--- /dev/null
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+// By downloading, copying, installing or using the software you agree to this license.
+// If you do not agree to this license, do not download, install,
+// copy or use the software.
+//
+//
+// License Agreement
+// For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+// * Redistribution's of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+//
+// * Redistribution's in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+//
+// * The name of the copyright holders may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "_cvaux.h"
+
+/****************************************************************************************\
+ The code below is implementation of HOG (Histogram-of-Oriented Gradients)
+ descriptor and object detection, introduced by Navneet Dalal and Bill Triggs.
+
+ The computed feature vectors are compatible with the
+ INRIA Object Detection and Localization Toolkit
+ (http://pascal.inrialpes.fr/soft/olt/)
+\****************************************************************************************/
+
+namespace cv
+{
+
+size_t HOGDescriptor::getDescriptorSize() const
+{
+ CV_Assert(blockSize.width % cellSize.width == 0 &&
+ 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)*
+ ((winSize.width - blockSize.width)/blockStride.width + 1)*
+ ((winSize.height - blockSize.height)/blockStride.height + 1);
+}
+
+double HOGDescriptor::getWinSigma() const
+{
+ return winSigma >= 0 ? winSigma : (blockSize.width + blockSize.height)/8.;
+}
+
+bool HOGDescriptor::checkDetectorSize() const
+{
+ size_t detectorSize = svmDetector.size(), descriptorSize = getDescriptorSize();
+ return detectorSize == 0 ||
+ detectorSize == descriptorSize ||
+ detectorSize == descriptorSize + 1;
+}
+
+void HOGDescriptor::setSVMDetector(const vector<float>& _svmDetector)
+{
+ svmDetector = _svmDetector;
+ CV_Assert( checkDetectorSize() );
+}
+
+bool HOGDescriptor::load(const String& filename, const String& objname)
+{
+ FileStorage fs(filename, FileStorage::READ);
+ FileNode obj = !objname.empty() ? fs[objname] : fs.getFirstTopLevelNode();
+ if( !obj.isMap() )
+ return false;
+ FileNodeIterator it = obj["winSize"].begin();
+ it >> winSize.width >> winSize.height;
+ it = obj["blockSize"].begin();
+ it >> blockSize.width >> blockSize.height;
+ it = obj["blockStride"].begin();
+ it >> blockStride.width >> blockStride.height;
+ it = obj["cellSize"].begin();
+ it >> cellSize.width >> cellSize.height;
+ obj["nbins"] >> nbins;
+ obj["derivAperture"] >> derivAperture;
+ obj["winSigma"] >> winSigma;
+ obj["histogramNormType"] >> histogramNormType;
+ obj["L2HysThreshold"] >> L2HysThreshold;
+ obj["gammaCorrection"] >> gammaCorrection;
+
+ FileNode vecNode = obj["SVMDetector"];
+ if( vecNode.isSeq() )
+ {
+ vecNode >> svmDetector;
+ CV_Assert(checkDetectorSize());
+ }
+ return true;
+}
+
+void HOGDescriptor::save(const String& filename, const String& objName) const
+{
+ FileStorage fs(filename, FileStorage::WRITE);
+ fs << (!objName.empty() ? objName : FileStorage::getDefaultObjectName(filename)) << "{";
+
+ fs << "winSize" << winSize
+ << "blockSize" << blockSize
+ << "blockStride" << blockStride
+ << "cellSize" << cellSize
+ << "nbins" << nbins
+ << "derivAperture" << derivAperture
+ << "winSigma" << getWinSigma()
+ << "histogramNormType" << histogramNormType
+ << "L2HysThreshold" << L2HysThreshold
+ << "gammaCorrection" << gammaCorrection;
+ if( !svmDetector.empty() )
+ fs << "SVMDetector" << "[:" << svmDetector << "]";
