1 /*M///////////////////////////////////////////////////////////////////////////////////////
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3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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5 // By downloading, copying, installing or using the software you agree to this license.
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6 // If you do not agree to this license, do not download, install,
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7 // copy or use the software.
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10 // License Agreement
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11 // For Open Source Computer Vision Library
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13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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14 // Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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15 // Third party copyrights are property of their respective owners.
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17 // Redistribution and use in source and binary forms, with or without modification,
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18 // are permitted provided that the following conditions are met:
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20 // * Redistribution's of source code must retain the above copyright notice,
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21 // this list of conditions and the following disclaimer.
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23 // * Redistribution's in binary form must reproduce the above copyright notice,
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24 // this list of conditions and the following disclaimer in the documentation
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25 // and/or other materials provided with the distribution.
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27 // * The name of the copyright holders may not be used to endorse or promote products
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28 // derived from this software without specific prior written permission.
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30 // This software is provided by the copyright holders and contributors "as is" and
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31 // any express or implied warranties, including, but not limited to, the implied
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32 // warranties of merchantability and fitness for a particular purpose are disclaimed.
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33 // In no event shall the Intel Corporation or contributors be liable for any direct,
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34 // indirect, incidental, special, exemplary, or consequential damages
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35 // (including, but not limited to, procurement of substitute goods or services;
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36 // loss of use, data, or profits; or business interruption) however caused
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37 // and on any theory of liability, whether in contract, strict liability,
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38 // or tort (including negligence or otherwise) arising in any way out of
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39 // the use of this software, even if advised of the possibility of such damage.
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45 /****************************************************************************************\
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46 The code below is implementation of HOG (Histogram-of-Oriented Gradients)
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47 descriptor and object detection, introduced by Navneet Dalal and Bill Triggs.
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49 The computed feature vectors are compatible with the
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50 INRIA Object Detection and Localization Toolkit
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51 (http://pascal.inrialpes.fr/soft/olt/)
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52 \****************************************************************************************/
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57 size_t HOGDescriptor::getDescriptorSize() const
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59 CV_Assert(blockSize.width % cellSize.width == 0 &&
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60 blockSize.height % cellSize.height == 0);
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61 CV_Assert((winSize.width - blockSize.width) % blockStride.width == 0 &&
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62 (winSize.height - blockSize.height) % blockStride.height == 0 );
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63 return (size_t)nbins*
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64 (blockSize.width/cellSize.width)*
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65 (blockSize.height/cellSize.height)*
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66 ((winSize.width - blockSize.width)/blockStride.width + 1)*
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67 ((winSize.height - blockSize.height)/blockStride.height + 1);
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70 double HOGDescriptor::getWinSigma() const
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72 return winSigma >= 0 ? winSigma : (blockSize.width + blockSize.height)/8.;
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75 bool HOGDescriptor::checkDetectorSize() const
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77 size_t detectorSize = svmDetector.size(), descriptorSize = getDescriptorSize();
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78 return detectorSize == 0 ||
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79 detectorSize == descriptorSize ||
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80 detectorSize == descriptorSize + 1;
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83 void HOGDescriptor::setSVMDetector(const Vector<float>& _svmDetector)
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85 svmDetector = _svmDetector;
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86 CV_Assert( checkDetectorSize() );
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89 bool HOGDescriptor::load(const String& filename, const String& objname)
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91 FileStorage fs(filename, FileStorage::READ);
