diff --git a/test/bug-hunting/cve/CVE-2019-15939/cmd.txt b/test/bug-hunting/cve/CVE-2019-15939/cmd.txt new file mode 100644 index 000000000..a12bdbf06 --- /dev/null +++ b/test/bug-hunting/cve/CVE-2019-15939/cmd.txt @@ -0,0 +1,2 @@ +-DCV_EXPORTS_W= +-DCV_EXPORTS= diff --git a/test/bug-hunting/cve/CVE-2019-15939/expected.txt b/test/bug-hunting/cve/CVE-2019-15939/expected.txt new file mode 100644 index 000000000..c1966f235 --- /dev/null +++ b/test/bug-hunting/cve/CVE-2019-15939/expected.txt @@ -0,0 +1,5 @@ +hog.cpp:92:bughuntingDivByZero +hog.cpp:93:bughuntingDivByZero +hog.cpp:94:bughuntingDivByZero +hog.cpp:95:bughuntingDivByZero + diff --git a/test/bug-hunting/cve/CVE-2019-15939/hog.cpp b/test/bug-hunting/cve/CVE-2019-15939/hog.cpp new file mode 100644 index 000000000..b55c710dc --- /dev/null +++ b/test/bug-hunting/cve/CVE-2019-15939/hog.cpp @@ -0,0 +1,3619 @@ +/*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 "precomp.hpp" +#include "cascadedetect.hpp" +#include "opencv2/core/core_c.h" +#include "opencv2/core/hal/intrin.hpp" +#include "opencl_kernels_objdetect.hpp" + +#include +#include +#include + +/****************************************************************************************\ + 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 +{ + +#define NTHREADS 256 + +enum {DESCR_FORMAT_COL_BY_COL, DESCR_FORMAT_ROW_BY_ROW}; + +static int numPartsWithin(int size, int part_size, int stride) +{ + CV_Assert(stride != 0); + return (size - part_size + stride) / stride; +} + +static Size numPartsWithin(cv::Size size, cv::Size part_size, + cv::Size stride) +{ + return Size(numPartsWithin(size.width, part_size.width, stride.width), + numPartsWithin(size.height, part_size.height, stride.height)); +} + +static size_t getBlockHistogramSize(Size block_size, Size cell_size, int nbins) +{ + CV_Assert(!cell_size.empty()); + Size cells_per_block = Size(block_size.width / cell_size.width, + block_size.height / cell_size.height); + return (size_t)(nbins * cells_per_block.area()); +} + +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(InputArray _svmDetector) +{ + _svmDetector.getMat().convertTo(svmDetector, CV_32F); + CV_Assert(checkDetectorSize()); + + Mat detector_reordered(1, (int)svmDetector.size(), CV_32FC1); + + size_t block_hist_size = getBlockHistogramSize(blockSize, cellSize, nbins); + cv::Size blocks_per_img = numPartsWithin(winSize, blockSize, blockStride); + + for (int i = 0; i < blocks_per_img.height; ++i) + for (int j = 0; j < blocks_per_img.width; ++j) + { + const float *src = &svmDetector[0] + (j * blocks_per_img.height + i) * block_hist_size; + float *dst = detector_reordered.ptr() + (i * blocks_per_img.width + j) * block_hist_size; + for (size_t k = 0; k < block_hist_size; ++k) + dst[k] = src[k]; + } + size_t descriptor_size = getDescriptorSize(); + free_coef = svmDetector.size() > descriptor_size ? svmDetector[descriptor_size] : 0; + detector_reordered.copyTo(oclSvmDetector); +} + +#define CV_TYPE_NAME_HOG_DESCRIPTOR "opencv-object-detector-hog" + +bool HOGDescriptor::read(FileNode& obj) +{ + CV_Assert(!obj["winSize"].empty()); + + if( !obj.isMap() ) + return false; + FileNodeIterator it = obj["winSize"].begin(); + it >> winSize.width >> winSize.height; CV_Assert(!winSize.empty()); + it = obj["blockSize"].begin(); + it >> blockSize.width >> blockSize.height; CV_Assert(!blockSize.empty()); + it = obj["blockStride"].begin(); + it >> blockStride.width >> blockStride.height; CV_Assert(!blockStride.empty()); + it = obj["cellSize"].begin(); + it >> cellSize.width >> cellSize.height; CV_Assert(!cellSize.empty()); + obj["nbins"] >> nbins; CV_Assert(nbins > 0); + obj["derivAperture"] >> derivAperture; + obj["winSigma"] >> winSigma; + obj["histogramNormType"] >> histogramNormType; + obj["L2HysThreshold"] >> L2HysThreshold; + obj["gammaCorrection"] >> gammaCorrection; + obj["nlevels"] >> nlevels; CV_Assert(nlevels > 0); + if (obj["signedGradient"].empty()) + signedGradient = false; + else + obj["signedGradient"] >> signedGradient; + + FileNode vecNode = obj["SVMDetector"]; + if( vecNode.isSeq() ) + { + std::vector _svmDetector; + vecNode >> _svmDetector; + setSVMDetector(_svmDetector); + } + return true; +} + +void HOGDescriptor::write(FileStorage& fs, const String& objName) const +{ + if( !objName.empty() ) + 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 + << "signedGradient" << signedGradient; + if( !svmDetector.empty() ) + fs << "SVMDetector" << svmDetector; + fs << "}"; +} + +bool HOGDescriptor::load(const String& filename, const String& objname) +{ + FileStorage fs(filename, FileStorage::READ); + FileNode obj = !objname.empty() ? fs[objname] : fs.getFirstTopLevelNode(); + return read(obj); +} + +void HOGDescriptor::save(const String& filename, const String& objName) const +{ + FileStorage fs(filename, FileStorage::WRITE); + write(fs, !objName.empty() ? objName : FileStorage::getDefaultObjectName(filename)); +} + +void HOGDescriptor::copyTo(HOGDescriptor& c) const +{ + c.winSize = winSize; + c.blockSize = blockSize; + c.blockStride = blockStride; + c.cellSize = cellSize; + c.nbins = nbins; + c.derivAperture = derivAperture; + c.winSigma = winSigma; + c.histogramNormType = histogramNormType; + c.L2HysThreshold = L2HysThreshold; + c.gammaCorrection = gammaCorrection; + c.setSVMDetector(svmDetector); + c.nlevels = nlevels; + c.signedGradient = signedGradient; +} + +void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle, + Size paddingTL, Size paddingBR) const +{ + CV_INSTRUMENT_REGION(); + + 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); // + 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_ _lut(1, 256); + const float* const lut = &_lut(0,0); +#if CV_SIMD128 + v_float32x4 idx(0.0f, 1.0f, 2.0f, 3.0f); + v_float32x4 ifour = v_setall_f32(4.0); + + float* const _data = &_lut(0, 0); + if ( gammaCorrection ) + for ( i = 0; i < 256; i += 4) + { + v_store(_data + i, v_sqrt(idx)); + idx += ifour; + } + else + for ( i = 0; i < 256; i += 4) + { + v_store(_data + i, idx); + 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 mapbuf(gradsize.width + gradsize.height + 4); + int* xmap = mapbuf.data() + 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 _dbuf(width*4); + float* const dbuf = _dbuf.data(); + 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); + + if (cn == 3) + { + int end = gradsize.width + 2; + xmap -= 1, x = 0; +#if CV_SIMD128 + for ( ; x <= end - 4; x += 4) + { + v_int32x4 mul_res = v_load(xmap + x); + mul_res += mul_res + mul_res; + v_store(xmap + x, mul_res); + } +#endif + for ( ; x < end; ++x) + xmap[x] *= 3; + xmap += 1; + } + + float angleScale = signedGradient ? (float)(nbins/(2.0*CV_PI)) : (float)(nbins/CV_PI); + for( y = 0; y < gradsize.height; y++ ) + { + const uchar* imgPtr = img.ptr(ymap[y]); + //In case subimage is used ptr() generates an assert for next and prev rows + //(see http://code.opencv.org/issues/4149) + const uchar* prevPtr = img.data + img.step*ymap[y-1]; + const uchar* nextPtr = img.data + img.step*ymap[y+1]; + + float* gradPtr = grad.ptr(y); + uchar* qanglePtr = 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 + { + x = 0; +#if CV_SIMD128 + for( ; x <= width - 4; x += 4 ) + { + 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; + + v_float32x4 _dx0 = v_float32x4(lut[p02[0]], lut[p12[0]], lut[p22[0]], lut[p32[0]]) - + v_float32x4(lut[p00[0]], lut[p10[0]], lut[p20[0]], lut[p30[0]]); + v_float32x4 _dx1 = v_float32x4(lut[p02[1]], lut[p12[1]], lut[p22[1]], lut[p32[1]]) - + v_float32x4(lut[p00[1]], lut[p10[1]], lut[p20[1]], lut[p30[1]]); + v_float32x4 _dx2 = v_float32x4(lut[p02[2]], lut[p12[2]], lut[p22[2]], lut[p32[2]]) - + v_float32x4(lut[p00[2]], lut[p10[2]], lut[p20[2]], lut[p30[2]]); + + v_float32x4 _dy0 = v_float32x4(lut[nextPtr[x0]], lut[nextPtr[x1]], lut[nextPtr[x2]], lut[nextPtr[x3]]) - + v_float32x4(lut[prevPtr[x0]], lut[prevPtr[x1]], lut[prevPtr[x2]], lut[prevPtr[x3]]); + v_float32x4 _dy1 = v_float32x4(lut[nextPtr[x0+1]], lut[nextPtr[x1+1]], lut[nextPtr[x2+1]], lut[nextPtr[x3+1]]) - + v_float32x4(lut[prevPtr[x0+1]], lut[prevPtr[x1+1]], lut[prevPtr[x2+1]], lut[prevPtr[x3+1]]); + v_float32x4 _dy2 = v_float32x4(lut[nextPtr[x0+2]], lut[nextPtr[x1+2]], lut[nextPtr[x2+2]], lut[nextPtr[x3+2]]) - + v_float32x4(lut[prevPtr[x0+2]], lut[prevPtr[x1+2]], lut[prevPtr[x2+2]], lut[prevPtr[x3+2]]); + + v_float32x4 _mag0 = (_dx0 * _dx0) + (_dy0 * _dy0); + v_float32x4 _mag1 = (_dx1 * _dx1) + (_dy1 * _dy1); + v_float32x4 _mag2 = (_dx2 * _dx2) + (_dy2 * _dy2); + + v_float32x4 mask = v_reinterpret_as_f32(_mag2 > _mag1); + _dx2 = v_select(mask, _dx2, _dx1); + _dy2 = v_select(mask, _dy2, _dy1); + + mask = v_reinterpret_as_f32(v_max(_mag2, _mag1) > _mag0); + _dx2 = v_select(mask, _dx2, _dx0); + _dy2 = v_select(mask, _dy2, _dy0); + + v_store(dbuf + x, _dx2); + v_store(dbuf + x + width, _dy2); + } +#endif + for( ; x < width; x++ ) + { + int x1 = xmap[x]; + float dx0, dy0, dx, dy, mag0, mag; + 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]]; + 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; + } + } + + // computing angles and magnidutes + cartToPolar( Dx, Dy, Mag, Angle, false ); + + // filling the result matrix + x = 0; +#if CV_SIMD128 + v_float32x4 fhalf = v_setall_f32(0.5f); + v_float32x4 _angleScale = v_setall_f32(angleScale), fone = v_setall_f32(1.0f); + v_int32x4 ione = v_setall_s32(1), _nbins = v_setall_s32(nbins), izero = v_setzero_s32(); + + for ( ; x <= width - 4; x += 4) + { + int x2 = x << 1; + v_float32x4 _mag = v_load(dbuf + x + (width << 1)); + v_float32x4 _angle = v_load(dbuf + x + width * 3); + _angle = (_angleScale * _angle) - fhalf; + + v_int32x4 _hidx = v_floor(_angle); + _angle -= v_cvt_f32(_hidx); + + v_float32x4 ft0 = _mag * (fone - _angle); + v_float32x4 ft1 = _mag * _angle; + + v_store_interleave(gradPtr + x2, ft0, ft1); + + v_int32x4 mask0 = _hidx >> 31; + v_int32x4 it0 = mask0 & _nbins; + mask0 = (_hidx >= _nbins); + v_int32x4 it1 = mask0 & _nbins; + _hidx += (it0 - it1); + + it0 = v_reinterpret_as_s32(v_pack(v_pack(_hidx, izero), v_reinterpret_as_s16(izero))); + _hidx += ione; + _hidx &= (_hidx < _nbins); + it1 = v_reinterpret_as_s32(v_pack(v_pack(_hidx, izero), v_reinterpret_as_s16(izero))); + v_uint8x16 it2, it3; + v_zip(v_reinterpret_as_u8(it0), v_reinterpret_as_u8(it1), it2, it3); + + v_store_low(qanglePtr + x2, it2); + } +#endif + for( ; 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; + + CV_Assert( (unsigned)hidx < (unsigned)nbins ); + + qanglePtr[x*2] = (uchar)hidx; + hidx++; + hidx &= hidx < nbins ? -1 : 0; + qanglePtr[x*2+1] = (uchar)hidx; + } + } +} + +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, const Size& paddingTL, const Size& paddingBR, + bool useCache, const Size& cacheStride); + virtual ~HOGCache() { } + virtual void init(const HOGDescriptor* descriptor, + const Mat& img, const Size& paddingTL, const Size& paddingBR, + bool useCache, const Size& cacheStride); + + 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; + std::vector blockData; + + bool useCache; + std::vector ymaxCached; + Size winSize; + Size cacheStride; + Size nblocks, ncells; + int blockHistogramSize; + int count1, count2, count4; + Point imgoffset; + Mat_ blockCache; + Mat_ blockCacheFlags; + + Mat grad, qangle; + const HOGDescriptor* descriptor; +}; + +HOGCache::HOGCache() : + blockHistogramSize(), count1(), count2(), count4() +{ + useCache = false; + descriptor = 0; +} + +HOGCache::HOGCache(const HOGDescriptor* _descriptor, + 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, const Size& _paddingTL, const Size& _paddingBR, + bool _useCache, const 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_ weights(blockSize); + float sigma = (float)descriptor->getWinSigma(); + float scale = 1.f/(sigma*sigma*2); + + { + AutoBuffer di(blockSize.height), dj(blockSize.width); + float* _di = di.data(), *_dj = dj.data(); + float bh = blockSize.height * 0.5f, bw = blockSize.width * 0.5f; + + i = 0; + #if CV_SIMD128 + v_float32x4 idx(0.0f, 1.0f, 2.0f, 3.0f); + v_float32x4 _bw = v_setall_f32(bw), _bh = v_setall_f32(bh); + v_float32x4 ifour = v_setall_f32(4.0); + + for (; i <= blockSize.height - 4; i += 4) + { + v_float32x4 t = idx - _bh; + t *= t; + idx += ifour; + v_store(_di + i, t); + } + #endif + for ( ; i < blockSize.height; ++i) + { + _di[i] = i - bh; + _di[i] *= _di[i]; + } + + j = 0; + #if CV_SIMD128 + idx = v_float32x4(0.0f, 1.0f, 2.0f, 3.0f); + + for (; j <= blockSize.height - 4; j += 4) + { + v_float32x4 t = idx - _bw; + t *= t; + idx += ifour; + v_store(_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); + + // 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_ 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 = grad.ptr(pt.y) + pt.x*2; + const uchar* qanglePtr = qangle.ptr(pt.y) + pt.x*2; + +// 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* 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_SIMD128 + float hist0[4], hist1[4]; + for( ; k < C2; 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]; + + v_float32x4 _a0 = v_setall_f32(a[0]), _a1 = v_setall_f32(a[1]); + v_float32x4 w = v_setall_f32(pk.gradWeight) * v_load(pk.histWeights); + v_float32x4 _t0 = _a0 * w, _t1 = _a1 * w; + + v_store(hist0, _t0); + v_store(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_SIMD128 + 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]; + + v_float32x4 _a0 = v_setall_f32(a[0]), _a1 = v_setall_f32(a[1]); + v_float32x4 w = v_setall_f32(pk.gradWeight) * v_load(pk.histWeights); + v_float32x4 _t0 = _a0 * w, _t1 = _a1 * w; + + v_store(hist0, _t0); + v_store(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; + } +#else + 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; + } +#endif + + normalizeBlockHistogram(blockHist); + + return blockHist; +} + +void HOGCache::normalizeBlockHistogram(float* _hist) const +{ + float* hist = &_hist[0], sum = 0.0f, partSum[4]; + size_t i = 0, sz = blockHistogramSize; + +#if CV_SIMD128 + v_float32x4 p0 = v_load(hist); + v_float32x4 s = p0 * p0; + + for (i = 4; i <= sz - 4; i += 4) + { + p0 = v_load(hist + i); + s += p0 * p0; + } + v_store(partSum, s); +#else + 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; + i = 0, sum = 0.0f; + +#if CV_SIMD128 + v_float32x4 _scale = v_setall_f32(scale); + static v_float32x4 _threshold = v_setall_f32(thresh); + + v_float32x4 p = _scale * v_load(hist); + p = v_min(p, _threshold); + s = p * p; + v_store(hist, p); + + for(i = 4 ; i <= sz - 4; i += 4) + { + p = v_load(hist + i); + p *= _scale; + p = v_min(p, _threshold); + s += p * p; + v_store(hist + i, p); + } + + v_store(partSum, s); +#else + 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]; + } + + scale = 1.f/(std::sqrt(sum)+1e-3f), i = 0; +#if CV_SIMD128 + v_float32x4 _scale2 = v_setall_f32(scale); + for ( ; i <= sz - 4; i += 4) + { + v_float32x4 t = _scale2 * v_load(hist + i); + v_store(hist + i, t); + } +#endif + for ( ; i < sz; ++i) + hist[i] *= scale; +} + +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); +} + +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; + int x = idx - nwindowsX*y; + return Rect( x*winStride.width, y*winStride.height, winSize.width, winSize.height ); +} + +static inline int gcd(int a, int b) +{ + if( a < b ) + std::swap(a, b); + while( b > 0 ) + { + int r = a % b; + a = b; + b = r; + } + return a; +} + +#ifdef HAVE_OPENCL + +static bool ocl_compute_gradients_8UC1(int height, int width, InputArray _img, float angle_scale, + UMat grad, UMat qangle, bool correct_gamma, int nbins) +{ + ocl::Kernel k("compute_gradients_8UC1_kernel", ocl::objdetect::objdetect_hog_oclsrc); + if(k.empty()) + return false; + + UMat img = _img.getUMat(); + + size_t localThreads[3] = { NTHREADS, 1, 1 }; + size_t globalThreads[3] = { (size_t)width, (size_t)height, 1 }; + char correctGamma = (correct_gamma) ? 1 : 0; + int grad_quadstep = (int)grad.step >> 3; + int qangle_elem_size = CV_ELEM_SIZE1(qangle.type()); + int qangle_step = (int)qangle.step / (2 * qangle_elem_size); + + int idx = 0; + idx = k.set(idx, height); + idx = k.set(idx, width); + idx = k.set(idx, (int)img.step1()); + idx = k.set(idx, grad_quadstep); + idx = k.set(idx, qangle_step); + idx = k.set(idx, ocl::KernelArg::PtrReadOnly(img)); + idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(grad)); + idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(qangle)); + idx = k.set(idx, angle_scale); + idx = k.set(idx, correctGamma); + idx = k.set(idx, nbins); + + return k.run(2, globalThreads, localThreads, false); +} + +static bool ocl_computeGradient(InputArray img, UMat grad, UMat qangle, int nbins, Size effect_size, bool gamma_correction, bool signedGradient) +{ + float angleScale = signedGradient ? (float)(nbins/(2.0*CV_PI)) : (float)(nbins/CV_PI); + + return ocl_compute_gradients_8UC1(effect_size.height, effect_size.width, img, + angleScale, grad, qangle, gamma_correction, nbins); +} + +#define CELL_WIDTH 8 +#define CELL_HEIGHT 8 +#define CELLS_PER_BLOCK_X 2 +#define CELLS_PER_BLOCK_Y 2 + +static bool ocl_compute_hists(int nbins, int block_stride_x, int block_stride_y, int height, int width, + UMat grad, UMat qangle, UMat gauss_w_lut, UMat block_hists, size_t block_hist_size) +{ + ocl::Kernel k("compute_hists_lut_kernel", ocl::objdetect::objdetect_hog_oclsrc); + if(k.empty()) + return false; + bool is_cpu = cv::ocl::Device::getDefault().type() == cv::ocl::Device::TYPE_CPU; + cv::String opts; + if(is_cpu) + opts = "-D CPU "; + else + opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple()); + k.create("compute_hists_lut_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts); + if(k.empty()) + return false; + + int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x)/block_stride_x; + int img_block_height = (height - CELLS_PER_BLOCK_Y * CELL_HEIGHT + block_stride_y)/block_stride_y; + int blocks_total = img_block_width * img_block_height; + + int qangle_elem_size = CV_ELEM_SIZE1(qangle.type()); + int grad_quadstep = (int)grad.step >> 2; + int qangle_step = (int)qangle.step / qangle_elem_size; + + int blocks_in_group = 4; + size_t localThreads[3] = { (size_t)blocks_in_group * 24, 2, 1 }; + size_t globalThreads[3] = {((img_block_width * img_block_height + blocks_in_group - 1)/blocks_in_group) * localThreads[0], 2, 1 }; + + int hists_size = (nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y * 12) * sizeof(float); + int final_hists_size = (nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y) * sizeof(float); + + int smem = (hists_size + final_hists_size) * blocks_in_group; + + int idx = 0; + idx = k.set(idx, block_stride_x); + idx = k.set(idx, block_stride_y); + idx = k.set(idx, nbins); + idx = k.set(idx, (int)block_hist_size); + idx = k.set(idx, img_block_width); + idx = k.set(idx, blocks_in_group); + idx = k.set(idx, blocks_total); + idx = k.set(idx, grad_quadstep); + idx = k.set(idx, qangle_step); + idx = k.set(idx, ocl::KernelArg::PtrReadOnly(grad)); + idx = k.set(idx, ocl::KernelArg::PtrReadOnly(qangle)); + idx = k.set(idx, ocl::KernelArg::PtrReadOnly(gauss_w_lut)); + idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(block_hists)); + idx = k.set(idx, (void*)NULL, (size_t)smem); + + return k.run(2, globalThreads, localThreads, false); +} + +static int power_2up(unsigned int n) +{ + for(unsigned int i = 1; i<=1024; i<<=1) + if(n < i) + return i; + return -1; // Input is too big +} + +static bool ocl_normalize_hists(int nbins, int block_stride_x, int block_stride_y, + int height, int width, UMat block_hists, float threshold) +{ + int block_hist_size = nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y; + int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) + / block_stride_x; + int img_block_height = (height - CELLS_PER_BLOCK_Y * CELL_HEIGHT + block_stride_y) + / block_stride_y; + int nthreads; + size_t globalThreads[3] = { 1, 1, 1 }; + size_t localThreads[3] = { 1, 1, 1 }; + + int idx = 0; + bool is_cpu = cv::ocl::Device::getDefault().type() == cv::ocl::Device::TYPE_CPU; + cv::String opts; + ocl::Kernel k; + if ( nbins == 9 ) + { + k.create("normalize_hists_36_kernel", ocl::objdetect::objdetect_hog_oclsrc, ""); + if(k.empty()) + return false; + if(is_cpu) + opts = "-D CPU "; + else + opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple()); + k.create("normalize_hists_36_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts); + if(k.empty()) + return false; + + int blocks_in_group = NTHREADS / block_hist_size; + nthreads = blocks_in_group * block_hist_size; + int num_groups = (img_block_width * img_block_height + blocks_in_group - 1)/blocks_in_group; + globalThreads[0] = nthreads * num_groups; + localThreads[0] = nthreads; + } + else + { + k.create("normalize_hists_kernel", ocl::objdetect::objdetect_hog_oclsrc, "-D WAVE_SIZE=32"); + if(k.empty()) + return false; + if(is_cpu) + opts = "-D CPU "; + else + opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple()); + k.create("normalize_hists_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts); + if(k.empty()) + return false; + + nthreads = power_2up(block_hist_size); + globalThreads[0] = img_block_width * nthreads; + globalThreads[1] = img_block_height; + localThreads[0] = nthreads; + + if ((nthreads < 32) || (nthreads > 512) ) + return false; + + idx = k.set(idx, nthreads); + idx = k.set(idx, block_hist_size); + idx = k.set(idx, img_block_width); + } + idx = k.set(idx, ocl::KernelArg::PtrReadWrite(block_hists)); + idx = k.set(idx, threshold); + idx = k.set(idx, (void*)NULL, nthreads * sizeof(float)); + + return k.run(2, globalThreads, localThreads, false); +} + +static bool ocl_extract_descrs_by_rows(int win_height, int win_width, int block_stride_y, int block_stride_x, int win_stride_y, int win_stride_x, + int height, int width, UMat block_hists, UMat descriptors, + int block_hist_size, int descr_size, int descr_width) +{ + ocl::Kernel k("extract_descrs_by_rows_kernel", ocl::objdetect::objdetect_hog_oclsrc); + if(k.empty()) + return false; + + int win_block_stride_x = win_stride_x / block_stride_x; + int win_block_stride_y = win_stride_y / block_stride_y; + int img_win_width = (width - win_width + win_stride_x) / win_stride_x; + int img_win_height = (height - win_height + win_stride_y) / win_stride_y; + int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / + block_stride_x; + + int descriptors_quadstep = (int)descriptors.step >> 2; + + size_t globalThreads[3] = { (size_t)img_win_width * NTHREADS, (size_t)img_win_height, 1 }; + size_t localThreads[3] = { NTHREADS, 1, 1 }; + + int idx = 0; + idx = k.set(idx, block_hist_size); + idx = k.set(idx, descriptors_quadstep); + idx = k.set(idx, descr_size); + idx = k.set(idx, descr_width); + idx = k.set(idx, img_block_width); + idx = k.set(idx, win_block_stride_x); + idx = k.set(idx, win_block_stride_y); + idx = k.set(idx, ocl::KernelArg::PtrReadOnly(block_hists)); + idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(descriptors)); + + return k.run(2, globalThreads, localThreads, false); +} + +static bool ocl_extract_descrs_by_cols(int win_height, int win_width, int block_stride_y, int block_stride_x, int win_stride_y, int win_stride_x, + int height, int width, UMat block_hists, UMat descriptors, + int block_hist_size, int descr_size, int nblocks_win_x, int nblocks_win_y) +{ + ocl::Kernel k("extract_descrs_by_cols_kernel", ocl::objdetect::objdetect_hog_oclsrc); + if(k.empty()) + return false; + + int win_block_stride_x = win_stride_x / block_stride_x; + int win_block_stride_y = win_stride_y / block_stride_y; + int img_win_width = (width - win_width + win_stride_x) / win_stride_x; + int img_win_height = (height - win_height + win_stride_y) / win_stride_y; + int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / + block_stride_x; + + int descriptors_quadstep = (int)descriptors.step >> 2; + + size_t globalThreads[3] = { (size_t)img_win_width * NTHREADS, (size_t)img_win_height, 1 }; + size_t localThreads[3] = { NTHREADS, 1, 1 }; + + int idx = 0; + idx = k.set(idx, block_hist_size); + idx = k.set(idx, descriptors_quadstep); + idx = k.set(idx, descr_size); + idx = k.set(idx, nblocks_win_x); + idx = k.set(idx, nblocks_win_y); + idx = k.set(idx, img_block_width); + idx = k.set(idx, win_block_stride_x); + idx = k.set(idx, win_block_stride_y); + idx = k.set(idx, ocl::KernelArg::PtrReadOnly(block_hists)); + idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(descriptors)); + + return k.run(2, globalThreads, localThreads, false); +} + +static bool ocl_compute(InputArray _img, Size win_stride, std::vector& _descriptors, int descr_format, Size blockSize, + Size cellSize, int nbins, Size blockStride, Size winSize, float sigma, bool gammaCorrection, double L2HysThreshold, bool signedGradient) +{ + Size imgSize = _img.size(); + Size effect_size = imgSize; + + UMat grad(imgSize, CV_32FC2); + int qangle_type = ocl::Device::getDefault().isIntel() ? CV_32SC2 : CV_8UC2; + UMat qangle(imgSize, qangle_type); + + const size_t block_hist_size = getBlockHistogramSize(blockSize, cellSize, nbins); + const Size blocks_per_img = numPartsWithin(imgSize, blockSize, blockStride); + UMat block_hists(1, static_cast(block_hist_size * blocks_per_img.area()) + 256, CV_32F); + + Size wins_per_img = numPartsWithin(imgSize, winSize, win_stride); + UMat labels(1, wins_per_img.area(), CV_8U); + + float scale = 1.f / (2.f * sigma * sigma); + Mat gaussian_lut(1, 512, CV_32FC1); + int idx = 0; + for(int i=-8; i<8; i++) + for(int j=-8; j<8; j++) + gaussian_lut.at(idx++) = std::exp(-(j * j + i * i) * scale); + for(int i=-8; i<8; i++) + for(int j=-8; j<8; j++) + gaussian_lut.at(idx++) = (8.f - fabs(j + 0.5f)) * (8.f - fabs(i + 0.5f)) / 64.f; + + if(!ocl_computeGradient(_img, grad, qangle, nbins, effect_size, gammaCorrection, signedGradient)) + return false; + + UMat gauss_w_lut; + gaussian_lut.copyTo(gauss_w_lut); + if(!ocl_compute_hists(nbins, blockStride.width, blockStride.height, effect_size.height, + effect_size.width, grad, qangle, gauss_w_lut, block_hists, block_hist_size)) + return false; + + if(!ocl_normalize_hists(nbins, blockStride.width, blockStride.height, effect_size.height, + effect_size.width, block_hists, (float)L2HysThreshold)) + return false; + + Size blocks_per_win = numPartsWithin(winSize, blockSize, blockStride); + wins_per_img = numPartsWithin(effect_size, winSize, win_stride); + + int descr_size = blocks_per_win.area()*(int)block_hist_size; + int descr_width = (int)block_hist_size*blocks_per_win.width; + + UMat descriptors(wins_per_img.area(), static_cast(blocks_per_win.area() * block_hist_size), CV_32F); + switch (descr_format) + { + case DESCR_FORMAT_ROW_BY_ROW: + if(!ocl_extract_descrs_by_rows(winSize.height, winSize.width, + blockStride.height, blockStride.width, win_stride.height, win_stride.width, effect_size.height, + effect_size.width, block_hists, descriptors, (int)block_hist_size, descr_size, descr_width)) + return false; + break; + case DESCR_FORMAT_COL_BY_COL: + if(!ocl_extract_descrs_by_cols(winSize.height, winSize.width, + blockStride.height, blockStride.width, win_stride.height, win_stride.width, effect_size.height, effect_size.width, + block_hists, descriptors, (int)block_hist_size, descr_size, blocks_per_win.width, blocks_per_win.height)) + return false; + break; + default: + return false; + } + descriptors.reshape(1, (int)descriptors.total()).getMat(ACCESS_READ).copyTo(_descriptors); + return true; +} +#endif //HAVE_OPENCL + +void HOGDescriptor::compute(InputArray _img, std::vector& descriptors, + Size winStride, Size padding, const std::vector& locations) const +{ + CV_INSTRUMENT_REGION(); + + if( winStride == Size() ) + winStride = cellSize; + Size cacheStride(gcd(winStride.width, blockStride.width), + gcd(winStride.height, blockStride.height)); + + Size imgSize = _img.size(); + + 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(imgSize.width + padding.width*2, imgSize.height + padding.height*2); + + CV_OCL_RUN(_img.dims() <= 2 && _img.type() == CV_8UC1 && _img.isUMat(), + ocl_compute(_img, winStride, descriptors, DESCR_FORMAT_COL_BY_COL, blockSize, + cellSize, nbins, blockStride, winSize, (float)getWinSigma(), gammaCorrection, L2HysThreshold, signedGradient)) + + Mat img = _img.getMat(); + 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 each window + 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 ) + memcpy(dst, src, blockHistogramSize * sizeof(float)); + } + } +} + +void HOGDescriptor::detect(const Mat& img, + std::vector& hits, std::vector& weights, double hitThreshold, + Size winStride, Size padding, const std::vector& locations) const +{ + CV_INSTRUMENT_REGION(); + + hits.clear(); + weights.