+ fs << "}";
+}
+
+void HOGDescriptor::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);
+ for( y = -1; y < gradsize.height + 1; y++ )
+ ymap[y] = borderInterpolate(y - paddingTL.height + roiofs.y,
+ wholeSize.height, borderType);
+
+ // 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;
+ const uchar* p2 = imgPtr + xmap[x+1]*3;
+ const uchar* p0 = imgPtr + xmap[x-1]*3;
+ float dx0, dy0, dx, dy, mag0, mag;
+
+ 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;
+ 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;
+ gradPtr[x*2] = mag*(1.f - angle);
+ gradPtr[x*2+1] = mag*angle;
+ }
+ }
+}
+
+
+struct HOGCache
+{
+ struct BlockData
+ {
+ BlockData() : histOfs(0), imgOffset() {}
+ int histOfs;
+ Point imgOffset;
+ };
+
+ struct PixData
+ {
+ size_t gradOfs, qangleOfs;
+ int histOfs[4];
+ float histWeights[4];
+ float gradWeight;
+ };
+
+ HOGCache();
+ HOGCache(const HOGDescriptor* descriptor,
+ const Mat& img, Size paddingTL, Size paddingBR,
+ bool useCache, Size cacheStride);
+ virtual ~HOGCache() {};
+ virtual void init(const HOGDescriptor* 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 HOGDescriptor* descriptor;
+};
+
+
+HOGCache::HOGCache()
+{
+ 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)
+{
+ init(_descriptor, _img, _paddingTL, _paddingBR, _useCache, _cacheStride);
+}
+
+void HOGCache::init(const HOGDescriptor* _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;
+ Size winSize = descriptor->winSize;
+ 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 i, cacheRows = blockCache.rows;
+ ymaxCached.resize(cacheRows);
+ for( i = 0; i < cacheRows; i++ )
+ ymaxCached[i] = -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* HOGCache::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 HOGCache::normalizeBlockHistogram(float* _hist) const
+{
+ float* hist = &_hist[0];
+ size_t i, sz = blockHistogramSize;
+
+ float sum = 0;
+ for( i = 0; i < sz; i++ )
+ sum += hist[i]*hist[i];
+ float scale = 1.f/(std::sqrt(sum)+sz*0.1f), thresh = (float)descriptor->L2HysThreshold;
+ for( i = 0, sum = 0; 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 HOGCache::windowsInImage(Size imageSize, Size winStride) const
+{
+ return Size((imageSize.width - winSize.width)/winStride.width + 1,
+ (imageSize.height - winSize.height)/winStride.height + 1);
+}
+
+Rect HOGCache::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 );
+}
+
+
+void HOGDescriptor::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);
+
+ HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);
+
+ if( !nwindows )
+ nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
+
+ const HOGCache::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 HOGCache::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];
+ }
+ }
+}
+
+
+void HOGDescriptor::detect(const Mat& img,
+ vector<Point>& hits, double hitThreshold,
+ Size winStride, Size padding, const vector<Point>& locations) const
+{
+ 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);
+
+ HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);
+
+ if( !nwindows )
+ nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
+
+ const HOGCache::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 HOGCache::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);
+ }
+}
+
+
+struct HOGThreadData
+{
+ vector<Rect> rectangles;
+ vector<Point> locations;
+ Mat smallerImgBuf;
+};
+
+void HOGDescriptor::detectMultiScale(
+ const Mat& img, vector<Rect>& foundLocations,
+ double hitThreshold, Size winStride, Size padding,
