3620 lines
165 KiB
C++
3620 lines
165 KiB
C++
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#include "cascadedetect.hpp"
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#include "opencv2/core/core_c.h"
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#include "opencv2/core/hal/intrin.hpp"
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#include "opencl_kernels_objdetect.hpp"
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#include <cstdio>
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#include <iterator>
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#include <limits>
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/****************************************************************************************\
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The code below is implementation of HOG (Histogram-of-Oriented Gradients)
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descriptor and object detection, introduced by Navneet Dalal and Bill Triggs.
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The computed feature vectors are compatible with the
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INRIA Object Detection and Localization Toolkit
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(http://pascal.inrialpes.fr/soft/olt/)
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\****************************************************************************************/
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namespace cv
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{
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#define NTHREADS 256
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enum {DESCR_FORMAT_COL_BY_COL, DESCR_FORMAT_ROW_BY_ROW};
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static int numPartsWithin(int size, int part_size, int stride)
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{
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CV_Assert(stride != 0);
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return (size - part_size + stride) / stride;
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}
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static Size numPartsWithin(cv::Size size, cv::Size part_size,
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cv::Size stride)
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{
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return Size(numPartsWithin(size.width, part_size.width, stride.width),
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numPartsWithin(size.height, part_size.height, stride.height));
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}
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static size_t getBlockHistogramSize(Size block_size, Size cell_size, int nbins)
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{
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CV_Assert(!cell_size.empty());
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Size cells_per_block = Size(block_size.width / cell_size.width,
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block_size.height / cell_size.height);
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return (size_t)(nbins * cells_per_block.area());
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}
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size_t HOGDescriptor::getDescriptorSize() const
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{
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CV_Assert(blockSize.width % cellSize.width == 0 &&
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blockSize.height % cellSize.height == 0);
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CV_Assert((winSize.width - blockSize.width) % blockStride.width == 0 &&
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(winSize.height - blockSize.height) % blockStride.height == 0 );
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return (size_t)nbins*
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(blockSize.width/cellSize.width)*
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(blockSize.height/cellSize.height)*
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((winSize.width - blockSize.width)/blockStride.width + 1)*
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((winSize.height - blockSize.height)/blockStride.height + 1);
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}
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double HOGDescriptor::getWinSigma() const
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{
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return winSigma > 0 ? winSigma : (blockSize.width + blockSize.height)/8.;
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}
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bool HOGDescriptor::checkDetectorSize() const
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{
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size_t detectorSize = svmDetector.size(), descriptorSize = getDescriptorSize();
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return detectorSize == 0 ||
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detectorSize == descriptorSize ||
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detectorSize == descriptorSize + 1;
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}
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void HOGDescriptor::setSVMDetector(InputArray _svmDetector)
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{
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_svmDetector.getMat().convertTo(svmDetector, CV_32F);
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CV_Assert(checkDetectorSize());
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Mat detector_reordered(1, (int)svmDetector.size(), CV_32FC1);
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size_t block_hist_size = getBlockHistogramSize(blockSize, cellSize, nbins);
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cv::Size blocks_per_img = numPartsWithin(winSize, blockSize, blockStride);
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for (int i = 0; i < blocks_per_img.height; ++i)