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92 FileNode obj = !objname.empty() ? fs[objname] : fs.getFirstTopLevelNode();
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95 FileNodeIterator it = obj["winSize"].begin();
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96 it >> winSize.width >> winSize.height;
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97 it = obj["blockSize"].begin();
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98 it >> blockSize.width >> blockSize.height;
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99 it = obj["blockStride"].begin();
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100 it >> blockStride.width >> blockStride.height;
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101 it = obj["cellSize"].begin();
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102 it >> cellSize.width >> cellSize.height;
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103 obj["nbins"] >> nbins;
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104 obj["derivAperture"] >> derivAperture;
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105 obj["winSigma"] >> winSigma;
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106 obj["histogramNormType"] >> histogramNormType;
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107 obj["L2HysThreshold"] >> L2HysThreshold;
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108 obj["gammaCorrection"] >> gammaCorrection;
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110 FileNode vecNode = obj["SVMDetector"];
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111 if( vecNode.isSeq() )
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113 vecNode >> svmDetector;
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114 CV_Assert(checkDetectorSize());
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119 void HOGDescriptor::save(const String& filename, const String& objName) const
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121 FileStorage fs(filename, FileStorage::WRITE);
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122 fs << (!objName.empty() ? objName : FileStorage::getDefaultObjectName(filename)) << "{";
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124 fs << "winSize" << winSize
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125 << "blockSize" << blockSize
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126 << "blockStride" << blockStride
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127 << "cellSize" << cellSize
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128 << "nbins" << nbins
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129 << "derivAperture" << derivAperture
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130 << "winSigma" << getWinSigma()
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131 << "histogramNormType" << histogramNormType
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132 << "L2HysThreshold" << L2HysThreshold
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133 << "gammaCorrection" << gammaCorrection;
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134 if( !svmDetector.empty() )
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135 fs << "SVMDetector" << "[:" << svmDetector << "]";
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139 void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
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140 Size paddingTL, Size paddingBR) const
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142 CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
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144 Size gradsize(img.cols + paddingTL.width + paddingBR.width,
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145 img.rows + paddingTL.height + paddingBR.height);
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146 grad.create(gradsize, CV_32FC2); // <magnitude*(1-alpha), magnitude*alpha>
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147 qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
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150 img.locateROI(wholeSize, roiofs);
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153 int cn = img.channels();
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155 Mat_<float> _lut(1, 256);
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156 const float* lut = &_lut(0,0);
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158 if( gammaCorrection )
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159 for( i = 0; i < 256; i++ )
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160 _lut(0,i) = std::sqrt((float)i);
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162 for( i = 0; i < 256; i++ )
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163 _lut(0,i) = (float)i;
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165 AutoBuffer<int> mapbuf(gradsize.width + gradsize.height + 4);
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166 int* xmap = (int*)mapbuf + 1;
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167 int* ymap = xmap + gradsize.width + 2;
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169 const int borderType = (int)BORDER_REFLECT_101;
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171 for( x = -1; x < gradsize.width + 1; x++ )
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172 xmap[x] = borderInterpolate(x - paddingTL.width + roiofs.x,
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173 wholeSize.width, borderType);
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174 for( y = -1; y < gradsize.height + 1; y++ )
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175 ymap[y] = borderInterpolate(y - paddingTL.height + roiofs.y,
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176 wholeSize.height, borderType);
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178 // x- & y- derivatives for the whole row
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179 int width = gradsize.width;
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180 AutoBuffer<float> _dbuf(width*4);
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181 float* dbuf = _dbuf;
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182 Mat Dx(1, width, CV_32F, dbuf);
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183 Mat Dy(1, width, CV_32F, dbuf + width);
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184 Mat Mag(1, width, CV_32F, dbuf + width*2);
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185 Mat Angle(1, width, CV_32F, dbuf + width*3);
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187 int _nbins = nbins;
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188 float angleScale = (float)(_nbins/CV_PI);
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190 for( y = 0; y < gradsize.height; y++ )