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; + std::vector blockHist(blockHistogramSize); + +#if CV_SIMD128 + float partSum[4]; +#endif + + 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]); +#if CV_SIMD128 + v_float32x4 _vec = v_load(vec); + v_float32x4 _svmVec = v_load(svmVec); + v_float32x4 sum = _svmVec * _vec; + + for( k = 4; k <= blockHistogramSize - 4; k += 4 ) + { + _vec = v_load(vec + k); + _svmVec = v_load(svmVec + k); + + sum += _vec * _svmVec; + } + + v_store(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]; + } + if( s >= hitThreshold ) + { + hits.push_back(pt0); + weights.push_back(s); + } + } +} + +void HOGDescriptor::detect(const Mat& img, std::vector& hits, double hitThreshold, + Size winStride, Size padding, const std::vector& locations) const +{ + CV_INSTRUMENT_REGION(); + + std::vector weightsV; + detect(img, hits, weightsV, hitThreshold, winStride, padding, locations); +} + +class HOGInvoker : + public ParallelLoopBody +{ +public: + HOGInvoker( const HOGDescriptor* _hog, const Mat& _img, + double _hitThreshold, const Size& _winStride, const Size& _padding, + const double* _levelScale, std::vector * _vec, Mutex* _mtx, + std::vector* _weights=0, std::vector* _scales=0 ) + { + hog = _hog; + img = _img; + hitThreshold = _hitThreshold; + winStride = _winStride; + padding = _padding; + levelScale = _levelScale; + vec = _vec; + weights = _weights; + scales = _scales; + mtx = _mtx; + } + + void operator()(const Range& range) const CV_OVERRIDE + { + 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()); + std::vector locations; + std::vector 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.ptr()); + if( sz == img.size() ) + smallerImg = Mat(sz, img.type(), img.data, img.step); + else + resize(img, smallerImg, sz, 0, 0, INTER_LINEAR_EXACT); + 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(); + } + } + } + +private: + const HOGDescriptor* hog; + Mat img; + double hitThreshold; + Size winStride; + Size padding; + const double* levelScale; + std::vector* vec; + std::vector* weights; + std::vector* scales; + Mutex* mtx; +}; + +#ifdef HAVE_OPENCL + +static bool ocl_classify_hists(int win_height, int win_width, int block_stride_y, int block_stride_x, + int win_stride_y, int win_stride_x, int height, int width, + const UMat& block_hists, UMat detector, + float free_coef, float threshold, UMat& labels, Size descr_size, int block_hist_size) +{ + int nthreads; + bool is_cpu = cv::ocl::Device::getDefault().type() == cv::ocl::Device::TYPE_CPU; + cv::String opts; + + ocl::Kernel k; + int idx = 0; + switch (descr_size.width) + { + case 180: + nthreads = 180; + k.create("classify_hists_180_kernel", ocl::objdetect::objdetect_hog_oclsrc, "-D WAVE_SIZE=32"); + if(k.empty()) + return false; + if(is_cpu) + opts = "-D CPU "; + else + opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple()); + k.create("classify_hists_180_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts); + if(k.empty()) + return false; + idx = k.set(idx, descr_size.width); + idx = k.set(idx, descr_size.height); + break; + + case 252: + nthreads = 256; + k.create("classify_hists_252_kernel", ocl::objdetect::objdetect_hog_oclsrc, "-D WAVE_SIZE=32"); + if(k.empty()) + return false; + if(is_cpu) + opts = "-D CPU "; + else + opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple()); + k.create("classify_hists_252_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts); + if(k.empty()) + return false; + idx = k.set(idx, descr_size.width); + idx = k.set(idx, descr_size.height); + break; + + default: + nthreads = 256; + k.create("classify_hists_kernel", ocl::objdetect::objdetect_hog_oclsrc, "-D WAVE_SIZE=32"); + if(k.empty()) + return false; + if(is_cpu) + opts = "-D CPU "; + else + opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple()); + k.create("classify_hists_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts); + if(k.empty()) + return false; + idx = k.set(idx, descr_size.area()); + idx = k.set(idx, descr_size.height); + } + + int win_block_stride_x = win_stride_x / block_stride_x; + int win_block_stride_y = win_stride_y / block_stride_y; + int img_win_width = (width - win_width + win_stride_x) / win_stride_x; + int img_win_height = (height - win_height + win_stride_y) / win_stride_y; + int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / + block_stride_x; + + size_t globalThreads[3] = { (size_t)img_win_width * nthreads, (size_t)img_win_height, 1 }; + size_t localThreads[3] = { (size_t)nthreads, 1, 1 }; + + idx = k.set(idx, block_hist_size); + idx = k.set(idx, img_win_width); + idx = k.set(idx, img_block_width); + idx = k.set(idx, win_block_stride_x); + idx = k.set(idx, win_block_stride_y); + idx = k.set(idx, ocl::KernelArg::PtrReadOnly(block_hists)); + idx = k.set(idx, ocl::KernelArg::PtrReadOnly(detector)); + idx = k.set(idx, free_coef); + idx = k.set(idx, threshold); + idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(labels)); + + return k.run(2, globalThreads, localThreads, false); +} + +static bool ocl_detect(InputArray img, std::vector &hits, double hit_threshold, Size win_stride, + const UMat& oclSvmDetector, Size blockSize, Size cellSize, int nbins, Size blockStride, Size winSize, + bool gammaCorrection, double L2HysThreshold, float sigma, float free_coef, bool signedGradient) +{ + hits.clear(); + if (oclSvmDetector.empty()) + return false; + + Size imgSize = img.size(); + Size effect_size = imgSize; + UMat grad(imgSize, CV_32FC2); + int qangle_type = ocl::Device::getDefault().isIntel() ? CV_32SC2 : CV_8UC2; + UMat qangle(imgSize, qangle_type); + + const size_t block_hist_size = getBlockHistogramSize(blockSize, cellSize, nbins); + const Size blocks_per_img = numPartsWithin(imgSize, blockSize, blockStride); + UMat block_hists(1, static_cast(block_hist_size * blocks_per_img.area()) + 256, CV_32F); + + Size wins_per_img = numPartsWithin(imgSize, winSize, win_stride); + UMat labels(1, wins_per_img.area(), CV_8U); + + float scale = 1.f / (2.f * sigma * sigma); + Mat gaussian_lut(1, 512, CV_32FC1); + int idx = 0; + for(int i=-8; i<8; i++) + for(int j=-8; j<8; j++) + gaussian_lut.at(idx++) = std::exp(-(j * j + i * i) * scale); + for(int i=-8; i<8; i++) + for(int j=-8; j<8; j++) + gaussian_lut.at(idx++) = (8.f - fabs(j + 0.5f)) * (8.f - fabs(i + 0.5f)) / 64.f; + + if(!ocl_computeGradient(img, grad, qangle, nbins, effect_size, gammaCorrection, signedGradient)) + return false; + + UMat gauss_w_lut; + gaussian_lut.copyTo(gauss_w_lut); + if(!ocl_compute_hists(nbins, blockStride.width, blockStride.height, effect_size.height, + effect_size.width, grad, qangle, gauss_w_lut, block_hists, block_hist_size)) + return false; + + if(!ocl_normalize_hists(nbins, blockStride.width, blockStride.height, effect_size.height, + effect_size.width, block_hists, (float)L2HysThreshold)) + return false; + + Size blocks_per_win = numPartsWithin(winSize, blockSize, blockStride); + + Size descr_size((int)block_hist_size*blocks_per_win.width, blocks_per_win.height); + + if(!ocl_classify_hists(winSize.height, winSize.width, blockStride.height, + blockStride.width, win_stride.height, win_stride.width, + effect_size.height, effect_size.width, block_hists, oclSvmDetector, + free_coef, (float)hit_threshold, labels, descr_size, (int)block_hist_size)) + return false; + + Mat labels_host = labels.getMat(ACCESS_READ); + unsigned char *vec = labels_host.ptr(); + for (int i = 0; i < wins_per_img.area(); i++) + { + int y = i / wins_per_img.width; + int x = i - wins_per_img.width * y; + if (vec[i]) + { + hits.push_back(Point(x * win_stride.width, y * win_stride.height)); + } + } + return true; +} + +static bool ocl_detectMultiScale(InputArray _img, std::vector &found_locations, std::vector& level_scale, + double hit_threshold, Size win_stride, double group_threshold, + const UMat& oclSvmDetector, Size blockSize, Size cellSize, + int nbins, Size blockStride, Size winSize, bool gammaCorrection, + double L2HysThreshold, float sigma, float free_coef, bool signedGradient) +{ + std::vector all_candidates; + std::vector locations; + UMat image_scale; + Size imgSize = _img.size(); + image_scale.create(imgSize, _img.type()); + + for (size_t i = 0; i& foundLocations, std::vector& foundWeights, + double hitThreshold, Size winStride, Size padding, + double scale0, double finalThreshold, bool useMeanshiftGrouping) const +{ + CV_INSTRUMENT_REGION(); + + double scale = 1.; + int levels = 0; + + Size imgSize = _img.size(); + std::vector levelScale; + for( levels = 0; levels < nlevels; levels++ ) + { + levelScale.push_back(scale); + if( cvRound(imgSize.width/scale) < winSize.width || + cvRound(imgSize.height/scale) < winSize.height || + scale0 <= 1 ) + break; + scale *= scale0; + } + levels = std::max(levels, 1); + levelScale.resize(levels); + + if(winStride == Size()) + winStride = blockStride; + + CV_OCL_RUN(_img.dims() <= 2 && _img.type() == CV_8UC1 && scale0 > 1 && winStride.width % blockStride.width == 0 && + winStride.height % blockStride.height == 0 && padding == Size(0,0) && _img.isUMat(), + ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, finalThreshold, oclSvmDetector, + blockSize, cellSize, nbins, blockStride, winSize, gammaCorrection, L2HysThreshold, (float)getWinSigma(), free_coef, signedGradient)); + + std::vector allCandidates; + std::vector tempScales; + std::vector tempWeights; + std::vector foundScales; + + Mutex mtx; + Mat img = _img.getMat(); + 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(); + 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); + clipObjects(imgSize, foundLocations, 0, &foundWeights); +} + +void HOGDescriptor::detectMultiScale(InputArray img, std::vector& foundLocations, + double hitThreshold, Size winStride, Size padding, + double scale0, double finalThreshold, bool useMeanshiftGrouping) const +{ + CV_INSTRUMENT_REGION(); + + std::vector