CV_Assert((winSize.width - blockSize.width) % blockStride.width == 0 &&
(winSize.height - blockSize.height) % blockStride.height == 0 );
+
return (size_t)nbins*
(blockSize.width/cellSize.width)*
(blockSize.height/cellSize.height)*
@@ -126,17 +127,17 @@ void HOGDescriptor::write(FileStorage& fs, const std::string& objName) const
fs << objName;
fs << "{" CV_TYPE_NAME_HOG_DESCRIPTOR
- << "winSize" << winSize
- << "blockSize" << blockSize
- << "blockStride" << blockStride
- << "cellSize" << cellSize
- << "nbins" << nbins
- << "derivAperture" << derivAperture
- << "winSigma" << getWinSigma()
- << "histogramNormType" << histogramNormType
- << "L2HysThreshold" << L2HysThreshold
- << "gammaCorrection" << gammaCorrection
- << "nlevels" << nlevels;
+ << "winSize" << winSize
+ << "blockSize" << blockSize
+ << "blockStride" << blockStride
+ << "cellSize" << cellSize
+ << "nbins" << nbins
+ << "derivAperture" << derivAperture
+ << "winSigma" << getWinSigma()
+ << "histogramNormType" << histogramNormType
+ << "L2HysThreshold" << L2HysThreshold
+ << "gammaCorrection" << gammaCorrection
+ << "nlevels" << nlevels;
if( !svmDetector.empty() )
fs << "SVMDetector" << svmDetector;
fs << "}";
@@ -172,14 +173,15 @@ void HOGDescriptor::copyTo(HOGDescriptor& c) const
}
void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
- Size paddingTL, Size paddingBR) const
+ Size paddingTL, Size paddingBR) const
{
CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
Size gradsize(img.cols + paddingTL.width + paddingBR.width,
- img.rows + paddingTL.height + paddingBR.height);
+ img.rows + paddingTL.height + paddingBR.height);
grad.create(gradsize, CV_32FC2); // <magnitude*(1-alpha), magnitude*alpha>
qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
+
Size wholeSize;
Point roiofs;
img.locateROI(wholeSize, roiofs);
@@ -188,14 +190,33 @@ void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
int cn = img.channels();
Mat_<float> _lut(1, 256);
- const float* lut = &_lut(0,0);
+ const float* const lut = &_lut(0,0);
+#if CV_SSE2
+ const int indeces[] = { 0, 1, 2, 3 };
+ __m128i idx = _mm_loadu_si128((const __m128i*)indeces);
+ __m128i ifour = _mm_set1_epi32(4);
+ float* const _data = &_lut(0, 0);
+ if( gammaCorrection )
+ for( i = 0; i < 256; i += 4 )
+ {
+ _mm_storeu_ps(_data + i, _mm_sqrt_ps(_mm_cvtepi32_ps(idx)));
+ idx = _mm_add_epi32(idx, ifour);
+ }
+ else
+ for( i = 0; i < 256; i += 4 )
+ {
+ _mm_storeu_ps(_data + i, _mm_cvtepi32_ps(idx));
+ idx = _mm_add_epi32(idx, ifour);
+ }
+#else
if( gammaCorrection )
for( i = 0; i < 256; i++ )
_lut(0,i) = std::sqrt((float)i);
else
for( i = 0; i < 256; i++ )
_lut(0,i) = (float)i;
+#endif
AutoBuffer<int> mapbuf(gradsize.width + gradsize.height + 4);
int* xmap = (int*)mapbuf + 1;
@@ -205,55 +226,42 @@ void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
for( x = -1; x < gradsize.width + 1; x++ )
xmap[x] = borderInterpolate(x - paddingTL.width + roiofs.x,
- wholeSize.width, borderType) - roiofs.x;
+ wholeSize.width, borderType) - roiofs.x;
for( y = -1; y < gradsize.height + 1; y++ )
ymap[y] = borderInterpolate(y - paddingTL.height + roiofs.y,
- wholeSize.height, borderType) - roiofs.y;
+ wholeSize.height, borderType) - roiofs.y;
// x- & y- derivatives for the whole row
int width = gradsize.width;
AutoBuffer<float> _dbuf(width*4);
- float* dbuf = _dbuf;
+ float* const dbuf = _dbuf;
Mat Dx(1, width, CV_32F, dbuf);
Mat Dy(1, width, CV_32F, dbuf + width);
Mat Mag(1, width, CV_32F, dbuf + width*2);
Mat Angle(1, width, CV_32F, dbuf + width*3);
- int _nbins = nbins;
- float angleScale = (float)(_nbins/CV_PI);
-#ifdef HAVE_IPP
- Mat lutimg(img.rows,img.cols,CV_MAKETYPE(CV_32F,cn));
- Mat hidxs(1, width, CV_32F);
- Ipp32f* pHidxs = (Ipp32f*)hidxs.data;
- Ipp32f* pAngles = (Ipp32f*)Angle.data;
-
- IppiSize roiSize;
- roiSize.width = img.cols;
- roiSize.height = img.rows;
-
- for( y = 0; y < roiSize.height; y++ )
+ if (cn == 3)
{
- const uchar* imgPtr = img.data + y*img.step;
- float* imglutPtr = (float*)(lutimg.data + y*lutimg.step);
-
- for( x = 0; x < roiSize.width*cn; x++ )
- {
- imglutPtr[x] = lut[imgPtr[x]];
- }
+ int end = gradsize.width + 2;
+ xmap -= 1, x = 0;
+#if CV_SSE2
+ __m128i ithree = _mm_set1_epi32(3);
+ for ( ; x <= end - 4; x += 4)
+ _mm_storeu_si128((__m128i*)(xmap + x), _mm_mullo_epi16(ithree,
+ _mm_loadu_si128((const __m128i*)(xmap + x))));
+#endif
+ for ( ; x < end; ++x)
+ xmap[x] *= 3;
+ xmap += 1;
}
-#endif
+ float angleScale = (float)(nbins/CV_PI);
for( y = 0; y < gradsize.height; y++ )
{
-#ifdef HAVE_IPP
- const float* imgPtr = (float*)(lutimg.data + lutimg.step*ymap[y]);
- const float* prevPtr = (float*)(lutimg.data + lutimg.step*ymap[y-1]);
- const float* nextPtr = (float*)(lutimg.data + lutimg.step*ymap[y+1]);
-#else
const uchar* imgPtr = img.data + img.step*ymap[y];
const uchar* prevPtr = img.data + img.step*ymap[y-1];
const uchar* nextPtr = img.data + img.step*ymap[y+1];
-#endif
+
float* gradPtr = (float*)grad.ptr(y);
uchar* qanglePtr = (uchar*)qangle.ptr(y);
@@ -262,46 +270,59 @@ void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
for( x = 0; x < width; x++ )
{
int x1 = xmap[x];
-#ifdef HAVE_IPP
- dbuf[x] = (float)(imgPtr[xmap[x+1]] - imgPtr[xmap[x-1]]);
- dbuf[width + x] = (float)(nextPtr[x1] - prevPtr[x1]);
-#else
dbuf[x] = (float)(lut[imgPtr[xmap[x+1]]] - lut[imgPtr[xmap[x-1]]]);
dbuf[width + x] = (float)(lut[nextPtr[x1]] - lut[prevPtr[x1]]);
-#endif
}
}
else
{
- for( x = 0; x < width; x++ )
+ x = 0;
+#if CV_SSE2
+ for( ; x <= width - 4; x += 4 )
{
- int x1 = xmap[x]*3;
+ int x0 = xmap[x], x1 = xmap[x+1], x2 = xmap[x+2], x3 = xmap[x+3];
+ typedef const uchar* const T;
+ T p02 = imgPtr + xmap[x+1], p00 = imgPtr + xmap[x-1];
+ T p12 = imgPtr + xmap[x+2], p10 = imgPtr + xmap[x];
+ T p22 = imgPtr + xmap[x+3], p20 = p02;
+ T p32 = imgPtr + xmap[x+4], p30 = p12;
+
+ __m128 _dx0 = _mm_sub_ps(_mm_set_ps(lut[p32[0]], lut[p22[0]], lut[p12[0]], lut[p02[0]]),
+ _mm_set_ps(lut[p30[0]], lut[p20[0]], lut[p10[0]], lut[p00[0]]));
+ __m128 _dx1 = _mm_sub_ps(_mm_set_ps(lut[p32[1]], lut[p22[1]], lut[p12[1]], lut[p02[1]]),
+ _mm_set_ps(lut[p30[1]], lut[p20[1]], lut[p10[1]], lut[p00[1]]));
+ __m128 _dx2 = _mm_sub_ps(_mm_set_ps(lut[p32[2]], lut[p22[2]], lut[p12[2]], lut[p02[2]]),
+ _mm_set_ps(lut[p30[2]], lut[p20[2]], lut[p10[2]], lut[p00[2]]));
+
+ __m128 _dy0 = _mm_sub_ps(_mm_set_ps(lut[nextPtr[x3]], lut[nextPtr[x2]], lut[nextPtr[x1]], lut[nextPtr[x0]]),
+ _mm_set_ps(lut[prevPtr[x3]], lut[prevPtr[x2]], lut[prevPtr[x1]], lut[prevPtr[x0]]));
+ __m128 _dy1 = _mm_sub_ps(_mm_set_ps(lut[nextPtr[x3+1]], lut[nextPtr[x2+1]], lut[nextPtr[x1+1]], lut[nextPtr[x0+1]]),
+ _mm_set_ps(lut[prevPtr[x3+1]], lut[prevPtr[x2+1]], lut[prevPtr[x1+1]], lut[prevPtr[x0+1]]));
+ __m128 _dy2 = _mm_sub_ps(_mm_set_ps(lut[nextPtr[x3+2]], lut[nextPtr[x2+2]], lut[nextPtr[x1+2]], lut[nextPtr[x0+2]]),
+ _mm_set_ps(lut[prevPtr[x3+2]], lut[prevPtr[x2+2]], lut[prevPtr[x1+2]], lut[prevPtr[x0+2]]));
+
+ __m128 _mag0 = _mm_add_ps(_mm_mul_ps(_dx0, _dx0), _mm_mul_ps(_dy0, _dy0));
+ __m128 _mag1 = _mm_add_ps(_mm_mul_ps(_dx1, _dx1), _mm_mul_ps(_dy1, _dy1));
+ __m128 _mag2 = _mm_add_ps(_mm_mul_ps(_dx2, _dx2), _mm_mul_ps(_dy2, _dy2));
+
+ __m128 mask = _mm_cmpgt_ps(_mag2, _mag1);
+ _dx2 = _mm_or_ps(_mm_and_ps(_dx2, mask), _mm_andnot_ps(mask, _dx1));
+ _dy2 = _mm_or_ps(_mm_and_ps(_dy2, mask), _mm_andnot_ps(mask, _dy1));
+
+ mask = _mm_cmpgt_ps(_mm_max_ps(_mag2, _mag1), _mag0);
+ _dx2 = _mm_or_ps(_mm_and_ps(_dx2, mask), _mm_andnot_ps(mask, _dx0));
+ _dy2 = _mm_or_ps(_mm_and_ps(_dy2, mask), _mm_andnot_ps(mask, _dy0));
+
+ _mm_storeu_ps(dbuf + x, _dx2);
+ _mm_storeu_ps(dbuf + x + width, _dy2);
+ }
+#endif
+ for( ; x < width; x++ )
+ {
+ int x1 = xmap[x];
float dx0, dy0, dx, dy, mag0, mag;
-#ifdef HAVE_IPP
- const float* p2 = imgPtr + xmap[x+1]*3;
- const float* p0 = imgPtr + xmap[x-1]*3;
-
- dx0 = p2[2] - p0[2];
- dy0 = nextPtr[x1+2] - prevPtr[x1+2];
- mag0 = dx0*dx0 + dy0*dy0;
-
- dx = p2[1] - p0[1];
- dy = nextPtr[x1+1] - prevPtr[x1+1];
- mag = dx*dx + dy*dy;
-
- if( mag0 < mag )
- {
- dx0 = dx;
- dy0 = dy;
- mag0 = mag;
- }
-
- dx = p2[0] - p0[0];
- dy = nextPtr[x1] - prevPtr[x1];
- mag = dx*dx + dy*dy;
-#else
- const uchar* p2 = imgPtr + xmap[x+1]*3;
- const uchar* p0 = imgPtr + xmap[x-1]*3;
+ const uchar* p2 = imgPtr + xmap[x+1];
+ const uchar* p0 = imgPtr + xmap[x-1];
dx0 = lut[p2[2]] - lut[p0[2]];
dy0 = lut[nextPtr[x1+2]] - lut[prevPtr[x1+2]];
@@ -310,7 +331,6 @@ void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
dx = lut[p2[1]] - lut[p0[1]];
dy = lut[nextPtr[x1+1]] - lut[prevPtr[x1+1]];
mag = dx*dx + dy*dy;
-
if( mag0 < mag )
{
dx0 = dx;
@@ -321,7 +341,6 @@ void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
dx = lut[p2[0]] - lut[p0[0]];
dy = lut[nextPtr[x1]] - lut[prevPtr[x1]];
mag = dx*dx + dy*dy;
- #endif
if( mag0 < mag )
{
dx0 = dx;
@@ -333,55 +352,83 @@ void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
dbuf[x+width] = dy0;
}
}
-#ifdef HAVE_IPP
- ippsCartToPolar_32f((const Ipp32f*)Dx.data, (const Ipp32f*)Dy.data, (Ipp32f*)Mag.data, pAngles, width);
- for( x = 0; x < width; x++ )
- {
- if(pAngles[x] < 0.f)
- pAngles[x] += (Ipp32f)(CV_PI*2.);
- }
- ippsNormalize_32f(pAngles, pAngles, width, 0.5f/angleScale, 1.f/angleScale);
- ippsFloor_32f(pAngles,(Ipp32f*)hidxs.data,width);
- ippsSub_32f_I((Ipp32f*)hidxs.data,pAngles,width);
- ippsMul_32f_I((Ipp32f*)Mag.data,pAngles,width);
-
- ippsSub_32f_I(pAngles,(Ipp32f*)Mag.data,width);
- ippsRealToCplx_32f((Ipp32f*)Mag.data,pAngles,(Ipp32fc*)gradPtr,width);
-#else
+ // computing angles and magnidutes
cartToPolar( Dx, Dy, Mag, Angle, false );
-#endif
- for( x = 0; x < width; x++ )
+
+ // filling the result matrix
+ x = 0;
+#if CV_SSE2
+ __m128 fhalf = _mm_set1_ps(0.5f), fzero = _mm_setzero_ps();
+ __m128 _angleScale = _mm_set1_ps(angleScale), fone = _mm_set1_ps(1.0f);
+ __m128i ione = _mm_set1_epi32(1), _nbins = _mm_set1_epi32(nbins), izero = _mm_setzero_si128();
+
+ for ( ; x <= width - 4; x += 4)
+ {
+ int x2 = x << 1;
+ __m128 _mag = _mm_loadu_ps(dbuf + x + (width << 1));
+ __m128 _angle = _mm_loadu_ps(dbuf + x + width * 3);
+ _angle = _mm_sub_ps(_mm_mul_ps(_angleScale, _angle), fhalf);
+
+ __m128 sign = _mm_and_ps(fone, _mm_cmplt_ps(_angle, fzero));
+ __m128i _hidx = _mm_cvttps_epi32(_angle);
+ _hidx = _mm_sub_epi32(_hidx, _mm_cvtps_epi32(sign));
+ _angle = _mm_sub_ps(_angle, _mm_cvtepi32_ps(_hidx));
+
+ __m128 ft0 = _mm_mul_ps(_mag, _mm_sub_ps(fone, _angle));
+ __m128 ft1 = _mm_mul_ps(_mag, _angle);