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192 const uchar* imgPtr = img.data + img.step*ymap[y];
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193 const uchar* prevPtr = img.data + img.step*ymap[y-1];
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194 const uchar* nextPtr = img.data + img.step*ymap[y+1];
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195 float* gradPtr = (float*)grad.ptr(y);
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196 uchar* qanglePtr = (uchar*)qangle.ptr(y);
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200 for( x = 0; x < width; x++ )
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203 dbuf[x] = (float)(lut[imgPtr[xmap[x+1]]] - lut[imgPtr[xmap[x-1]]]);
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204 dbuf[width + x] = (float)(lut[nextPtr[x1]] - lut[prevPtr[x1]]);
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209 for( x = 0; x < width; x++ )
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211 int x1 = xmap[x]*3;
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212 const uchar* p2 = imgPtr + xmap[x+1]*3;
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213 const uchar* p0 = imgPtr + xmap[x-1]*3;
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214 float dx0, dy0, dx, dy, mag0, mag;
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216 dx0 = lut[p2[2]] - lut[p0[2]];
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217 dy0 = lut[nextPtr[x1+2]] - lut[prevPtr[x1+2]];
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218 mag0 = dx0*dx0 + dy0*dy0;
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220 dx = lut[p2[1]] - lut[p0[1]];
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221 dy = lut[nextPtr[x1+1]] - lut[prevPtr[x1+1]];
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222 mag = dx*dx + dy*dy;
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231 dx = lut[p2[0]] - lut[p0[0]];
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232 dy = lut[nextPtr[x1]] - lut[prevPtr[x1]];
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233 mag = dx*dx + dy*dy;
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243 dbuf[x+width] = dy0;
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247 cartToPolar( Dx, Dy, Mag, Angle, false );
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249 for( x = 0; x < width; x++ )
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251 float mag = dbuf[x+width*2], angle = dbuf[x+width*3]*angleScale - 0.5f;
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252 int hidx = cvFloor(angle);
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256 else if( hidx >= _nbins )
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258 assert( (unsigned)hidx < (unsigned)_nbins );
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260 qanglePtr[x*2] = (uchar)hidx;
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262 hidx &= hidx < _nbins ? -1 : 0;
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263 qanglePtr[x*2+1] = (uchar)hidx;
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264 gradPtr[x*2] = mag*(1.f - angle);
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265 gradPtr[x*2+1] = mag*angle;
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270 void HOGDescriptor::normalizeBlockHistogram(Vector<float>& _hist) const
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272 float* hist = &_hist[0];
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273 size_t i, sz = _hist.size();
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276 for( i = 0; i < sz; i++ )
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277 sum += hist[i]*hist[i];
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278 float scale = 1.f/(std::sqrt(sum)+sz*0.1f), thresh = (float)L2HysThreshold;
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279 for( i = 0, sum = 0; i < sz; i++ )
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281 hist[i] = std::min(hist[i]*scale, thresh);
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282 sum += hist[i]*hist[i];
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284 scale = 1.f/(std::sqrt(sum)+1e-3f);
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285 for( i = 0; i < sz; i++ )
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294 BlockData() : histOfs(0), imgOffset() {}
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301 size_t gradOfs, qangleOfs;
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303 float histWeights[4];
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308 HOGCache(const HOGDescriptor* descriptor,
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309 const Mat& img, Size paddingTL, Size paddingBR,
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310 bool useCache, Size cacheStride);
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311 virtual ~HOGCache() {};
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312 virtual void init(const HOGDescriptor* descriptor,
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313 const Mat& img, Size paddingTL, Size paddingBR,
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314 bool useCache, Size cacheStride);
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316 Size windowsInImage(Size imageSize, Size winStride) const;
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317 Rect getWindow(Size imageSize, Size winStride, int idx) const;
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319 const float* getBlock(Point pt, float* buf);
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321 Vector<PixData> pixData;
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322 Vector<BlockData> blockData;
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325 Vector<int> ymaxCached;
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326 Size winSize, cacheStride;
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327 Size nblocks, ncells;
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328 int blockHistogramSize;
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329 int count1, count2, count4;
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331 Mat_<float> blockCache;
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332 Mat_<uchar> blockCacheFlags;
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335 const HOGDescriptor* descriptor;
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339 HOGCache::HOGCache()
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342 blockHistogramSize = count1 = count2 = count4 = 0;
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346 HOGCache::HOGCache(const HOGDescriptor* _descriptor,
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347 const Mat& _img, Size _paddingTL, Size _paddingBR,