foundWeights; + detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride, + padding, scale0, finalThreshold, useMeanshiftGrouping); +} + +template struct RTTIImpl +{ +public: + static int isInstance(const void* ptr) + { + static _ClsName dummy; + static void* dummyp = &dummy; + union + { + const void* p; + const void** pp; + } a, b; + a.p = dummyp; + b.p = ptr; + return *a.pp == *b.pp; + } + static void release(void** dbptr) + { + if(dbptr && *dbptr) + { + delete (_ClsName*)*dbptr; + *dbptr = 0; + } + } + static void* read(CvFileStorage* fs, CvFileNode* n) + { + FileNode fn(fs, n); + _ClsName* obj = new _ClsName; + if(obj->read(fn)) + return obj; + delete obj; + return 0; + } + + static void write(CvFileStorage* _fs, const char* name, const void* ptr, CvAttrList) + { + if(ptr && _fs) + { + FileStorage fs(_fs, false); + ((const _ClsName*)ptr)->write(fs, String(name)); + } + } + + static void* clone(const void* ptr) + { + if(!ptr) + return 0; + return new _ClsName(*(const _ClsName*)ptr); + } +}; + +typedef RTTIImpl HOGRTTI; + +CvType hog_type( CV_TYPE_NAME_HOG_DESCRIPTOR, HOGRTTI::isInstance, + HOGRTTI::release, HOGRTTI::read, HOGRTTI::write, HOGRTTI::clone); + +std::vector 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, 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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(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) +std::vector HOGDescriptor::getDaimlerPeopleDetector() +{ + static const float detector[] = { + 0.294350f, -0.098796f, -0.129522f, 0.078753f, + 0.387527f, 0.261529f, 0.145939f, 0.061520f, + 0.328699f, 0.227148f, -0.066467f, -0.086723f, + 0.047559f, 0.106714f, 0.037897f, 0.111461f, + -0.024406f, 0.304769f, 0.254676f, -0.069235f, + 0.082566f, 0.147260f, 0.326969f, 0.148888f, + 0.055270f, -0.087985f, 0.261720f, 0.143442f, + 0.026812f, 0.238212f, 0.194020f, 0.056341f, + -0.025854f, -0.034444f, -0.156631f, 0.205174f, + 0.089008f, -0.139811f, 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-0.174477f, -0.067627f, -0.001979f, + 0.142913f, 0.040597f, 0.019907f, 0.025963f, + -0.043585f, -0.120732f, 0.099937f, 0.091059f, + 0.247307f, 0.204226f, -0.042753f, -0.068580f, + -0.119002f, 0.026722f, 0.034853f, -0.060934f, + -0.025054f, -0.093026f, -0.035372f, -0.233209f, + -0.049869f, -0.039151f, -0.022279f, -0.065380f, + -9.063785f}; + return std::vector(detector, detector + sizeof(detector)/sizeof(detector[0])); +} + +class HOGConfInvoker : + public ParallelLoopBody +{ +public: + HOGConfInvoker( const HOGDescriptor* _hog, const Mat& _img, + double _hitThreshold, const Size& _padding, + std::vector* locs, + std::vector* _vec, Mutex* _mtx ) + { + hog = _hog; + img = _img; + hitThreshold = _hitThreshold; + padding = _padding; + locations = locs; + vec = _vec; + mtx = _mtx; + } + + void operator()(const Range& range) const CV_OVERRIDE + { + CV_INSTRUMENT_REGION(); + + 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 dets; + + 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.ptr()); + + if( sz == img.size() ) + smallerImg = Mat(sz, img.type(), img.data, img.step); + else + resize(img, smallerImg, sz, 0, 0, INTER_LINEAR_EXACT); + + 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* locations; + Size padding; + std::vector* vec; + Mutex* mtx; +}; + +void HOGDescriptor::detectROI(const cv::Mat& img, const std::vector &locations, + CV_OUT std::vector& foundLocations, CV_OUT std::vector& confidences, + double hitThreshold, cv::Size winStride, cv::Size padding) const +{ + CV_INSTRUMENT_REGION(); + + foundLocations.clear(); + confidences.clear(); + + if( svmDetector.empty() || locations.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); + HOGCache cache(this, img, padding, padding, true, 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; + std::vector blockHist(blockHistogramSize); + +#if CV_SIMD128 + float partSum[4]; +#endif + + 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 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; + + // need to divide this into 4 parts! + const float* vec = cache.getBlock(pt, &blockHist[0]); +#if CV_SIMD128 + v_float32x4 _vec = v_load(vec); + v_float32x4 _svmVec = v_load(svmVec); + v_float32x4 sum = _svmVec * _vec; + + for( k = 4; k <= blockHistogramSize - 4; k += 4 ) + { + _vec = v_load(vec + k); + _svmVec = v_load(svmVec + k); + + sum += _vec * _svmVec; + } + + v_store(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& foundLocations, std::vector& locations, + double hitThreshold, int groupThreshold) const +{ + CV_INSTRUMENT_REGION(); + + std::vector allCandidates; + Mutex 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); +} + +void HOGDescriptor::readALTModel(String modelfile) +{ + // read model from SVMlight format.. + FILE *modelfl; + if ((modelfl = fopen(modelfile.c_str(), "rb")) == NULL) + { + String eerr("file not exist"); + String efile(__FILE__); + String efunc(__FUNCTION__); + throw Exception(Error::StsError, eerr, efile, efunc, __LINE__); + } + char version_buffer[10]; + if (!fread (&version_buffer,sizeof(char),10,modelfl)) + { + String eerr("version?"); + String efile(__FILE__); + String efunc(__FUNCTION__); + fclose(modelfl); + + throw Exception(Error::StsError, eerr, efile, efunc, __LINE__); + } + if(strcmp(version_buffer,"V6.01")) { + String eerr("version does not match"); + String efile(__FILE__); + String efunc(__FUNCTION__); + fclose(modelfl); + + throw Exception(Error::StsError, eerr, efile, efunc, __LINE__); + } + /* read version number */ + int version = 0; + if (!fread (&version,sizeof(int),1,modelfl)) + { + fclose(modelfl); + throw Exception(); + } + if (version < 200) + { + String eerr("version does not match"); + String efile(__FILE__); + String efunc(__FUNCTION__); + fclose(modelfl); + 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); + + 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); + CV_Assert(l >= 0 && l < 0xFFFF); + 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); + + std::vector detector; + detector.clear(); + if(kernel_type == 0) { /* linear kernel */ + /* save linear wts also */ + CV_Assert(totwords + 1 > 0 && totwords < 0xFFFF); + double *linearwt = new double[totwords+1]; + int length = totwords; + nread = fread(linearwt, sizeof(double), totwords + 1, modelfl); + if(nread != static_cast(length) + 1) { + delete [] linearwt; + fclose(modelfl); + throw Exception(); + } + + for(int i = 0; i < length; i++) + detector.push_back((float)linearwt[i]); + + detector.push_back((float)-linearbias); + setSVMDetector(detector); + delete [] linearwt; + } else { + fclose(modelfl); + throw Exception(); + } + fclose(modelfl); +} + +void HOGDescriptor::groupRectangles(std::vector& rectList, std::vector& weights, int groupThreshold, double eps) const +{ + CV_INSTRUMENT_REGION(); + + if( groupThreshold <= 0 || rectList.empty() ) + { + return; + } + + CV_Assert(rectList.size() == weights.size()); + + std::vector labels; + int nclasses = partition(rectList, labels, SimilarRects(eps)); + + std::vector > rrects(nclasses); + std::vector numInClass(nclasses, 0); + std::vector foundWeights(nclasses, -std::numeric_limits::max()); + int i, j, nlabels = (int)labels.size(); + + for( i = 0; i < nlabels; i++ ) + { + int cls = labels[i]; + rrects[cls].x += rectList[i].x; + rrects[cls].y += rectList[i].y; + rrects[cls].width += rectList[i].width; + rrects[cls].height += rectList[i].height; + foundWeights[cls] = max(foundWeights[cls], weights[i]); + numInClass[cls]++; + } + + for( i = 0; i < nclasses; i++ ) + { + // find the average of all ROI in the cluster + cv::Rect_ r = rrects[i]; + double s = 1.0/numInClass[i]; + rrects[i] = cv::Rect_(cv::saturate_cast(r.x*s), + cv::saturate_cast(r.y*s), + cv::saturate_cast(r.width*s), + cv::saturate_cast(r.height*s)); + } + + rectList.clear(); + weights.clear(); + + for( i = 0; i < nclasses; i++ ) + { + cv::Rect r1 = rrects[i]; + int n1 = numInClass[i]; + double w1 = foundWeights[i]; + if( n1 <= groupThreshold ) + continue; + // filter out small rectangles inside large rectangles + for( j = 0; j < nclasses; j++ ) + { + int n2 = numInClass[j]; + + if( j == i || n2 <= groupThreshold ) + continue; + + cv::Rect r2 = rrects[j]; + + int dx = cv::saturate_cast( r2.width * eps ); + int dy = cv::saturate_cast( r2.height * eps ); + + if( r1.x >= r2.x - dx && + r1.y >= r2.y - dy && + r1.x + r1.width <= r2.x + r2.width + dx && + r1.y + r1.height <= r2.y + r2.height + dy && + (n2 > std::max(3, n1) || n1 < 3) ) + break; + } + + if( j == nclasses ) + { + rectList.push_back(r1); + weights.push_back(w1); + } + } +} +} diff --git a/test/bug-hunting/cve/CVE-2019-15939/opencv2/objdetect.hpp b/test/bug-hunting/cve/CVE-2019-15939/opencv2/objdetect.hpp new file mode 100644 index 000000000..097c5923a --- /dev/null +++ b/test/bug-hunting/cve/CVE-2019-15939/opencv2/objdetect.hpp @@ -0,0 +1,753 @@ +/*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. +// Copyright (C) 2013, OpenCV Foundation, 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*/ + +#ifndef OPENCV_OBJDETECT_HPP +#define OPENCV_OBJDETECT_HPP + +#include "opencv2/core.hpp" + +/** +@defgroup objdetect Object Detection + +Haar Feature-based Cascade Classifier for Object Detection +---------------------------------------------------------- + +The object detector described below has been initially proposed by Paul Viola @cite Viola01 and +improved by Rainer Lienhart @cite Lienhart02 . + +First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is +trained with a few hundred sample views of a particular object (i.e., a face or a car), called +positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary +images of the same size. + +After a classifier is trained, it can be applied to a region of interest (of the same size as used +during the training) in an input image. The classifier outputs a "1" if the region is likely to show +the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can +move the search window across the image and check every location using the classifier. The +classifier is designed so that it can be easily "resized" in order to be able to find the objects of +interest at different sizes, which is more efficient than resizing the image itself. So, to find an +object of an unknown size in the image the scan procedure should be done several times at different +scales. + +The word "cascade" in the classifier name means that the resultant classifier consists of several +simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some +stage the candidate is rejected or all the stages are passed. The word "boosted" means that the +classifiers at every stage of the cascade are complex themselves and they are built out of basic +classifiers using one of four different boosting techniques (weighted voting). Currently Discrete +Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are +decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic +classifiers, and are calculated as described below. The current algorithm uses the following +Haar-like features: + +![image](pics/haarfeatures.png) + +The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within +the region of interest and the scale (this scale is not the same as the scale used at the detection +stage, though these two scales are multiplied). For example, in the case of the third line feature +(2c) the response is calculated as the difference between the sum of image pixels under the +rectangle covering the whole feature (including the two white stripes and the black stripe in the +middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to +compensate for the differences in the size of areas. The sums of pixel values over a rectangular +regions are calculated rapidly using integral images (see below and the integral description). + +To see the object detector at work, have a look at the facedetect demo: + + +The following reference is for the detection part only. There is a separate application called +opencv_traincascade that can train a cascade of boosted classifiers from a set of samples. + +@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in +addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection +using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at + + +@{ + @defgroup objdetect_c C API +@} + */ + +typedef struct CvHaarClassifierCascade CvHaarClassifierCascade; + +namespace cv +{ + +//! @addtogroup objdetect +//! @{ + +///////////////////////////// Object Detection //////////////////////////// + +//! class for grouping object candidates, detected by Cascade Classifier, HOG etc. +//! instance of the class is to be passed to cv::partition (see cxoperations.hpp) +class CV_EXPORTS SimilarRects +{ +public: + SimilarRects(double _eps) : eps(_eps) {} + inline bool operator()(const Rect& r1, const Rect& r2) const + { + double delta = eps * ((std::min)(r1.width, r2.width) + (std::min)(r1.height, r2.height)) * 0.5; + return std::abs(r1.x - r2.x) <= delta && + std::abs(r1.y - r2.y) <= delta && + std::abs(r1.x + r1.width - r2.x - r2.width) <= delta && + std::abs(r1.y + r1.height - r2.y - r2.height) <= delta; + } + double eps; +}; + +/** @brief Groups the object candidate rectangles. + +@param rectList Input/output vector of rectangles. Output vector includes retained and grouped +rectangles. (The Python list is not modified in place.) +@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a +group of rectangles to retain it. +@param eps Relative difference between sides of the rectangles to merge them into a group. + +The function is a wrapper for the generic function partition . It clusters all the input rectangles +using the rectangle equivalence criteria that combines rectangles with similar sizes and similar +locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If +\f$\texttt{eps}\rightarrow +\inf\f$ , all the rectangles are put in one cluster. Then, the small +clusters containing less than or equal to groupThreshold rectangles are rejected. In each other +cluster, the average rectangle is computed and put into the output rectangle list. + */ +CV_EXPORTS void groupRectangles(std::vector& rectList, int groupThreshold, double eps = 0.2); +/** @overload */ +CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector& rectList, CV_OUT std::vector& weights, + int groupThreshold, double eps = 0.2); +/** @overload */ +CV_EXPORTS void groupRectangles(std::vector& rectList, int groupThreshold, + double eps, std::vector* weights, std::vector* levelWeights ); +/** @overload */ +CV_EXPORTS void groupRectangles(std::vector& rectList, std::vector& rejectLevels, + std::vector& levelWeights, int groupThreshold, double eps = 0.2); +/** @overload */ +CV_EXPORTS void groupRectangles_meanshift(std::vector& rectList, std::vector& foundWeights, + std::vector& foundScales, + double detectThreshold = 0.0, Size winDetSize = Size(64, 128)); + +template<> struct DefaultDeleter{ CV_EXPORTS void operator ()(CvHaarClassifierCascade* obj) const; }; + +enum { CASCADE_DO_CANNY_PRUNING = 1, + CASCADE_SCALE_IMAGE = 2, + CASCADE_FIND_BIGGEST_OBJECT = 4, + CASCADE_DO_ROUGH_SEARCH = 8 + }; + +class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm +{ +public: + virtual ~BaseCascadeClassifier(); + virtual bool empty() const CV_OVERRIDE = 0; + virtual bool load( const String& filename ) = 0; + virtual void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + double scaleFactor, + int minNeighbors, int flags, + Size minSize, Size maxSize ) = 0; + + virtual void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& numDetections, + double scaleFactor, + int minNeighbors, int flags, + Size minSize, Size maxSize ) = 0; + + virtual void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& rejectLevels, + CV_OUT std::vector& levelWeights, + double scaleFactor, + int minNeighbors, int flags, + Size minSize, Size maxSize, + bool outputRejectLevels ) = 0; + + virtual bool isOldFormatCascade() const = 0; + virtual Size getOriginalWindowSize() const = 0; + virtual int getFeatureType() const = 0; + virtual void* getOldCascade() = 0; + + class CV_EXPORTS MaskGenerator + { + public: + virtual ~MaskGenerator() {} + virtual Mat generateMask(const Mat& src)=0; + virtual void initializeMask(const Mat& /*src*/) { } + }; + virtual void setMaskGenerator(const Ptr& maskGenerator) = 0; + virtual Ptr getMaskGenerator() = 0; +}; + +/** @example samples/cpp/facedetect.cpp +This program demonstrates usage of the Cascade classifier class +\image html Cascade_Classifier_Tutorial_Result_Haar.jpg "Sample screenshot" width=321 height=254 +*/ +/** @brief Cascade classifier class for object detection. + */ +class CV_EXPORTS_W CascadeClassifier +{ +public: + CV_WRAP CascadeClassifier(); + /** @brief Loads a classifier from a file. + + @param filename Name of the file from which the classifier is loaded. + */ + CV_WRAP CascadeClassifier(const String& filename); + ~CascadeClassifier(); + /** @brief Checks whether the classifier has been loaded. + */ + CV_WRAP bool empty() const; + /** @brief Loads a classifier from a file. + + @param filename Name of the file from which the classifier is loaded. The file may contain an old + HAAR classifier trained by the haartraining application or a new cascade classifier trained by the + traincascade application. + */ + CV_WRAP bool load( const String& filename ); + /** @brief Reads a classifier from a FileStorage node. + + @note The file may contain a new cascade classifier (trained traincascade application) only. + */ + CV_WRAP bool read( const FileNode& node ); + + /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list + of rectangles. + + @param image Matrix of the type CV_8U containing an image where objects are detected. + @param objects Vector of rectangles where each rectangle contains the detected object, the + rectangles may be partially outside the original image. + @param scaleFactor Parameter specifying how much the image size is reduced at each image scale. + @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have + to retain it. + @param flags Parameter with the same meaning for an old cascade as in the function + cvHaarDetectObjects. It is not used for a new cascade. + @param minSize Minimum possible object size. Objects smaller than that are ignored. + @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale. + + The function is parallelized with the TBB library. + + @note + - (Python) A face detection example using cascade classifiers can be found at + opencv_source_code/samples/python/facedetect.py + */ + CV_WRAP void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + double scaleFactor = 1.1, + int minNeighbors = 3, int flags = 0, + Size minSize = Size(), + Size maxSize = Size() ); + + /** @overload + @param image Matrix of the type CV_8U containing an image where objects are detected. + @param objects Vector of rectangles where each rectangle contains the detected object, the + rectangles may be partially outside the original image. + @param numDetections Vector of detection numbers for the corresponding objects. An object's number + of detections is the number of neighboring positively classified rectangles that were joined + together to form the object. + @param scaleFactor Parameter specifying how much the image size is reduced at each image