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éŒðÿÿ‰”$d#‰Öéwðÿÿè!-ûÿf-À~D‰É¶Ôé;èÿÿD‰ÆéŒúÿÿ‰´$d#A‰÷éPèÿÿ1ÿéÎíÿÿ1ÒéÇíÿÿ‰ñ1ÿéžùÿÿD‰¼$d#éQøÿÿ‰ñ1ÿéµüÿÿD‰„$d#é<úÿÿD‰þé:øÿÿH‹D$‰¸Ð"é‚÷ÿÿHƒìHƒÄÃ0 <= scaleIdx && scaleIdx < (int)scaleData->size()/home/dburd/build/opencv/modules/objdetect/src/cascadedetect.cppvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)total() == 0 || data != NULL/home/dburd/build/opencv/modules/core/include/opencv2/core/mat.inl.hpp/home/dburd/build/opencv/modules/objdetect/src/cascadedetect.hppStep must be a multiple of esz1sqsum.u == u && sqsum.size() == s.szi && sqsum.type()==CV_32Srectstiltedrect!cascade.data.stumps.empty()!empty()vector::reservevector::_M_default_appendnodes[root][PARENT] < 0/home/dburd/build/opencv/modules/core/include/opencv2/core/operations.hppn > 0-D LOCAL_SIZE_X=%d -D LOCAL_SIZE_Y=%d -D SUM_BUF_SIZE=%d -D SUM_BUF_STEP=%d -D NODE_COUNT=%d -D SPLIT_STAGE=%d -D N_STAGES=%d -D MAX_FACES=%d -D HAAR-D LOCAL_SIZE_X=%d -D LOCAL_SIZE_Y=%d -D NODE_COUNT=%d -D SPLIT_STAGE=%d -D N_STAGES=%d -D MAX_FACES=%d -D HAAR-D LOCAL_SIZE_X=%d -D LOCAL_SIZE_Y=%d -D SUM_BUF_SIZE=%d -D SUM_BUF_STEP=%d -D SPLIT_STAGE=%d -D N_STAGES=%d -D MAX_FACES=%d -D LBP-D LOCAL_SIZE_X=%d -D LOCAL_SIZE_Y=%d -D SPLIT_STAGE=%d -D N_STAGES=%d -D MAX_FACES=%d -D LBPrunHaarClassifierrunLBPClassifierStumpSimplea->size() == nb->size() == nstageTypeBOOSTfeatureTypeHAARLBPHOGfeatureParamsmaxCatCountstagesstageThresholdweakClassifiersinternalNodesleafValuesHOG cascade is not supported in 3.0origWinSize.height > 0 && origWinSize.width > 0features!_seq || _seq->elem_size == sizeof(_Tp)/home/dburd/build/opencv/modules/core/include/opencv2/core/core_c.hscaleFactor > 1 && _image.depth() == CV_8UscaleFactor > 1 && image.depth() == CV_8UgetMaskGeneratorsetMaskGeneratorgetOldCascadegetFeatureTypegetOriginalWindowSizeisOldFormatCascadevoid cv::CascadeClassifier::detectMultiScale(cv::InputArray, std::vector<cv::Rect_<int> >&, std::vector<int>&, std::vector<double, std::allocator<double> >&, double, int, int, cv::Size, cv::Size, bool)detectMultiScalevoid cv::CascadeClassifier::detectMultiScale(cv::InputArray, std::vector<cv::Rect_<int> >&, std::vector<int>&, double, int, int, cv::Size, cv::Size)detectMultiScalevoid cv::CascadeClassifier::detectMultiScale(cv::InputArray, std::vector<cv::Rect_<int> >&, double, int, int, cv::Size, cv::Size)detectMultiScaleclipObjectsbool cv::CascadeClassifierImpl::Data::read(const cv::FileNode&)readvirtual void cv::CascadeClassifierImpl::detectMultiScale(cv::InputArray, std::vector<cv::Rect_<int> >&, std::vector<int>&, double, int, int, cv::Size, cv::Size)detectMultiScalevirtual void cv::CascadeClassifierImpl::detectMultiScale(cv::InputArray, std::vector<cv::Rect_<int> >&, double, int, int, cv::Size, cv::Size)Seqvirtual void cv::CascadeClassifierImpl::detectMultiScale(cv::InputArray, std::vector<cv::Rect_<int> >&, std::vector<int>&, std::vector<double, std::allocator<double> >&, double, int, int, cv::Size, cv::Size, bool)detectMultiScalevirtual void cv::CascadeClassifierInvoker::operator()(const cv::Range&) constvoid cv::CascadeClassifierImpl::detectMultiScaleNoGrouping(cv::InputArray, std::vector<cv::Rect_<int> >&, std::vector<int>&, std::vector<double, std::allocator<double> >&, double, cv::Size, cv::Size, bool)int cv::predictCategorical(cv::CascadeClassifierImpl&, cv::Ptr<cv::FeatureEvaluator>&, double&) [with FEval = cv::LBPEvaluator]int