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for (int j = 0; j < blocks_per_img.width; ++j)
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{
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const float *src = &svmDetector[0] + (j * blocks_per_img.height + i) * block_hist_size;
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float *dst = detector_reordered.ptr<float>() + (i * blocks_per_img.width + j) * block_hist_size;
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for (size_t k = 0; k < block_hist_size; ++k)
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dst[k] = src[k];
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}
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size_t descriptor_size = getDescriptorSize();
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free_coef = svmDetector.size() > descriptor_size ? svmDetector[descriptor_size] : 0;
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detector_reordered.copyTo(oclSvmDetector);
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}
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#define CV_TYPE_NAME_HOG_DESCRIPTOR "opencv-object-detector-hog"
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bool HOGDescriptor::read(FileNode& obj)
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{
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CV_Assert(!obj["winSize"].empty());
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if( !obj.isMap() )
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return false;
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FileNodeIterator it = obj["winSize"].begin();
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it >> winSize.width >> winSize.height; CV_Assert(!winSize.empty());
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it = obj["blockSize"].begin();
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it >> blockSize.width >> blockSize.height; CV_Assert(!blockSize.empty());
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it = obj["blockStride"].begin();
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it >> blockStride.width >> blockStride.height; CV_Assert(!blockStride.empty());
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it = obj["cellSize"].begin();
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it >> cellSize.width >> cellSize.height; CV_Assert(!cellSize.empty());
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obj["nbins"] >> nbins; CV_Assert(nbins > 0);
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obj["derivAperture"] >> derivAperture;
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obj["winSigma"] >> winSigma;
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obj["histogramNormType"] >> histogramNormType;
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obj["L2HysThreshold"] >> L2HysThreshold;
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obj["gammaCorrection"] >> gammaCorrection;
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obj["nlevels"] >> nlevels; CV_Assert(nlevels > 0);
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if (obj["signedGradient"].empty())
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signedGradient = false;
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else
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obj["signedGradient"] >> signedGradient;
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FileNode vecNode = obj["SVMDetector"];
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if( vecNode.isSeq() )
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{
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std::vector<float> _svmDetector;
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vecNode >> _svmDetector;
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setSVMDetector(_svmDetector);
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}
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return true;
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}
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void HOGDescriptor::write(FileStorage& fs, const String& objName) const
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{
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if( !objName.empty() )
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fs << objName;
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fs << "{" CV_TYPE_NAME_HOG_DESCRIPTOR
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<< "winSize" << winSize
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<< "blockSize" << blockSize
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<< "blockStride" << blockStride
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<< "cellSize" << cellSize
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<< "nbins" << nbins
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<< "derivAperture" << derivAperture
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<< "winSigma" << getWinSigma()
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<< "histogramNormType" << histogramNormType
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<< "L2HysThreshold" << L2HysThreshold
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<< "gammaCorrection" << gammaCorrection
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<< "nlevels" << nlevels
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<< "signedGradient" << signedGradient;
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if( !svmDetector.empty() )
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fs << "SVMDetector" << svmDetector;
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fs << "}";
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}
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bool HOGDescriptor::load(const String& filename, const String& objname)
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{
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FileStorage fs(filename, FileStorage::READ);
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FileNode obj = !objname.empty() ? fs[objname] : fs.getFirstTopLevelNode();