+ __m128 ft2 = _mm_unpacklo_ps(ft0, ft1);
+ __m128 ft3 = _mm_unpackhi_ps(ft0, ft1);
+
+ _mm_storeu_ps(gradPtr + x2, ft2);
+ _mm_storeu_ps(gradPtr + x2 + 4, ft3);
+
+ __m128i mask0 = _mm_sub_epi32(izero, _mm_srli_epi32(_hidx, 31));
+ __m128i it0 = _mm_and_si128(mask0, _nbins);
+ mask0 = _mm_cmplt_epi32(_hidx, _nbins);
+ __m128i it1 = _mm_andnot_si128(mask0, _nbins);
+ _hidx = _mm_add_epi32(_hidx, _mm_sub_epi32(it0, it1));
+
+ it0 = _mm_packus_epi16(_mm_packs_epi32(_hidx, izero), izero);
+ _hidx = _mm_add_epi32(ione, _hidx);
+ _hidx = _mm_and_si128(_hidx, _mm_cmplt_epi32(_hidx, _nbins));
+ it1 = _mm_packus_epi16(_mm_packs_epi32(_hidx, izero), izero);
+ it0 = _mm_unpacklo_epi8(it0, it1);
+
+ _mm_storel_epi64((__m128i*)(qanglePtr + x2), it0);
+ }
+#endif
+ for( ; x < width; x++ )
{
-#ifdef HAVE_IPP
- int hidx = (int)pHidxs[x];
-#else
float mag = dbuf[x+width*2], angle = dbuf[x+width*3]*angleScale - 0.5f;
int hidx = cvFloor(angle);
angle -= hidx;
gradPtr[x*2] = mag*(1.f - angle);
gradPtr[x*2+1] = mag*angle;
-#endif
+
if( hidx < 0 )
- hidx += _nbins;
- else if( hidx >= _nbins )
- hidx -= _nbins;
- assert( (unsigned)hidx < (unsigned)_nbins );
+ hidx += nbins;
+ else if( hidx >= nbins )
+ hidx -= nbins;
+
+ CV_Assert( (unsigned)hidx < (unsigned)nbins );
qanglePtr[x*2] = (uchar)hidx;
hidx++;
- hidx &= hidx < _nbins ? -1 : 0;
+ hidx &= hidx < nbins ? -1 : 0;
qanglePtr[x*2+1] = (uchar)hidx;
}
}
}
-
struct HOGCache
{
struct BlockData
{
- BlockData() : histOfs(0), imgOffset() {}
+ BlockData() :
+ histOfs(0), imgOffset()
+ { }
+
int histOfs;
Point imgOffset;
};
@@ -396,25 +443,26 @@ struct HOGCache
HOGCache();
HOGCache(const HOGDescriptor* descriptor,
- const Mat& img, Size paddingTL, Size paddingBR,
- bool useCache, Size cacheStride);
- virtual ~HOGCache() {};
+ const Mat& img, const Size& paddingTL, const Size& paddingBR,
+ bool useCache, const Size& cacheStride);
+ virtual ~HOGCache() { }
virtual void init(const HOGDescriptor* descriptor,
- const Mat& img, Size paddingTL, Size paddingBR,
- bool useCache, Size cacheStride);
+ const Mat& img, const Size& paddingTL, const Size& paddingBR,
+ bool useCache, const Size& cacheStride);
- Size windowsInImage(Size imageSize, Size winStride) const;
- Rect getWindow(Size imageSize, Size winStride, int idx) const;
+ Size windowsInImage(const Size& imageSize, const Size& winStride) const;
+ Rect getWindow(const Size& imageSize, const Size& winStride, int idx) const;
const float* getBlock(Point pt, float* buf);
virtual void normalizeBlockHistogram(float* histogram) const;
-
+
std::vector<PixData> pixData;
std::vector<BlockData> blockData;
bool useCache;
std::vector<int> ymaxCached;
- Size winSize, cacheStride;
+ Size winSize;
+ Size cacheStride;
Size nblocks, ncells;
int blockHistogramSize;
int count1, count2, count4;
@@ -426,24 +474,23 @@ struct HOGCache
const HOGDescriptor* descriptor;
};
-
-HOGCache::HOGCache()
+HOGCache::HOGCache() :
+ blockHistogramSize(), count1(), count2(), count4()
{
useCache = false;
- blockHistogramSize = count1 = count2 = count4 = 0;
descriptor = 0;
}
HOGCache::HOGCache(const HOGDescriptor* _descriptor,
- const Mat& _img, Size _paddingTL, Size _paddingBR,
- bool _useCache, Size _cacheStride)
+ const Mat& _img, const Size& _paddingTL, const Size& _paddingBR,
+ bool _useCache, const Size& _cacheStride)
{
init(_descriptor, _img, _paddingTL, _paddingBR, _useCache, _cacheStride);
}
void HOGCache::init(const HOGDescriptor* _descriptor,
- const Mat& _img, Size _paddingTL, Size _paddingBR,
- bool _useCache, Size _cacheStride)
+ const Mat& _img, const Size& _paddingTL, const Size& _paddingBR,
+ bool _useCache, const Size& _cacheStride)
{
descriptor = _descriptor;
cacheStride = _cacheStride;
@@ -460,16 +507,18 @@ void HOGCache::init(const HOGDescriptor* _descriptor,
int rawBlockSize = blockSize.width*blockSize.height;
nblocks = Size((winSize.width - blockSize.width)/blockStride.width + 1,
- (winSize.height - blockSize.height)/blockStride.height + 1);
+ (winSize.height - blockSize.height)/blockStride.height + 1);
ncells = Size(blockSize.width/cellSize.width, blockSize.height/cellSize.height);
blockHistogramSize = ncells.width*ncells.height*nbins;
if( useCache )
{
Size cacheSize((grad.cols - blockSize.width)/cacheStride.width+1,
- (winSize.height/cacheStride.height)+1);
+ (winSize.height/cacheStride.height)+1);
+
blockCache.create(cacheSize.height, cacheSize.width*blockHistogramSize);
blockCacheFlags.create(cacheSize);
+
size_t cacheRows = blockCache.rows;
ymaxCached.resize(cacheRows);
for(size_t ii = 0; ii < cacheRows; ii++ )
@@ -480,13 +529,53 @@ void HOGCache::init(const HOGDescriptor* _descriptor,
float sigma = (float)descriptor->getWinSigma();
float scale = 1.f/(sigma*sigma*2);
- for(i = 0; i < blockSize.height; i++)
- for(j = 0; j < blockSize.width; j++)
+ {
+ AutoBuffer<float> di(blockSize.height), dj(blockSize.width);
+ float* _di = (float*)di, *_dj = (float*)dj;
+ float bh = blockSize.height * 0.5f, bw = blockSize.width * 0.5f;
+
+ i = 0;
+ #if CV_SSE2
+ const int a[] = { 0, 1, 2, 3 };
+ __m128i idx = _mm_loadu_si128((__m128i*)a);
+ __m128 _bw = _mm_set1_ps(bw), _bh = _mm_set1_ps(bh);
+ __m128i ifour = _mm_set1_epi32(4);
+
+ for (; i <= blockSize.height - 4; i += 4)
{
- float di = i - blockSize.height*0.5f;
- float dj = j - blockSize.width*0.5f;
- weights(i,j) = std::exp(-(di*di + dj*dj)*scale);
+ __m128 t = _mm_sub_ps(_mm_cvtepi32_ps(idx), _bh);
+ t = _mm_mul_ps(t, t);
+ idx = _mm_add_epi32(idx, ifour);
+ _mm_storeu_ps(_di + i, t);
}
+ #endif
+ for ( ; i < blockSize.height; ++i)
+ {
+ _di[i] = i - bh;
+ _di[i] *= _di[i];
+ }
+
+ j = 0;
+ #if CV_SSE2
+ idx = _mm_loadu_si128((__m128i*)a);
+ for (; j <= blockSize.width - 4; j += 4)
+ {
+ __m128 t = _mm_sub_ps(_mm_cvtepi32_ps(idx), _bw);
+ t = _mm_mul_ps(t, t);
+ idx = _mm_add_epi32(idx, ifour);
+ _mm_storeu_ps(_dj + j, t);
+ }
+ #endif
+ for ( ; j < blockSize.width; ++j)
+ {
+ _dj[j] = j - bw;
+ _dj[j] *= _dj[j];
+ }
+
+ for(i = 0; i < blockSize.height; i++)
+ for(j = 0; j < blockSize.width; j++)
+ weights(i,j) = std::exp(-(_di[i] + _dj[j])*scale);
+ }
blockData.resize(nblocks.width*nblocks.height);
pixData.resize(rawBlockSize*3);
@@ -515,6 +604,7 @@ void HOGCache::init(const HOGDescriptor* _descriptor,
// and also unrolled. Inside we use PixData[k] to access the gradient values and
// update the histogram
//
+
count1 = count2 = count4 = 0;
for( j = 0; j < blockSize.width; j++ )
for( i = 0; i < blockSize.height; i++ )
@@ -527,12 +617,12 @@ void HOGCache::init(const HOGDescriptor* _descriptor,
int icellX1 = icellX0 + 1, icellY1 = icellY0 + 1;
cellX -= icellX0;
cellY -= icellY0;
-
+
if( (unsigned)icellX0 < (unsigned)ncells.width &&
- (unsigned)icellX1 < (unsigned)ncells.width )
+ (unsigned)icellX1 < (unsigned)ncells.width )
{
if( (unsigned)icellY0 < (unsigned)ncells.height &&
- (unsigned)icellY1 < (unsigned)ncells.height )
+ (unsigned)icellY1 < (unsigned)ncells.height )
{
data = &pixData[rawBlockSize*2 + (count4++)];
data->histOfs[0] = (icellX0*ncells.height + icellY0)*nbins;
@@ -567,9 +657,9 @@ void HOGCache::init(const HOGDescriptor* _descriptor,
icellX1 = icellX0;
cellX = 1.f - cellX;
}
-
+
if( (unsigned)icellY0 < (unsigned)ncells.height &&
- (unsigned)icellY1 < (unsigned)ncells.height )
+ (unsigned)icellY1 < (unsigned)ncells.height )
{
data = &pixData[rawBlockSize + (count2++)];
data->histOfs[0] = (icellX1*ncells.height + icellY0)*nbins;
@@ -597,7 +687,7 @@ void HOGCache::init(const HOGDescriptor* _descriptor,
data->qangleOfs = (qangle.cols*i + j)*2;
data->gradWeight = weights(i,j);
}
-
+
assert( count1 + count2 + count4 == rawBlockSize );
// defragment pixData
for( j = 0; j < count2; j++ )
@@ -606,7 +696,7 @@ void HOGCache::init(const HOGDescriptor* _descriptor,
pixData[j + count1 + count2] = pixData[j + rawBlockSize*2];
count2 += count1;
count4 += count2;
-
+
// initialize blockData
for( j = 0; j < nblocks.width; j++ )
for( i = 0; i < nblocks.height; i++ )
@@ -617,24 +707,23 @@ void HOGCache::init(const HOGDescriptor* _descriptor,
}
}
-
const float* HOGCache::getBlock(Point pt, float* buf)
{
float* blockHist = buf;
assert(descriptor != 0);
- Size blockSize = descriptor->blockSize;
+// Size blockSize = descriptor->blockSize;
pt += imgoffset;
- CV_Assert( (unsigned)pt.x <= (unsigned)(grad.cols - blockSize.width) &&
- (unsigned)pt.y <= (unsigned)(grad.rows - blockSize.height) );
+// CV_Assert( (unsigned)pt.x <= (unsigned)(grad.cols - blockSize.width) &&
+// (unsigned)pt.y <= (unsigned)(grad.rows - blockSize.height) );
if( useCache )
{
CV_Assert( pt.x % cacheStride.width == 0 &&
pt.y % cacheStride.height == 0 );
Point cacheIdx(pt.x/cacheStride.width,
- (pt.y/cacheStride.height) % blockCache.rows);
+ (pt.y/cacheStride.height) % blockCache.rows);
if( pt.y != ymaxCached[cacheIdx.y] )
{
Mat_<uchar> cacheRow = blockCacheFlags.row(cacheIdx.y);
@@ -653,50 +742,131 @@ const float* HOGCache::getBlock(Point pt, float* buf)
const float* gradPtr = (const float*)(grad.data + grad.step*pt.y) + pt.x*2;
const uchar* qanglePtr = qangle.data + qangle.step*pt.y + pt.x*2;
- CV_Assert( blockHist != 0 );
-#ifdef HAVE_IPP
- ippsZero_32f(blockHist,blockHistogramSize);
-#else
- for( k = 0; k < blockHistogramSize; k++ )
- blockHist[k] = 0.f;
-#endif
-
+// CV_Assert( blockHist != 0 );
+ memset(blockHist, 0, sizeof(float) * blockHistogramSize);
+
const PixData* _pixData = &pixData[0];
-
+
for( k = 0; k < C1; k++ )
{
const PixData& pk = _pixData[k];
- const float* a = gradPtr + pk.gradOfs;
+ const float* const a = gradPtr + pk.gradOfs;
float w = pk.gradWeight*pk.histWeights[0];
const uchar* h = qanglePtr + pk.qangleOfs;
int h0 = h[0], h1 = h[1];
+
float* hist = blockHist + pk.histOfs[0];
float t0 = hist[h0] + a[0]*w;
float t1 = hist[h1] + a[1]*w;
hist[h0] = t0; hist[h1] = t1;
}
+#if CV_SSE2
+ float hist0[4], hist1[4];
for( ; k < C2; k++ )
{
const PixData& pk = _pixData[k];
- const float* a = gradPtr + pk.gradOfs;
- float w, t0, t1, a0 = a[0], a1 = a[1];
- const uchar* h = qanglePtr + pk.qangleOfs;
+ const float* const a = gradPtr + pk.gradOfs;
+ const uchar* const h = qanglePtr + pk.qangleOfs;
int h0 = h[0], h1 = h[1];
-
+
+ __m128 _a0 = _mm_set1_ps(a[0]), _a1 = _mm_set1_ps(a[1]);
+ __m128 _w = _mm_mul_ps(_mm_set1_ps(pk.gradWeight), _mm_loadu_ps(pk.histWeights));
+ __m128 _t0 = _mm_mul_ps(_a0, _w), _t1 = _mm_mul_ps(_a1, _w);
+
+ _mm_storeu_ps(hist0, _t0);
+ _mm_storeu_ps(hist1, _t1);
+
+ float* hist = blockHist + pk.histOfs[0];
+ float t0 = hist[h0] + hist0[0];
+ float t1 = hist[h1] + hist1[0];
+ hist[h0] = t0; hist[h1] = t1;