cv::predictOrdered(cv::CascadeClassifierImpl&, cv::Ptr<cv::FeatureEvaluator>&, double&) [with FEval = cv::HaarEvaluator]int cv::predictCategoricalStump(cv::CascadeClassifierImpl&, cv::Ptr<cv::FeatureEvaluator>&, double&) [with FEval = cv::LBPEvaluator]predictCategoricalStumpint cv::predictOrderedStump(cv::CascadeClassifierImpl&, cv::Ptr<cv::FeatureEvaluator>&, double&) [with FEval = cv::HaarEvaluator]predictOrderedStumpint cv::CascadeClassifierImpl::runAt(cv::Ptr<cv::FeatureEvaluator>&, cv::Point, int, double&)setWindowbool cv::LBPEvaluator::Feature::read(const cv::FileNode&, const Size&)getScaleDatavirtual void cv::HaarEvaluator::computeOptFeatures()Matvirtual void cv::HaarEvaluator::computeChannels(int, cv::InputArray)computeChannelsreadbool cv::HaarEvaluator::Feature::read(const cv::FileNode&, const Size&)Matvirtual bool cv::FeatureEvaluator::setImage(cv::InputArray, const std::vector<float>&)void cv::groupRectangles_meanshift(std::vector<cv::Rect_<int> >&, std::vector<double, std::allocator<double> >&, std::vector<double, std::allocator<double> >&, double, cv::Size)void cv::groupRectangles(std::vector<cv::Rect_<int> >&, std::vector<int>&, std::vector<double, std::allocator<double> >&, int, double)void cv::groupRectangles(std::vector<cv::Rect_<int> >&, std::vector<int>&, int, double)void cv::groupRectangles(std::vector<cv::Rect_<int> >&, int, double)partitionvoid cv::groupRectangles(std::vector<cv::Rect_<int> >&, int, double, std::vector<int>*, std::vector<double, std::allocator<double> >*)N2cv6detail8PtrOwnerEN2cv21BaseCascadeClassifierEN2cv16FeatureEvaluatorEN2cv21CascadeClassifierImplEN2cv13HaarEvaluatorEN2cv12LBPEvaluatorEN2cv24CascadeClassifierInvokerEN2cv6detail12PtrOwnerImplINS_13HaarEvaluatorENS_14DefaultDeleterIS2_EEEEN2cv6detail12PtrOwnerImplINS_12LBPEvaluatorENS_14DefaultDeleterIS2_EEEEN2cv6detail12PtrOwnerImplI12CvMemStorageNS_14DefaultDeleterIS2_EEEEN2cv6detail12PtrOwnerImplISt6vectorINS_16FeatureEvaluator9ScaleDataESaIS4_EENS_14DefaultDeleterIS6_EEEEN2cv6detail12PtrOwnerImplISt6vectorINS_13HaarEvaluator7FeatureESaIS4_EENS_14DefaultDeleterIS6_EEEEN2cv6detail12PtrOwnerImplISt6vectorINS_13HaarEvaluator10OptFeatureESaIS4_EENS_14DefaultDeleterIS6_EEEEN2cv6detail12PtrOwnerImplISt6vectorINS_12LBPEvaluator7FeatureESaIS4_EENS_14DefaultDeleterIS6_EEEEN2cv6detail12PtrOwnerImplISt6vectorINS_12LBPEvaluator10OptFeatureESaIS4_EENS_14DefaultDeleterIS6_EEEEN2cv6detail12PtrOwnerImplI23CvHaarClassifierCascadeNS_14DefaultDeleterIS2_EEEEN2cv6detail12PtrOwnerImplINS_21CascadeClassifierImplENS_14DefaultDeleterIS2_EEEEInvalid window size (too large)origWinSize.height1000000origWinSize.widthInvalid LBP featurerect.y + rect.heightHrect.x + rect.widthWrect.yrect.xInvalid HAAR featurerw.r.y + rw.r.heightrw.r.x + rw.r.widthrw.r.yrw.r.x€?H7@¬Å'7ð?š™™™™™¹?à?ܹá{“ÊÐ?ñh㈵øä> ?𿚙™™™™É?ÿBÿÿÿÿÿÿÿÿ



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sizestage_thresholdtreesleft_valleft_noderight_valright_nodeNo element name has been given/home/dburd/build/opencv/modules/core/include/opencv2/core/persistence.hppoperator<<operator<<operator<<{:opencv-cascade4/home/dburd/build/opencv/modules/objdetect/src/detection_based_tracker.cpp_detectorstateThread==STATE_THREAD_WORKING_SLEEPING
 %s: ERROR: OpenCV Exception caught: 
'%s'


 %s: ERROR: Exception caught: 
'%s'


 %s: ERROR: UNKNOWN Exception caught