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return read(obj);
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}
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void HOGDescriptor::save(const String& filename, const String& objName) const
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{
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FileStorage fs(filename, FileStorage::WRITE);
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write(fs, !objName.empty() ? objName : FileStorage::getDefaultObjectName(filename));
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}
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void HOGDescriptor::copyTo(HOGDescriptor& c) const
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{
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c.winSize = winSize;
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c.blockSize = blockSize;
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c.blockStride = blockStride;
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c.cellSize = cellSize;
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c.nbins = nbins;
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c.derivAperture = derivAperture;
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c.winSigma = winSigma;
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c.histogramNormType = histogramNormType;
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c.L2HysThreshold = L2HysThreshold;
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c.gammaCorrection = gammaCorrection;
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c.setSVMDetector(svmDetector);
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c.nlevels = nlevels;
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c.signedGradient = signedGradient;
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}
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void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
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Size paddingTL, Size paddingBR) const
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{
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CV_INSTRUMENT_REGION();
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CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
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Size gradsize(img.cols + paddingTL.width + paddingBR.width,
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img.rows + paddingTL.height + paddingBR.height);
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grad.create(gradsize, CV_32FC2); // <magnitude*(1-alpha), magnitude*alpha>
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qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
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Size wholeSize;
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Point roiofs;
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img.locateROI(wholeSize, roiofs);
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int i, x, y;
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int cn = img.channels();
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Mat_<float> _lut(1, 256);
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const float* const lut = &_lut(0,0);
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#if CV_SIMD128
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v_float32x4 idx(0.0f, 1.0f, 2.0f, 3.0f);
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v_float32x4 ifour = v_setall_f32(4.0);
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float* const _data = &_lut(0, 0);
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if ( gammaCorrection )
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for ( i = 0; i < 256; i += 4)
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{
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v_store(_data + i, v_sqrt(idx));
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idx += ifour;
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}
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else
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for ( i = 0; i < 256; i += 4)
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{
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v_store(_data + i, idx);
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idx += ifour;
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}
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#else
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if( gammaCorrection )
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for( i = 0; i < 256; i++ )
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_lut(0,i) = std::sqrt((float)i);
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else
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for( i = 0; i < 256; i++ )
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_lut(0,i) = (float)i;
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#endif
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AutoBuffer<int> mapbuf(gradsize.width + gradsize.height + 4);
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int* xmap = mapbuf.data() + 1;
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int* ymap = xmap + gradsize.width + 2;
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const int borderType = (int)BORDER_REFLECT_101;
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for( x = -1; x < gradsize.width + 1; x++ )
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xmap[x] = borderInterpolate(x - paddingTL.width + roiofs.x,
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wholeSize.width, borderType) - roiofs.x;
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for( y = -1; y < gradsize.height + 1; y++ )
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ymap[y] = borderInterpolate(y - paddingTL.height + roiofs.y,
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wholeSize.height, borderType) - roiofs.y;
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// x- & y- derivatives for the whole row
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int width = gradsize.width;