+
+ hist = blockHist + pk.histOfs[1];
+ t0 = hist[h0] + hist0[1];
+ t1 = hist[h1] + hist1[1];
+ hist[h0] = t0; hist[h1] = t1;
+ }
+#else
+ for( ; k < C2; k++ )
+ {
+ const PixData& pk = _pixData[k];
+ const float* const a = gradPtr + pk.gradOfs;
+ float w, t0, t1, a0 = a[0], a1 = a[1];
+ const uchar* const h = qanglePtr + pk.qangleOfs;
+ int h0 = h[0], h1 = h[1];
+
float* hist = blockHist + pk.histOfs[0];
w = pk.gradWeight*pk.histWeights[0];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
-
+
hist = blockHist + pk.histOfs[1];
w = pk.gradWeight*pk.histWeights[1];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
}
-
+#endif
+
+#if CV_SSE2
+ for( ; k < C4; k++ )
+ {
+ const PixData& pk = _pixData[k];
+ const float* const a = gradPtr + pk.gradOfs;
+ const uchar* const h = qanglePtr + pk.qangleOfs;
+ int h0 = h[0], h1 = h[1];
+
+ __m128 _a0 = _mm_set1_ps(a[0]), _a1 = _mm_set1_ps(a[1]);
+ __m128 _w = _mm_mul_ps(_mm_set1_ps(pk.gradWeight), _mm_loadu_ps(pk.histWeights));
+ __m128 _t0 = _mm_mul_ps(_a0, _w), _t1 = _mm_mul_ps(_a1, _w);
+
+ _mm_storeu_ps(hist0, _t0);
+ _mm_storeu_ps(hist1, _t1);
+
+ float* hist = blockHist + pk.histOfs[0];
+ float t0 = hist[h0] + hist0[0];
+ float t1 = hist[h1] + hist1[0];
+ hist[h0] = t0; hist[h1] = t1;
+
+ hist = blockHist + pk.histOfs[1];
+ t0 = hist[h0] + hist0[1];
+ t1 = hist[h1] + hist1[1];
+ hist[h0] = t0; hist[h1] = t1;
+
+ hist = blockHist + pk.histOfs[2];
+ t0 = hist[h0] + hist0[2];
+ t1 = hist[h1] + hist1[2];
+ hist[h0] = t0; hist[h1] = t1;
+
+ hist = blockHist + pk.histOfs[3];
+ t0 = hist[h0] + hist0[3];
+ t1 = hist[h1] + hist1[3];
+ hist[h0] = t0; hist[h1] = t1;
+
+// __m128 _hist0 = _mm_set_ps((blockHist + pk.histOfs[3])[h0], (blockHist + pk.histOfs[2])[h0],
+// (blockHist + pk.histOfs[1])[h0], (blockHist + pk.histOfs[0])[h0]);
+// __m128 _hist1 = _mm_set_ps((blockHist + pk.histOfs[3])[h1], (blockHist + pk.histOfs[2])[h1],
+// (blockHist + pk.histOfs[1])[h1], (blockHist + pk.histOfs[0])[h1]);
+//
+// _hist0 = _mm_add_ps(_t0, _hist0);
+// _hist1 = _mm_add_ps(_t1, _hist1);
+//
+// _mm_storeu_ps(hist0, _hist0);
+// _mm_storeu_ps(hist1, _hist1);
+//
+// (pk.histOfs[0] + blockHist)[h0] = hist0[0];
+// (pk.histOfs[1] + blockHist)[h0] = hist0[1];
+// (pk.histOfs[2] + blockHist)[h0] = hist0[2];
+// (pk.histOfs[3] + blockHist)[h0] = hist0[3];
+//
+// (pk.histOfs[0] + blockHist)[h1] = hist1[0];
+// (pk.histOfs[1] + blockHist)[h1] = hist1[1];
+// (pk.histOfs[2] + blockHist)[h1] = hist1[2];
+// (pk.histOfs[3] + blockHist)[h1] = hist1[3];
+ }
+#else
for( ; k < C4; k++ )
{
const PixData& pk = _pixData[k];
@@ -704,85 +874,140 @@ const float* HOGCache::getBlock(Point pt, float* buf)
float w, t0, t1, a0 = a[0], a1 = a[1];
const uchar* h = qanglePtr + pk.qangleOfs;
int h0 = h[0], h1 = h[1];
-
+
float* hist = blockHist + pk.histOfs[0];
w = pk.gradWeight*pk.histWeights[0];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
-
+
hist = blockHist + pk.histOfs[1];
w = pk.gradWeight*pk.histWeights[1];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
-
+
hist = blockHist + pk.histOfs[2];
w = pk.gradWeight*pk.histWeights[2];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
-
+
hist = blockHist + pk.histOfs[3];
w = pk.gradWeight*pk.histWeights[3];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
}
+#endif
normalizeBlockHistogram(blockHist);
return blockHist;
}
-
void HOGCache::normalizeBlockHistogram(float* _hist) const
{
- float* hist = &_hist[0];
-#ifdef HAVE_IPP
- size_t sz = blockHistogramSize;
-#else
- size_t i, sz = blockHistogramSize;
-#endif
+ float* hist = &_hist[0], sum = 0.0f, partSum[4];
+ size_t i = 0, sz = blockHistogramSize;
- float sum = 0;
-#ifdef HAVE_IPP
- ippsDotProd_32f(hist,hist,sz,&sum);
+#if CV_SSE2
+ __m128 p0 = _mm_loadu_ps(hist);
+ __m128 s = _mm_mul_ps(p0, p0);
+
+ for (i = 4; i <= sz - 4; i += 4)
+ {
+ p0 = _mm_loadu_ps(hist + i);
+ s = _mm_add_ps(s, _mm_mul_ps(p0, p0));
+ }
+ _mm_storeu_ps(partSum, s);
#else
- for( i = 0; i < sz; i++ )
- sum += hist[i]*hist[i];
+ partSum[0] = 0.0f;
+ partSum[1] = 0.0f;
+ partSum[2] = 0.0f;
+ partSum[3] = 0.0f;
+ for ( ; i <= sz - 4; i += 4)
+ {
+ partSum[0] += hist[i] * hist[i];
+ partSum[1] += hist[i+1] * hist[i+1];
+ partSum[2] += hist[i+2] * hist[i+2];
+ partSum[3] += hist[i+3] * hist[i+3];
+ }
#endif
+ float t0 = partSum[0] + partSum[1];
+ float t1 = partSum[2] + partSum[3];
+ sum = t0 + t1;
+ for ( ; i < sz; ++i)
+ sum += hist[i]*hist[i];
float scale = 1.f/(std::sqrt(sum)+sz*0.1f), thresh = (float)descriptor->L2HysThreshold;
-#ifdef HAVE_IPP
- ippsMulC_32f_I(scale,hist,sz);
- ippsThreshold_32f_I( hist, sz, thresh, ippCmpGreater );
- ippsDotProd_32f(hist,hist,sz,&sum);
+ i = 0, sum = 0.0f;
+
+#if CV_SSE2
+ __m128 _scale = _mm_set1_ps(scale);
+ static __m128 _threshold = _mm_set1_ps(thresh);
+
+ __m128 p = _mm_mul_ps(_scale, _mm_loadu_ps(hist));
+ p = _mm_min_ps(p, _threshold);
+ s = _mm_mul_ps(p, p);
+ _mm_storeu_ps(hist, p);
+
+ for(i = 4 ; i <= sz - 4; i += 4)
+ {
+ p = _mm_loadu_ps(hist + i);
+ p = _mm_mul_ps(p, _scale);
+ p = _mm_min_ps(p, _threshold);
+ s = _mm_add_ps(s, _mm_mul_ps(p, p));
+ _mm_storeu_ps(hist + i, p);
+ }
+
+ _mm_storeu_ps(partSum, s);
#else
- for( i = 0, sum = 0; i < sz; i++ )
+ partSum[0] = 0.0f;
+ partSum[1] = 0.0f;
+ partSum[2] = 0.0f;
+ partSum[3] = 0.0f;
+ for( ; i <= sz - 4; i += 4)
+ {
+ hist[i] = std::min(hist[i]*scale, thresh);
+ hist[i+1] = std::min(hist[i+1]*scale, thresh);
+ hist[i+2] = std::min(hist[i+2]*scale, thresh);
+ hist[i+3] = std::min(hist[i+3]*scale, thresh);
+ partSum[0] += hist[i]*hist[i];
+ partSum[1] += hist[i+1]*hist[i+1];
+ partSum[2] += hist[i+2]*hist[i+2];
+ partSum[3] += hist[i+3]*hist[i+3];
+ }
+#endif
+ t0 = partSum[0] + partSum[1];
+ t1 = partSum[2] + partSum[3];
+ sum = t0 + t1;
+ for( ; i < sz; ++i)
{
hist[i] = std::min(hist[i]*scale, thresh);
sum += hist[i]*hist[i];
}
-#endif
- scale = 1.f/(std::sqrt(sum)+1e-3f);
-#ifdef HAVE_IPP
- ippsMulC_32f_I(scale,hist,sz);
-#else
- for( i = 0; i < sz; i++ )
- hist[i] *= scale;
+ scale = 1.f/(std::sqrt(sum)+1e-3f), i = 0;
+#if CV_SSE2
+ __m128 _scale2 = _mm_set1_ps(scale);
+ for ( ; i <= sz - 4; i += 4)
+ {
+ __m128 t = _mm_mul_ps(_scale2, _mm_loadu_ps(hist + i));
+ _mm_storeu_ps(hist + i, t);
+ }
#endif
+ for ( ; i < sz; ++i)
+ hist[i] *= scale;
}
-
-Size HOGCache::windowsInImage(Size imageSize, Size winStride) const
+Size HOGCache::windowsInImage(const Size& imageSize, const Size& winStride) const
{
return Size((imageSize.width - winSize.width)/winStride.width + 1,
- (imageSize.height - winSize.height)/winStride.height + 1);
+ (imageSize.height - winSize.height)/winStride.height + 1);
}
-Rect HOGCache::getWindow(Size imageSize, Size winStride, int idx) const
+Rect HOGCache::getWindow(const Size& imageSize, const Size& winStride, int idx) const
{
int nwindowsX = (imageSize.width - winSize.width)/winStride.width + 1;
int y = idx / nwindowsX;
@@ -790,15 +1015,14 @@ Rect HOGCache::getWindow(Size imageSize, Size winStride, int idx) const
return Rect( x*winStride.width, y*winStride.height, winSize.width, winSize.height );
}
-
void HOGDescriptor::compute(const Mat& img, std::vector<float>& descriptors,
- Size winStride, Size padding,
- const std::vector<Point>& locations) const
+ Size winStride, Size padding, const std::vector<Point>& locations) const
{
if( winStride == Size() )
winStride = cellSize;
Size cacheStride(gcd(winStride.width, blockStride.width),
gcd(winStride.height, blockStride.height));
+
size_t nwindows = locations.size();
padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
@@ -816,6 +1040,7 @@ void HOGDescriptor::compute(const Mat& img, std::vector<float>& descriptors,
size_t dsize = getDescriptorSize();
descriptors.resize(dsize*nwindows);
+ // for each window
for( size_t i = 0; i < nwindows; i++ )
{
float* descriptor = &descriptors[i*dsize];
@@ -831,7 +1056,7 @@ void HOGDescriptor::compute(const Mat& img, std::vector<float>& descriptors,
else
{
pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
- CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
+// CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
}
for( int j = 0; j < nblocks; j++ )
@@ -842,17 +1067,11 @@ void HOGDescriptor::compute(const Mat& img, std::vector<float>& descriptors,
float* dst = descriptor + bj.histOfs;
const float* src = cache.getBlock(pt, dst);
if( src != dst )
-#ifdef HAVE_IPP
- ippsCopy_32f(src,dst,blockHistogramSize);
-#else
- for( int k = 0; k < blockHistogramSize; k++ )
- dst[k] = src[k];
-#endif
+ memcpy(dst, src, blockHistogramSize * sizeof(float));
}
}
}
-
void HOGDescriptor::detect(const Mat& img,
std::vector<Point>& hits, std::vector<double>& weights, double hitThreshold,
Size winStride, Size padding, const std::vector<Point>& locations) const
@@ -864,7 +1083,8 @@ void HOGDescriptor::detect(const Mat& img,
if( winStride == Size() )
winStride = cellSize;
Size cacheStride(gcd(winStride.width, blockStride.width),
- gcd(winStride.height, blockStride.height));
+ gcd(winStride.height, blockStride.height));
+
size_t nwindows = locations.size();
padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
@@ -884,6 +1104,10 @@ void HOGDescriptor::detect(const Mat& img,
double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
std::vector<float> blockHist(blockHistogramSize);
+#if CV_SSE2
+ float partSum[4];
+#endif
+
for( size_t i = 0; i < nwindows; i++ )
{
Point pt0;
@@ -891,7 +1115,7 @@ void HOGDescriptor::detect(const Mat& img,
{
pt0 = locations[i];
if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
- pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
+ pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
continue;
}
else
@@ -901,28 +1125,38 @@ void HOGDescriptor::detect(const Mat& img,
}
double s = rho;
const float* svmVec = &svmDetector[0];
-#ifdef HAVE_IPP
- int j;
-#else
+
int j, k;
-#endif
for( j = 0; j < nblocks; j++, svmVec += blockHistogramSize )
{
const HOGCache::BlockData& bj = blockData[j];
Point pt = pt0 + bj.imgOffset;
const float* vec = cache.getBlock(pt, &blockHist[0]);
-#ifdef HAVE_IPP
- Ipp32f partSum;
- ippsDotProd_32f(vec,svmVec,blockHistogramSize,&partSum);
- s += (double)partSum;
+#if CV_SSE2
+ __m128 _vec = _mm_loadu_ps(vec);
+ __m128 _svmVec = _mm_loadu_ps(svmVec);
+ __m128 sum = _mm_mul_ps(_svmVec, _vec);
+
+ for( k = 4; k <= blockHistogramSize - 4; k += 4 )
+ {
+ _vec = _mm_loadu_ps(vec + k);
+ _svmVec = _mm_loadu_ps(svmVec + k);
+
+ sum = _mm_add_ps(sum, _mm_mul_ps(_vec, _svmVec));
+ }
+
+ _mm_storeu_ps(partSum, sum);