scale. + @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have + to retain it. + @param flags Parameter with the same meaning for an old cascade as in the function + cvHaarDetectObjects. It is not used for a new cascade. + @param minSize Minimum possible object size. Objects smaller than that are ignored. + @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale. + */ + CV_WRAP_AS(detectMultiScale2) void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& numDetections, + double scaleFactor=1.1, + int minNeighbors=3, int flags=0, + Size minSize=Size(), + Size maxSize=Size() ); + + /** @overload + This function allows you to retrieve the final stage decision certainty of classification. + For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter. + For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage. + This value can then be used to separate strong from weaker classifications. + + A code sample on how to use it efficiently can be found below: + @code + Mat img; + vector weights; + vector levels; + vector detections; + CascadeClassifier model("/path/to/your/model.xml"); + model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true); + cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl; + @endcode + */ + CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& rejectLevels, + CV_OUT std::vector& levelWeights, + double scaleFactor = 1.1, + int minNeighbors = 3, int flags = 0, + Size minSize = Size(), + Size maxSize = Size(), + bool outputRejectLevels = false ); + + CV_WRAP bool isOldFormatCascade() const; + CV_WRAP Size getOriginalWindowSize() const; + CV_WRAP int getFeatureType() const; + void* getOldCascade(); + + CV_WRAP static bool convert(const String& oldcascade, const String& newcascade); + + void setMaskGenerator(const Ptr& maskGenerator); + Ptr getMaskGenerator(); + + Ptr cc; +}; + +CV_EXPORTS Ptr createFaceDetectionMaskGenerator(); + +//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// + +//! struct for detection region of interest (ROI) +struct DetectionROI +{ + //! scale(size) of the bounding box + double scale; + //! set of requested locations to be evaluated + std::vector locations; + //! vector that will contain confidence values for each location + std::vector confidences; +}; + +/**@brief Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector. + +the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs @cite Dalal2005 . + +useful links: + +https://hal.inria.fr/inria-00548512/document/ + +https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients + +https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor + +http://www.learnopencv.com/histogram-of-oriented-gradients + +http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial + + */ +struct CV_EXPORTS_W HOGDescriptor +{ +public: + enum HistogramNormType { L2Hys = 0 //!< Default histogramNormType + }; + enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value. + }; + enum DescriptorStorageFormat { DESCR_FORMAT_COL_BY_COL, DESCR_FORMAT_ROW_BY_ROW }; + + /**@brief Creates the HOG descriptor and detector with default params. + + aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 ) + */ + CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8), + cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1), + histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true), + free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false) + {} + + /** @overload + @param _winSize sets winSize with given value. + @param _blockSize sets blockSize with given value. + @param _blockStride sets blockStride with given value. + @param _cellSize sets cellSize with given value. + @param _nbins sets nbins with given value. + @param _derivAperture sets derivAperture with given value. + @param _winSigma sets winSigma with given value. + @param _histogramNormType sets histogramNormType with given value. + @param _L2HysThreshold sets L2HysThreshold with given value. + @param _gammaCorrection sets gammaCorrection with given value. + @param _nlevels sets nlevels with given value. + @param _signedGradient sets signedGradient with given value. + */ + CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, + Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1, + HOGDescriptor::HistogramNormType _histogramNormType=HOGDescriptor::L2Hys, + double _L2HysThreshold=0.2, bool _gammaCorrection=false, + int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false) + : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize), + nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma), + histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold), + gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient) + {} + + /** @overload + @param filename The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier. + */ + CV_WRAP HOGDescriptor(const String& filename) + { + load(filename); + } + + /** @overload + @param d the HOGDescriptor which cloned to create a new one. + */ + HOGDescriptor(const HOGDescriptor& d) + { + d.copyTo(*this); + } + + /**@brief Default destructor. + */ + virtual ~HOGDescriptor() {} + + /**@brief Returns the number of coefficients required for the classification. + */ + CV_WRAP size_t getDescriptorSize() const; + + /** @brief Checks if detector size equal to descriptor size. + */ + CV_WRAP bool checkDetectorSize() const; + + /** @brief Returns winSigma value + */ + CV_WRAP double getWinSigma() const; + + /**@example samples/cpp/peopledetect.cpp + */ + /**@brief Sets coefficients for the linear SVM classifier. + @param svmdetector coefficients for the linear SVM classifier. + */ + CV_WRAP virtual void setSVMDetector(InputArray svmdetector); + + /** @brief Reads HOGDescriptor parameters from a cv::FileNode. + @param fn File node + */ + virtual bool read(FileNode& fn); + + /** @brief Stores HOGDescriptor parameters in a cv::FileStorage. + @param fs File storage + @param objname Object name + */ + virtual void write(FileStorage& fs, const String& objname) const; + + /** @brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file. + @param filename Path of the file to read. + @param objname The optional name of the node to read (if empty, the first top-level node will be used). + */ + CV_WRAP virtual bool load(const String& filename, const String& objname = String()); + + /** @brief saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file + @param filename File name + @param objname Object name + */ + CV_WRAP virtual void save(const String& filename, const String& objname = String()) const; + + /** @brief clones the HOGDescriptor + @param c cloned HOGDescriptor + */ + virtual void copyTo(HOGDescriptor& c) const; + + /**@example samples/cpp/train_HOG.cpp + */ + /** @brief Computes HOG descriptors of given image. + @param img Matrix of the type CV_8U containing an image where HOG features will be calculated. + @param descriptors Matrix of the type CV_32F + @param winStride Window stride. It must be a multiple of block stride. + @param padding Padding + @param locations Vector of Point + */ + CV_WRAP virtual void compute(InputArray img, + CV_OUT std::vector& descriptors, + Size winStride = Size(), Size padding = Size(), + const std::vector& locations = std::vector()) const; + + /** @brief Performs object detection without a multi-scale window. + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. + @param weights Vector that will contain confidence values for each detected object. + @param hitThreshold Threshold for the distance between features and SVM classifying plane. + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). + But if the free coefficient is omitted (which is allowed), you can specify it manually here. + @param winStride Window stride. It must be a multiple of block stride. + @param padding Padding + @param searchLocations Vector of Point includes set of requested locations to be evaluated. + */ + CV_WRAP virtual void detect(InputArray img, CV_OUT std::vector& foundLocations, + CV_OUT std::vector& weights, + double hitThreshold = 0, Size winStride = Size(), + Size padding = Size(), + const std::vector& searchLocations = std::vector()) const; + + /** @brief Performs object detection without a multi-scale window. + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. + @param hitThreshold Threshold for the distance between features and SVM classifying plane. + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). + But if the free coefficient is omitted (which is allowed), you can specify it manually here. + @param winStride Window stride. It must be a multiple of block stride. + @param padding Padding + @param searchLocations Vector of Point includes locations to search. + */ + virtual void detect(InputArray img, CV_OUT std::vector& foundLocations, + double hitThreshold = 0, Size winStride = Size(), + Size padding = Size(), + const std::vector& searchLocations=std::vector()) const; + + /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list + of rectangles. + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param foundLocations Vector of rectangles where each rectangle contains the detected object. + @param foundWeights Vector that will contain confidence values for each detected object. + @param hitThreshold Threshold for the distance between features and SVM classifying plane. + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). + But if the free coefficient is omitted (which is allowed), you can specify it manually here. + @param winStride Window stride. It must be a multiple of block stride. + @param padding Padding + @param scale Coefficient of the detection window increase. + @param