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AutoBuffer<float> _dbuf(width*4);
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float* const dbuf = _dbuf.data();
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Mat Dx(1, width, CV_32F, dbuf);
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Mat Dy(1, width, CV_32F, dbuf + width);
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Mat Mag(1, width, CV_32F, dbuf + width*2);
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Mat Angle(1, width, CV_32F, dbuf + width*3);
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if (cn == 3)
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{
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int end = gradsize.width + 2;
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xmap -= 1, x = 0;
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#if CV_SIMD128
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for ( ; x <= end - 4; x += 4)
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{
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v_int32x4 mul_res = v_load(xmap + x);
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mul_res += mul_res + mul_res;
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v_store(xmap + x, mul_res);
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}
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#endif
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for ( ; x < end; ++x)
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xmap[x] *= 3;
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xmap += 1;
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}
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float angleScale = signedGradient ? (float)(nbins/(2.0*CV_PI)) : (float)(nbins/CV_PI);
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for( y = 0; y < gradsize.height; y++ )
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{
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const uchar* imgPtr = img.ptr(ymap[y]);
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//In case subimage is used ptr() generates an assert for next and prev rows
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//(see http://code.opencv.org/issues/4149)
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const uchar* prevPtr = img.data + img.step*ymap[y-1];
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const uchar* nextPtr = img.data + img.step*ymap[y+1];
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float* gradPtr = grad.ptr<float>(y);
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uchar* qanglePtr = qangle.ptr(y);
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if( cn == 1 )
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{
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for( x = 0; x < width; x++ )
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{
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int x1 = xmap[x];
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dbuf[x] = (float)(lut[imgPtr[xmap[x+1]]] - lut[imgPtr[xmap[x-1]]]);
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dbuf[width + x] = (float)(lut[nextPtr[x1]] - lut[prevPtr[x1]]);
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}
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}
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else
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{
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x = 0;
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#if CV_SIMD128
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for( ; x <= width - 4; x += 4 )
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{
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int x0 = xmap[x], x1 = xmap[x+1], x2 = xmap[x+2], x3 = xmap[x+3];
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typedef const uchar* const T;
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T p02 = imgPtr + xmap[x+1], p00 = imgPtr + xmap[x-1];
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|
|
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> pixData;
|
||
|
|
std::vector<BlockData> blockData;
|
||
|
|
|
||
|
|
bool useCache;
|
||
|
|
std::vector<int> ymaxCached;
|
||
|
|
Size winSize;
|
||
|
|
Size cacheStride;
|
||
|
|
Size nblocks, ncells;
|
||
|
|
int blockHistogramSize;
|
||
|
|
int count1, count2, count4;
|
||
|
|
Point imgoffset;
|
||
|
|
Mat_<float> blockCache;
|
||
|
|
Mat_<uchar> blockCacheFlags;
|
||
|
|
|
||
|
|
Mat grad, qangle;
|
||
|
|
const HOGDescriptor* descriptor;
|
||
|
|
};
|
||
|
|
|
||
|
|
HOGCache::HOGCache() :
|
||
|
|
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_<float> weights(blockSize);
|
||
|
|
float sigma = (float)descriptor->getWinSigma();
|
||
|
|
float scale = 1.f/(sigma*sigma*2);
|
||
|
|
|
||
|
|
{
|
||
|
|
AutoBuffer<float> 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_<uchar> cacheRow = blockCacheFlags.row(cacheIdx.y);
|
||
|
|
cacheRow = (uchar)0;
|
||
|
|
ymaxCached[cacheIdx.y] = pt.y;
|
||
|
|
}
|
||
|
|
|
||
|
|
blockHist = &blockCache[cacheIdx.y][cacheIdx.x*blockHistogramSize];
|
||
|
|
uchar& computedFlag = blockCacheFlags(cacheIdx.y, cacheIdx.x);
|
||
|
|
if( computedFlag != 0 )
|
||
|
|
return blockHist;
|
||
|
|
computedFlag = (uchar)1; // set it at once, before actual computing
|
||
|
|
}
|
||
|
|
|
||
|
|
int k, C1 = count1, C2 = count2, C4 = count4;
|
||
|
|
const float* gradPtr = grad.ptr<float>(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<float>& _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<int>(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<float>(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<float>(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<int>(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<float>& descriptors,
|
||
|
|
Size winStride, Size padding, const std::vector<Point>& 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<Point>& hits, std::vector<double>& weights, double hitThreshold,
|
||
|
|
Size winStride, Size padding, const std::vector<Point>& 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<float> 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<Point>& hits, double hitThreshold,