+ double scale0, int groupThreshold) const
+{
+ double scale = 1.;
+ foundLocations.clear();
+ int i, levels = 0;
+ const int maxLevels = 64;
+
+ int t, nthreads = getNumThreads();
+ vector<HOGThreadData> threadData(nthreads);
+
+ for( t = 0; t < nthreads; t++ )
+ threadData[t].smallerImgBuf.create(img.size(), img.type());
+
+ vector<double> levelScale(maxLevels);
+ for( levels = 0; levels < maxLevels; levels++ )
+ {
+ levelScale[levels] = scale;
+ if( cvRound(img.cols/scale) < winSize.width ||
+ cvRound(img.rows/scale) < winSize.height ||
+ scale0 <= 1 )
+ break;
+ scale *= scale0;
+ }
+ levels = std::max(levels, 1);
+ levelScale.resize(levels);
+
+ {
+#ifdef _OPENMP
+ #pragma omp parallel for num_threads(nthreads) schedule(dynamic)
+#endif // _OPENMP
+ for( i = 0; i < levels; i++ )
+ {
+ HOGThreadData& tdata = threadData[getThreadNum()];
+ double scale = levelScale[i];
+ Size sz(cvRound(img.cols/scale), cvRound(img.rows/scale));
+ Mat smallerImg(sz, img.type(), tdata.smallerImgBuf.data);
+ if( sz == img.size() )
+ smallerImg = Mat(sz, img.type(), img.data, img.step);
+ else
+ resize(img, smallerImg, sz);
+ detect(smallerImg, tdata.locations, hitThreshold, winStride, padding);
+ Size scaledWinSize = Size(cvRound(winSize.width*scale), cvRound(winSize.height*scale));
+ for( size_t j = 0; j < tdata.locations.size(); j++ )
+ tdata.rectangles.push_back(Rect(
+ cvRound(tdata.locations[j].x*scale),
+ cvRound(tdata.locations[j].y*scale),
+ scaledWinSize.width, scaledWinSize.height));
+ }
+ }
+
+ for( t = 0; t < nthreads; t++ )
+ {
+ HOGThreadData& tdata = threadData[t];
+ std::copy(tdata.rectangles.begin(), tdata.rectangles.end(),
+ std::back_inserter(foundLocations));
+ }
+
+ groupRectangles(foundLocations, groupThreshold, 0.2);
+}
+
+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,
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+ 6.40467042e-003f, -0.01271779f, 6.89014705e-005f, 0.04541415f,
+ -0.01899539f, -0.05020239f, 0.03000903f, 0.01090422f, 4.52452758e-003f,
+ 0.02573632f, -0.02388454f, -0.04200457f, 1.72783900e-003f,
+ -0.05978370f, -0.02720562f, 0.06573715f, 0.01154317f, 0.01265615f,
+ 0.07375994f, -9.19828378e-003f, -0.04914120f, 0.02124831f, 0.06455322f,
+ 0.04372910f, -0.03310043f, 0.03605788f, -6.78055827e-003f,
+ 9.36202332e-003f, 0.01747596f, -0.06406314f, -0.06812935f, 0.08080816f,
+ -0.02778088f, 0.02735260f, 0.06393493f, 0.06652229f, 0.05676993f,
+ 0.08640018f, -7.59188086e-003f, -0.02012847f, -0.04741159f,
+ -0.01657069f, -0.01624399f, 0.05547778f, -2.33309763e-003f,
+ 0.01120033f, 0.06141156f, -0.06285004f, -0.08732341f, -0.09313398f,
+ -0.04267832f, 5.57443965e-003f, 0.04809862f, 0.01773641f,
+ 5.37361018e-003f, 0.14842421f, -0.06298012f, -0.02935147f, 0.11443478f,
+ -0.05034208f, 5.65494271e-003f, 0.02076526f, -0.04577984f,
+ -0.04735741f, 0.02961071f, -0.09307127f, -0.04417921f, -0.04990027f,
+ -0.03940028f, 0.01306016f, 0.06267900f, 0.03758737f, 0.08460117f,
+ 0.13858789f, 0.04862388f, -0.06319809f, -0.05655516f, 0.01885816f,
+ -0.03285607f, 0.03371567f, -0.07040928f, -0.04514049f, 0.01392166f,
+ 0.08184422f, -0.07230316f, 0.02386871f, 0.02184591f, 0.02605764f,
+ -0.01033954f, 9.29878280e-003f, 7.67351175e-003f, 0.15189242f,
+ 0.02069071f, -0.09738296f, -0.08894105f, -0.07768748f, 0.02332268f,
+ -0.01778995f, -0.03258888f, -0.08180822f, -0.08492987f, 0.02290156f,
+ -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 vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
+}
+
+}
projects / opencv / blobdiff