DetectionBasedTracker: workcycleObjectDetectorFunction: ERROR concerning pointer, received as the function parameter(params.maxTrackLifetime >= 0) && trackingDetectorvector::_M_fill_insertnumpos > 0imageGray.type()==CV_8UC1updateTrackedObjectsvirtual void cv::DetectionBasedTracker::process(const cv::Mat&)processDetectionBasedTrackerworkcycleObjectDetectorworkcycleObjectDetectorFunctionSeparateDetectionWorkN2cv21DetectionBasedTrackerEN2cv21DetectionBasedTracker21SeparateDetectionWorkEN2cv6detail12PtrOwnerImplINS_21DetectionBasedTracker21SeparateDetectionWorkENS_14DefaultDeleterIS3_EEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJPFPvS3_ES3_EEEEEEstateThread==STATE_THREAD_WORKINnumpositions > 0?@@opencv-haar-classifiertree %dnode %dparentnext/home/dburd/build/opencv/modules/objdetect/src/haar.cppHidden cascade has not been created.
Use cvSetImagesForHaarClassifierCascadeInvalid cascade pointerhid_cascade has been already createdNegative number of cascade stagesheader of the stage classifier #%d is invalid (has null pointers or non-positive classfier count)rectangle #%d of the classifier #%d of the stage classifier #%d is not inside the reference (original) cascade windowInvalid classifier pointerInvalid stages nodeInvalid stage %dNumber of stages should be positivesize node is not a valid sequence.Invalid size node: width must be positive integerInvalid size node: height must be positive integerTrees node is not a valid sequence. (stage %d)Tree node is not a valid sequence. (stage %d, tree %d)Tree node %d is not a valid map. (stage %d, tree %d)Feature node is not a valid map. (stage %d, tree %d, node %d)Rects node is not a valid sequence. (stage %d, tree %d, node %d)Rect %d is not a valid sequence. (stage %d, tree %d, node %d)x coordinate must be non-negative integer. (stage %d, tree %d, node %d, rect %d)y coordinate must be non-negative integer. (stage %d, tree %d, node %d, rect %d)width must be positive integer and (x + width) must not exceed window width. (stage %d, tree %d, node %d, rect %d)height must be positive integer and (y + height) must not exceed window height. (stage %d, tree %d, node %d, rect %d)weight must be real number. (stage %d, tree %d, node %d, rect %d)tilted must be 0 or 1. (stage %d, tree %d, node %d)threshold must be real number. (stage %d, tree %d, node %d)left node must be valid node number. (stage %d, tree %d, node %d)left node or left value must be specified. (stage %d, tree %d, node %d)left value must be real number. (stage %d, tree %d, node %d)Tree structure is broken: too many values. (stage %d, tree %d, node %d)right node must be valid node number. (stage %d, tree %d, node %d)right node or right value must be specified. (stage %d, tree %d, node %d)right value must be real number. (stage %d, tree %d, node %d)Tree structure is broken: too few values. (stage %d, tree %d)stage threshold must be real number. (stage %d)parent must be integer number. (stage %d)next must be integer number. (stage %d)Scale must be positiveCOI is not supportedAll integral images must have the same sizeOnly (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowedSum and tilted_sum must have the same stride (step, widthStep)/Null path is passedrbInvalid path%d%d%d%d%d%f%n%99s%n%f%d%d%nparent >= 0 && parent < i%s%s%d/AdaBoostCARTHaarClassifier.txtCould not allocate memory for input_cascade%s/%d/AdaBoostCARTHaarClassifier.txtelements_read == (size_t)(size)count > 0 && count < CV_HAAR_STAGE_MAXclassifier->count > 0 && classifier->count< CV_HAAR_STAGE_MAXInvalid