|
||
|
|
Size winStride, Size padding, const std::vector<Point>& locations) const
|
||
|
|
{
|
||
|
|
CV_INSTRUMENT_REGION();
|
||
|
|
|
||
|
|
std::vector<double> 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<Rect> * _vec, Mutex* _mtx,
|
||
|
|
std::vector<double>* _weights=0, std::vector<double>* _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<Point> locations;
|
||
|
|
std::vector<double> hitsWeights;
|
||
|
|
|
||
|
|
for( i = i1; i < i2; i++ )
|
||
|
|
{
|
||
|
|
double scale = levelScale[i];
|
||
|
|
Size sz(cvRound(img.cols/scale), cvRound(img.rows/scale));
|
||
|
|
Mat smallerImg(sz, img.type(), smallerImgBuf.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<Rect>* vec;
|
||
|
|
std::vector<double>* weights;
|
||
|
|
std::vector<double>* 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<Point> &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<int>(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<float>(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<float>(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<Rect> &found_locations, std::vector<double>& 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<Rect> all_candidates;
|
||
|
|
std::vector<Point> locations;
|
||
|
|
UMat image_scale;
|
||
|
|
Size imgSize = _img.size();
|
||
|
|
image_scale.create(imgSize, _img.type());
|
||
|
|
|
||
|
|
for (size_t i = 0; i<level_scale.size() ; i++)
|
||
|
|
{
|
||
|
|
double scale = level_scale[i];
|
||
|
|
Size effect_size = Size(cvRound(imgSize.width / scale), cvRound(imgSize.height / scale));
|
||
|
|
if (effect_size == imgSize)
|
||
|
|
{
|
||
|
|
if(!ocl_detect(_img, locations, hit_threshold, win_stride, oclSvmDetector, blockSize, cellSize, nbins,
|
||
|
|
blockStride, winSize, gammaCorrection, L2HysThreshold, sigma, free_coef, signedGradient))
|
||
|
|
return false;
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
resize(_img, image_scale, effect_size, 0, 0, INTER_LINEAR_EXACT);
|
||
|
|
if(!ocl_detect(image_scale, locations, hit_threshold, win_stride, oclSvmDetector, blockSize, cellSize, nbins,
|
||
|
|
blockStride, winSize, gammaCorrection, L2HysThreshold, sigma, free_coef, signedGradient))
|
||
|
|
return false;
|
||
|
|
}
|
||
|
|
Size scaled_win_size(cvRound(winSize.width * scale),
|
||
|
|
cvRound(winSize.height * scale));
|
||
|
|
for (size_t j = 0; j < locations.size(); j++)
|
||
|
|
all_candidates.push_back(Rect(Point2d(locations[j]) * scale, scaled_win_size));
|
||
|
|
}
|
||
|
|
found_locations.assign(all_candidates.begin(), all_candidates.end());
|
||
|
|
groupRectangles(found_locations, (int)group_threshold, 0.2);
|
||
|
|
clipObjects(imgSize, found_locations, 0, 0);
|
||
|
|
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
#endif //HAVE_OPENCL
|
||
|
|
|
||
|
|
void HOGDescriptor::detectMultiScale(
|
||
|
|
InputArray _img, std::vector<Rect>& foundLocations, std::vector<double>& 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<double> 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<Rect> allCandidates;
|
||
|
|
std::vector<double> tempScales;
|
||
|
|
std::vector<double> tempWeights;
|
||
|
|
std::vector<double> 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<Rect>& foundLocations,
|
||
|
|
double hitThreshold, Size winStride, Size padding,
|
||
|
|
double scale0, double finalThreshold, bool useMeanshiftGrouping) const
|
||
|
|
{
|
||
|
|
CV_INSTRUMENT_REGION();
|
||
|
|
|
||
|
|
std::vector<double> foundWeights;
|
||
|
|
detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride,
|
||
|
|
padding, scale0, finalThreshold, useMeanshiftGrouping);
|
||
|
|
}
|
||
|
|
|
||
|
|
template<typename _ClsName> 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<HOGDescriptor> HOGRTTI;
|
||
|
|
|
||
|
|
CvType hog_type( CV_TYPE_NAME_HOG_DESCRIPTOR, HOGRTTI::isInstance,
|
||
|
|
HOGRTTI::release, HOGRTTI::read, HOGRTTI::write, HOGRTTI::clone);
|
||
|
|
|
||
|
|
std::vector<float> HOGDescriptor::getDefaultPeopleDetector()
|
||
|
|
{
|
||
|
|
static const float detector[] = {
|
||
|
|
0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
|
||
|
|
0.11547081f, -0.04268804f, 0.04635834f, -0.05468199f, 0.08232084f,
|
||
|
|
0.10424068f, -0.02294518f, 0.01108519f, 0.01378693f, 0.11193510f,
|
||
|
|
0.01268418f, 0.08528346f, -0.06309239f, 0.13054633f, 0.08100729f,
|
||
|
|
-0.05209739f, -0.04315529f, 0.09341384f, 0.11035026f, -0.07596218f,
|
||
|
|
-0.05517511f, -0.04465296f, 0.02947334f, 0.04555536f,
|
||
|
|
-3.55954492e-003f, 0.07818956f, 0.07730991f, 0.07890715f, 0.06222893f,
|
||
|
|
0.09001380f, -0.03574381f, 0.03414327f, 0.05677258f, -0.04773581f,
|
||
|
|
0.03746637f, -0.03521175f, 0.06955440f, -0.03849038f, 0.01052293f,
|
||
|
|
0.01736112f, 0.10867710f, 0.08748853f, 3.29739624e-003f, 0.10907028f,
|
||
|
|
0.07913758f, 0.10393070f, 0.02091867f, 0.11594022f, 0.13182420f,
|
||
|
|
0.09879354f, 0.05362710f, -0.06745391f, -7.01260753e-003f,
|
||
|
|
5.24702156e-003f, 0.03236255f, 0.01407916f, 0.02207983f, 0.02537322f,
|
||
|
|
0.04547948f, 0.07200756f, 0.03129894f, -0.06274468f, 0.02107014f,
|
||
|
|
0.06035208f, 0.08636236f, 4.53164103e-003f, 0.02193363f, 0.02309801f,
|
||
|
|
0.05568166f, -0.02645093f, 0.04448695f, 0.02837519f, 0.08975694f,
|
||
|
|
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|
|
0.05682293f, 0.05925463f, -0.01165808f, -0.07936699f, -0.04208954f,
|
||
|
|
0.01333987f, 0.09051196f, 0.10098671f, -0.03974256f, 0.01238771f,
|
||
|
|
-0.07501741f, -0.03655440f, -0.04301528f, 0.09216860f,
|
||
|
|
4.63579083e-004f, 0.02851115f, 0.02142735f, 1.28244064e-004f,
|
||
|
|
0.02879687f, -0.08554889f, -0.04838862f, 0.08135369f, -0.05756533f,
|
||
|
|
0.01413900f, 0.03451880f, -0.06619488f, -0.03053130f, 0.02961676f,
|
||
|
|
-0.07384635f, 0.01135692f, 0.05283910f, -0.07778034f, -0.02107482f,
|
||
|
|
-0.05511716f, -0.13473752f, 0.03030157f, 0.06722020f, -0.06218817f,
|
||
|
|
-0.05826827f, 0.06254654f, 0.02895772f, -0.01664000f, -0.03620280f,
|
||
|
|
-0.01612278f, -1.46097376e-003f, 0.14013411f, -8.96181818e-003f,
|
||
|
|
-0.03250246f, 3.38630192e-003f, 2.64779478e-003f, 0.03359732f,
