??????? 參考文章：OpenCV中的HOG+SVM物體分類

??????? 此文主要描述出HOG分類的調(diào)用堆棧。

??????? 使用OpenCV作圖像檢測(cè)， 使用HOG檢測(cè)過程，其中一部分源代碼如下：

1.HOG 檢測(cè)底層棧的檢測(cè)計(jì)算代碼：

???????? 貌似在計(jì)算過程中僅使用滑窗方法？

void HOGDescriptor::detect(const Mat& img,vector<Point>& hits, vector<double>& weights, 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]; #ifdef HAVE_IPPint j; #elseint j, k; #endiffor( 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_IPPIpp32f partSum;ippsDotProd_32f(vec,svmVec,blockHistogramSize,&partSum);s += (double)partSum; #elsefor( 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]; #endif}if( s >= hitThreshold ){hits.push_back(pt0);weights.push_back(s);}} }
2. HOG invoker的對(duì)象重載：

void operator()( const Range& range ) const{int i, i1 = range.start, i2 = range.end;double minScale = i1 > 0 ? levelScale[i1] : i2 > 1 ? levelScale[i1+1] : std::max(img.cols, img.rows);Size maxSz(cvCeil(img.cols/minScale), cvCeil(img.rows/minScale));Mat smallerImgBuf(maxSz, img.type());vector<Point> locations;vector<double> hitsWeights;for( i = i1; i < i2; i++ ){double scale = levelScale[i];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);elseresize(img, smallerImg, sz);//使用HOG 進(jìn)行檢測(cè)hog->detect(smallerImg, locations, hitsWeights, hitThreshold, winStride, padding);Size scaledWinSize = Size(cvRound(hog->winSize.width*scale), cvRound(hog->winSize.height*scale));mtx->lock();for( size_t j = 0; j < locations.size(); j++ ){vec->push_back(Rect(cvRound(locations[j].x*scale),cvRound(locations[j].y*scale),scaledWinSize.width, scaledWinSize.height));if (scales){scales->push_back(scale);}}mtx->unlock();if (weights && (!hitsWeights.empty())){mtx->lock();for (size_t j = 0; j < locations.size(); j++){weights->push_back(hitsWeights[j]);}mtx->unlock();}}}
3.使用HOG特征進(jìn)行多尺度檢測(cè)

void HOGDescriptor::detectMultiScale(const Mat& img, vector<Rect>& foundLocations, vector<double>& foundWeights,double hitThreshold, Size winStride, Size padding,double scale0, double finalThreshold, bool useMeanshiftGrouping) const {double scale = 1.;int levels = 0;vector<double> levelScale;for( levels = 0; levels < nlevels; levels++ ){levelScale.push_back(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);std::vector<Rect> allCandidates;std::vector<double> tempScales;std::vector<double> tempWeights;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));std::copy(tempScales.begin(), tempScales.end(), back_inserter(foundScales));foundLocations.clear();std::copy(allCandidates.begin(), allCandidates.end(), back_inserter(foundLocations));foundWeights.clear();std::copy(tempWeights.begin(), tempWeights.end(), back_inserter(foundWeights));if ( useMeanshiftGrouping ){groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize);}else{groupRectangles(foundLocations, foundWeights, (int)finalThreshold, 0.2);} }

其中得到HogCache也是重要的一環(huán)：

? 獨(dú)立為init函數(shù)：

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;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 pixDatafor( 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 blockDatafor( 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);} }

總結(jié)：

?????? 以上大致為HOG檢測(cè)計(jì)算大致的函數(shù)調(diào)用堆棧。

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