classifier cascadeNull storage pointerscale factor must be > 1Only 8-bit images are supportedvector::_M_range_inserticvReadHaarClassifiericvCreateHaarClassifierCascadeicvLoadCascadeCARTcvLoadHaarClassifierCascadevirtual void cv::HaarDetectObjects_ScaleCascade_Invoker::operator()(const cv::Range&) constvirtual void cv::HaarDetectObjects_ScaleImage_Invoker::operator()(const cv::Range&) constCvSeq* cvHaarDetectObjectsForROC(const CvArr*, CvHaarClassifierCascade*, CvMemStorage*, std::vector<int>&, std::vector<double, std::allocator<double> >&, double, int, int, CvSize, CvSize, bool)cvHaarDetectObjectsForROCcvRunHaarClassifierCascadeSumint cvRunHaarClassifierCascade(const CvHaarClassifierCascade*, CvPoint, int)icvCreateHidHaarClassifierCascadecvSetImagesForHaarClassifierCascadeN2cv36HaarDetectObjects_ScaleImage_InvokerEN2cv38HaarDetectObjects_ScaleCascade_InvokerEN2cv6detail12PtrOwnerImplI5CvMatNS_14DefaultDeleterIS2_EEEE@I@333333Ã?ffffffæ?333333ã?š™™™™™Ù?·Ñ8€%rects >= 2 && rects <= CV_HAAR_Fcompute_gradients_8UC1_kernelcompute_hists_lut_kernel-D WAVE_SIZE=%dnormalize_hists_36_kernelnormalize_hists_kernelstride != 0/home/dburd/build/opencv/modules/objdetect/src/hog.cppimg.type() == CV_8U || img.type() == CV_8UC3(unsigned)hidx < (unsigned)nbins!cellSize.empty()!blockStride.empty()blockSize.width % cellSize.width == 0 && blockSize.height % cellSize.height == 0(winSize.width - blockSize.width) % blockStride.width == 0 && (winSize.height - blockSize.height) % blockStride.height == 0checkDetectorSize()!cell_size.empty()pt.x % cacheStride.width == 0 && pt.y % cacheStride.height == 0extract_descrs_by_cols_kernelwinSize!obj["winSize"].empty()blockSize!winSize.empty()blockStride!blockSize.empty()cellSizenbinsnbins > 0derivAperturewinSigmahistogramNormTypeL2HysThresholdgammaCorrectionnlevelsnlevels > 0signedGradientSVMDetectorrectList.size() == weights.size()pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0file not existversion?V6.01version does not matchl >= 0 && l < 0xFFFFtotwords + 1 > 0 && totwords < 0xFFFF-D WAVE_SIZE=32classify_hists_180_kernel-D CPU classify_hists_252_kernelclassify_hists_kernelopencv-object-detector-hogvoid cv::HOGDescriptor::groupRectangles(std::vector<cv::Rect_<int> >&, std::vector<double, std::allocator<double> >&, int, double) constgroupRectanglesreadALTModelreadALTModelvirtual void cv::HOGConfInvoker::operator()(const cv::Range&) constvirtual void cv::HOGDescriptor::detectMultiScaleROI(const cv::Mat&, std::vector<cv::Rect_<int> >&, std::vector<cv::DetectionROI>&, double, int) constvirtual void cv::HOGDescriptor::detectROI(const cv::Mat&, const std::vector<cv::Point_<int> >&, std::vector<cv::Point_<int> >&, std::vector<double, std::allocator<double> >&, double, cv::Size, cv::Size) constvirtual void cv::HOGDescriptor::detectMultiScale(cv::InputArray, std::vector<cv::Rect_<int> >&, double, cv::Size, cv::Size, double, double, bool) constMatvirtual void cv::HOGDescriptor::detectMultiScale(cv::InputArray, std::vector<cv::Rect_<int> >&, std::vector<double, std::allocator<double> >&, double, cv::Size, cv::Size, double, double, bool) constvirtual void cv::HOGDescriptor::detect(const cv::Mat&, std::vector<cv::Point_<int> >&, double, cv::Size, cv::Size, const std::vector<cv::Point_<int> >&) constvirtual void cv::HOGDescriptor::detect(const cv::Mat&, std::vector<cv::Point_<int> >&, std::vector<double, std::allocator<double> >&, double, cv::Size, cv::Size, const