|
||
|
|
-0.02411991f, -0.04229729f, 0.10666174f, -6.66579151f };
|
||
|
|
return std::vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
|
||
|
|
}
|
||
|
|
|
||
|
|
// This function renurn 1981 SVM coeffs obtained from daimler's base.
|
||
|
|
// To use these coeffs the detection window size should be (48,96)
|
||
|
|
std::vector<float> 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, -0.100147f, -0.037830f,
|
||
|
|
-0.029230f, -0.055641f, 0.033248f, -0.016512f,
|
||
|
|
0.155244f, 0.247315f, -0.124694f, -0.048414f,
|
||
|
|
-0.062219f, 0.193683f, 0.004574f, 0.055089f,
|
||
|
|
0.093565f, 0.167712f, 0.167581f, 0.018895f,
|
||
|
|
0.215258f, 0.122609f, 0.090520f, -0.067219f,
|
||
|
|
-0.049029f, -0.099615f, 0.241804f, -0.094893f,
|
||
|
|
-0.176248f, 0.001727f, -0.134473f, 0.104442f,
|
||
|
|
0.050942f, 0.081165f, 0.072156f, 0.121646f,
|
||
|
|
0.002656f, -0.297974f, -0.133587f, -0.060121f,
|
||
|
|
-0.092515f, -0.048974f, -0.084754f, -0.180111f,
|
||
|
|
-0.038590f, 0.086283f, -0.134636f, -0.107249f,
|
||
|
|
0.132890f, 0.141556f, 0.249425f, 0.130273f,
|
||
|
|
-0.030031f, 0.073212f, -0.008155f, 0.019931f,
|
||
|
|
0.071688f, 0.000300f, -0.019525f, -0.021725f,
|
||
|
|
-0.040993f, -0.086841f, 0.070124f, 0.240033f,
|
||
|
|
0.265350f, 0.043208f, 0.166754f, 0.091453f,
|
||
|
|
0.060916f, -0.036972f, -0.091043f, 0.079873f,
|
||
|
|
0.219781f, 0.158102f, -0.140618f, -0.043016f,
|
||
|
|
0.124802f, 0.093668f, 0.103208f, 0.094872f,
|
||
|
|
0.080541f, 0.137711f, 0.160566f, -0.169231f,
|
||
|
|
0.013983f, 0.309508f, -0.004217f, -0.057200f,
|
||
|
|
-0.064489f, 0.014066f, 0.361009f, 0.251328f,
|
||
|
|
-0.080983f, -0.044183f, 0.061436f, -0.037381f,
|
||
|
|
-0.078786f, 0.030993f, 0.066314f, 0.037683f,
|
||
|
|
0.152325f, -0.091683f, 0.070203f, 0.217856f,
|
||
|
|
0.036435f, -0.076462f, 0.006254f, -0.094431f,
|
||
|
|
0.154829f, -0.023038f, -0.196961f, -0.024594f,
|
||
|
|
0.178465f, -0.050139f, -0.045932f, -0.000965f,
|
||
|
|
0.109112f, 0.046165f, -0.159373f, -0.008713f,
|
||
|
|
0.041307f, 0.097129f, -0.057211f, -0.064599f,
|
||
|
|
0.077165f, 0.176167f, 0.138322f, 0.065753f,
|
||
|
|
-0.104950f, 0.017933f, 0.136255f, -0.011598f,
|
||
|
|
0.047007f, 0.080550f, 0.068619f, 0.084661f,
|
||
|
|
-0.035493f, -0.091314f, -0.041411f, 0.060971f,
|
||
|
|
-0.101912f, -0.079870f, -0.085977f, -0.022686f,
|
||
|
|
0.079788f, -0.098064f, -0.054603f, 0.040383f,
|
||
|
|
0.300794f, 0.128603f, 0.094844f, 0.047407f,
|
||
|
|
0.101825f, 0.061832f, -0.162160f, -0.204553f,
|
||
|
|
-0.035165f, 0.101450f, -0.016641f, -0.027140f,
|
||
|
|
-0.134392f, -0.008743f, 0.102331f, 0.114853f,
|
||
|
|
0.009644f, 0.062823f, 0.237339f, 0.167843f,
|
||
|
|
0.053066f, -0.012592f, 0.043158f, 0.002305f,
|
||
|
|
0.065001f, -0.038929f, -0.020356f, 0.152343f,
|
||
|
|
0.043469f, -0.029967f, -0.042948f, 0.032481f,
|
||
|
|
0.068488f, -0.110840f, -0.111083f, 0.111980f,
|
||
|
|
-0.002072f, -0.005562f, 0.082926f, 0.006635f,
|
||
|
|
-0.108153f, 0.024242f, -0.086464f, -0.189884f,
|
||
|
|
-0.017492f, 0.191456f, -0.007683f, -0.128769f,
|
||
|
|
-0.038017f, -0.132380f, 0.091926f, 0.079696f,
|
||
|
|
-0.106728f, -0.007656f, 0.172744f, 0.011576f,
|
||
|
|
0.009883f, 0.083258f, -0.026516f, 0.145534f,
|
||
|
|
0.153924f, -0.130290f, -0.108945f, 0.124490f,
|
||
|
|
-0.003186f, -0.100485f, 0.015024f, -0.060512f,
|
||
|
|
0.026288f, -0.086713f, -0.169012f, 0.076517f,
|
||
|
|
0.215778f, 0.043701f, -0.131642f, -0.012585f,
|
||
|
|
-0.045181f, -0.118183f, -0.241544f, -0.167293f,
|
||
|
|
-0.020107f, -0.019917f, -0.101827f, -0.107096f,
|
||
|
|
-0.010503f, 0.044938f, 0.189680f, 0.217119f,
|
||
|
|
-0.046086f, 0.044508f, 0.199716f, -0.036004f,
|
||
|
|
-0.148927f, 0.013355f, -0.078279f, 0.030451f,
|
||
|
|
0.056301f, -0.024609f, 0.083224f, 0.099533f,
|
||
|
|
-0.039432f, -0.138880f, 0.005482f, -0.024120f,
|
||
|
|
-0.140468f, -0.066381f, -0.017057f, 0.009260f,
|
||
|
|
-0.058004f, -0.028486f, -0.061610f, 0.007483f,
|
||
|
|
-0.158309f, -0.150687f, -0.044595f, -0.105121f,
|
||
|
|
-0.045763f, -0.006618f, -0.024419f, -0.117713f,
|
||
|
|
-0.119366f, -0.175941f, -0.071542f, 0.119027f,
|
||
|
|
0.111362f, 0.043080f, 0.034889f, 0.093003f,
|
||
|
|
0.007842f, 0.057368f, -0.108834f, -0.079968f,
|
||
|
|
0.230959f, 0.020205f, 0.011470f, 0.098877f,
|
||
|
|
0.101310f, -0.030215f, -0.018018f, -0.059552f,
|
||
|
|
-0.106157f, 0.021866f, -0.036471f, 0.080051f,
|
||
|
|
0.041165f, -0.082101f, 0.117726f, 0.030961f,
|
||
|
|
-0.054763f, -0.084102f, -0.185778f, -0.061305f,
|
||
|
|
-0.038089f, -0.110728f, -0.264010f, 0.076675f,
|
||
|
|
-0.077111f, -0.137644f, 0.036232f, 0.277995f,
|
||
|
|
0.019116f, 0.107738f, 0.144003f, 0.080304f,
|
||
|
|
0.215036f, 0.228897f, 0.072713f, 0.077773f,
|
||
|
|
0.120168f, 0.075324f, 0.062730f, 0.122478f,
|
||
|
|
-0.049008f, 0.164912f, 0.162450f, 0.041246f,
|
||
|
|
0.009891f, -0.097827f, -0.038700f, -0.023027f,
|
||
|
|
-0.120020f, 0.203364f, 0.248474f, 0.149810f,
|
||
|
|
-0.036276f, -0.082814f, -0.090343f, -0.027143f,
|
||
|
|
-0.075689f, -0.320310f, -0.000500f, -0.143334f,
|
||
|
|
-0.065077f, -0.186936f, 0.129372f, 0.116431f,
|
||
|
|
0.181699f, 0.170436f, 0.418854f, 0.460045f,
|
||
|
|
0.333719f, 0.230515f, 0.047822f, -0.044954f,
|
||
|
|
-0.068086f, 0.140179f, -0.044821f, 0.085550f,
|
||
|
|
0.092483f, -0.107296f, -0.130670f, -0.206629f,
|
||
|
|
0.114601f, -0.317869f, -0.076663f, 0.038680f,
|
||
|
|
0.212753f, -0.016059f, -0.126526f, -0.163602f,
|
||
|
|
0.210154f, 0.099887f, -0.126366f, 0.118453f,
|
||
|
|
0.019309f, -0.021611f, -0.096499f, -0.111809f,
|
||
|
|
-0.200489f, 0.142854f, 0.228840f, -0.353346f,
|
||
|
|
-0.179151f, 0.116834f, 0.252389f, -0.031728f,
|
||
|
|
-0.188135f, -0.158998f, 0.386523f, 0.122315f,
|
||
|
|
0.209944f, 0.394023f, 0.359030f, 0.260717f,
|
||
|
|
0.170335f, 0.013683f, -0.142596f, -0.026138f,
|
||
|
|
-0.011878f, -0.150519f, 0.047159f, -0.107062f,
|
||
|
|
-0.147347f, -0.187689f, -0.186027f, -0.208048f,
|
||
|
|
0.058468f, -0.073026f, -0.236556f, -0.079788f,
|
||
|
|
-0.146216f, -0.058563f, -0.101361f, -0.071294f,
|
||
|
|
-0.071093f, 0.116919f, 0.234304f, 0.306781f,
|
||
|
|
0.321866f, 0.240000f, 0.073261f, -0.012173f,
|
||
|
|
0.026479f, 0.050173f, 0.166127f, 0.228955f,
