fixes
This commit is contained in:
Konstantin Matskevich
parent
5c38519c65
commit
3495c5953c
@ -313,8 +313,6 @@ public:
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Size winStride = Size(), Size padding = Size(),
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const std::vector<Point>& locations = std::vector<Point>()) const;
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virtual bool ocl_compute(InputArray _img, Size win_stride, std::vector<float>& descriptors, int descr_format) const;
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//with found weights output
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CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
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CV_OUT std::vector<double>& weights,
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@ -326,9 +324,7 @@ public:
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double hitThreshold = 0, Size winStride = Size(),
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Size padding = Size(),
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const std::vector<Point>& searchLocations=std::vector<Point>()) const;
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//ocl
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virtual bool ocl_detect(InputArray img, std::vector<Point> &hits,
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double hitThreshold = 0, Size winStride = Size()) const;
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//with result weights output
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CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
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CV_OUT std::vector<double>& foundWeights, double hitThreshold = 0,
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@ -339,9 +335,6 @@ public:
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double hitThreshold = 0, Size winStride = Size(),
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Size padding = Size(), double scale = 1.05,
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double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;
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//ocl
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virtual bool ocl_detectMultiScale(InputArray img, std::vector<Rect> &found_locations, std::vector<double>& level_scale,
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double hit_threshold, Size winStride, double groupThreshold) const;
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CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
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Size paddingTL = Size(), Size paddingBR = Size()) const;
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@ -361,25 +354,26 @@ public:
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CV_PROP bool gammaCorrection;
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CV_PROP std::vector<float> svmDetector;
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UMat oclSvmDetector;
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float free_coef;
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CV_PROP int nlevels;
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// evaluate specified ROI and return confidence value for each location
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virtual void detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
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// evaluate specified ROI and return confidence value for each location
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virtual void detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
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CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
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double hitThreshold = 0, cv::Size winStride = Size(),
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cv::Size padding = Size()) const;
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// evaluate specified ROI and return confidence value for each location in multiple scales
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virtual void detectMultiScaleROI(const cv::Mat& img,
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// evaluate specified ROI and return confidence value for each location in multiple scales
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virtual void detectMultiScaleROI(const cv::Mat& img,
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CV_OUT std::vector<cv::Rect>& foundLocations,
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std::vector<DetectionROI>& locations,
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double hitThreshold = 0,
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int groupThreshold = 0) const;
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// read/parse Dalal's alt model file
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void readALTModel(String modelfile);
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void groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const;
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// read/parse Dalal's alt model file
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void readALTModel(String modelfile);
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void groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const;
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};
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@ -74,10 +74,10 @@ OCL_PERF_TEST(HOGFixture, HOG)
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{
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UMat src;
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imread(getDataPath("gpu/hog/road.png"), cv::IMREAD_GRAYSCALE).copyTo(src);
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CV_Assert(!src.empty());
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ASSERT_FALSE(src.empty());
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vector<cv::Rect> found_locations;
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declare.in(src).time(5);
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declare.in(src);
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HOGDescriptor hog;
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hog.setSVMDetector(hog.getDefaultPeopleDetector());
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@ -112,6 +112,7 @@ bool HOGDescriptor::checkDetectorSize() const