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348 bool _useCache, Size _cacheStride)
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350 init(_descriptor, _img, _paddingTL, _paddingBR, _useCache, _cacheStride);
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353 void HOGCache::init(const HOGDescriptor* _descriptor,
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354 const Mat& _img, Size _paddingTL, Size _paddingBR,
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355 bool _useCache, Size _cacheStride)
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357 descriptor = _descriptor;
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358 cacheStride = _cacheStride;
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359 useCache = _useCache;
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361 descriptor->computeGradient(_img, grad, qangle, _paddingTL, _paddingBR);
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362 imgoffset = _paddingTL;
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364 winSize = descriptor->winSize;
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365 Size blockSize = descriptor->blockSize;
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366 Size blockStride = descriptor->blockStride;
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367 Size cellSize = descriptor->cellSize;
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368 Size winSize = descriptor->winSize;
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369 int i, j, nbins = descriptor->nbins;
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370 int rawBlockSize = blockSize.width*blockSize.height;
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372 nblocks = Size((winSize.width - blockSize.width)/blockStride.width + 1,
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373 (winSize.height - blockSize.height)/blockStride.height + 1);
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374 ncells = Size(blockSize.width/cellSize.width, blockSize.height/cellSize.height);
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375 blockHistogramSize = ncells.width*ncells.height*nbins;
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379 Size cacheSize((grad.cols - blockSize.width)/cacheStride.width+1,
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380 (winSize.height/cacheStride.height)+1);
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381 blockCache.create(cacheSize.height, cacheSize.width*blockHistogramSize);
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382 blockCacheFlags.create(cacheSize);
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383 size_t i, cacheRows = blockCache.rows;
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384 ymaxCached.resize(cacheRows);
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385 for( i = 0; i < cacheRows; i++ )
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386 ymaxCached[i] = -1;
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389 Mat_<float> weights(blockSize);
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390 float sigma = (float)descriptor->getWinSigma();
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391 float scale = 1.f/(sigma*sigma*2);
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393 for(i = 0; i < blockSize.height; i++)
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394 for(j = 0; j < blockSize.width; j++)
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396 float di = i - blockSize.height*0.5f;
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397 float dj = j - blockSize.width*0.5f;
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398 weights(i,j) = std::exp(-(di*di + dj*dj)*scale);
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401 blockData.resize(nblocks.width*nblocks.height);
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402 pixData.resize(rawBlockSize*3);
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404 // Initialize 2 lookup tables, pixData & blockData.
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407 // The detection algorithm runs in 4 nested loops (at each pyramid layer):
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408 // loop over the windows within the input image
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409 // loop over the blocks within each window
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410 // loop over the cells within each block
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411 // loop over the pixels in each cell
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413 // As each of the loops runs over a 2-dimensional array,
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414 // we could get 8(!) nested loops in total, which is very-very slow.
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416 // To speed the things up, we do the following:
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417 // 1. loop over windows is unrolled in the HOGDescriptor::{compute|detect} methods;
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418 // inside we compute the current search window using getWindow() method.
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419 // Yes, it involves some overhead (function call + couple of divisions),
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420 // but it's tiny in fact.
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421 // 2. loop over the blocks is also unrolled. Inside we use pre-computed blockData[j]
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422 // to set up gradient and histogram pointers.
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423 // 3. loops over cells and pixels in each cell are merged
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424 // (since there is no overlap between cells, each pixel in the block is processed once)
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425 // and also unrolled. Inside we use PixData[k] to access the gradient values and
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426 // update the histogram
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428 count1 = count2 = count4 = 0;
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429 for( j = 0; j < blockSize.width; j++ )
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430 for( i = 0; i < blockSize.height; i++ )
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433 float cellX = (j+0.5f)/cellSize.width - 0.5f;
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434 float cellY = (i+0.5f)/cellSize.height - 0.5f;
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435 int icellX0 = cvFloor(cellX);
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436 int icellY0 = cvFloor(cellY);