|
||
|
|
0.061905f, 0.156460f, 0.205990f, 0.120672f,
|
||
|
|
0.037350f, 0.167884f, 0.290099f, 0.420900f,
|
||
|
|
-0.012601f, 0.189839f, 0.306378f, 0.118383f,
|
||
|
|
-0.095598f, -0.072360f, -0.132496f, -0.224259f,
|
||
|
|
-0.126021f, 0.022714f, 0.284039f, 0.051369f,
|
||
|
|
-0.000927f, -0.058735f, -0.083354f, -0.141254f,
|
||
|
|
-0.187578f, -0.202669f, 0.048902f, 0.246597f,
|
||
|
|
0.441863f, 0.342519f, 0.066979f, 0.215286f,
|
||
|
|
0.188191f, -0.072240f, -0.208142f, -0.030196f,
|
||
|
|
0.178141f, 0.136985f, -0.043374f, -0.181098f,
|
||
|
|
0.091815f, 0.116177f, -0.126690f, -0.386625f,
|
||
|
|
0.368165f, 0.269149f, -0.088042f, -0.028823f,
|
||
|
|
0.092961f, 0.024099f, 0.046112f, 0.176756f,
|
||
|
|
0.135849f, 0.124955f, 0.195467f, -0.037218f,
|
||
|
|
0.167217f, 0.188938f, 0.053528f, -0.066561f,
|
||
|
|
0.133721f, -0.070565f, 0.115898f, 0.152435f,
|
||
|
|
-0.116993f, -0.110592f, -0.179005f, 0.026668f,
|
||
|
|
0.080530f, 0.075084f, -0.070401f, 0.012497f,
|
||
|
|
0.021849f, -0.139764f, -0.022020f, -0.096301f,
|
||
|
|
-0.064954f, -0.127446f, -0.013806f, -0.108315f,
|
||
|
|
0.156285f, 0.149867f, -0.011382f, 0.064532f,
|
||
|
|
0.029168f, 0.027393f, 0.069716f, 0.153735f,
|
||
|
|
0.038459f, 0.230714f, 0.253840f, 0.059522f,
|
||
|
|
-0.045053f, 0.014083f, 0.071103f, 0.068747f,
|
||
|
|
0.095887f, 0.005832f, 0.144887f, 0.026357f,
|
||
|
|
-0.067359f, -0.044151f, -0.123283f, -0.019911f,
|
||
|
|
0.005318f, 0.109208f, -0.003201f, -0.021734f,
|
||
|
|
0.142025f, -0.066907f, -0.120070f, -0.188639f,
|
||
|
|
0.012472f, -0.048704f, -0.012366f, -0.184828f,
|
||
|
|
0.168591f, 0.267166f, 0.058208f, -0.044101f,
|
||
|
|
0.033500f, 0.178558f, 0.104550f, 0.122418f,
|
||
|
|
0.080177f, 0.173246f, 0.298537f, 0.064173f,
|
||
|
|
0.053397f, 0.174341f, 0.230984f, 0.117025f,
|
||
|
|
0.166242f, 0.227781f, 0.120623f, 0.176952f,
|
||
|
|
-0.011393f, -0.086483f, -0.008270f, 0.051700f,
|
||
|
|
-0.153369f, -0.058837f, -0.057639f, -0.060115f,
|
||
|
|
0.026349f, -0.160745f, -0.037894f, -0.048575f,
|
||
|
|
0.041052f, -0.022112f, 0.060365f, 0.051906f,
|
||
|
|
0.162657f, 0.138519f, -0.050185f, -0.005938f,
|
||
|
|
0.071301f, 0.127686f, 0.062342f, 0.144400f,
|
||
|
|
0.072600f, 0.198436f, 0.246219f, -0.078185f,
|
||
|
|
-0.036169f, 0.075934f, 0.047328f, -0.013601f,
|
||
|
|
0.087205f, 0.019900f, 0.022606f, -0.015365f,
|
||
|
|
-0.092506f, 0.075275f, -0.116375f, 0.050500f,
|
||
|
|
0.045118f, 0.166567f, 0.072073f, 0.060371f,
|
||
|
|
0.131747f, -0.169863f, -0.039352f, -0.047486f,
|
||
|
|
-0.039797f, -0.204312f, 0.021710f, 0.129443f,
|
||
|
|
-0.021173f, 0.173416f, -0.070794f, -0.063986f,
|
||
|
|
0.069689f, -0.064099f, -0.123201f, -0.017372f,
|
||
|
|
-0.206870f, 0.065863f, 0.113226f, 0.024707f,
|
||
|
|
-0.071341f, -0.066964f, -0.098278f, -0.062927f,
|
||
|
|
0.075840f, 0.014716f, 0.019378f, 0.132699f,
|
||
|
|
-0.074191f, -0.089557f, -0.078446f, -0.197488f,
|
||
|
|
-0.173665f, 0.052583f, 0.044361f, 0.113549f,
|
||
|
|
0.098492f, 0.077379f, -0.011146f, -0.192593f,
|
||
|
|
-0.164435f, 0.045568f, 0.205699f, 0.049187f,
|
||
|
|
-0.082281f, 0.134874f, 0.185499f, 0.034968f,
|
||
|
|
-0.119561f, -0.112372f, -0.115091f, -0.054042f,
|
||
|
|
-0.183816f, -0.078100f, 0.190695f, 0.091617f,
|
||
|
|
0.004257f, -0.041135f, -0.061453f, -0.141592f,
|
||
|
|
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|
|
-0.252515f, -0.002638f, 0.042812f, 0.096776f,
|
||
|
|
-0.123280f, 0.064858f, -0.010455f, -0.219760f,
|
||
|
|
-0.239331f, -0.104363f, -0.058022f, -0.053584f,
|
||
|
|
0.025611f, 0.005129f, -0.100418f, -0.045712f,
|
||
|
|
-0.194418f, -0.126366f, -0.030530f, 0.051168f,
|
||
|
|
0.215959f, 0.172402f, -0.054700f, -0.185995f,
|
||
|
|
-0.278360f, -0.193693f, -0.040309f, 0.003735f,
|
||
|
|
-0.007770f, 0.123556f, 0.190179f, -0.077315f,
|
||
|
|
0.117403f, 0.212942f, 0.012160f, 0.000113f,
|
||
|
|
0.027331f, 0.040202f, 0.033293f, 0.219438f,
|
||
|
|
0.184174f, 0.259349f, 0.311206f, 0.082547f,
|
||
|
|
-0.047875f, -0.078417f, 0.010746f, 0.082620f,
|
||
|
|
0.311931f, 0.307605f, 0.003863f, 0.021405f,
|
||
|
|
-0.026388f, -0.019572f, 0.020582f, -0.059353f,
|
||
|
|
0.025199f, 0.261319f, 0.086316f, 0.143614f,
|
||
|
|
0.107780f, 0.003900f, -0.188397f, -0.038563f,
|
||
|
|
-0.106045f, -0.125154f, -0.010509f, 0.054021f,
|
||
|
|
0.242130f, 0.279152f, 0.215546f, 0.346995f,
|
||
|
|
0.440856f, 0.237452f, 0.234154f, 0.301646f,
|
||
|
|
0.168929f, -0.208358f, -0.126848f, 0.010260f,
|
||
|
|
0.121018f, -0.062975f, -0.052848f, 0.050341f,
|
||
|
|
-0.061103f, -0.266482f, 0.107186f, 0.140221f,
|
||
|
|
0.280065f, 0.287889f, 0.373198f, 0.151596f,
|
||
|
|
0.013593f, 0.115616f, 0.014616f, -0.281710f,
|
||
|
|
-0.237597f, -0.117305f, -0.000034f, -0.136739f,
|
||
|
|
-0.196275f, -0.095225f, -0.125310f, -0.250514f,
|
||
|
|
0.236804f, -0.071805f, -0.037421f, 0.048230f,
|
||
|
|
0.321596f, 0.063632f, 0.024039f, -0.029133f,
|
||
|
|
0.230983f, 0.160593f, -0.154355f, -0.013086f,
|
||
|
|
-0.079929f, 0.094692f, 0.160391f, 0.180239f,
|
||
|
|
0.053895f, 0.100759f, 0.288631f, 0.038191f,
|
||
|
|
0.181692f, 0.229682f, 0.440166f, 0.063401f,
|
||
|
|
0.006273f, 0.020865f, 0.338695f, 0.256244f,
|
||
|
|
-0.043927f, 0.115617f, 0.003296f, 0.173965f,
|
||
|
|
0.021318f, -0.040936f, -0.118932f, 0.182380f,
|
||
|
|
0.235922f, -0.053233f, -0.015053f, -0.101057f,
|
||
|
|
0.095341f, 0.051111f, 0.161831f, 0.032614f,
|
||
|
|
0.159496f, 0.072375f, 0.025089f, 0.023748f,
|
||
|
|
0.029151f, 0.161284f, -0.117717f, -0.036191f,
|
||
|
|
-0.176822f, -0.162006f, 0.226542f, -0.078329f,
|
||
|
|
0.043079f, -0.119172f, 0.054614f, -0.101365f,
|
||
|
|
-0.064541f, -0.115304f, 0.135170f, 0.298872f,
|
||
|
|
0.098060f, 0.089428f, -0.007497f, 0.110391f,
|
||
|
|
-0.028824f, 0.020835f, -0.036804f, 0.125411f,
|
||
|
|
0.192105f, -0.048931f, 0.003086f, -0.010681f,
|
||
|
|
0.074698f, -0.016263f, 0.096063f, 0.060267f,
|
||
|
|
-0.007277f, 0.139139f, -0.080635f, 0.036628f,
|
||
|
|
0.086058f, 0.131979f, 0.085707f, 0.025301f,
|
||
|
|
0.226094f, 0.194759f, 0.042193f, -0.157846f,
|
||
|
|
-0.068402f, -0.141450f, -0.112659f, -0.076305f,
|
||
|
|
-0.069085f, -0.114332f, -0.102005f, 0.132193f,
|
||
|
|
-0.067042f, 0.106643f, 0.198964f, 0.171616f,
|
||
|
|