+ double t0 = partSum[0] + partSum[1];
+ double t1 = partSum[2] + partSum[3];
+ s += t0 + t1;
#else
for( k = 0; k <= blockHistogramSize - 4; k += 4 )
s += vec[k]*svmVec[k] + vec[k+1]*svmVec[k+1] +
vec[k+2]*svmVec[k+2] + vec[k+3]*svmVec[k+3];
+#endif
for( ; k < blockHistogramSize; k++ )
s += vec[k]*svmVec[k];
-#endif
}
if( s >= hitThreshold )
{
@@ -933,19 +1167,20 @@ void HOGDescriptor::detect(const Mat& img,
}
void HOGDescriptor::detect(const Mat& img, std::vector<Point>& hits, double hitThreshold,
- Size winStride, Size padding, const std::vector<Point>& locations) const
+ Size winStride, Size padding, const std::vector<Point>& locations) const
{
std::vector<double> weightsV;
detect(img, hits, weightsV, hitThreshold, winStride, padding, locations);
}
-class HOGInvoker : public ParallelLoopBody
+class HOGInvoker :
+ public ParallelLoopBody
{
public:
HOGInvoker( const HOGDescriptor* _hog, const Mat& _img,
- double _hitThreshold, Size _winStride, Size _padding,
- const double* _levelScale, std::vector<Rect> * _vec, Mutex* _mtx,
- std::vector<double>* _weights=0, std::vector<double>* _scales=0 )
+ double _hitThreshold, const Size& _winStride, const Size& _padding,
+ const double* _levelScale, std::vector<Rect> * _vec, Mutex* _mtx,
+ std::vector<double>* _weights=0, std::vector<double>* _scales=0 )
{
hog = _hog;
img = _img;
@@ -987,9 +1222,7 @@ public:
cvRound(locations[j].y*scale),
scaledWinSize.width, scaledWinSize.height));
if (scales)
- {
scales->push_back(scale);
- }
}
mtx->unlock();
@@ -997,14 +1230,13 @@ public:
{
mtx->lock();
for (size_t j = 0; j < locations.size(); j++)
- {
weights->push_back(hitsWeights[j]);
- }
mtx->unlock();
}
}
}
+private:
const HOGDescriptor* hog;
Mat img;
double hitThreshold;
@@ -1017,7 +1249,6 @@ public:
Mutex* mtx;
};
-
void HOGDescriptor::detectMultiScale(
const Mat& img, std::vector<Rect>& foundLocations, std::vector<double>& foundWeights,
double hitThreshold, Size winStride, Size padding,
@@ -1031,8 +1262,8 @@ void HOGDescriptor::detectMultiScale(
{
levelScale.push_back(scale);
if( cvRound(img.cols/scale) < winSize.width ||
- cvRound(img.rows/scale) < winSize.height ||
- scale0 <= 1 )
+ cvRound(img.rows/scale) < winSize.height ||
+ scale0 <= 1 )
break;
scale *= scale0;
}
@@ -1045,8 +1276,9 @@ void HOGDescriptor::detectMultiScale(
std::vector<double> foundScales;
Mutex mtx;
- parallel_for_(Range(0, (int)levelScale.size()),
- HOGInvoker(this, img, hitThreshold, winStride, padding, &levelScale[0], &allCandidates, &mtx, &tempWeights, &tempScales));
+ Range range(0, (int)levelScale.size());
+ HOGInvoker invoker(this, img, hitThreshold, winStride, padding, &levelScale[0], &allCandidates, &mtx, &tempWeights, &tempScales);
+ parallel_for_(range, invoker);
std::copy(tempScales.begin(), tempScales.end(), back_inserter(foundScales));
foundLocations.clear();
@@ -1055,841 +1287,838 @@ void HOGDescriptor::detectMultiScale(
std::copy(tempWeights.begin(), tempWeights.end(), back_inserter(foundWeights));
if ( useMeanshiftGrouping )
- {
groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize);
- }
else
- {
groupRectangles(foundLocations, (int)finalThreshold, 0.2);
- }
}
void HOGDescriptor::detectMultiScale(const Mat& img, std::vector<Rect>& foundLocations,
- double hitThreshold, Size winStride, Size padding,
- double scale0, double finalThreshold, bool useMeanshiftGrouping) const
+ double hitThreshold, Size winStride, Size padding,
+ double scale0, double finalThreshold, bool useMeanshiftGrouping) const
{
std::vector<double> foundWeights;
detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride,
- padding, scale0, finalThreshold, useMeanshiftGrouping);
+ padding, scale0, finalThreshold, useMeanshiftGrouping);
}
typedef RTTIImpl<HOGDescriptor> HOGRTTI;
CvType hog_type( CV_TYPE_NAME_HOG_DESCRIPTOR, HOGRTTI::isInstance,
- HOGRTTI::release, HOGRTTI::read, HOGRTTI::write, HOGRTTI::clone);
+ HOGRTTI::release, HOGRTTI::read, HOGRTTI::write, HOGRTTI::clone);
std::vector<float> HOGDescriptor::getDefaultPeopleDetector()
{
static const float detector[] = {
- 0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
- 0.11547081f, -0.04268804f, 0.04635834f, -0.05468199f, 0.08232084f,
- 0.10424068f, -0.02294518f, 0.01108519f, 0.01378693f, 0.11193510f,
- 0.01268418f, 0.08528346f, -0.06309239f, 0.13054633f, 0.08100729f,
- -0.05209739f, -0.04315529f, 0.09341384f, 0.11035026f, -0.07596218f,
- -0.05517511f, -0.04465296f, 0.02947334f, 0.04555536f,
- -3.55954492e-003f, 0.07818956f, 0.07730991f, 0.07890715f, 0.06222893f,
- 0.09001380f, -0.03574381f, 0.03414327f, 0.05677258f, -0.04773581f,
- 0.03746637f, -0.03521175f, 0.06955440f, -0.03849038f, 0.01052293f,
- 0.01736112f, 0.10867710f, 0.08748853f, 3.29739624e-003f, 0.10907028f,
- 0.07913758f, 0.10393070f, 0.02091867f, 0.11594022f, 0.13182420f,
- 0.09879354f, 0.05362710f, -0.06745391f, -7.01260753e-003f,
- 5.24702156e-003f, 0.03236255f, 0.01407916f, 0.02207983f, 0.02537322f,
- 0.04547948f, 0.07200756f, 0.03129894f, -0.06274468f, 0.02107014f,
- 0.06035208f, 0.08636236f, 4.53164103e-003f, 0.02193363f, 0.02309801f,
- 0.05568166f, -0.02645093f, 0.04448695f, 0.02837519f, 0.08975694f,
- 0.04461516f, 0.08975355f, 0.07514391f, 0.02306982f, 0.10410084f,
- 0.06368385f, 0.05943464f, 4.58420580e-003f, 0.05220337f, 0.06675851f,
- 0.08358569f, 0.06712101f, 0.06559004f, -0.03930482f, -9.15936660e-003f,
- -0.05897915f, 0.02816453f, 0.05032348f, 0.06780671f, 0.03377650f,
- -6.09417039e-004f, -0.01795146f, -0.03083684f, -0.01302475f,
- -0.02972313f, 7.88706727e-003f, -0.03525961f, -2.50397739e-003f,
- 0.05245084f, 0.11791293f, -0.02167498f, 0.05299332f, 0.06640524f,
- 0.05190265f, -8.27316567e-003f, 0.03033127f, 0.05842173f,
- -4.01050318e-003f, -6.25105947e-003f, 0.05862958f, -0.02465461f,
- 0.05546781f, -0.08228195f, -0.07234028f, 0.04640540f, -0.01308254f,
- -0.02506191f, 0.03100746f, -0.04665651f, -0.04591486f, 0.02949927f,
- 0.06035462f, 0.02244646f, -0.01698639f, 0.01040041f, 0.01131170f,
- 0.05419579f, -0.02130277f, -0.04321722f, -0.03665198f, 0.01126490f,
- -0.02606488f, -0.02228328f, -0.02255680f, -0.03427236f,
- -7.75165204e-003f, -0.06195229f, 8.21638294e-003f, 0.09535975f,
- -0.03709979f, -0.06942501f, 0.14579427f, -0.05448192f, -0.02055904f,
- 0.05747357f, 0.02781788f, -0.07077577f, -0.05178314f, -0.10429011f,
- -0.11235505f, 0.07529039f, -0.07559302f, -0.08786739f, 0.02983843f,
- 0.02667585f, 0.01382199f, -0.01797496f, -0.03141199f, -0.02098101f,
- 0.09029204f, 0.04955018f, 0.13718739f, 0.11379953f, 1.80019124e-003f,
- -0.04577610f, -1.11108483e-003f, -0.09470536f, -0.11596080f,
- 0.04489342f, 0.01784211f, 3.06850672e-003f, 0.10781866f,
- 3.36498418e-003f, -0.10842580f, -0.07436839f, -0.10535070f,
- -0.01866805f, 0.16057891f, -5.07316366e-003f, -0.04295658f,
- -5.90488780e-003f, 8.82003549e-003f, -0.01492646f, -0.05029279f,
- -0.12875880f, 8.78831954e-004f, -0.01297184f, -0.07592774f,
- -0.02668831f, -6.93787413e-004f, 0.02406698f, -0.01773298f,
- -0.03855745f, -0.05877856f, 0.03259695f, 0.12826584f, 0.06292590f,
- -4.10733931e-003f, 0.10996531f, 0.01332991f, 0.02088735f, 0.04037504f,
- -0.05210760f, 0.07760046f, 0.06399347f, -0.05751930f, -0.10053057f,
- 0.07505023f, -0.02139782f, 0.01796176f, 2.34400877e-003f, -0.04208319f,
- 0.07355055f, 0.05093350f, -0.02996780f, -0.02219072f, 0.03355330f,
- 0.04418742f, -0.05580705f, -0.05037573f, -0.04548179f, 0.01379514f,
- 0.02150671f, -0.02194211f, -0.13682702f, 0.05464972f, 0.01608082f,
- 0.05309116f, 0.04701022f, 1.33690401e-003f, 0.07575664f, 0.09625306f,
- 8.92647635e-003f, -0.02819123f, 0.10866830f, -0.03439325f,
- -0.07092371f, -0.06004780f, -0.02712298f, -7.07467366e-003f,
- -0.01637020f, 0.01336790f, -0.10313606f, 0.04906582f, -0.05732445f,
- -0.02731079f, 0.01042235f, -0.08340668f, 0.03686501f, 0.06108340f,
- 0.01322748f, -0.07809529f, 0.03774724f, -0.03413248f, -0.06096525f,
- -0.04212124f, -0.07982176f, -1.25973229e-003f, -0.03045501f,
- -0.01236493f, -0.06312395f, 0.04789570f, -0.04602066f, 0.08576570f,
- 0.02521080f, 0.02988098f, 0.10314583f, 0.07060035f, 0.04520544f,
- -0.04426654f, 0.13146530f, 0.08386490f, 0.02164590f, -2.12280243e-003f,
- -0.03686353f, -0.02074944f, -0.03829959f, -0.01530596f, 0.02689708f,
- 0.11867401f, -0.06043470f, -0.02785023f, -0.04775074f, 0.04878745f,
- 0.06350956f, 0.03494788f, 0.01467400f, 1.17890188e-003f, 0.04379614f,
- 2.03681854e-003f, -0.03958609f, -0.01072688f, 6.43705716e-003f,
- 0.02996500f, -0.03418507f, -0.01960307f, -0.01219154f,
- -4.37000440e-003f, -0.02549453f, 0.02646318f, -0.01632513f,
- 6.46516960e-003f, -0.01929734f, 4.78711911e-003f, 0.04962371f,
- 0.03809111f, 0.07265724f, 0.05758125f, -0.03741554f, 0.01648608f,
- -8.45285598e-003f, 0.03996826f, -0.08185477f, 0.02638875f,
- -0.04026615f, -0.02744674f, -0.04071517f, 1.05096330e-003f,
- -0.04741232f, -0.06733172f, 8.70434940e-003f, -0.02192543f,
- 1.35350740e-003f, -0.03056974f, -0.02975521f, -0.02887780f,
- -0.01210713f, -0.04828526f, -0.09066251f, -0.09969629f, -0.03665164f,
- -8.88111943e-004f, -0.06826669f, -0.01866150f, -0.03627640f,
- -0.01408288f, 0.01874239f, -0.02075835f, 0.09145175f, -0.03547291f,
- 0.05396780f, 0.04198981f, 0.01301925f, -0.03384354f, -0.12201976f,
- 0.06830920f, -0.03715654f, 9.55848210e-003f, 5.05685573e-003f,
- 0.05659294f, 3.90764466e-003f, 0.02808490f, -0.05518097f, -0.03711621f,
- -0.02835565f, -0.04420464f, -0.01031947f, 0.01883466f,
- -8.49525444e-003f, -0.09419250f, -0.01269387f, -0.02133371f,
- -0.10190815f, -0.07844430f, 2.43644323e-003f, -4.09610150e-003f,
- 0.01202551f, -0.06452291f, -0.10593818f, -0.02464746f, -0.02199699f,
- -0.07401930f, 0.07285886f, 8.87513801e-004f, 9.97662079e-003f,
- 8.46779719e-003f, 0.03730333f, -0.02905126f, 0.03573337f, -0.04393689f,
- -0.12014472f, 0.03176554f, -2.76015815e-003f, 0.10824566f, 0.05090732f,
- -3.30179278e-003f, -0.05123822f, 5.04784798e-003f, -0.05664124f,
- -5.99415926e-003f, -0.05341901f, -0.01221393f, 0.01291318f,
- 9.91760660e-003f, -7.56987557e-003f, -0.06193124f, -2.24549137e-003f,
- 0.01987562f, -0.02018840f, -0.06975540f, -0.06601523f, -0.03349112f,
- -0.08910118f, -0.03371435f, -0.07406893f, -0.02248047f, -0.06159951f,
- 2.77751544e-003f, -0.05723337f, -0.04792468f, 0.07518548f,
- 2.77279224e-003f, 0.04211938f, 0.03100502f, 0.05278448f, 0.03954679f,
- -0.03006846f, -0.03851741f, -0.02792403f, -0.02875333f, 0.01531280f,
- 0.02186953f, -0.01989829f, 2.50679464e-003f, -0.10258728f,
- -0.04785743f, -0.02887216f, 3.85063468e-003f, 0.01112236f,
- 8.29218887e-003f, -0.04822981f, -0.04503597f, -0.03713100f,