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437 int icellX1 = icellX0 + 1, icellY1 = icellY0 + 1;
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441 if( (unsigned)icellX0 < (unsigned)ncells.width &&
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442 (unsigned)icellX1 < (unsigned)ncells.width )
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444 if( (unsigned)icellY0 < (unsigned)ncells.height &&
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445 (unsigned)icellY1 < (unsigned)ncells.height )
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447 data = &pixData[rawBlockSize*2 + (count4++)];
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448 data->histOfs[0] = (icellX0*ncells.height + icellY0)*nbins;
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449 data->histWeights[0] = (1.f - cellX)*(1.f - cellY);
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450 data->histOfs[1] = (icellX1*ncells.height + icellY0)*nbins;
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451 data->histWeights[1] = cellX*(1.f - cellY);
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452 data->histOfs[2] = (icellX0*ncells.height + icellY1)*nbins;
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453 data->histWeights[2] = (1.f - cellX)*cellY;
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454 data->histOfs[3] = (icellX1*ncells.height + icellY1)*nbins;
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455 data->histWeights[3] = cellX*cellY;
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459 data = &pixData[rawBlockSize + (count2++)];
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460 if( (unsigned)icellY0 < (unsigned)ncells.height )
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463 cellY = 1.f - cellY;
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465 data->histOfs[0] = (icellX0*ncells.height + icellY1)*nbins;
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466 data->histWeights[0] = (1.f - cellX)*cellY;
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467 data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
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468 data->histWeights[1] = cellX*cellY;
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469 data->histOfs[2] = data->histOfs[3] = 0;
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470 data->histWeights[2] = data->histWeights[3] = 0;
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475 if( (unsigned)icellX0 < (unsigned)ncells.width )
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478 cellX = 1.f - cellX;
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481 if( (unsigned)icellY0 < (unsigned)ncells.height &&
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482 (unsigned)icellY1 < (unsigned)ncells.height )
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484 data = &pixData[rawBlockSize + (count2++)];
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485 data->histOfs[0] = (icellX1*ncells.height + icellY0)*nbins;
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486 data->histWeights[0] = cellX*(1.f - cellY);
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487 data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
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488 data->histWeights[1] = cellX*cellY;
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489 data->histOfs[2] = data->histOfs[3] = 0;
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490 data->histWeights[2] = data->histWeights[3] = 0;
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494 data = &pixData[count1++];
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495 if( (unsigned)icellY0 < (unsigned)ncells.height )
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498 cellY = 1.f - cellY;
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500 data->histOfs[0] = (icellX1*ncells.height + icellY1)*nbins;
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501 data->histWeights[0] = cellX*cellY;
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502 data->histOfs[1] = data->histOfs[2] = data->histOfs[3] = 0;
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503 data->histWeights[1] = data->histWeights[2] = data->histWeights[3] = 0;
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506 data->gradOfs = (grad.cols*i + j)*2;
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507 data->qangleOfs = (qangle.cols*i + j)*2;
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508 data->gradWeight = weights(i,j);
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511 assert( count1 + count2 + count4 == rawBlockSize );
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512 // defragment pixData
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513 for( j = 0; j < count2; j++ )
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514 pixData[j + count1] = pixData[j + rawBlockSize];
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515 for( j = 0; j < count4; j++ )
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516 pixData[j + count1 + count2] = pixData[j + rawBlockSize*2];
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520 // initialize blockData
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521 for( j = 0; j < nblocks.width; j++ )
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522 for( i = 0; i < nblocks.height; i++ )
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524 BlockData& data = blockData[j*nblocks.height + i];
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525 data.histOfs = (j*nblocks.height + i)*blockHistogramSize;
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526 data.imgOffset = Point(j*blockStride.width,i*blockStride.height);
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531 const float* HOGCache::getBlock(Point pt, float* buf)
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533 float* blockHist = buf;
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534 assert(descriptor != 0);
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536 Size blockSize = descriptor->blockSize;
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539 CV_Assert( (unsigned)pt.x <= (unsigned)(grad.cols - blockSize.width) &&
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540 (unsigned)pt.y <= (unsigned)(grad.rows - blockSize.height) );
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544 CV_Assert( pt.x % cacheStride.width == 0 &&
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545 pt.y % cacheStride.height == 0 );
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546 Point cacheIdx(pt.x/cacheStride.width,
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547 (pt.y/cacheStride.height) % blockCache.rows);