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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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@ -126,8 +127,9 @@ void HOGDescriptor::setSVMDetector(InputArray _svmDetector)
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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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CV_Assert(checkDetectorSize());
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}
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#define CV_TYPE_NAME_HOG_DESCRIPTOR "opencv-object-detector-hog"
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@ -1068,6 +1070,8 @@ static inline int gcd(int a, int b)
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return a;
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}
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#ifdef HAVE_OPENCL
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static bool ocl_compute_gradients_8UC1(int height, int width, InputArray _img, float angle_scale,
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UMat grad, UMat qangle, bool correct_gamma, int nbins)
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{
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@ -1309,7 +1313,8 @@ static bool ocl_extract_descrs_by_cols(int win_height, int win_width, int block_
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return k.run(2, globalThreads, localThreads, false);
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}
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bool HOGDescriptor::ocl_compute(InputArray _img, Size win_stride, std::vector<float>& _descriptors, int descr_format) const
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static bool ocl_compute(InputArray _img, Size win_stride, std::vector<float>& _descriptors, int descr_format, Size blockSize,
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Size cellSize, int nbins, Size blockStride, Size winSize, float sigma, bool gammaCorrection, double L2HysThreshold)
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{
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Size imgSize = _img.size();
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Size effect_size = imgSize;
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@ -1324,7 +1329,6 @@ bool HOGDescriptor::ocl_compute(InputArray _img, Size win_stride, std::vector<fl
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Size wins_per_img = numPartsWithin(imgSize, winSize, win_stride);
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UMat labels(1, wins_per_img.area(), CV_8U);
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float sigma = (float)getWinSigma();
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float scale = 1.f / (2.f * sigma * sigma);
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Mat gaussian_lut(1, 512, CV_32FC1);
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int idx = 0;
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@ -1335,16 +1339,18 @@ bool HOGDescriptor::ocl_compute(InputArray _img, Size win_stride, std::vector<fl
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for(int j=-8; j<8; j++)
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gaussian_lut.at<float>(idx++) = (8.f - fabs(j + 0.5f)) * (8.f - fabs(i + 0.5f)) / 64.f;
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if(!ocl_computeGradient(_img, grad, qangle, nbins, effect_size, gammaCorrection))
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return false;
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UMat gauss_w_lut;
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gaussian_lut.copyTo(gauss_w_lut);
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if(!ocl_computeGradient(_img, grad, qangle, nbins, effect_size, gammaCorrection)) return false;
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if(!ocl_compute_hists(nbins, blockStride.width, blockStride.height, effect_size.height,
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effect_size.width, grad, qangle, gauss_w_lut, block_hists, block_hist_size)) return false;
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effect_size.width, grad, qangle, gauss_w_lut, block_hists, block_hist_size))
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return false;
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if(!ocl_normalize_hists(nbins, blockStride.width, blockStride.height, effect_size.height,
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effect_size.width, block_hists, (float)L2HysThreshold)) return false;
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effect_size.width, block_hists, (float)L2HysThreshold))
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return false;
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Size blocks_per_win = numPartsWithin(winSize, blockSize, blockStride);
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wins_per_img = numPartsWithin(effect_size, winSize, win_stride);
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@ -1358,12 +1364,14 @@ bool HOGDescriptor::ocl_compute(InputArray _img, Size win_stride, std::vector<fl
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case DESCR_FORMAT_ROW_BY_ROW:
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if(!ocl_extract_descrs_by_rows(winSize.height, winSize.width,
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blockStride.height, blockStride.width, win_stride.height, win_stride.width, effect_size.height,
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effect_size.width, block_hists, descriptors, (int)block_hist_size, descr_size, descr_width)) return false;
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effect_size.width, block_hists, descriptors, (int)block_hist_size, descr_size, descr_width))
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return false;
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break;
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case DESCR_FORMAT_COL_BY_COL:
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if(!ocl_extract_descrs_by_cols(winSize.height, winSize.width,
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blockStride.height, blockStride.width, win_stride.height, win_stride.width, effect_size.height, effect_size.width,
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block_hists, descriptors, (int)block_hist_size, descr_size, blocks_per_win.width, blocks_per_win.height)) return false;
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block_hists, descriptors, (int)block_hist_size, descr_size, blocks_per_win.width, blocks_per_win.height))