- -0.06988008f, -0.11002295f, -2.69209221e-003f, 1.85383670e-003f,
- -0.05921049f, -0.06105053f, -0.08458050f, -0.04527602f,
- 8.90329306e-004f, -0.05875023f, -2.68602883e-003f, -0.01591195f,
- 0.03631859f, 0.05493166f, 0.07300330f, 5.53333294e-003f, 0.06400407f,
- 0.01847740f, -5.76280477e-003f, -0.03210877f, 4.25160583e-003f,
- 0.01166520f, -1.44864211e-003f, 0.02253744f, -0.03367080f, 0.06983195f,
- -4.22323542e-003f, -8.89401045e-003f, -0.07943393f, 0.05199728f,
- 0.06065201f, 0.04133492f, 1.44032843e-003f, -0.09585235f, -0.03964731f,
- 0.04232114f, 0.01750465f, -0.04487902f, -7.59733608e-003f, 0.02011171f,
- 0.04673622f, 0.09011173f, -0.07869188f, -0.04682482f, -0.05080139f,
- -3.99383716e-003f, -0.05346331f, 0.01085723f, -0.03599333f,
- -0.07097908f, 0.03551549f, 0.02680387f, 0.03471529f, 0.01790393f,
- 0.05471273f, 9.62048303e-003f, -0.03180215f, 0.05864431f, 0.02330614f,
- 0.01633144f, -0.05616681f, -0.10245429f, -0.08302189f, 0.07291322f,
- -0.01972590f, -0.02619633f, -0.02485327f, -0.04627592f,
- 1.48853404e-003f, 0.05514185f, -0.01270860f, -0.01948900f, 0.06373586f,
- 0.05002292f, -0.03009798f, 8.76216311e-003f, -0.02474238f,
- -0.05504891f, 1.74034527e-003f, -0.03333667f, 0.01524987f, 0.11663762f,
- -1.32344989e-003f, -0.06608453f, 0.05687166f, -6.89525274e-004f,
- -0.04402352f, 0.09450210f, -0.04222684f, -0.05360983f, 0.01779531f,
- 0.02561388f, -0.11075410f, -8.77790991e-003f, -0.01099504f,
- -0.10380266f, 0.03103457f, -0.02105741f, -0.07371717f, 0.05146710f,
- 0.10581432f, -0.08617968f, -0.02892107f, 0.01092199f, 0.14551543f,
- -2.24320893e-003f, -0.05818033f, -0.07390742f, 0.05701261f,
- 0.12937020f, -0.04986651f, 0.10182415f, 0.05028650f, 0.12515625f,
- 0.09175041f, 0.06404983f, 0.01523394f, 0.09460562f, 0.06106631f,
- -0.14266998f, -0.02926703f, 0.02762171f, 0.02164151f,
- -9.58488265e-004f, -0.04231362f, -0.09866509f, 0.04322244f,
- 0.05872034f, -0.04838847f, 0.06319253f, 0.02443798f, -0.03606876f,
- 9.38737206e-003f, 0.04289991f, -0.01027411f, 0.08156885f, 0.08751175f,
- -0.13191354f, 8.16054735e-003f, -0.01452161f, 0.02952677f, 0.03615945f,
- -2.09128903e-003f, 0.02246693f, 0.09623287f, 0.09412123f, -0.02924758f,
- -0.07815186f, -0.02203079f, -2.02566991e-003f, 0.01094733f,
- -0.01442332f, 0.02838561f, 0.11882371f, 7.28798332e-003f, -0.10345965f,
- 0.07561217f, -0.02049661f, 4.44177445e-003f, 0.01609347f, -0.04893158f,
- -0.08758243f, -7.67420698e-003f, 0.08862378f, 0.06098121f, 0.06565887f,
- 7.32981879e-003f, 0.03558407f, -0.03874352f, -0.02490055f,
- -0.06771075f, 0.09939223f, -0.01066077f, 0.01382995f, -0.07289080f,
- 7.47184316e-003f, 0.10621431f, -0.02878659f, 0.02383525f, -0.03274646f,
- 0.02137008f, 0.03837290f, 0.02450992f, -0.04296818f, -0.02895143f,
- 0.05327370f, 0.01499020f, 0.04998732f, 0.12938657f, 0.09391870f,
- 0.04292390f, -0.03359194f, -0.06809492f, 0.01125796f, 0.17290455f,
- -0.03430733f, -0.06255233f, -0.01813114f, 0.11726857f, -0.06127599f,
- -0.08677909f, -0.03429872f, 0.04684938f, 0.08161420f, 0.03538774f,
- 0.01833884f, 0.11321855f, 0.03261845f, -0.04826299f, 0.01752407f,
- -0.01796414f, -0.10464549f, -3.30041884e-003f, 2.29343961e-004f,
- 0.01457292f, -0.02132982f, -0.02602923f, -9.87351313e-003f,
- 0.04273872f, -0.02103316f, -0.07994065f, 0.02614958f, -0.02111666f,
- -0.06964913f, -0.13453490f, -0.06861878f, -6.09341264e-003f,
- 0.08251446f, 0.15612499f, 2.46531400e-003f, 8.88424646e-003f,
- -0.04152999f, 0.02054853f, 0.05277953f, -0.03087788f, 0.02817579f,
- 0.13939077f, 0.07641046f, -0.03627627f, -0.03015098f, -0.04041540f,
- -0.01360690f, -0.06227205f, -0.02738223f, 0.13577610f, 0.15235767f,
- -0.05392922f, -0.11175954f, 0.02157129f, 0.01146481f, -0.05264937f,
- -0.06595174f, -0.02749175f, 0.11812254f, 0.17404149f, -0.06137035f,
- -0.11003478f, -0.01351621f, -0.01745916f, -0.08577441f, -0.04469909f,
- -0.06106115f, 0.10559758f, 0.20806813f, -0.09174948f, 7.09621934e-004f,
- 0.03579374f, 0.07215115f, 0.02221742f, 0.01827742f, -7.90785067e-003f,
- 0.01489554f, 0.14519960f, -0.06425831f, 0.02990399f, -1.80181325e-003f,
- -0.01401528f, -0.04171134f, -3.70530109e-003f, -0.09090481f,
- 0.09520713f, 0.08845516f, -0.02651753f, -0.03016730f, 0.02562448f,
- 0.03563816f, -0.03817881f, 0.01433385f, 0.02256983f, 0.02872120f,
- 0.01001934f, -0.06332260f, 0.04338406f, 0.07001807f, -0.04705722f,
- -0.07318907f, 0.02630457f, 0.03106382f, 0.06648342f, 0.10913180f,
- -0.01630815f, 0.02910308f, 0.02895109f, 0.08040254f, 0.06969310f,
- 0.06797734f, 6.08639978e-003f, 4.16588830e-003f, 0.08926726f,
- -0.03123648f, 0.02700146f, 0.01168734f, -0.01631594f, 4.61015804e-003f,
- 8.51359498e-003f, -0.03544224f, 0.03571994f, 4.29766066e-003f,
- -0.01970077f, -8.79793242e-003f, 0.09607988f, 0.01544222f,
- -0.03923707f, 0.07308586f, 0.06061262f, 1.31683104e-004f,
- -7.98222050e-003f, 0.02399261f, -0.06084389f, -0.02743429f,
- -0.05475523f, -0.04131311f, 0.03559756f, 0.03055342f, 0.02981433f,
- 0.14860515f, 0.01766787f, 0.02945257f, 0.04898238f, 0.01026922f,
- 0.02811658f, 0.08267091f, 0.02732154f, -0.01237693f, 0.11760156f,
- 0.03802063f, -0.03309754f, 5.24957618e-003f, -0.02460510f, 0.02691451f,
- 0.05399988f, -0.10133506f, 0.06385437f, -0.01818005f, 0.02259503f,
- 0.03573135f, 0.01042848f, -0.04153402f, -0.04043029f, 0.01643575f,
- 0.08326677f, 4.61383024e-004f, -0.05308095f, -0.08536223f,
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+ -0.11368170f, -0.03554465f, -0.04533844f, -0.02861580f, 0.06782424f,
+ 0.01113123f, 0.02453644f, 0.12721945f, 0.08084814f, -0.03607795f,
+ 0.01109122f, 0.04803548f, -0.03489929f, 0.03399536f, -0.05682014f,
+ 8.59533902e-003f, -4.27904585e-003f, 0.03230887f, -0.01300198f,
+ -0.01038137f, -0.07930113f, 8.33097473e-003f, 0.02296994f,
+ -0.01306500f, -0.01881626f, 0.04413369f, 0.05729880f, -0.03761553f,
+ 0.01942326f, 1.64540811e-003f, -0.03811319f, 0.04190650f, -0.14978096f,
+ -0.04514487f, 0.01209545f, -5.46460645e-003f, -0.01647195f,
+ 7.63064111e-003f, -0.07494587f, 0.08415288f, 0.10020141f, -0.01228561f,
+ 0.06553826f, 0.04554005f, 0.07890417f, 0.03041138f, 0.01752007f,
+ 0.09208256f, -3.74419295e-004f, 0.10549527f, 0.04686913f, 0.01894833f,
+ -0.02651412f, -4.34682379e-003f, 5.44942822e-003f, 0.01444484f,
+ 0.05882156f, -0.03336544f, 0.04603891f, -0.10432546f, 0.01923928f,
+ 0.01842845f, -0.01712168f, -0.02222766f, 0.04693324f, -0.06202956f,
+ -0.01422159f, 0.08732220f, -0.07706107f, 0.02661049f, -0.04300238f,
+ -0.03092422f, -0.03552184f, -0.01886088f, -0.04979934f, 0.03906401f,
+ 0.04608644f, 0.04966111f, 0.04275464f, -0.04621769f, -0.02653212f,
+ 8.57011229e-003f, 0.03839684f, 0.05818764f, 0.03880796f,
+ -2.76100676e-004f, 0.03076511f, -0.03266929f, -0.05374557f,
+ 0.04986527f, -9.45429131e-003f, 0.03582499f, -2.64564669e-003f,
+ -1.07461517e-003f, 0.02962313f, -0.01483363f, 0.03060869f, 0.02448327f,
+ 0.01845641f, 0.03282966f, -0.03534438f, -0.01084059f, -0.01119136f,
+ -1.85360224e-003f, -5.94652840e-004f, -0.04451817f, 2.98327743e-003f,
+ 0.06272484f, -0.02152076f, -3.05971340e-003f, -0.05070828f,
+ 0.01531762f, 0.01282815f, 0.05167150f, 9.46266949e-003f,
+ -3.34558333e-003f, 0.11442288f, -0.03906701f, -2.67325155e-003f,
+ 0.03069184f, -0.01134165f, 0.02949462f, 0.02879886f, 0.03855566f,
+ -0.03450781f, 0.09142872f, -0.02156654f, 0.06075062f, -0.06220816f,
+ 0.01944680f, 6.68372354e-003f, -0.06656796f, 8.70784000e-003f,
+ 0.03456013f, 0.02434320f, -0.13236357f, -0.04177035f, -0.02069627f,
+ 0.01068112f, 0.01505432f, -0.07517391f, -3.83571628e-003f,
+ -0.06298508f, -0.02881260f, -0.13101046f, -0.07221562f,
+ -5.79945277e-003f, -8.57300125e-003f, 0.03782469f, 0.02762164f,
+ 0.04942456f, -0.02936396f, 0.09597211f, 0.01921411f, 0.06101191f,
+ -0.04787507f, -0.01379578f, -7.40224449e-003f, -0.02220136f,
+ -0.01313756f, 7.77558051e-003f, 0.12296968f, 0.02939998f, 0.03594062f,
+ -0.07788624f, -0.01133144f, 3.99316690e-004f, -0.06090347f,
+ -0.01122066f, -4.68682544e-003f, 0.07633100f, -0.06748922f,
+ -0.05640298f, -0.05265681f, -0.01139122f, -0.01624347f, -0.04715714f,
+ -0.01099092f, 0.01048561f, 3.28499987e-003f, -0.05810167f,
+ -0.07699911f, -0.03330683f, 0.04185145f, 0.03478536f, 0.02275165f,
+ 0.02304766f, 6.66040834e-003f, 0.10968148f, -5.93013782e-003f,
+ -0.04858336f, -0.04203213f, -0.09316786f, -6.13074889e-003f,
+ -0.02544625f, 0.01366201f, 9.18555818e-003f, -0.01846578f,
+ -0.05622401f, -0.03989377f, -0.07810296f, 6.91275718e-003f,
+ 0.05957597f, -0.03901334f, 0.01572002f, -0.01193903f,
+ -6.89400872e-003f, -0.03093356f, -0.04136098f, -0.01562869f,
+ -0.04604580f, 0.02865234f, -0.08678447f, -0.03232484f, -0.05364593f,
+ -0.01445016f, -0.07003860f, -0.08669746f, -0.04520775f, 0.04274122f,
+ 0.03117515f, 0.08175703f, 0.01081109f, 0.06379741f, 0.06199206f,
+ 0.02865988f, 0.02360346f, 0.06725410f, -0.03248780f, -9.37702879e-003f,
+ 0.08265898f, -0.02245839f, 0.05125763f, -0.01862395f, 0.01973453f,
+ -0.01994494f, -0.10770868f, 0.03180375f, 3.23935156e-003f,
+ -0.02142080f, -0.04256190f, 0.04760900f, 0.04282863f, 0.05635953f,
+ -0.01870849f, 0.05540622f, -0.03042666f, 0.01455277f, -0.06630179f,
+ -0.05843807f, -0.03739681f, -0.09739155f, -0.03220233f, -0.05620182f,
+ -0.10381401f, 0.07400211f, 4.20676917e-003f, 0.03258535f,
+ 2.14308966e-003f, 0.05121966f, -0.01274337f, 0.02384761f, 0.06335578f,
+ -0.07905591f, 0.08375625f, -0.07898903f, -0.06508528f, -0.02498444f,
+ 0.06535810f, 0.03970535f, 0.04895468f, -0.01169566f, -0.03980601f,
+ 0.05682293f, 0.05925463f, -0.01165808f, -0.07936699f, -0.04208954f,
+ 0.01333987f, 0.09051196f, 0.10098671f, -0.03974256f, 0.01238771f,
+ -0.07501741f, -0.03655440f, -0.04301528f, 0.09216860f,
+ 4.63579083e-004f, 0.02851115f, 0.02142735f, 1.28244064e-004f,
+ 0.02879687f, -0.08554889f, -0.04838862f, 0.08135369f, -0.05756533f,
+ 0.01413900f, 0.03451880f, -0.06619488f, -0.03053130f, 0.02961676f,
+ -0.07384635f, 0.01135692f, 0.05283910f, -0.07778034f, -0.02107482f,
+ -0.05511716f, -0.13473752f, 0.03030157f, 0.06722020f, -0.06218817f,
+ -0.05826827f, 0.06254654f, 0.02895772f, -0.01664000f, -0.03620280f,
+ -0.01612278f, -1.46097376e-003f, 0.14013411f, -8.96181818e-003f,
+ -0.03250246f, 3.38630192e-003f, 2.64779478e-003f, 0.03359732f,
+ -0.02411991f, -0.04229729f, 0.10666174f, -6.66579151f };
return std::vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
}
-//This function renurn 1981 SVM coeffs obtained from daimler's base.
-//To use these coeffs the detection window size should be (48,96)
+
+// This function renurn 1981 SVM coeffs obtained from daimler's base.