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548 if( pt.y != ymaxCached[cacheIdx.y] )
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550 Mat_<uchar> cacheRow = blockCacheFlags.row(cacheIdx.y);
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551 cacheRow = (uchar)0;
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552 ymaxCached[cacheIdx.y] = pt.y;
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555 blockHist = &blockCache[cacheIdx.y][cacheIdx.x*blockHistogramSize];
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556 uchar& computedFlag = blockCacheFlags(cacheIdx.y, cacheIdx.x);
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557 if( computedFlag != 0 )
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559 computedFlag = (uchar)1; // set it at once, before actual computing
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562 int k, C1 = count1, C2 = count2, C4 = count4;
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563 const float* gradPtr = (const float*)(grad.data + grad.step*pt.y) + pt.x*2;
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564 const uchar* qanglePtr = qangle.data + qangle.step*pt.y + pt.x*2;
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566 CV_Assert( blockHist != 0 );
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568 for( k = 0; k < blockHistogramSize; k++ )
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569 blockHist[k] = 0.f;
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571 const PixData* _pixData = &pixData[0];
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573 for( k = 0; k < C1; k++ )
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575 const PixData& pk = _pixData[k];
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576 const float* a = gradPtr + pk.gradOfs;
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577 float w = pk.gradWeight*pk.histWeights[0];
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578 const uchar* h = qanglePtr + pk.qangleOfs;
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579 int h0 = h[0], h1 = h[1];
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580 float* hist = blockHist + pk.histOfs[0];
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581 float t0 = hist[h0] + a[0]*w;
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582 float t1 = hist[h1] + a[1]*w;
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583 hist[h0] = t0; hist[h1] = t1;
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586 for( ; k < C2; k++ )
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588 const PixData& pk = _pixData[k];
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589 const float* a = gradPtr + pk.gradOfs;
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590 float w, t0, t1, a0 = a[0], a1 = a[1];
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591 const uchar* h = qanglePtr + pk.qangleOfs;
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592 int h0 = h[0], h1 = h[1];
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594 float* hist = blockHist + pk.histOfs[0];
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595 w = pk.gradWeight*pk.histWeights[0];
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596 t0 = hist[h0] + a0*w;
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597 t1 = hist[h1] + a1*w;
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598 hist[h0] = t0; hist[h1] = t1;
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600 hist = blockHist + pk.histOfs[1];
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601 w = pk.gradWeight*pk.histWeights[1];
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602 t0 = hist[h0] + a0*w;
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603 t1 = hist[h1] + a1*w;
\r
604 hist[h0] = t0; hist[h1] = t1;
\r
607 for( ; k < C4; k++ )
\r
609 const PixData& pk = _pixData[k];
\r
610 const float* a = gradPtr + pk.gradOfs;
\r
611 float w, t0, t1, a0 = a[0], a1 = a[1];
\r
612 const uchar* h = qanglePtr + pk.qangleOfs;
\r
613 int h0 = h[0], h1 = h[1];
\r
615 float* hist = blockHist + pk.histOfs[0];
\r
616 w = pk.gradWeight*pk.histWeights[0];
\r
617 t0 = hist[h0] + a0*w;
\r
618 t1 = hist[h1] + a1*w;
\r
619 hist[h0] = t0; hist[h1] = t1;
\r
621 hist = blockHist + pk.histOfs[1];
\r
622 w = pk.gradWeight*pk.histWeights[1];
\r
623 t0 = hist[h0] + a0*w;
\r
624 t1 = hist[h1] + a1*w;
\r
625 hist[h0] = t0; hist[h1] = t1;
\r
627 hist = blockHist + pk.histOfs[2];
\r
628 w = pk.gradWeight*pk.histWeights[2];
\r
629 t0 = hist[h0] + a0*w;
\r
630 t1 = hist[h1] + a1*w;
\r
631 hist[h0] = t0; hist[h1] = t1;
\r
633 hist = blockHist + pk.histOfs[3];
\r
634 w = pk.gradWeight*pk.histWeights[3];
\r
635 t0 = hist[h0] + a0*w;
\r
636 t1 = hist[h1] + a1*w;
\r
637 hist[h0] = t0; hist[h1] = t1;
\r
640 // normalize the block histogram
\r
641 Vector<float> d(blockHist, (size_t)blockHistogramSize);
\r
642 descriptor->normalizeBlockHistogram(d);
\r
648 Size HOGCache::windowsInImage(Size imageSize, Size winStride) const
\r
650 return Size((imageSize.width - winSize.width)/winStride.width + 1,
\r
651 (imageSize.height - winSize.height)/winStride.height + 1);
\r
654 Rect HOGCache::getWindow(Size imageSize, Size winStride, int idx) const
\r
656 int nwindowsX = (imageSize.width - winSize.width)/winStride.width + 1;
\r
657 int y = idx / nwindowsX;
\r
658 int x = idx - nwindowsX*y;
\r
659 return Rect( x*winStride.width, y*winStride.height, winSize.width, winSize.height );
\r
663 void HOGDescriptor::compute(const Mat& img, Vector<float>& descriptors,
\r
664 Size winStride, Size padding,
\r
665 const Vector<Point>& locations) const
\r
667 if( winStride == Size() )
\r
668 winStride = cellSize;
\r
669 Size cacheStride(gcd(winStride.width, blockStride.width),
\r
670 gcd(winStride.height, blockStride.height));
\r
671 size_t nwindows = locations.size();
\r
672 padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
\r
673 padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
\r
674 Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
\r
676 HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);
\r
679 nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
\r
681 const HOGCache::BlockData* blockData = &cache.blockData[0];
\r
683 int nblocks = cache.nblocks.area();
\r
684 int blockHistogramSize = cache.blockHistogramSize;
\r
685 size_t dsize = getDescriptorSize();
\r
686 descriptors.resize(dsize*nwindows);
\r
688 for( size_t i = 0; i < nwindows; i++ )
\r
690 float* descriptor = &descriptors[i*dsize];
\r
693 if( !locations.empty() )
\r
695 pt0 = locations[i];
\r
696 if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
\r
697 pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
\r
702 pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
\r