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return false;
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break;
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default:
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return false;
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@ -1371,6 +1379,7 @@ bool HOGDescriptor::ocl_compute(InputArray _img, Size win_stride, std::vector<fl
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descriptors.reshape(1, (int)descriptors.total()).getMat(ACCESS_READ).copyTo(_descriptors);
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return true;
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}
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#endif //HAVE_OPENCL
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void HOGDescriptor::compute(InputArray _img, std::vector<float>& descriptors,
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Size winStride, Size padding, const std::vector<Point>& locations) const
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@ -1388,7 +1397,8 @@ void HOGDescriptor::compute(InputArray _img, std::vector<float>& descriptors,
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Size paddedImgSize(imgSize.width + padding.width*2, imgSize.height + padding.height*2);
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CV_OCL_RUN(_img.dims() <= 2 && _img.type() == CV_8UC1 && _img.isUMat(),
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ocl_compute(_img, winStride, descriptors, DESCR_FORMAT_COL_BY_COL))
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ocl_compute(_img, winStride, descriptors, DESCR_FORMAT_COL_BY_COL, blockSize,
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cellSize, nbins, blockStride, winSize, (float)getWinSigma(), gammaCorrection, L2HysThreshold))
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Mat img = _img.getMat();
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HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);
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@ -1612,6 +1622,8 @@ private:
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Mutex* mtx;
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};
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#ifdef HAVE_OPENCL
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static bool ocl_classify_hists(int win_height, int win_width, int block_stride_y, int block_stride_x,
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int win_stride_y, int win_stride_x, int height, int width,
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const UMat& block_hists, UMat detector,
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@ -1697,11 +1709,12 @@ static bool ocl_classify_hists(int win_height, int win_width, int block_stride_y
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return k.run(2, globalThreads, localThreads, false);
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}
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bool HOGDescriptor::ocl_detect(InputArray img, std::vector<Point> &hits,
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double hit_threshold, Size win_stride) const
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static bool ocl_detect(InputArray img, std::vector<Point> &hits, double hit_threshold, Size win_stride,
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const UMat& oclSvmDetector, Size blockSize, Size cellSize, int nbins, Size blockStride, Size winSize,
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bool gammaCorrection, double L2HysThreshold, float sigma, float free_coef)
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{
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hits.clear();
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if (svmDetector.empty())
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if (oclSvmDetector.empty())
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return false;
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Size imgSize = img.size();
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@ -1716,7 +1729,6 @@ bool HOGDescriptor::ocl_detect(InputArray img, std::vector<Point> &hits,
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Size wins_per_img = numPartsWithin(imgSize, winSize, win_stride);
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UMat labels(1, wins_per_img.area(), CV_8U);
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float sigma = (float)getWinSigma();
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float scale = 1.f / (2.f * sigma * sigma);
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Mat gaussian_lut(1, 512, CV_32FC1);
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int idx = 0;
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@ -1727,19 +1739,18 @@ bool HOGDescriptor::ocl_detect(InputArray img, std::vector<Point> &hits,
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for(int j=-8; j<8; j++)
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gaussian_lut.at<float>(idx++) = (8.f - fabs(j + 0.5f)) * (8.f - fabs(i + 0.5f)) / 64.f;
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if(!ocl_computeGradient(img, grad, qangle, nbins, effect_size, gammaCorrection))
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return false;
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UMat gauss_w_lut;
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gaussian_lut.copyTo(gauss_w_lut);
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if(!ocl_computeGradient(img, grad, qangle, nbins, effect_size, gammaCorrection)) return false;
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if(!ocl_compute_hists(nbins, blockStride.width, blockStride.height, effect_size.height,
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effect_size.width, grad, qangle, gauss_w_lut, block_hists, block_hist_size)) return false;
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effect_size.width, grad, qangle, gauss_w_lut, block_hists, block_hist_size))
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return false;
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if(!ocl_normalize_hists(nbins, blockStride.width, blockStride.height, effect_size.height,
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effect_size.width, block_hists, (float)L2HysThreshold)) return false;
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size_t descriptor_size = getDescriptorSize();