+// To use these coeffs the detection window size should be (48,96)
std::vector<float> HOGDescriptor::getDaimlerPeopleDetector()
{
static const float detector[] = {
@@ -2389,249 +2618,265 @@ std::vector<float> HOGDescriptor::getDaimlerPeopleDetector()
-0.025054f, -0.093026f, -0.035372f, -0.233209f,
-0.049869f, -0.039151f, -0.022279f, -0.065380f,
-9.063785f};
- return std::vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
+ return std::vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
}
-class HOGConfInvoker : public ParallelLoopBody
+class HOGConfInvoker :
+ public ParallelLoopBody
{
public:
- HOGConfInvoker( const HOGDescriptor* _hog, const Mat& _img,
- double _hitThreshold, Size _padding,
- std::vector<DetectionROI>* locs,
- std::vector<Rect>* _vec, Mutex* _mtx )
- {
- hog = _hog;
- img = _img;
- hitThreshold = _hitThreshold;
- padding = _padding;
- locations = locs;
- vec = _vec;
- mtx = _mtx;
- }
+ HOGConfInvoker( const HOGDescriptor* _hog, const Mat& _img,
+ double _hitThreshold, const Size& _padding,
+ std::vector<DetectionROI>* locs,
+ std::vector<Rect>* _vec, Mutex* _mtx )
+ {
+ hog = _hog;
+ img = _img;
+ hitThreshold = _hitThreshold;
+ padding = _padding;
+ locations = locs;
+ vec = _vec;
+ mtx = _mtx;
+ }
- void operator()( const Range& range ) const
- {
- int i, i1 = range.start, i2 = range.end;
+ void operator()( const Range& range ) const
+ {
+ int i, i1 = range.start, i2 = range.end;
- Size maxSz(cvCeil(img.cols/(*locations)[0].scale), cvCeil(img.rows/(*locations)[0].scale));
- Mat smallerImgBuf(maxSz, img.type());
- std::vector<Point> dets;
+ Size maxSz(cvCeil(img.cols/(*locations)[0].scale), cvCeil(img.rows/(*locations)[0].scale));
+ Mat smallerImgBuf(maxSz, img.type());
+ std::vector<Point> dets;
- for( i = i1; i < i2; i++ )
- {
- double scale = (*locations)[i].scale;
+ for( i = i1; i < i2; i++ )
+ {
+ double scale = (*locations)[i].scale;
- Size sz(cvRound(img.cols / scale), cvRound(img.rows / scale));
- Mat smallerImg(sz, img.type(), smallerImgBuf.data);
+ Size sz(cvRound(img.cols / scale), cvRound(img.rows / scale));
+ Mat smallerImg(sz, img.type(), smallerImgBuf.data);
- if( sz == img.size() )
- smallerImg = Mat(sz, img.type(), img.data, img.step);
- else
- resize(img, smallerImg, sz);
+ if( sz == img.size() )
+ smallerImg = Mat(sz, img.type(), img.data, img.step);
+ else
+ resize(img, smallerImg, sz);
- hog->detectROI(smallerImg, (*locations)[i].locations, dets, (*locations)[i].confidences, hitThreshold, Size(), padding);
- Size scaledWinSize = Size(cvRound(hog->winSize.width*scale), cvRound(hog->winSize.height*scale));
- mtx->lock();
- for( size_t j = 0; j < dets.size(); j++ )
- {
- vec->push_back(Rect(cvRound(dets[j].x*scale),
- cvRound(dets[j].y*scale),
- scaledWinSize.width, scaledWinSize.height));
- }
- mtx->unlock();
- }
- }
+ hog->detectROI(smallerImg, (*locations)[i].locations, dets, (*locations)[i].confidences, hitThreshold, Size(), padding);
+ Size scaledWinSize = Size(cvRound(hog->winSize.width*scale), cvRound(hog->winSize.height*scale));
+ mtx->lock();
+ for( size_t j = 0; j < dets.size(); j++ )
+ vec->push_back(Rect(cvRound(dets[j].x*scale),
+ cvRound(dets[j].y*scale),
+ scaledWinSize.width, scaledWinSize.height));
+ mtx->unlock();
+ }
+ }
- const HOGDescriptor* hog;
- Mat img;
- double hitThreshold;
- std::vector<DetectionROI>* locations;
- Size padding;
- std::vector<Rect>* vec;
- Mutex* mtx;
+ const HOGDescriptor* hog;
+ Mat img;
+ double hitThreshold;
+ std::vector<DetectionROI>* locations;
+ Size padding;
+ std::vector<Rect>* vec;
+ Mutex* mtx;
};
void HOGDescriptor::detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
- CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
- double hitThreshold, cv::Size winStride,
- cv::Size padding) const
+ CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
+ double hitThreshold, cv::Size winStride, cv::Size padding) const
{
- foundLocations.clear();
+ foundLocations.clear();
+ confidences.clear();
- confidences.clear();
+ if( svmDetector.empty() || locations.empty())
+ return;
- if( svmDetector.empty() )
- return;
+ if( winStride == Size() )
+ winStride = cellSize;
+ Size cacheStride(gcd(winStride.width, blockStride.width),
+ gcd(winStride.height, blockStride.height));
- if( locations.empty() )
- return;
+ size_t nwindows = locations.size();
+ padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
+ padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
+ Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
- if( winStride == Size() )
- winStride = cellSize;
+ // HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);
+ HOGCache cache(this, img, padding, padding, true, cacheStride);
+ if( !nwindows )
+ nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
- Size cacheStride(gcd(winStride.width, blockStride.width),
- gcd(winStride.height, blockStride.height));
+ const HOGCache::BlockData* blockData = &cache.blockData[0];
- size_t nwindows = locations.size();
- padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
- padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
- Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
+ int nblocks = cache.nblocks.area();
+ int blockHistogramSize = cache.blockHistogramSize;
+ size_t dsize = getDescriptorSize();
- // HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);
- HOGCache cache(this, img, padding, padding, true, cacheStride);
- if( !nwindows )
- nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
+ double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
+ std::vector<float> blockHist(blockHistogramSize);
- const HOGCache::BlockData* blockData = &cache.blockData[0];
+#if CV_SSE2
+ float partSum[4];
+#endif
- int nblocks = cache.nblocks.area();
- int blockHistogramSize = cache.blockHistogramSize;
- size_t dsize = getDescriptorSize();
+ for( size_t i = 0; i < nwindows; i++ )
+ {
+ Point pt0;
+ pt0 = locations[i];
+ if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
+ pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
+ {
+ // out of image
+ confidences.push_back(-10.0);
+ continue;
+ }
- double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
- std::vector<float> blockHist(blockHistogramSize);
+ double s = rho;
+ const float* svmVec = &svmDetector[0];
+ int j, k;
- for( size_t i = 0; i < nwindows; i++ )
- {
- Point pt0;
- pt0 = locations[i];
- if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
- pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
- {
- // out of image
- confidences.push_back(-10.0);
- continue;
- }
+ for( j = 0; j < nblocks; j++, svmVec += blockHistogramSize )
+ {
+ const HOGCache::BlockData& bj = blockData[j];
+ Point pt = pt0 + bj.imgOffset;
- double s = rho;
- const float* svmVec = &svmDetector[0];
- int j, k;
+ // need to devide this into 4 parts!
+ const float* vec = cache.getBlock(pt, &blockHist[0]);
+#if CV_SSE2
+ __m128 _vec = _mm_loadu_ps(vec);
+ __m128 _svmVec = _mm_loadu_ps(svmVec);
+ __m128 sum = _mm_mul_ps(_svmVec, _vec);
- for( j = 0; j < nblocks; j++, svmVec += blockHistogramSize )
- {
- const HOGCache::BlockData& bj = blockData[j];
- Point pt = pt0 + bj.imgOffset;
- // need to devide this into 4 parts!
- const float* vec = cache.getBlock(pt, &blockHist[0]);
- for( k = 0; k <= blockHistogramSize - 4; k += 4 )
- s += vec[k]*svmVec[k] + vec[k+1]*svmVec[k+1] +
- vec[k+2]*svmVec[k+2] + vec[k+3]*svmVec[k+3];
- for( ; k < blockHistogramSize; k++ )
- s += vec[k]*svmVec[k];
- }
- // cv::waitKey();
- confidences.push_back(s);
+ for( k = 4; k <= blockHistogramSize - 4; k += 4 )
+ {
+ _vec = _mm_loadu_ps(vec + k);
+ _svmVec = _mm_loadu_ps(svmVec + k);
- if( s >= hitThreshold )
- foundLocations.push_back(pt0);
- }
- }
+ sum = _mm_add_ps(sum, _mm_mul_ps(_vec, _svmVec));
+ }
+
+ _mm_storeu_ps(partSum, sum);
+ double t0 = partSum[0] + partSum[1];
+ double t1 = partSum[2] + partSum[3];
+ s += t0 + t1;
+#else
+ for( k = 0; k <= blockHistogramSize - 4; k += 4 )
+ s += vec[k]*svmVec[k] + vec[k+1]*svmVec[k+1] +
+ vec[k+2]*svmVec[k+2] + vec[k+3]*svmVec[k+3];
+#endif
+ for( ; k < blockHistogramSize; k++ )
+ s += vec[k]*svmVec[k];
+ }
+ confidences.push_back(s);
+
+ if( s >= hitThreshold )
+ foundLocations.push_back(pt0);
+ }
+}
void HOGDescriptor::detectMultiScaleROI(const cv::Mat& img,
- CV_OUT std::vector<cv::Rect>& foundLocations,
- std::vector<DetectionROI>& locations,
- double hitThreshold,
- int groupThreshold) const
+ CV_OUT std::vector<cv::Rect>& foundLocations, std::vector<DetectionROI>& locations,
+ double hitThreshold, int groupThreshold) const
{
- std::vector<Rect> allCandidates;
- Mutex mtx;
+ std::vector<Rect> allCandidates;
+ Mutex mtx;
- parallel_for_(Range(0, (int)locations.size()),
- HOGConfInvoker(this, img, hitThreshold, Size(8, 8), &locations, &allCandidates, &mtx));
+ parallel_for_(Range(0, (int)locations.size()),
+ HOGConfInvoker(this, img, hitThreshold, Size(8, 8),
+ &locations, &allCandidates, &mtx));
- foundLocations.resize(allCandidates.size());
- std::copy(allCandidates.begin(), allCandidates.end(), foundLocations.begin());
- cv::groupRectangles(foundLocations, groupThreshold, 0.2);
+ foundLocations.resize(allCandidates.size());
+ std::copy(allCandidates.begin(), allCandidates.end(), foundLocations.begin());
+ cv::groupRectangles(foundLocations, groupThreshold, 0.2);
}
void HOGDescriptor::readALTModel(std::string modelfile)
{
- // read model from SVMlight format..
- FILE *modelfl;
- if ((modelfl = fopen(modelfile.c_str(), "rb")) == NULL)
- {
- std::string eerr("file not exist");
- std::string efile(__FILE__);
- std::string efunc(__FUNCTION__);
- throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
- }
- char version_buffer[10];
- if (!fread (&version_buffer,sizeof(char),10,modelfl))
- {
- std::string eerr("version?");
- std::string efile(__FILE__);
- std::string efunc(__FUNCTION__);
- throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
- }
- if(strcmp(version_buffer,"V6.01")) {
- std::string eerr("version doesnot match");
- std::string efile(__FILE__);
- std::string efunc(__FUNCTION__);
- throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
- }
- /* read version number */
- int version = 0;
- if (!fread (&version,sizeof(int),1,modelfl))
- { throw Exception(); }
- if (version < 200)
- {
- std::string eerr("version doesnot match");
- std::string efile(__FILE__);
- std::string efunc(__FUNCTION__);
- throw Exception();
- }
- int kernel_type;
- size_t nread;
- nread=fread(&(kernel_type),sizeof(int),1,modelfl);
+ // read model from SVMlight format..
+ FILE *modelfl;
+ if ((modelfl = fopen(modelfile.c_str(), "rb")) == NULL)
+ {
+ std::string eerr("file not exist");
+ std::string efile(__FILE__);
+ std::string efunc(__FUNCTION__);
+ throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
+ }
+ char version_buffer[10];
+ if (!fread (&version_buffer,sizeof(char),10,modelfl))
+ {
+ std::string eerr("version?");
+ std::string efile(__FILE__);
+ std::string efunc(__FUNCTION__);
+ throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
+ }
+ if(strcmp(version_buffer,"V6.01")) {
+ std::string eerr("version doesnot match");
+ std::string efile(__FILE__);
+ std::string efunc(__FUNCTION__);
+ throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
+ }
+ /* read version number */
+ int version = 0;
+ if (!fread (&version,sizeof(int),1,modelfl))
+ { throw Exception(); }
+ if (version < 200)
+ {
+ std::string eerr("version doesnot match");
+ std::string efile(__FILE__);
+ std::string efunc(__FUNCTION__);
+ throw Exception();
+ }
+ int kernel_type;
+ size_t nread;
+ nread=fread(&(kernel_type),sizeof(int),1,modelfl);
- {// ignore these
- int poly_degree;
- nread=fread(&(poly_degree),sizeof(int),1,modelfl);
+ {// ignore these
+ int poly_degree;
+ nread=fread(&(poly_degree),sizeof(int),1,modelfl);
- double rbf_gamma;
- nread=fread(&(rbf_gamma),sizeof(double), 1, modelfl);
- double coef_lin;
- nread=fread(&(coef_lin),sizeof(double),1,modelfl);
- double coef_const;
- nread=fread(&(coef_const),sizeof(double),1,modelfl);
- int l;
- nread=fread(&l,sizeof(int),1,modelfl);
- char* custom = new char[l];
- nread=fread(custom,sizeof(char),l,modelfl);
- delete[] custom;
- }
- int totwords;
- nread=fread(&(totwords),sizeof(int),1,modelfl);
- {// ignore these
- int totdoc;
- nread=fread(&(totdoc),sizeof(int),1,modelfl);
- int sv_num;
- nread=fread(&(sv_num), sizeof(int),1,modelfl);
- }
+ double rbf_gamma;
+ nread=fread(&(rbf_gamma),sizeof(double), 1, modelfl);
+ double coef_lin;
+ nread=fread(&(coef_lin),sizeof(double),1,modelfl);
+ double coef_const;
+ nread=fread(&(coef_const),sizeof(double),1,modelfl);
+ int l;
+ nread=fread(&l,sizeof(int),1,modelfl);
+ char* custom = new char[l];
+ nread=fread(custom,sizeof(char),l,modelfl);
+ delete[] custom;
+ }
+ int totwords;
+ nread=fread(&(totwords),sizeof(int),1,modelfl);
+ {// ignore these
+ int totdoc;
+ nread=fread(&(totdoc),sizeof(int),1,modelfl);
+ int sv_num;
+ nread=fread(&(sv_num), sizeof(int),1,modelfl);
+ }
- double linearbias;
- nread=fread(&linearbias, sizeof(double), 1, modelfl);
+ double linearbias;