finalThreshold Final threshold + @param useMeanshiftGrouping indicates grouping algorithm + */ + CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, + CV_OUT std::vector& foundWeights, double hitThreshold = 0, + Size winStride = Size(), Size padding = Size(), double scale = 1.05, + double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const; + + /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list + of rectangles. + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param foundLocations Vector of rectangles where each rectangle contains the detected object. + @param hitThreshold Threshold for the distance between features and SVM classifying plane. + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). + But if the free coefficient is omitted (which is allowed), you can specify it manually here. + @param winStride Window stride. It must be a multiple of block stride. + @param padding Padding + @param scale Coefficient of the detection window increase. + @param finalThreshold Final threshold + @param useMeanshiftGrouping indicates grouping algorithm + */ + virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, + double hitThreshold = 0, Size winStride = Size(), + Size padding = Size(), double scale = 1.05, + double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const; + + /** @brief Computes gradients and quantized gradient orientations. + @param img Matrix contains the image to be computed + @param grad Matrix of type CV_32FC2 contains computed gradients + @param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations + @param paddingTL Padding from top-left + @param paddingBR Padding from bottom-right + */ + CV_WRAP virtual void computeGradient(InputArray img, InputOutputArray grad, InputOutputArray angleOfs, + Size paddingTL = Size(), Size paddingBR = Size()) const; + + /** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows). + */ + CV_WRAP static std::vector getDefaultPeopleDetector(); + + /**@example samples/tapi/hog.cpp + */ + /** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows). + */ + CV_WRAP static std::vector getDaimlerPeopleDetector(); + + //! Detection window size. Align to block size and block stride. Default value is Size(64,128). + CV_PROP Size winSize; + + //! Block size in pixels. Align to cell size. Default value is Size(16,16). + CV_PROP Size blockSize; + + //! Block stride. It must be a multiple of cell size. Default value is Size(8,8). + CV_PROP Size blockStride; + + //! Cell size. Default value is Size(8,8). + CV_PROP Size cellSize; + + //! Number of bins used in the calculation of histogram of gradients. Default value is 9. + CV_PROP int nbins; + + //! not documented + CV_PROP int derivAperture; + + //! Gaussian smoothing window parameter. + CV_PROP double winSigma; + + //! histogramNormType + CV_PROP HOGDescriptor::HistogramNormType histogramNormType; + + //! L2-Hys normalization method shrinkage. + CV_PROP double L2HysThreshold; + + //! Flag to specify whether the gamma correction preprocessing is required or not. + CV_PROP bool gammaCorrection; + + //! coefficients for the linear SVM classifier. + CV_PROP std::vector svmDetector; + + //! coefficients for the linear SVM classifier used when OpenCL is enabled + UMat oclSvmDetector; + + //! not documented + float free_coef; + + //! Maximum number of detection window increases. Default value is 64 + CV_PROP int nlevels; + + //! Indicates signed gradient will be used or not + CV_PROP bool signedGradient; + + /** @brief evaluate specified ROI and return confidence value for each location + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param locations Vector of Point + @param foundLocations Vector of Point where each Point is detected object's top-left point. + @param confidences confidences + @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually + it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if + the free coefficient is omitted (which is allowed), you can specify it manually here + @param winStride winStride + @param padding padding + */ + virtual void detectROI(InputArray img, const std::vector &locations, + CV_OUT std::vector& foundLocations, CV_OUT std::vector& confidences, + double hitThreshold = 0, cv::Size winStride = Size(), + cv::Size padding = Size()) const; + + /** @brief evaluate specified ROI and return confidence value for each location in multiple scales + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param foundLocations Vector of rectangles where each rectangle contains the detected object. + @param locations Vector of DetectionROI + @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified + in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. + @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. + */ + virtual void detectMultiScaleROI(InputArray img, + CV_OUT std::vector& foundLocations, + std::vector& locations, + double hitThreshold = 0, + int groupThreshold = 0) const; + + /** @brief Groups the object candidate rectangles. + @param rectList Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.) + @param weights Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.) + @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. + @param eps Relative difference between sides of the rectangles to merge them into a group. + */ + void groupRectangles(std::vector& rectList, std::vector& weights, int groupThreshold, double eps) const; +}; + +class CV_EXPORTS_W QRCodeDetector +{ +public: + CV_WRAP QRCodeDetector(); + ~QRCodeDetector(); + + /** @brief sets the epsilon used during the horizontal scan of QR code stop marker detection. + @param epsX Epsilon neighborhood, which allows you to determine the horizontal pattern + of the scheme 1:1:3:1:1 according to QR code standard. + */ + CV_WRAP void setEpsX(double epsX); + /** @brief sets the epsilon used during the vertical scan of QR code stop marker detection. + @param epsY Epsilon neighborhood, which allows you to determine the vertical pattern + of the scheme 1:1:3:1:1 according to QR code standard. + */ + CV_WRAP void setEpsY(double epsY); + + /** @brief Detects QR code in image and returns the quadrangle containing the code. + @param img grayscale or color (BGR) image containing (or not) QR code. + @param points Output vector of vertices of the minimum-area quadrangle containing the code. + */ + CV_WRAP bool detect(InputArray img, OutputArray points) const; + + /** @brief Decodes QR code in image once it's found by the detect() method. + + Returns UTF8-encoded output string or empty string if the code cannot be decoded. + @param img grayscale or color (BGR) image containing QR code. + @param points Quadrangle vertices found by detect() method (or some other algorithm). + @param straight_qrcode The optional output image containing rectified and binarized QR code + */ + CV_WRAP std::string decode(InputArray img, InputArray points, OutputArray straight_qrcode = noArray()); + + /** @brief Both detects and decodes QR code + + @param img grayscale or color (BGR) image containing QR code. + @param points optional output array of vertices of the found QR code quadrangle. Will be empty if not found. + @param straight_qrcode The optional output image containing rectified and binarized QR code + */ + CV_WRAP std::string detectAndDecode(InputArray img, OutputArray points=noArray(), + OutputArray straight_qrcode = noArray()); + /** @brief Detects QR codes in image and returns the vector of the quadrangles containing the codes. + @param img grayscale or color (BGR) image containing (or not) QR codes. + @param points Output vector of vector of vertices of the minimum-area quadrangle containing the codes. + */ + CV_WRAP + bool detectMulti(InputArray img, OutputArray points) const; + + /** @brief Decodes QR codes in image once it's found by the detect() method. + @param img grayscale or color (BGR) image containing QR codes. + @param decoded_info UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded. + @param points vector of Quadrangle vertices found by detect() method (or some other algorithm). + @param straight_qrcode The optional output vector of images containing rectified and binarized QR codes + */ + CV_WRAP + bool decodeMulti( + InputArray img, InputArray points, + CV_OUT std::vector& decoded_info, + OutputArrayOfArrays straight_qrcode = noArray() + ) const; + + /** @brief Both detects and decodes QR codes + @param img grayscale or color (BGR) image containing QR codes. + @param decoded_info UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded. + @param points optional output vector of vertices of the found QR code quadrangles. Will be empty if not found. + @param straight_qrcode The optional output vector of images containing rectified and binarized QR codes + */ + CV_WRAP + bool detectAndDecodeMulti( + InputArray img, CV_OUT std::vector& decoded_info, + OutputArray points = noArray(), + OutputArrayOfArrays straight_qrcode = noArray() + ) const; + +protected: + struct Impl; + Ptr p; +}; + +//! @} objdetect +} + +#include "opencv2/objdetect/detection_based_tracker.hpp" + +#endif diff --git a/test/bug-hunting/cve/CVE-2019-15939/precomp.hpp b/test/bug-hunting/cve/CVE-2019-15939/precomp.hpp new file mode 100644 index 000000000..cbefc396b --- /dev/null +++ b/test/bug-hunting/cve/CVE-2019-15939/precomp.hpp @@ -0,0 +1,53 @@ +/*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*/ + +#ifndef __OPENCV_PRECOMP_H__ +#define __OPENCV_PRECOMP_H__ + +#include "opencv2/objdetect.hpp" +#include "opencv2/imgproc.hpp" + +#include "opencv2/core/utility.hpp" +#include "opencv2/core/ocl.hpp" +#include "opencv2/core/private.hpp" + +#endif