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added the signedGradient varaible to the HOGDescriptor class.

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when true, use signed gradient for the angular histogram computation.
default to false for backward compatibility.
pull/4000/head
Michaël Sdika 10 years ago
parent a31b29308f
commit ab40c2acf7
2 changed files with 23 additions and 16 deletions
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  1. 7
      modules/objdetect/include/opencv2/objdetect.hpp
  2. 32
      modules/objdetect/src/hog.cpp

7
modules/objdetect/include/opencv2/objdetect.hpp
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@ -345,18 +345,18 @@ public:
CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS)
free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false)
{}
CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
int _histogramNormType=HOGDescriptor::L2Hys,
double _L2HysThreshold=0.2, bool _gammaCorrection=false,
int _nlevels=HOGDescriptor::DEFAULT_NLEVELS)
int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false)
: winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize),
nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma),
histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),
gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels)
gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient)
{}
CV_WRAP HOGDescriptor(const String& filename)
@ -432,6 +432,7 @@ public:
UMat oclSvmDetector;
float free_coef;
CV_PROP int nlevels;
CV_PROP bool signedGradient;
//! evaluate specified ROI and return confidence value for each location

32
modules/objdetect/src/hog.cpp
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@ -153,6 +153,10 @@ bool HOGDescriptor::read(FileNode& obj)
obj["L2HysThreshold"] >> L2HysThreshold;
obj["gammaCorrection"] >> gammaCorrection;
obj["nlevels"] >> nlevels;
if (obj["signedGradient"].empty())
signedGradient = false;
else
obj["signedGradient"] >> signedGradient;
FileNode vecNode = obj["SVMDetector"];
if( vecNode.isSeq() )
@ -179,7 +183,8 @@ void HOGDescriptor::write(FileStorage& fs, const String& objName) const
<< "histogramNormType" << histogramNormType
<< "L2HysThreshold" << L2HysThreshold
<< "gammaCorrection" << gammaCorrection
<< "nlevels" << nlevels;
<< "nlevels" << nlevels
<< "signedGradient" << signedGradient;
if( !svmDetector.empty() )
fs << "SVMDetector" << svmDetector;
fs << "}";
@ -212,6 +217,7 @@ void HOGDescriptor::copyTo(HOGDescriptor& c) const
c.gammaCorrection = gammaCorrection;
c.svmDetector = svmDetector;
c.nlevels = nlevels;
c.signedGradient = signedGradient;
}
void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
@ -297,7 +303,7 @@ void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
xmap += 1;
}
float angleScale = (float)(nbins/CV_PI);
float angleScale = signedGradient ? (float)(nbins/(2.0*CV_PI)) : (float)(nbins/CV_PI);
for( y = 0; y < gradsize.height; y++ )
{
const uchar* imgPtr = img.ptr(ymap[y]);
@ -1106,9 +1112,9 @@ static bool ocl_compute_gradients_8UC1(int height, int width, InputArray _img, f
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)
static bool ocl_computeGradient(InputArray img, UMat grad, UMat qangle, int nbins, Size effect_size, bool gamma_correction, bool signedGradient)
{
float angleScale = (float)(nbins / CV_PI);
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);
@ -1316,7 +1322,7 @@ static bool ocl_extract_descrs_by_cols(int win_height, int win_width, int block_
}
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)
Size cellSize, int nbins, Size blockStride, Size winSize, float sigma, bool gammaCorrection, double L2HysThreshold, bool signedGradient)
{
Size imgSize = _img.size();
Size effect_size = imgSize;
@ -1342,7 +1348,7 @@ static bool ocl_compute(InputArray _img, Size win_stride, std::vector<float>& _d
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))
if(!ocl_computeGradient(_img, grad, qangle, nbins, effect_size, gammaCorrection, signedGradient))
return false;
UMat gauss_w_lut;
@ -1401,7 +1407,7 @@ void HOGDescriptor::compute(InputArray _img, std::vector<float>& descriptors,
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))
cellSize, nbins, blockStride, winSize, (float)getWinSigma(), gammaCorrection, L2HysThreshold, signedGradient))
Mat img = _img.getMat();
HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);
@ -1714,7 +1720,7 @@ static bool ocl_classify_hists(int win_height, int win_width, int block_stride_y
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 gammaCorrection, double L2HysThreshold, float sigma, float free_coef, bool signedGradient)
{
hits.clear();
if (oclSvmDetector.empty())
@ -1743,7 +1749,7 @@ static bool ocl_detect(InputArray img, std::vector<Point> &hits, double hit_thre
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))
if(!ocl_computeGradient(img, grad, qangle, nbins, effect_size, gammaCorrection, signedGradient))
return false;
UMat gauss_w_lut;
@ -1784,7 +1790,7 @@ static bool ocl_detectMultiScale(InputArray _img, std::vector<Rect> &found_locat
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)
double L2HysThreshold, float sigma, float free_coef, bool signedGradient)
{
std::vector<Rect> all_candidates;
std::vector<Point> locations;
@ -1799,14 +1805,14 @@ static bool ocl_detectMultiScale(InputArray _img, std::vector<Rect> &found_locat
if (effect_size == imgSize)
{
if(!ocl_detect(_img, locations, hit_threshold, win_stride, oclSvmDetector, blockSize, cellSize, nbins,
blockStride, winSize, gammaCorrection, L2HysThreshold, sigma, free_coef))
blockStride, winSize, gammaCorrection, L2HysThreshold, sigma, free_coef, signedGradient))
return false;
}
else
{
resize(_img, image_scale, effect_size);
if(!ocl_detect(image_scale, locations, hit_threshold, win_stride, oclSvmDetector, blockSize, cellSize, nbins,
blockStride, winSize, gammaCorrection, L2HysThreshold, sigma, free_coef))
blockStride, winSize, gammaCorrection, L2HysThreshold, sigma, free_coef, signedGradient))
return false;
}
Size scaled_win_size(cvRound(winSize.width * scale),
@ -1848,7 +1854,7 @@ void HOGDescriptor::detectMultiScale(
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));
blockSize, cellSize, nbins, blockStride, winSize, gammaCorrection, L2HysThreshold, (float)getWinSigma(), free_coef, signedGradient));
std::vector<Rect> allCandidates;
std::vector<double> tempScales;

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