+ nread=fread(&linearbias, sizeof(double), 1, modelfl);
- std::vector<float> detector;
- detector.clear();
- if(kernel_type == 0) { /* linear kernel */
- /* save linear wts also */
- double *linearwt = new double[totwords+1];
- int length = totwords;
- nread = fread(linearwt, sizeof(double), totwords + 1, modelfl);
- if(nread != static_cast<size_t>(length) + 1)
- throw Exception();
+ std::vector<float> detector;
+ detector.clear();
+ if(kernel_type == 0) { /* linear kernel */
+ /* save linear wts also */
+ double *linearwt = new double[totwords+1];
+ int length = totwords;
+ nread = fread(linearwt, sizeof(double), totwords + 1, modelfl);
+ if(nread != static_cast<size_t>(length) + 1)
+ throw Exception();
- for(int i = 0; i < length; i++)
- detector.push_back((float)linearwt[i]);
+ for(int i = 0; i < length; i++)
+ detector.push_back((float)linearwt[i]);
- detector.push_back((float)-linearbias);
- setSVMDetector(detector);
- delete linearwt;
- } else {
- throw Exception();
- }
- fclose(modelfl);
+ detector.push_back((float)-linearbias);
+ setSVMDetector(detector);
+ delete linearwt;
+ } else {
+ throw Exception();
+ }
+ fclose(modelfl);
}
}
+
diff --git a/modules/objdetect/test/test_cascadeandhog.cpp b/modules/objdetect/test/test_cascadeandhog.cpp
index 38a9e210d..a165d9deb 100644
--- a/modules/objdetect/test/test_cascadeandhog.cpp
+++ b/modules/objdetect/test/test_cascadeandhog.cpp
@@ -543,3 +543,815 @@ TEST(Objdetect_HOGDetectorReadWrite, regression)
TEST(Objdetect_CascadeDetector, regression) { CV_CascadeDetectorTest test; test.safe_run(); }
TEST(Objdetect_HOGDetector, regression) { CV_HOGDetectorTest test; test.safe_run(); }
+
+
+//----------------------------------------------- HOG SSE2 compatible test -----------------------------------
+
+class HOGDescriptorTester :
+ public cv::HOGDescriptor
+{
+ HOGDescriptor* actual_hog;
+ cvtest::TS* ts;
+ mutable bool failed;
+
+public:
+ HOGDescriptorTester(HOGDescriptor& instance) :
+ cv::HOGDescriptor(instance), actual_hog(&instance),
+ ts(cvtest::TS::ptr()), failed(false)
+ { }
+
+ virtual void computeGradient(const Mat& img, Mat& grad, Mat& qangle,
+ Size paddingTL, Size paddingBR) const;
+
+ virtual void detect(const Mat& img,
+ vector<Point>& hits, vector<double>& weights, double hitThreshold = 0.0,
+ Size winStride = Size(), Size padding = Size(),
+ const vector<Point>& locations = vector<Point>()) const;
+
+ virtual void detect(const Mat& img, vector<Point>& hits, double hitThreshold = 0.0,
+ Size winStride = Size(), Size padding = Size(),
+ const vector<Point>& locations = vector<Point>()) const;
+
+ virtual void compute(const Mat& img, vector<float>& descriptors,
+ Size winStride = Size(), Size padding = Size(),
+ const vector<Point>& locations = vector<Point>()) const;
+
+ bool is_failed() const;
+};
+
+struct HOGCacheTester
+{
+ struct BlockData
+ {
+ BlockData() : histOfs(0), imgOffset() {}
+ int histOfs;
+ Point imgOffset;
+ };
+
+ struct PixData
+ {
+ size_t gradOfs, qangleOfs;
+ int histOfs[4];
+ float histWeights[4];
+ float gradWeight;
+ };
+
+ HOGCacheTester();
+ HOGCacheTester(const HOGDescriptorTester* descriptor,
+ const Mat& img, Size paddingTL, Size paddingBR,
+ bool useCache, Size cacheStride);
+ virtual ~HOGCacheTester() { }
+ virtual void init(const HOGDescriptorTester* descriptor,
+ const Mat& img, Size paddingTL, Size paddingBR,
+ bool useCache, Size cacheStride);
+
+ Size windowsInImage(Size imageSize, Size winStride) const;
+ Rect getWindow(Size imageSize, Size winStride, int idx) const;
+
+ const float* getBlock(Point pt, float* buf);
+ virtual void normalizeBlockHistogram(float* histogram) const;
+
+ vector<PixData> pixData;
+ vector<BlockData> blockData;
+
+ bool useCache;
+ vector<int> ymaxCached;
+ Size winSize, cacheStride;
+ Size nblocks, ncells;
+ int blockHistogramSize;
+ int count1, count2, count4;
+ Point imgoffset;
+ Mat_<float> blockCache;
+ Mat_<uchar> blockCacheFlags;
+
+ Mat grad, qangle;
+ const HOGDescriptorTester* descriptor;
+};
+
+HOGCacheTester::HOGCacheTester()
+{
+ useCache = false;
+ blockHistogramSize = count1 = count2 = count4 = 0;
+ descriptor = 0;
+}
+
+HOGCacheTester::HOGCacheTester(const HOGDescriptorTester* _descriptor,
+ const Mat& _img, Size _paddingTL, Size _paddingBR,
+ bool _useCache, Size _cacheStride)
+{
+ init(_descriptor, _img, _paddingTL, _paddingBR, _useCache, _cacheStride);
+}
+
+void HOGCacheTester::init(const HOGDescriptorTester* _descriptor,
+ const Mat& _img, Size _paddingTL, Size _paddingBR,
+ bool _useCache, Size _cacheStride)
+{
+ descriptor = _descriptor;
+ cacheStride = _cacheStride;
+ useCache = _useCache;
+
+ descriptor->computeGradient(_img, grad, qangle, _paddingTL, _paddingBR);
+ imgoffset = _paddingTL;
+
+ winSize = descriptor->winSize;
+ Size blockSize = descriptor->blockSize;
+ Size blockStride = descriptor->blockStride;
+ Size cellSize = descriptor->cellSize;
+ int i, j, nbins = descriptor->nbins;
+ int rawBlockSize = blockSize.width*blockSize.height;
+
+ nblocks = Size((winSize.width - blockSize.width)/blockStride.width + 1,
+ (winSize.height - blockSize.height)/blockStride.height + 1);
+ ncells = Size(blockSize.width/cellSize.width, blockSize.height/cellSize.height);
+ blockHistogramSize = ncells.width*ncells.height*nbins;
+
+ if( useCache )
+ {
+ Size cacheSize((grad.cols - blockSize.width)/cacheStride.width+1,
+ (winSize.height/cacheStride.height)+1);
+ blockCache.create(cacheSize.height, cacheSize.width*blockHistogramSize);
+ blockCacheFlags.create(cacheSize);
+ size_t cacheRows = blockCache.rows;
+ ymaxCached.resize(cacheRows);
+ for(size_t ii = 0; ii < cacheRows; ii++ )
+ ymaxCached[ii] = -1;
+ }
+
+ Mat_<float> weights(blockSize);
+ float sigma = (float)descriptor->getWinSigma();
+ float scale = 1.f/(sigma*sigma*2);
+
+ for(i = 0; i < blockSize.height; i++)
+ for(j = 0; j < blockSize.width; j++)
+ {
+ float di = i - blockSize.height*0.5f;
+ float dj = j - blockSize.width*0.5f;
+ weights(i,j) = std::exp(-(di*di + dj*dj)*scale);
+ }
+
+ blockData.resize(nblocks.width*nblocks.height);
+ pixData.resize(rawBlockSize*3);
+
+ // Initialize 2 lookup tables, pixData & blockData.
+ // Here is why:
+ //
+ // The detection algorithm runs in 4 nested loops (at each pyramid layer):
+ // loop over the windows within the input image
+ // loop over the blocks within each window
+ // loop over the cells within each block
+ // loop over the pixels in each cell
+ //
+ // As each of the loops runs over a 2-dimensional array,
+ // we could get 8(!) nested loops in total, which is very-very slow.
+ //
+ // To speed the things up, we do the following:
+ // 1. loop over windows is unrolled in the HOGDescriptor::{compute|detect} methods;
+ // inside we compute the current search window using getWindow() method.
+ // Yes, it involves some overhead (function call + couple of divisions),
+ // but it's tiny in fact.
+ // 2. loop over the blocks is also unrolled. Inside we use pre-computed blockData[j]
+ // to set up gradient and histogram pointers.
+ // 3. loops over cells and pixels in each cell are merged
+ // (since there is no overlap between cells, each pixel in the block is processed once)
+ // and also unrolled. Inside we use PixData[k] to access the gradient values and
+ // update the histogram
+ //
+ count1 = count2 = count4 = 0;
+ for( j = 0; j < blockSize.width; j++ )
+ for( i = 0; i < blockSize.height; i++ )
+ {
+ PixData* data = 0;
+ float cellX = (j+0.5f)/cellSize.width - 0.5f;
+ float cellY = (i+0.5f)/cellSize.height - 0.5f;
+ int icellX0 = cvFloor(cellX);
+ int icellY0 = cvFloor(cellY);
+ int icellX1 = icellX0 + 1, icellY1 = icellY0 + 1;
+ cellX -= icellX0;
+ cellY -= icellY0;
+
+ if( (unsigned)icellX0 < (unsigned)ncells.width &&
+ (unsigned)icellX1 < (unsigned)ncells.width )
+ {
+ if( (unsigned)icellY0 < (unsigned)ncells.height &&
+ (unsigned)icellY1 < (unsigned)ncells.height )
+ {
+ data = &pixData[rawBlockSize*2 + (count4++)];
+ data->histOfs[0] = (icellX0*ncells.height + icellY0)*nbins;
+ data->histWeights[0] = (1.f - cellX)*(1.f - cellY);
+ data->histOfs[1] = (icellX1*ncells.height + icellY0)*nbins;
+ data->histWeights[1] = cellX*(1.f - cellY);
+ data->histOfs[2] = (icellX0*ncells.height + icellY1)*nbins;
+ data->histWeights[2] = (1.f - cellX)*cellY;
+ data->histOfs[3] = (icellX1*ncells.height + icellY1)*nbins;
+ data->histWeights[3] = cellX*cellY;
+ }
+ else
+ {
+ data = &pixData[rawBlockSize + (count2++)];
+ if( (unsigned)icellY0 < (unsigned)ncells.height )
+ {
+ icellY1 = icellY0;
+ cellY = 1.f - cellY;
+ }
+ data->histOfs[0] = (icellX0*ncells.height + icellY1)*nbins;
+ data->histWeights[0] = (1.f - cellX)*cellY;
+ data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
+ data->histWeights[1] = cellX*cellY;
+ data->histOfs[2] = data->histOfs[3] = 0;
+ data->histWeights[2] = data->histWeights[3] = 0;
+ }
+ }
+ else
+ {
+ if( (unsigned)icellX0 < (unsigned)ncells.width )
+ {
+ icellX1 = icellX0;
+ cellX = 1.f - cellX;
+ }
+
+ if( (unsigned)icellY0 < (unsigned)ncells.height &&
+ (unsigned)icellY1 < (unsigned)ncells.height )
+ {
+ data = &pixData[rawBlockSize + (count2++)];
+ data->histOfs[0] = (icellX1*ncells.height + icellY0)*nbins;
+ data->histWeights[0] = cellX*(1.f - cellY);
+ data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
+ data->histWeights[1] = cellX*cellY;
+ data->histOfs[2] = data->histOfs[3] = 0;
+ data->histWeights[2] = data->histWeights[3] = 0;
+ }
+ else
+ {
+ data = &pixData[count1++];
+ if( (unsigned)icellY0 < (unsigned)ncells.height )
+ {
+ icellY1 = icellY0;
+ cellY = 1.f - cellY;
+ }
+ data->histOfs[0] = (icellX1*ncells.height + icellY1)*nbins;
+ data->histWeights[0] = cellX*cellY;
+ data->histOfs[1] = data->histOfs[2] = data->histOfs[3] = 0;
+ data->histWeights[1] = data->histWeights[2] = data->histWeights[3] = 0;
+ }
+ }
+ data->gradOfs = (grad.cols*i + j)*2;
+ data->qangleOfs = (qangle.cols*i + j)*2;
+ data->gradWeight = weights(i,j);
+ }
+
+ assert( count1 + count2 + count4 == rawBlockSize );
+ // defragment pixData
+ for( j = 0; j < count2; j++ )
+ pixData[j + count1] = pixData[j + rawBlockSize];
+ for( j = 0; j < count4; j++ )
+ pixData[j + count1 + count2] = pixData[j + rawBlockSize*2];
+ count2 += count1;
+ count4 += count2;
+
+ // initialize blockData
+ for( j = 0; j < nblocks.width; j++ )
+ for( i = 0; i < nblocks.height; i++ )
+ {
+ BlockData& data = blockData[j*nblocks.height + i];
+ data.histOfs = (j*nblocks.height + i)*blockHistogramSize;
+ data.imgOffset = Point(j*blockStride.width,i*blockStride.height);
+ }
+}
+
+const float* HOGCacheTester::getBlock(Point pt, float* buf)
+{
+ float* blockHist = buf;
+ assert(descriptor != 0);
+
+ Size blockSize = descriptor->blockSize;
+ pt += imgoffset;
+
+ CV_Assert( (unsigned)pt.x <= (unsigned)(grad.cols - blockSize.width) &&
+ (unsigned)pt.y <= (unsigned)(grad.rows - blockSize.height) );
+
+ if( useCache )
+ {
+ CV_Assert( pt.x % cacheStride.width == 0 &&
+ pt.y % cacheStride.height == 0 );
+ Point cacheIdx(pt.x/cacheStride.width,
+ (pt.y/cacheStride.height) % blockCache.rows);
+ if( pt.y != ymaxCached[cacheIdx.y] )
+ {
+ Mat_<uchar> cacheRow = blockCacheFlags.row(cacheIdx.y);
+ cacheRow = (uchar)0;
+ ymaxCached[cacheIdx.y] = pt.y;
+ }
+
+ blockHist = &blockCache[cacheIdx.y][cacheIdx.x*blockHistogramSize];
+ uchar& computedFlag = blockCacheFlags(cacheIdx.y, cacheIdx.x);
+ if( computedFlag != 0 )
+ return blockHist;
+ computedFlag = (uchar)1; // set it at once, before actual computing
+ }
+
+ int k, C1 = count1, C2 = count2, C4 = count4;
+ const float* gradPtr = (const float*)(grad.data + grad.step*pt.y) + pt.x*2;
+ const uchar* qanglePtr = qangle.data + qangle.step*pt.y + pt.x*2;
+
+ CV_Assert( blockHist != 0 );
+ for( k = 0; k < blockHistogramSize; k++ )
+ blockHist[k] = 0.f;
+
+ const PixData* _pixData = &pixData[0];
+
+ for( k = 0; k < C1; k++ )
+ {
+ const PixData& pk = _pixData[k];
+ const float* a = gradPtr + pk.gradOfs;
+ float w = pk.gradWeight*pk.histWeights[0];
+ const uchar* h = qanglePtr + pk.qangleOfs;
+ int h0 = h[0], h1 = h[1];
+ float* hist = blockHist + pk.histOfs[0];
+ float t0 = hist[h0] + a[0]*w;
+ float t1 = hist[h1] + a[1]*w;
+ hist[h0] = t0; hist[h1] = t1;
+ }
+
+ for( ; k < C2; k++ )
+ {
+ const PixData& pk = _pixData[k];
+ const float* a = gradPtr + pk.gradOfs;
+ float w, t0, t1, a0 = a[0], a1 = a[1];
+ const uchar* h = qanglePtr + pk.qangleOfs;
+ int h0 = h[0], h1 = h[1];
+
+ float* hist = blockHist + pk.histOfs[0];
+ w = pk.gradWeight*pk.histWeights[0];
+ t0 = hist[h0] + a0*w;
+ t1 = hist[h1] + a1*w;
+ hist[h0] = t0; hist[h1] = t1;
+
+ hist = blockHist + pk.histOfs[1];
+ w = pk.gradWeight*pk.histWeights[1];
+ t0 = hist[h0] + a0*w;
+ t1 = hist[h1] + a1*w;
+ hist[h0] = t0; hist[h1] = t1;
+ }
+
+ for( ; k < C4; k++ )
+ {
+ const PixData& pk = _pixData[k];
+ const float* a = gradPtr + pk.gradOfs;
+ float w, t0, t1, a0 = a[0], a1 = a[1];
+ const uchar* h = qanglePtr + pk.qangleOfs;
+ int h0 = h[0], h1 = h[1];
+
+ float* hist = blockHist + pk.histOfs[0];
+ w = pk.gradWeight*pk.histWeights[0];
+ t0 = hist[h0] + a0*w;