std::vector<cv::Point_<int> >&) constdetectvirtual void cv::HOGDescriptor::compute(cv::InputArray, std::vector<float>&, cv::Size, cv::Size, const std::vector<cv::Point_<int> >&) constgetBlockMatvirtual void cv::HOGDescriptor::computeGradient(const cv::Mat&, cv::Mat&, cv::Mat&, cv::Size, cv::Size) constcomputeGradientoperator<<operator<<operator<<operator<<operator<<readnumPartsWithingetBlockHistogramSizesetSVMDetectorgetDescriptorSizeN2cv13HOGDescriptorEN2cv8HOGCacheEN2cv10HOGInvokerEN2cv14HOGConfInvokerEµ–>Uʽj¡¾AI¡=ðiÆ>!ç…>	q>eü{=<K¨>|™h>Úˆ½ԛ±½:ÍB=ߌÚ=â9=ªEä=ïǼ¯
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ÿÿÿÿÿÿïÿÿÿÿÿÿÿïÿ@@00!src.empty()/home/dburd/build/opencv/modules/objdetect/src/qrcode.cpp!inarr.empty()inarr.depth() == CV_8Usrc_points.size() == 4basic_string::_M_construct null not validlength > 0detectAndDecodecv::String cv::QRCodeDetector::decode(cv::InputArray, cv::InputArray, cv::OutputArray)decodebool cv::QRDecode::samplingForVersion()bool cv::QRDecode::versionDefinition()bool cv::QRDecode::updatePerspective()void cv::QRDecode::init(const cv::Mat&, const std::vector<cv::Point_<float> >&)detectstd::vector<cv::Point_<float> > cv::QRDetect::getQuadrilateral(std::vector<cv::Point_<float> >)bool cv::QRDetect::testBypassRoute(std::vector<cv::Point_<float> >, int, int)bool cv::QRDetect::computeTransformationPoints()bool cv::QRDetect::localization()void cv::QRDetect::fixationPoints(std::vector<cv::Point_<float> >&)std::vector<cv::Point_<float> > cv::QRDetect::separateVerticalLines(const std::vector<cv::Vec<double, 3> >&)separateVerticalLinesstd::vector<cv::Vec<double, 3> > cv::QRDetect::searchHorizontalLines()void cv::QRDetect::init(const cv::Mat&, double, double)initN2cv6detail12PtrOwnerImplINS_14QRCodeDetector4ImplENS_14DefaultDeleterIS3_EEEEInvalid QR code source pointscontourArea(src_points)0.0ào@ÿÿÿÿÿÿÿ€@`?ào@ÐCÐ?ðCÿÿÿÿÿÿïü©ñÒMbP?’$I’$IÂ?۶m۶mÛ?ףp=
×ï?@۶m۶mË?333333ë?$@š™™™™™©?objdetectobjdetect_hogcascadedetect#define CELL_WIDTH 8
#define CELL_HEIGHT 8
#define CELLS_PER_BLOCK_X 2
#define CELLS_PER_BLOCK_Y 2
#define NTHREADS 256
#define CV_PI_F M_PI_F
#ifdef INTEL_DEVICE
#define QANGLE_TYPE     int
#define QANGLE_TYPE2    int2
#else
#define QANGLE_TYPE     uchar
#define QANGLE_TYPE2    uchar2
#endif
__kernel void compute_hists_lut_kernel(
const int cblock_stride_x, const int cblock_stride_y,
const int cnbins, const int cblock_hist_size, const int img_block_width,
const int blocks_in_group, const int blocks_total,
const int grad_quadstep, const int qangle_step,
__global const float* grad, __global const QANGLE_TYPE* qangle,
__global const float* gauss_w_lut,
__global float* block_hists, __local float* smem)
{
const int lx = get_local_id(0);
const int lp = lx / 24; 
const int gid = get_group_id(0) * blocks_in_group + lp;
const int gidY = gid / img_block_width;
const int gidX = gid - gidY * img_block_width;
const int lidX = lx - lp * 24;
const int lidY = get_local_id(1);
const int cell_x = lidX / 12;
const int cell_y = lidY;
const int cell_thread_x = lidX - cell_x * 12;
__local float* hists = smem + lp * cnbins * (CELLS_PER_BLOCK_X *
CELLS_PER_BLOCK_Y * 12 + CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y);
__local float* final_hist = hists + cnbins *
(CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y * 12);
const int offset_x = gidX * cblock_stride_x + (cell_x << 2) + cell_thread_x;
const int offset_y = gidY * cblock_stride_y + (cell_y << 2);