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float free_coef = svmDetector.size() > descriptor_size ? svmDetector[descriptor_size] : 0;
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effect_size.width, block_hists, (float)L2HysThreshold))
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return false;
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Size blocks_per_win = numPartsWithin(winSize, blockSize, blockStride);
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@ -1748,7 +1759,8 @@ bool HOGDescriptor::ocl_detect(InputArray img, std::vector<Point> &hits,
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if(!ocl_classify_hists(winSize.height, winSize.width, blockStride.height,
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blockStride.width, win_stride.height, win_stride.width,
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effect_size.height, effect_size.width, block_hists, oclSvmDetector,
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(float)free_coef, (float)hit_threshold, labels, descr_size, (int)block_hist_size)) return false;
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free_coef, (float)hit_threshold, labels, descr_size, (int)block_hist_size))
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return false;
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Mat labels_host = labels.getMat(ACCESS_READ);
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unsigned char *vec = labels_host.ptr();
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@ -1764,8 +1776,11 @@ bool HOGDescriptor::ocl_detect(InputArray img, std::vector<Point> &hits,
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return true;
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}
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bool HOGDescriptor::ocl_detectMultiScale(InputArray _img, std::vector<Rect> &found_locations, std::vector<double>& level_scale,
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double hit_threshold, Size win_stride, double group_threshold) const
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static bool ocl_detectMultiScale(InputArray _img, std::vector<Rect> &found_locations, std::vector<double>& level_scale,
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double hit_threshold, Size win_stride, double group_threshold,
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const UMat& oclSvmDetector, Size blockSize, Size cellSize,
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int nbins, Size blockStride, Size winSize, bool gammaCorrection,
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double L2HysThreshold, float sigma, float free_coef)
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{
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std::vector<Rect> all_candidates;
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std::vector<Point> locations;
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@ -1779,12 +1794,16 @@ bool HOGDescriptor::ocl_detectMultiScale(InputArray _img, std::vector<Rect> &fou
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Size effect_size = Size(cvRound(imgSize.width / scale), cvRound(imgSize.height / scale));
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if (effect_size == imgSize)
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{
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if(!ocl_detect(_img, locations, hit_threshold, win_stride)) return false;
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if(!ocl_detect(_img, locations, hit_threshold, win_stride, oclSvmDetector, blockSize, cellSize, nbins,
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blockStride, winSize, gammaCorrection, L2HysThreshold, sigma, free_coef))
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return false;
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}
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else
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{
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resize(_img, image_scale, effect_size);
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if(!ocl_detect(image_scale, locations, hit_threshold, win_stride)) return false;
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if(!ocl_detect(image_scale, locations, hit_threshold, win_stride, oclSvmDetector, blockSize, cellSize, nbins,
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blockStride, winSize, gammaCorrection, L2HysThreshold, sigma, free_coef))
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return false;
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}
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Size scaled_win_size(cvRound(winSize.width * scale),
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cvRound(winSize.height * scale));
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@ -1795,6 +1814,7 @@ bool HOGDescriptor::ocl_detectMultiScale(InputArray _img, std::vector<Rect> &fou
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cv::groupRectangles(found_locations, (int)group_threshold, 0.2);
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return true;
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}
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#endif //HAVE_OPENCL
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void HOGDescriptor::detectMultiScale(
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InputArray _img, std::vector<Rect>& foundLocations, std::vector<double>& foundWeights,
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@ -1823,7 +1843,8 @@ void HOGDescriptor::detectMultiScale(
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CV_OCL_RUN(_img.dims() <= 2 && _img.type() == CV_8UC1 && scale0 > 1 && winStride.width % blockStride.width == 0 &&
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winStride.height % blockStride.height == 0 && padding == Size(0,0) && _img.isUMat(),
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ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, finalThreshold));
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ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, finalThreshold, oclSvmDetector,
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blockSize, cellSize, nbins, blockStride, winSize, gammaCorrection, L2HysThreshold, (float)getWinSigma(), free_coef));
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std::vector<Rect> allCandidates;
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std::vector<double> tempScales;
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