+ t1 = hist[h1] + a1*w;
+ hist[h0] = t0; hist[h1] = t1;
+
+ hist = blockHist + pk.histOfs[1];
+ w = pk.gradWeight*pk.histWeights[1];
+ t0 = hist[h0] + a0*w;
+ t1 = hist[h1] + a1*w;
+ hist[h0] = t0; hist[h1] = t1;
+
+ hist = blockHist + pk.histOfs[2];
+ w = pk.gradWeight*pk.histWeights[2];
+ t0 = hist[h0] + a0*w;
+ t1 = hist[h1] + a1*w;
+ hist[h0] = t0; hist[h1] = t1;
+
+ hist = blockHist + pk.histOfs[3];
+ w = pk.gradWeight*pk.histWeights[3];
+ t0 = hist[h0] + a0*w;
+ t1 = hist[h1] + a1*w;
+ hist[h0] = t0; hist[h1] = t1;
+ }
+
+ normalizeBlockHistogram(blockHist);
+
+ return blockHist;
+}
+
+void HOGCacheTester::normalizeBlockHistogram(float* _hist) const
+{
+ float* hist = &_hist[0], partSum[4] = { 0.0f, 0.0f, 0.0f, 0.0f };
+ size_t i, sz = blockHistogramSize;
+
+ for (i = 0; i <= sz - 4; i += 4)
+ {
+ partSum[0] += hist[i] * hist[i];
+ partSum[1] += hist[i+1] * hist[i+1];
+ partSum[2] += hist[i+2] * hist[i+2];
+ partSum[3] += hist[i+3] * hist[i+3];
+ }
+ float t0 = partSum[0] + partSum[1];
+ float t1 = partSum[2] + partSum[3];
+ float sum = t0 + t1;
+ for( ; i < sz; i++ )
+ sum += hist[i]*hist[i];
+
+ float scale = 1.f/(std::sqrt(sum)+sz*0.1f), thresh = (float)descriptor->L2HysThreshold;
+ partSum[0] = partSum[1] = partSum[2] = partSum[3] = 0.0f;
+ for(i = 0; i <= sz - 4; i += 4)
+ {
+ hist[i] = std::min(hist[i]*scale, thresh);
+ hist[i+1] = std::min(hist[i+1]*scale, thresh);
+ hist[i+2] = std::min(hist[i+2]*scale, thresh);
+ hist[i+3] = std::min(hist[i+3]*scale, thresh);
+ partSum[0] += hist[i]*hist[i];
+ partSum[1] += hist[i+1]*hist[i+1];
+ partSum[2] += hist[i+2]*hist[i+2];
+ partSum[3] += hist[i+3]*hist[i+3];
+ }
+ t0 = partSum[0] + partSum[1];
+ t1 = partSum[2] + partSum[3];
+ sum = t0 + t1;
+ for( ; i < sz; i++ )
+ {
+ hist[i] = std::min(hist[i]*scale, thresh);
+ sum += hist[i]*hist[i];
+ }
+
+ scale = 1.f/(std::sqrt(sum)+1e-3f);
+ for( i = 0; i < sz; i++ )
+ hist[i] *= scale;
+}
+
+Size HOGCacheTester::windowsInImage(Size imageSize, Size winStride) const
+{
+ return Size((imageSize.width - winSize.width)/winStride.width + 1,
+ (imageSize.height - winSize.height)/winStride.height + 1);
+}
+
+Rect HOGCacheTester::getWindow(Size imageSize, Size winStride, int idx) const
+{
+ int nwindowsX = (imageSize.width - winSize.width)/winStride.width + 1;
+ int y = idx / nwindowsX;
+ int x = idx - nwindowsX*y;
+ return Rect( x*winStride.width, y*winStride.height, winSize.width, winSize.height );
+}
+
+inline bool HOGDescriptorTester::is_failed() const
+{
+ return failed;
+}
+
+void HOGDescriptorTester::detect(const Mat& img,
+ vector<Point>& hits, vector<double>& weights, double hitThreshold,
+ Size winStride, Size padding, const vector<Point>& locations) const
+{
+ if (failed)
+ return;
+
+ hits.clear();
+ if( svmDetector.empty() )
+ return;
+
+ if( winStride == Size() )
+ winStride = cellSize;
+ Size cacheStride(gcd(winStride.width, blockStride.width),
+ gcd(winStride.height, blockStride.height));
+ size_t nwindows = locations.size();
+ padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
+ padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
+ Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
+
+ HOGCacheTester cache(this, img, padding, padding, nwindows == 0, cacheStride);
+
+ if( !nwindows )
+ nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
+
+ const HOGCacheTester::BlockData* blockData = &cache.blockData[0];
+
+ int nblocks = cache.nblocks.area();
+ int blockHistogramSize = cache.blockHistogramSize;
+ size_t dsize = getDescriptorSize();
+
+ double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
+ vector<float> blockHist(blockHistogramSize);
+
+ for( size_t i = 0; i < nwindows; i++ )
+ {
+ Point pt0;
+ if( !locations.empty() )
+ {
+ pt0 = locations[i];
+ if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
+ pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
+ continue;
+ }
+ else
+ {
+ pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
+ CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
+ }
+ double s = rho;
+ const float* svmVec = &svmDetector[0];
+ int j, k;
+ for( j = 0; j < nblocks; j++, svmVec += blockHistogramSize )
+ {
+ const HOGCacheTester::BlockData& bj = blockData[j];
+ Point pt = pt0 + bj.imgOffset;
+
+ const float* vec = cache.getBlock(pt, &blockHist[0]);
+ for( k = 0; k <= blockHistogramSize - 4; k += 4 )
+ s += vec[k]*svmVec[k] + vec[k+1]*svmVec[k+1] +
+ vec[k+2]*svmVec[k+2] + vec[k+3]*svmVec[k+3];
+ for( ; k < blockHistogramSize; k++ )
+ s += vec[k]*svmVec[k];
+ }
+ if( s >= hitThreshold )
+ {
+ hits.push_back(pt0);
+ weights.push_back(s);
+ }
+ }
+
+ // validation
+ std::vector<Point> actual_find_locations;
+ std::vector<double> actual_weights;
+ actual_hog->detect(img, actual_find_locations, actual_weights,
+ hitThreshold, winStride, padding, locations);
+
+ if (!std::equal(hits.begin(), hits.end(),
+ actual_find_locations.begin()))
+ {
+ ts->printf(cvtest::TS::SUMMARY, "Found locations are not equal (see detect function)\n");
+ ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
+ ts->set_gtest_status();
+ failed = true;
+ return;
+ }
+
+ const double eps = 0.0;
+ double diff_norm = norm(Mat(actual_weights) - Mat(weights), CV_L2);
+ if (diff_norm > eps)
+ {
+ ts->printf(cvtest::TS::SUMMARY, "Weights for found locations aren't equal.\n"
+ "Norm of the difference is %lf\n", diff_norm);
+ ts->printf(cvtest::TS::LOG, "Channels: %d\n", img.channels());
+ failed = true;
+ ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
+ ts->set_gtest_status();
+ return;
+ }
+}
+
+void HOGDescriptorTester::detect(const Mat& img, vector<Point>& hits, double hitThreshold,
+ Size winStride, Size padding, const vector<Point>& locations) const
+{
+ vector<double> weightsV;
+ detect(img, hits, weightsV, hitThreshold, winStride, padding, locations);
+}
+
+void HOGDescriptorTester::compute(const Mat& img, vector<float>& descriptors,
+ Size winStride, Size padding, const vector<Point>& locations) const
+{
+ if( winStride == Size() )
+ winStride = cellSize;
+ Size cacheStride(gcd(winStride.width, blockStride.width),
+ gcd(winStride.height, blockStride.height));
+ size_t nwindows = locations.size();
+ padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
+ padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
+ Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
+
+ HOGCacheTester cache(this, img, padding, padding, nwindows == 0, cacheStride);
+
+ if( !nwindows )
+ nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
+
+ const HOGCacheTester::BlockData* blockData = &cache.blockData[0];
+
+ int nblocks = cache.nblocks.area();
+ int blockHistogramSize = cache.blockHistogramSize;
+ size_t dsize = getDescriptorSize();
+ descriptors.resize(dsize*nwindows);
+
+ for( size_t i = 0; i < nwindows; i++ )
+ {
+ float* descriptor = &descriptors[i*dsize];
+
+ Point pt0;
+ if( !locations.empty() )
+ {
+ pt0 = locations[i];
+ if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
+ pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
+ continue;
+ }
+ else
+ {
+ pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
+ CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
+ }
+
+ for( int j = 0; j < nblocks; j++ )
+ {
+ const HOGCacheTester::BlockData& bj = blockData[j];
+ Point pt = pt0 + bj.imgOffset;
+
+ float* dst = descriptor + bj.histOfs;
+ const float* src = cache.getBlock(pt, dst);
+ if( src != dst )
+ for( int k = 0; k < blockHistogramSize; k++ )
+ dst[k] = src[k];
+ }
+ }
+
+ // validation
+ std::vector<float> actual_descriptors;
+ actual_hog->compute(img, actual_descriptors, winStride, padding, locations);
+
+ double diff_norm = cv::norm(Mat(actual_descriptors) - Mat(descriptors), CV_L2);
+ const double eps = 0.0;
+ if (diff_norm > eps)
+ {
+ ts->printf(cvtest::TS::SUMMARY, "Norm of the difference: %lf\n", diff_norm);
+ ts->printf(cvtest::TS::SUMMARY, "Found descriptors are not equal (see compute function)\n");
+ ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
+ ts->printf(cvtest::TS::LOG, "Channels: %d\n", img.channels());
+ ts->set_gtest_status();
+ failed = true;
+ return;
+ }
+}
+
+void HOGDescriptorTester::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
+ Size paddingTL, Size paddingBR) const
+{
+ CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
+
+ Size gradsize(img.cols + paddingTL.width + paddingBR.width,
+ img.rows + paddingTL.height + paddingBR.height);
+ grad.create(gradsize, CV_32FC2); // <magnitude*(1-alpha), magnitude*alpha>
+ qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
+ Size wholeSize;
+ Point roiofs;
+ img.locateROI(wholeSize, roiofs);
+
+ int i, x, y;
+ int cn = img.channels();
+
+ Mat_<float> _lut(1, 256);
+ const float* lut = &_lut(0,0);
+
+ if( gammaCorrection )
+ for( i = 0; i < 256; i++ )
+ _lut(0,i) = std::sqrt((float)i);
+ else
+ for( i = 0; i < 256; i++ )
+ _lut(0,i) = (float)i;
+
+ AutoBuffer<int> mapbuf(gradsize.width + gradsize.height + 4);
+ int* xmap = (int*)mapbuf + 1;
+ int* ymap = xmap + gradsize.width + 2;
+
+ const int borderType = (int)BORDER_REFLECT_101;
+
+ for( x = -1; x < gradsize.width + 1; x++ )
+ xmap[x] = borderInterpolate(x - paddingTL.width + roiofs.x,
+ wholeSize.width, borderType) - roiofs.x;
+ for( y = -1; y < gradsize.height + 1; y++ )
+ ymap[y] = borderInterpolate(y - paddingTL.height + roiofs.y,
+ wholeSize.height, borderType) - roiofs.y;
+
+ // x- & y- derivatives for the whole row
+ int width = gradsize.width;
+ AutoBuffer<float> _dbuf(width*4);
+ float* dbuf = _dbuf;
+ Mat Dx(1, width, CV_32F, dbuf);
+ Mat Dy(1, width, CV_32F, dbuf + width);
+ Mat Mag(1, width, CV_32F, dbuf + width*2);
+ Mat Angle(1, width, CV_32F, dbuf + width*3);
+
+ int _nbins = nbins;
+ float angleScale = (float)(_nbins/CV_PI);
+ for( y = 0; y < gradsize.height; y++ )
+ {
+ const uchar* imgPtr = img.data + img.step*ymap[y];
+ const uchar* prevPtr = img.data + img.step*ymap[y-1];
+ const uchar* nextPtr = img.data + img.step*ymap[y+1];
+ float* gradPtr = (float*)grad.ptr(y);
+ uchar* qanglePtr = (uchar*)qangle.ptr(y);
+
+ if( cn == 1 )
+ {
+ for( x = 0; x < width; x++ )
+ {
+ int x1 = xmap[x];
+ dbuf[x] = (float)(lut[imgPtr[xmap[x+1]]] - lut[imgPtr[xmap[x-1]]]);
+ dbuf[width + x] = (float)(lut[nextPtr[x1]] - lut[prevPtr[x1]]);
+ }
+ }
+ else
+ {
+ for( x = 0; x < width; x++ )
+ {
+ int x1 = xmap[x]*3;
+ float dx0, dy0, dx, dy, mag0, mag;
+ const uchar* p2 = imgPtr + xmap[x+1]*3;
+ const uchar* p0 = imgPtr + xmap[x-1]*3;
+
+ dx0 = lut[p2[2]] - lut[p0[2]];
+ dy0 = lut[nextPtr[x1+2]] - lut[prevPtr[x1+2]];
+ mag0 = dx0*dx0 + dy0*dy0;
+
+ dx = lut[p2[1]] - lut[p0[1]];
+ dy = lut[nextPtr[x1+1]] - lut[prevPtr[x1+1]];
+ mag = dx*dx + dy*dy;
+
+ if( mag0 < mag )
+ {
+ dx0 = dx;
+ dy0 = dy;
+ mag0 = mag;
+ }
+
+ dx = lut[p2[0]] - lut[p0[0]];
+ dy = lut[nextPtr[x1]] - lut[prevPtr[x1]];
+ mag = dx*dx + dy*dy;
+
+ if( mag0 < mag )
+ {
+ dx0 = dx;
+ dy0 = dy;
+ mag0 = mag;
+ }
+
+ dbuf[x] = dx0;
+ dbuf[x+width] = dy0;
+ }
+ }
+
+ cartToPolar( Dx, Dy, Mag, Angle, false );
+ for( x = 0; x < width; x++ )
+ {
+ float mag = dbuf[x+width*2], angle = dbuf[x+width*3]*angleScale - 0.5f;
+ int hidx = cvFloor(angle);
+ angle -= hidx;
+ gradPtr[x*2] = mag*(1.f - angle);
+ gradPtr[x*2+1] = mag*angle;
+ if( hidx < 0 )
+ hidx += _nbins;
+ else if( hidx >= _nbins )
+ hidx -= _nbins;
+ assert( (unsigned)hidx < (unsigned)_nbins );
+
+ qanglePtr[x*2] = (uchar)hidx;
+ hidx++;
+ hidx &= hidx < _nbins ? -1 : 0;
+ qanglePtr[x*2+1] = (uchar)hidx;
+ }
+ }
+
+ // validation
+ Mat actual_mats[2], reference_mats[2] = { grad, qangle };
+ const char* args[] = { "Gradient's", "Qangles's" };
+ actual_hog->computeGradient(img, actual_mats[0], actual_mats[1], paddingTL, paddingBR);
+
+ const double eps = 0.0;
+ for (i = 0; i < 2; ++i)
+ {
+ double diff_norm = norm(reference_mats[i] - actual_mats[i], CV_L2);
+ if (diff_norm > eps)
+ {
+ ts->printf(cvtest::TS::LOG, "%s matrices are not equal\n"
+ "Norm of the difference is %lf\n", args[i], diff_norm);
+ ts->printf(cvtest::TS::LOG, "Channels: %d\n", img.channels());
+ ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
+ ts->set_gtest_status();
+ failed = true;
+ return;
+ }
+ }
+}
+
+TEST(Objdetect_HOGDetector_Strict, accuracy)
+{
+ cvtest::TS* ts = cvtest::TS::ptr();
+ RNG& rng = ts->get_rng();
+
+ HOGDescriptor actual_hog;
+ actual_hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
+ HOGDescriptorTester reference_hog(actual_hog);
+
+ const unsigned int test_case_count = 5;
+ for (unsigned int i = 0; i < test_case_count && !reference_hog.is_failed(); ++i)
+ {
+ // creating a matrix
+ Size ssize(rng.uniform(1, 10) * actual_hog.winSize.width,
+ rng.uniform(1, 10) * actual_hog.winSize.height);
+ int type = rng.uniform(0, 1) > 0 ? CV_8UC1 : CV_8UC3;
+ Mat image(ssize, type);
+ rng.fill(image, RNG::UNIFORM, 0, 256, true);
+
+ // checking detect
+ std::vector<Point> hits;
+ std::vector<double> weights;
+ reference_hog.detect(image, hits, weights);
+
+ // checking compute
+ std::vector<float> descriptors;
+ reference_hog.compute(image, descriptors);
+ }
+ }