__global const float* grad_ptr = (gid < blocks_total) ?
grad + offset_y * grad_quadstep + (offset_x << 1) : grad;
__global const QANGLE_TYPE* qangle_ptr = (gid < blocks_total) ?
qangle + offset_y * qangle_step + (offset_x << 1) : qangle;
__local float* hist = hists + 12 * (cell_y * CELLS_PER_BLOCK_Y + cell_x) +
cell_thread_x;
for (int bin_id = 0; bin_id < cnbins; ++bin_id)
hist[bin_id * 48] = 0.f;
const int dist_x = -4 + cell_thread_x - 4 * cell_x;
const int dist_center_x = dist_x - 4 * (1 - 2 * cell_x);
const int dist_y_begin = -4 - 4 * lidY;
for (int dist_y = dist_y_begin; dist_y < dist_y_begin + 12; ++dist_y)
{
float2 vote = (float2) (grad_ptr[0], grad_ptr[1]);
QANGLE_TYPE2 bin = (QANGLE_TYPE2) (qangle_ptr[0], qangle_ptr[1]);
grad_ptr += grad_quadstep;
qangle_ptr += qangle_step;
int dist_center_y = dist_y - 4 * (1 - 2 * cell_y);
int idx = (dist_center_y + 8) * 16 + (dist_center_x + 8);
float gaussian = gauss_w_lut[idx];
idx = (dist_y + 8) * 16 + (dist_x + 8);
float interp_weight = gauss_w_lut[256+idx];
hist[bin.x * 48] += gaussian * interp_weight * vote.x;
hist[bin.y * 48] += gaussian * interp_weight * vote.y;
}
barrier(CLK_LOCAL_MEM_FENCE);
volatile __local float* hist_ = hist;
for (int bin_id = 0; bin_id < cnbins; ++bin_id, hist_ += 48)
{
if (cell_thread_x < 6)
hist_[0] += hist_[6];
barrier(CLK_LOCAL_MEM_FENCE);
if (cell_thread_x < 3)
hist_[0] += hist_[3];
#ifdef CPU
barrier(CLK_LOCAL_MEM_FENCE);
#endif
if (cell_thread_x == 0)
final_hist[(cell_x * 2 + cell_y) * cnbins + bin_id] =
hist_[0] + hist_[1] + hist_[2];
}
barrier(CLK_LOCAL_MEM_FENCE);
int tid = (cell_y * CELLS_PER_BLOCK_Y + cell_x) * 12 + cell_thread_x;
if ((tid < cblock_hist_size) && (gid < blocks_total))
{
__global float* block_hist = block_hists +
(gidY * img_block_width + gidX) * cblock_hist_size;
block_hist[tid] = final_hist[tid];
}
}
__kernel void normalize_hists_36_kernel(__global float* block_hists,
const float threshold, __local float *squares)
{
const int tid = get_local_id(0);
const int gid = get_global_id(0);
const int bid = tid / 36;      
const int boffset = bid * 36;  
const int hid = tid - boffset; 
float elem = block_hists[gid];
squares[tid] = elem * elem;
barrier(CLK_LOCAL_MEM_FENCE);
__local float* smem = squares + boffset;
float sum = smem[hid];
if (hid < 18)
smem[hid] = sum = sum + smem[hid + 18];
barrier(CLK_LOCAL_MEM_FENCE);
if (hid < 9)
smem[hid] = sum = sum + smem[hid + 9];
barrier(CLK_LOCAL_MEM_FENCE);
if (hid < 4)
smem[hid] = sum + smem[hid + 4];
barrier(CLK_LOCAL_MEM_FENCE);
sum = smem[0] + smem[1] + smem[2] + smem[3] + smem[8];
elem = elem / (sqrt(sum) + 3.6f);
elem = min(elem, threshold);
barrier(CLK_LOCAL_MEM_FENCE);
squares[tid] = elem * elem;
barrier(CLK_LOCAL_MEM_FENCE);
sum = smem[hid];
if (hid < 18)
smem[hid] = sum = sum + smem[hid + 18];
barrier(CLK_LOCAL_MEM_FENCE);
if (hid < 9)
smem[hid] = sum = sum + smem[hid + 9];
barrier(CLK_LOCAL_MEM_FENCE);
if (hid < 4)
smem[hid] = sum + smem[hid + 4];
barrier(CLK_LOCAL_MEM_FENCE);
sum = smem[0] + smem[1] + smem[2] + smem[3] + smem[8];
block_hists[gid] = elem / (sqrt(sum) + 1e-3f);
}