703 CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
\r
706 for( int j = 0; j < nblocks; j++ )
\r
708 const HOGCache::BlockData& bj = blockData[j];
\r
709 Point pt = pt0 + bj.imgOffset;
\r
711 float* dst = descriptor + bj.histOfs;
\r
712 const float* src = cache.getBlock(pt, dst);
\r
714 for( int k = 0; k < blockHistogramSize; k++ )
\r
721 void HOGDescriptor::detect(const Mat& img,
\r
722 Vector<Point>& hits, double hitThreshold,
\r
723 Size winStride, Size padding, const Vector<Point>& locations) const
\r
726 if( svmDetector.empty() )
\r
729 if( winStride == Size() )
\r
730 winStride = cellSize;
\r
731 Size cacheStride(gcd(winStride.width, blockStride.width),
\r
732 gcd(winStride.height, blockStride.height));
\r
733 size_t nwindows = locations.size();
\r
734 padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
\r
735 padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
\r
736 Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
\r
738 HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);
\r
741 nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
\r
743 const HOGCache::BlockData* blockData = &cache.blockData[0];
\r
745 int nblocks = cache.nblocks.area();
\r
746 int blockHistogramSize = cache.blockHistogramSize;
\r
747 size_t dsize = getDescriptorSize();
\r
749 double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
\r
750 Vector<float> blockHist(blockHistogramSize);
\r
752 for( size_t i = 0; i < nwindows; i++ )
\r
755 if( !locations.empty() )
\r
757 pt0 = locations[i];
\r
758 if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
\r
759 pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
\r
764 pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
\r
765 CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
\r
768 const float* svmVec = &svmDetector[0];
\r
770 for( j = 0; j < nblocks; j++, svmVec += blockHistogramSize )
\r
772 const HOGCache::BlockData& bj = blockData[j];
\r
773 Point pt = pt0 + bj.imgOffset;
\r
775 const float* vec = cache.getBlock(pt, &blockHist[0]);
\r
776 for( k = 0; k <= blockHistogramSize - 4; k += 4 )
\r
777 s += vec[k]*svmVec[k] + vec[k+1]*svmVec[k+1] +
\r
778 vec[k+2]*svmVec[k+2] + vec[k+3]*svmVec[k+3];
\r
779 for( ; k < blockHistogramSize; k++ )
\r
780 s += vec[k]*svmVec[k];
\r
782 if( s >= hitThreshold )
\r
783 hits.push_back(pt0);
\r
788 struct SimilarRects
\r
790 SimilarRects(double _eps) : eps(_eps) {}
\r
791 inline bool operator()(const Rect& r1, const Rect& r2) const
\r
793 double delta = eps*(std::min(r1.width, r2.width) + std::min(r1.height, r2.height))*0.5;
\r
794 return std::abs(r1.x - r2.x) <= delta &&
\r
795 std::abs(r1.y - r2.y) <= delta &&
\r
796 std::abs(r1.x + r1.width - r2.x - r2.width) <= delta &&
\r
797 std::abs(r1.y + r1.height - r2.y - r2.height) <= delta;
\r
802 struct HOGThreadData
\r
804 Vector<Rect> rectangles;
\r
805 Vector<Point> locations;
\r
809 void HOGDescriptor::detectMultiScale(
\r
810 const Mat& img, Vector<Rect>& foundLocations,
\r
811 double hitThreshold, Size winStride, Size padding,
\r
812 double scale0, int groupThreshold) const
\r
815 foundLocations.clear();
\r
817 const int maxLevels = 64;
\r
819 int t, nthreads = getNumThreads();
\r
820 Vector<HOGThreadData> threadData(nthreads);
\r
822 for( t = 0; t < nthreads; t++ )
\r
823 threadData[t].smallerImgBuf.create(img.size(), img.type());
\r
825 Vector<double> levelScale(maxLevels);
\r
826 for( levels = 0; levels < maxLevels; levels++ )
\r
828 levelScale[levels] = scale;
\r
829 if( cvRound(img.cols/scale) < winSize.width ||
\r
830 cvRound(img.rows/scale) < winSize.height ||
\r
835 levels = std::max(levels, 1);
\r
836 levelScale.resize(levels);
\r
840 #pragma omp parallel for num_threads(nthreads) schedule(dynamic)
\r
842 for( i = 0; i < levels; i++ )
\r
844 HOGThreadData& tdata = threadData[getThreadNum()];
\r
845 double scale = levelScale[i];
\r
846 Size sz(cvRound(img.cols/scale), cvRound(img.rows/scale));
\r
847 Mat smallerImg(sz, img.type(), tdata.smallerImgBuf.data);
\r
848 if( sz == img.size() )
\r
849 smallerImg = Mat(sz, img.type(), img.data, img.step);
\r
851 resize(img, smallerImg, sz);
\r
852 detect(smallerImg, tdata.locations, hitThreshold, winStride, padding);
\r
853 Size scaledWinSize = Size(cvRound(winSize.width*scale), cvRound(winSize.height*scale));
\r
854 for( size_t j = 0; j < tdata.locations.size(); j++ )
\r
855 tdata.rectangles.push_back(Rect(
\r
856 cvRound(tdata.locations[j].x*scale),
\r
857 cvRound(tdata.locations[j].y*scale),
\r
858 scaledWinSize.width, scaledWinSize.height));
\r
862 for( t = 0; t < nthreads; t++ )
\r
864 HOGThreadData& tdata = threadData[t];
\r
865 std::copy(tdata.rectangles.begin(), tdata.rectangles.end(),
\r
866 std::back_inserter(foundLocations));
\r
869 if( groupThreshold <= 0 )
\r
872 Vector<int> labels;
\r
873 int nclasses = partition(foundLocations, labels, SimilarRects(0.2));
\r
874 Vector<Rect> rrects(nclasses);
\r
875 Vector<int> rweights(nclasses, 0);
\r
876 int nlabels = (int)labels.size();
\r
877 for( i = 0; i < nlabels; i++ )
\r
879 int cls = labels[i];
\r
880 rrects[cls].x += foundLocations[i].x;
\r
881 rrects[cls].y += foundLocations[i].y;
\r
882 rrects[cls].width += foundLocations[i].width;
\r
883 rrects[cls].height += foundLocations[i].height;
\r
886 foundLocations.clear();
\r
887 for( i = 0; i < nclasses; i++ )
\r
889 Rect r = rrects[i];
\r
890 if( rweights[i] <= groupThreshold )
\r
892 float s = 1.f/rweights[i];
\r
893 foundLocations.push_back(
\r
894 Rect(cvRound(r.x*s), cvRound(r.y*s), cvRound(r.width*s), cvRound(r.height*s)));
\r
898 Vector<float> HOGDescriptor::getDefaultPeopleDetector()
\r
900 static const float detector[] = {
\r
901 0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
\r
902 0.11547081f, -0.04268804f, 0.04635834f, -0.05468199f, 0.08232084f,
\r
903 0.10424068f, -0.02294518f, 0.01108519f, 0.01378693f, 0.11193510f,
\r
904 0.01268418f, 0.08528346f, -0.06309239f, 0.13054633f, 0.08100729f,
\r
905 -0.05209739f, -0.04315529f, 0.09341384f, 0.11035026f, -0.07596218f,
\r
906 -0.05517511f, -0.04465296f, 0.02947334f, 0.04555536f,
\r
907 -3.55954492e-003f, 0.07818956f, 0.07730991f, 0.07890715f, 0.06222893f,
\r
908 0.09001380f, -0.03574381f, 0.03414327f, 0.05677258f, -0.04773581f,
\r
909 0.03746637f, -0.03521175f, 0.06955440f, -0.03849038f, 0.01052293f,
\r
910 0.01736112f, 0.10867710f, 0.08748853f, 3.29739624e-003f, 0.10907028f,
\r
911 0.07913758f, 0.10393070f, 0.02091867f, 0.11594022f, 0.13182420f,
\r
912 0.09879354f, 0.05362710f, -0.06745391f, -7.01260753e-003f,
\r
913 5.24702156e-003f, 0.03236255f, 0.01407916f, 0.02207983f, 0.02537322f,
\r
914 0.04547948f, 0.07200756f, 0.03129894f, -0.06274468f, 0.02107014f,
\r
915 0.06035208f, 0.08636236f, 4.53164103e-003f, 0.02193363f, 0.02309801f,
\r
916 0.05568166f, -0.02645093f, 0.04448695f, 0.02837519f, 0.08975694f,