0.167237f, -0.033730f, -0.026755f, 0.083621f,
|
||
|
|
0.149459f, -0.002799f, -0.000318f, 0.011753f,
|
||
|
|
0.065889f, -0.089375f, -0.049610f, 0.224579f,
|
||
|
|
0.216548f, -0.034908f, -0.017851f, -0.088144f,
|
||
|
|
0.007530f, 0.240268f, 0.073270f, 0.013263f,
|
||
|
|
0.175323f, 0.012082f, 0.093993f, 0.015282f,
|
||
|
|
0.105854f, 0.107990f, 0.077798f, -0.096166f,
|
||
|
|
-0.079607f, 0.177820f, 0.142392f, 0.033337f,
|
||
|
|
-0.078100f, -0.081616f, -0.046993f, 0.139459f,
|
||
|
|
0.020272f, -0.123161f, 0.175269f, 0.105217f,
|
||
|
|
0.057328f, 0.080909f, -0.012612f, -0.097081f,
|
||
|
|
0.082060f, -0.096716f, -0.063921f, 0.201884f,
|
||
|
|
0.128166f, -0.035051f, -0.032227f, -0.068139f,
|
||
|
|
-0.115915f, 0.095080f, -0.086007f, -0.067543f,
|
||
|
|
0.030776f, 0.032712f, 0.088937f, 0.054336f,
|
||
|
|
-0.039329f, -0.114022f, 0.171672f, -0.112321f,
|
||
|
|
-0.217646f, 0.065186f, 0.060223f, 0.192174f,
|
||
|
|
0.055580f, -0.131107f, -0.144338f, 0.056730f,
|
||
|
|
-0.034707f, -0.081616f, -0.135298f, -0.000614f,
|
||
|
|
0.087189f, 0.014614f, 0.067709f, 0.107689f,
|
||
|
|
0.225780f, 0.084361f, -0.008544f, 0.051649f,
|
||
|
|
-0.048369f, -0.037739f, -0.060710f, 0.002654f,
|
||
|
|
0.016935f, 0.085563f, -0.015961f, -0.019265f,
|
||
|
|
0.111788f, 0.062376f, 0.202019f, 0.047713f,
|
||
|
|
0.042261f, 0.069716f, 0.242913f, 0.021052f,
|
||
|
|
-0.072812f, -0.155920f, -0.026436f, 0.035621f,
|
||
|
|
-0.079300f, -0.028787f, -0.048329f, 0.084718f,
|
||
|
|
-0.060565f, -0.083750f, -0.164075f, -0.040742f,
|
||
|
|
-0.086219f, 0.015271f, -0.005204f, -0.016038f,
|
||
|
|
0.045816f, -0.050433f, -0.077652f, 0.117109f,
|
||
|
|
0.009611f, -0.009045f, -0.008634f, -0.055373f,
|
||
|
|
-0.085968f, 0.028527f, -0.054736f, -0.168089f,
|
||
|
|
0.175839f, 0.071205f, -0.023603f, 0.037907f,
|
||
|
|
-0.004561f, -0.022634f, 0.123831f, 0.094469f,
|
||
|
|
-0.072920f, -0.133642f, -0.014032f, -0.142754f,
|
||
|
|
-0.026999f, -0.199409f, 0.013268f, 0.226989f,
|
||
|
|
0.048650f, -0.170988f, -0.050141f, 0.007880f,
|
||
|
|
0.061880f, 0.019078f, -0.043578f, -0.038139f,
|
||
|
|
0.134814f, 0.054097f, -0.081670f, 0.176838f,
|
||
|
|
0.047920f, -0.038176f, 0.050406f, -0.107181f,
|
||
|
|
-0.036279f, 0.027060f, 0.081594f, -0.002820f,
|
||
|
|
0.090507f, -0.033338f, -0.059571f, 0.013404f,
|
||
|
|
-0.099860f, 0.073371f, 0.342805f, 0.098305f,
|
||
|
|
-0.150910f, -0.020822f, -0.056960f, 0.046262f,
|
||
|
|
-0.043413f, -0.149405f, -0.129105f, -0.010899f,
|
||
|
|
-0.014229f, -0.179949f, -0.113044f, -0.049468f,
|
||
|
|
-0.065513f, 0.090269f, -0.011919f, 0.087846f,
|
||
|
|
0.095796f, 0.146127f, 0.101599f, 0.078066f,
|
||
|
|
-0.084348f, -0.100002f, -0.020134f, -0.050169f,
|
||
|
|
0.062122f, 0.014640f, 0.019143f, 0.036543f,
|
||
|
|
0.180924f, -0.013976f, -0.066768f, -0.001090f,
|
||
|
|
-0.070419f, -0.004839f, -0.001504f, 0.034483f,
|
||
|
|
-0.044954f, -0.050336f, -0.088638f, -0.174782f,
|
||
|
|
-0.116082f, -0.205507f, 0.015587f, -0.042839f,
|
||
|
|
-0.096879f, -0.144097f, -0.050268f, -0.196796f,
|
||
|
|
0.109639f, 0.271411f, 0.173732f, 0.108070f,
|
||
|
|
0.156437f, 0.124255f, 0.097242f, 0.238693f,
|
||
|
|
0.083941f, 0.109105f, 0.223940f, 0.267188f,
|
||
|
|
0.027385f, 0.025819f, 0.125070f, 0.093738f,
|
||
|
|
0.040353f, 0.038645f, -0.012730f, 0.144063f,
|
||
|
|
0.052931f, -0.009138f, 0.084193f, 0.160272f,
|
||
|
|
-0.041366f, 0.011951f, -0.121446f, -0.106713f,
|
||
|
|
-0.047566f, 0.047984f, -0.255224f, -0.076116f,
|
||
|
|
0.098685f, -0.150845f, -0.171513f, -0.156590f,
|
||
|
|
0.058331f, 0.187493f, 0.413018f, 0.554265f,
|
||
|
|
0.372242f, 0.237943f, 0.124571f, 0.110829f,
|
||
|
|
0.010322f, -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<float>(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<DetectionROI>* locs,
|
||
|
|
std::vector<Rect>* _vec, Mutex* _mtx )
|
||
|
|
{
|
||
|
|
hog = _hog;
|
||
|
|
img = _img;
|
||
|
|
hitThreshold = _hitThreshold;
|
||
|
|
padding = _padding;
|
||
|
|
locations = locs;
|
||
|
|
vec = _vec;
|
||
|
|
mtx = _mtx;
|
||
|
|
}
|
||
|
|
|
||
|
|
void operator()(const Range& range) const 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<Point> 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<DetectionROI>* locations;
|
||
|
|
Size padding;
|
||
|
|
std::vector<Rect>* vec;
|
||
|
|
Mutex* mtx;
|
||
|
|
};
|
||
|
|
|
||
|
|
void HOGDescriptor::detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
|
||
|
|
CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
|
||
|
|
double hitThreshold, cv::Size winStride, cv::Size padding) const
|
||
|
|
{
|
||
|
|
CV_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<float> 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<cv::Rect>& foundLocations, std::vector<DetectionROI>& locations,
|
||
|
|
double hitThreshold, int groupThreshold) const
|
||
|
|
{
|
||
|
|
CV_INSTRUMENT_REGION();
|
||
|
|
|
||
|
|
std::vector<Rect> 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<float> 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<size_t>(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<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const
|
||
|
|
{
|
||
|
|
CV_INSTRUMENT_REGION();
|
||
|
|
|
||
|
|
if( groupThreshold <= 0 || rectList.empty() )
|
||
|
|
{
|
||
|
|
return;
|
||
|
|
}
|
||
|
|
|
||
|
|
CV_Assert(rectList.size() == weights.size());
|
||
|
|
|
||
|
|
std::vector<int> labels;
|
||
|
|
int nclasses = partition(rectList, labels, SimilarRects(eps));
|
||
|
|
|
||
|
|
std::vector<cv::Rect_<double> > rrects(nclasses);
|
||
|
|
std::vector<int> numInClass(nclasses, 0);
|
||
|
|
std::vector<double> foundWeights(nclasses, -std::numeric_limits<double>::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_<double> r = rrects[i];
|
||
|
|
double s = 1.0/numInClass[i];
|
||
|
|
rrects[i] = cv::Rect_<double>(cv::saturate_cast<double>(r.x*s),
|
||
|
|
cv::saturate_cast<double>(r.y*s),
|
||
|
|
cv::saturate_cast<double>(r.width*s),
|
||
|
|
cv::saturate_cast<double>(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<int>( r2.width * eps );
|
||
|
|
int dy = cv::saturate_cast<int>( 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);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|