\r
917 0.04461516f, 0.08975355f, 0.07514391f, 0.02306982f, 0.10410084f,
\r
918 0.06368385f, 0.05943464f, 4.58420580e-003f, 0.05220337f, 0.06675851f,
\r
919 0.08358569f, 0.06712101f, 0.06559004f, -0.03930482f, -9.15936660e-003f,
\r
920 -0.05897915f, 0.02816453f, 0.05032348f, 0.06780671f, 0.03377650f,
\r
921 -6.09417039e-004f, -0.01795146f, -0.03083684f, -0.01302475f,
\r
922 -0.02972313f, 7.88706727e-003f, -0.03525961f, -2.50397739e-003f,
\r
923 0.05245084f, 0.11791293f, -0.02167498f, 0.05299332f, 0.06640524f,
\r
924 0.05190265f, -8.27316567e-003f, 0.03033127f, 0.05842173f,
\r
925 -4.01050318e-003f, -6.25105947e-003f, 0.05862958f, -0.02465461f,
\r
926 0.05546781f, -0.08228195f, -0.07234028f, 0.04640540f, -0.01308254f,
\r
927 -0.02506191f, 0.03100746f, -0.04665651f, -0.04591486f, 0.02949927f,
\r
928 0.06035462f, 0.02244646f, -0.01698639f, 0.01040041f, 0.01131170f,
\r
929 0.05419579f, -0.02130277f, -0.04321722f, -0.03665198f, 0.01126490f,
\r
930 -0.02606488f, -0.02228328f, -0.02255680f, -0.03427236f,
\r
931 -7.75165204e-003f, -0.06195229f, 8.21638294e-003f, 0.09535975f,
\r
932 -0.03709979f, -0.06942501f, 0.14579427f, -0.05448192f, -0.02055904f,
\r
933 0.05747357f, 0.02781788f, -0.07077577f, -0.05178314f, -0.10429011f,
\r
934 -0.11235505f, 0.07529039f, -0.07559302f, -0.08786739f, 0.02983843f,
\r
935 0.02667585f, 0.01382199f, -0.01797496f, -0.03141199f, -0.02098101f,
\r
936 0.09029204f, 0.04955018f, 0.13718739f, 0.11379953f, 1.80019124e-003f,
\r
937 -0.04577610f, -1.11108483e-003f, -0.09470536f, -0.11596080f,
\r
938 0.04489342f, 0.01784211f, 3.06850672e-003f, 0.10781866f,
\r
939 3.36498418e-003f, -0.10842580f, -0.07436839f, -0.10535070f,
\r
940 -0.01866805f, 0.16057891f, -5.07316366e-003f, -0.04295658f,
\r
941 -5.90488780e-003f, 8.82003549e-003f, -0.01492646f, -0.05029279f,
\r
942 -0.12875880f, 8.78831954e-004f, -0.01297184f, -0.07592774f,
\r
943 -0.02668831f, -6.93787413e-004f, 0.02406698f, -0.01773298f,
\r
944 -0.03855745f, -0.05877856f, 0.03259695f, 0.12826584f, 0.06292590f,
\r
945 -4.10733931e-003f, 0.10996531f, 0.01332991f, 0.02088735f, 0.04037504f,
\r
946 -0.05210760f, 0.07760046f, 0.06399347f, -0.05751930f, -0.10053057f,
\r
947 0.07505023f, -0.02139782f, 0.01796176f, 2.34400877e-003f, -0.04208319f,
\r
948 0.07355055f, 0.05093350f, -0.02996780f, -0.02219072f, 0.03355330f,
\r
949 0.04418742f, -0.05580705f, -0.05037573f, -0.04548179f, 0.01379514f,
\r
950 0.02150671f, -0.02194211f, -0.13682702f, 0.05464972f, 0.01608082f,
\r
951 0.05309116f, 0.04701022f, 1.33690401e-003f, 0.07575664f, 0.09625306f,
\r
952 8.92647635e-003f, -0.02819123f, 0.10866830f, -0.03439325f,
\r
953 -0.07092371f, -0.06004780f, -0.02712298f, -7.07467366e-003f,
\r
954 -0.01637020f, 0.01336790f, -0.10313606f, 0.04906582f, -0.05732445f,
\r
955 -0.02731079f, 0.01042235f, -0.08340668f, 0.03686501f, 0.06108340f,
\r
956 0.01322748f, -0.07809529f, 0.03774724f, -0.03413248f, -0.06096525f,
\r
957 -0.04212124f, -0.07982176f, -1.25973229e-003f, -0.03045501f,
\r
958 -0.01236493f, -0.06312395f, 0.04789570f, -0.04602066f, 0.08576570f,
\r
959 0.02521080f, 0.02988098f, 0.10314583f, 0.07060035f, 0.04520544f,
\r
960 -0.04426654f, 0.13146530f, 0.08386490f, 0.02164590f, -2.12280243e-003f,
\r
961 -0.03686353f, -0.02074944f, -0.03829959f, -0.01530596f, 0.02689708f,
\r
962 0.11867401f, -0.06043470f, -0.02785023f, -0.04775074f, 0.04878745f,
\r
963 0.06350956f, 0.03494788f, 0.01467400f, 1.17890188e-003f, 0.04379614f,
\r
964 2.03681854e-003f, -0.03958609f, -0.01072688f, 6.43705716e-003f,
\r
965 0.02996500f, -0.03418507f, -0.01960307f, -0.01219154f,
\r
966 -4.37000440e-003f, -0.02549453f, 0.02646318f, -0.01632513f,
\r
967 6.46516960e-003f, -0.01929734f, 4.78711911e-003f, 0.04962371f,
\r
968 0.03809111f, 0.07265724f, 0.05758125f, -0.03741554f, 0.01648608f,
\r
969 -8.45285598e-003f, 0.03996826f, -0.08185477f, 0.02638875f,
\r
970 -0.04026615f, -0.02744674f, -0.04071517f, 1.05096330e-003f,
\r
971 -0.04741232f, -0.06733172f, 8.70434940e-003f, -0.02192543f,
\r
972 1.35350740e-003f, -0.03056974f, -0.02975521f, -0.02887780f,
\r
973 -0.01210713f, -0.04828526f, -0.09066251f, -0.09969629f, -0.03665164f,
\r
974 -8.88111943e-004f, -0.06826669f, -0.01866150f, -0.03627640f,
\r
975 -0.01408288f, 0.01874239f, -0.02075835f, 0.09145175f, -0.03547291f,
\r
976 0.05396780f, 0.04198981f, 0.01301925f, -0.03384354f, -0.12201976f,
\r
977 0.06830920f, -0.03715654f, 9.55848210e-003f, 5.05685573e-003f,
\r
978 0.05659294f, 3.90764466e-003f, 0.02808490f, -0.05518097f, -0.03711621f,
\r
979 -0.02835565f, -0.04420464f, -0.01031947f, 0.01883466f,
\r
980 -8.49525444e-003f, -0.09419250f, -0.01269387f, -0.02133371f,
\r
981 -0.10190815f, -0.07844430f, 2.43644323e-003f, -4.09610150e-003f,
\r
982 0.01202551f, -0.06452291f, -0.10593818f, -0.02464746f, -0.02199699f,
\r
983 -0.07401930f, 0.07285886f, 8.87513801e-004f, 9.97662079e-003f,
\r
984 8.46779719e-003f, 0.03730333f, -0.02905126f, 0.03573337f, -0.04393689f,
\r
985 -0.12014472f, 0.03176554f, -2.76015815e-003f, 0.10824566f, 0.05090732f,
\r
986 -3.30179278e-003f, -0.05123822f, 5.04784798e-003f, -0.05664124f,
\r
987 -5.99415926e-003f, -0.05341901f, -0.01221393f, 0.01291318f,
\r
988 9.91760660e-003f, -7.56987557e-003f, -0.06193124f, -2.24549137e-003f,
\r
989 0.01987562f, -0.02018840f, -0.06975540f, -0.06601523f, -0.03349112f,
\r
990 -0.08910118f, -0.03371435f, -0.07406893f, -0.02248047f, -0.06159951f,
\r
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1689 -0.05843807f, -0.03739681f, -0.09739155f, -0.03220233f, -0.05620182f,
\r
1690 -0.10381401f, 0.07400211f, 4.20676917e-003f, 0.03258535f,
\r
1691 2.14308966e-003f, 0.05121966f, -0.01274337f, 0.02384761f, 0.06335578f,
\r
1692 -0.07905591f, 0.08375625f, -0.07898903f, -0.06508528f, -0.02498444f,
\r
1693 0.06535810f, 0.03970535f, 0.04895468f, -0.01169566f, -0.03980601f,
\r
1694 0.05682293f, 0.05925463f, -0.01165808f, -0.07936699f, -0.04208954f,
\r
1695 0.01333987f, 0.09051196f, 0.10098671f, -0.03974256f, 0.01238771f,
\r
1696 -0.07501741f, -0.03655440f, -0.04301528f, 0.09216860f,
\r
1697 4.63579083e-004f, 0.02851115f, 0.02142735f, 1.28244064e-004f,
\r
1698 0.02879687f, -0.08554889f, -0.04838862f, 0.08135369f, -0.05756533f,
\r
1699 0.01413900f, 0.03451880f, -0.06619488f, -0.03053130f, 0.02961676f,
\r
1700 -0.07384635f, 0.01135692f, 0.05283910f, -0.07778034f, -0.02107482f,
\r
1701 -0.05511716f, -0.13473752f, 0.03030157f, 0.06722020f, -0.06218817f,
\r
1702 -0.05826827f, 0.06254654f, 0.02895772f, -0.01664000f, -0.03620280f,
\r
1703 -0.01612278f, -1.46097376e-003f, 0.14013411f, -8.96181818e-003f,
\r
1704 -0.03250246f, 3.38630192e-003f, 2.64779478e-003f, 0.03359732f,
\r
1705 -0.02411991f, -0.04229729f, 0.10666174f, -6.66579151f };
\r
1706 return Vector<float>((float*)detector, sizeof(detector)/sizeof(detector[0]), true);
\r
projects / opencv.git / blob