inline float reduce_smem(volatile __local float* smem, int size)
{
unsigned int tid = get_local_id(0);
float sum = smem[tid];
if (size >= 512) { if (tid < 256) smem[tid] = sum = sum + smem[tid + 256];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 256) { if (tid < 128) smem[tid] = sum = sum + smem[tid + 128];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 128) { if (tid < 64) smem[tid] = sum = sum + smem[tid + 64];
barrier(CLK_LOCAL_MEM_FENCE); }
#ifdef CPU
if (size >= 64) { if (tid < 32) smem[tid] = sum = sum + smem[tid + 32];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 32) { if (tid < 16) smem[tid] = sum = sum + smem[tid + 16];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 16) { if (tid < 8) smem[tid] = sum = sum + smem[tid + 8];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 8) { if (tid < 4) smem[tid] = sum = sum + smem[tid + 4];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 4) { if (tid < 2) smem[tid] = sum = sum + smem[tid + 2];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 2) { if (tid < 1) smem[tid] = sum = sum + smem[tid + 1];
barrier(CLK_LOCAL_MEM_FENCE); }
#else
if (tid < 32)
{
if (size >= 64) smem[tid] = sum = sum + smem[tid + 32];
#if WAVE_SIZE < 32
} barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16) {
#endif
if (size >= 32) smem[tid] = sum = sum + smem[tid + 16];
if (size >= 16) smem[tid] = sum = sum + smem[tid + 8];
if (size >= 8) smem[tid] = sum = sum + smem[tid + 4];
if (size >= 4) smem[tid] = sum = sum + smem[tid + 2];
if (size >= 2) smem[tid] = sum = sum + smem[tid + 1];
}
#endif
return sum;
}
__kernel void normalize_hists_kernel(
const int nthreads, const int block_hist_size, const int img_block_width,
__global float* block_hists, const float threshold, __local float *squares)
{
const int tid = get_local_id(0);
const int gidX = get_group_id(0);
const int gidY = get_group_id(1);
__global float* hist = block_hists + (gidY * img_block_width + gidX) *
block_hist_size + tid;
float elem = 0.f;
if (tid < block_hist_size)
elem = hist[0];
squares[tid] = elem * elem;
barrier(CLK_LOCAL_MEM_FENCE);
float sum = reduce_smem(squares, nthreads);
float scale = 1.0f / (sqrt(sum) + 0.1f * block_hist_size);
elem = min(elem * scale, threshold);
barrier(CLK_LOCAL_MEM_FENCE);
squares[tid] = elem * elem;
barrier(CLK_LOCAL_MEM_FENCE);
sum = reduce_smem(squares, nthreads);
scale = 1.0f / (sqrt(sum) + 1e-3f);
if (tid < block_hist_size)
hist[0] = elem * scale;
}
__kernel void classify_hists_180_kernel(
const int cdescr_width, const int cdescr_height, const int cblock_hist_size,
const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
__global const float * block_hists, __global const float* coefs,
float free_coef, float threshold, __global uchar* labels)
{
const int tid = get_local_id(0);
const int gidX = get_group_id(0);
const int gidY = get_group_id(1);
__global const float* hist = block_hists + (gidY * win_block_stride_y *
img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
float product = 0.f;
for (int i = 0; i < cdescr_height; i++)
{
product += coefs[i * cdescr_width + tid] *
hist[i * img_block_width * cblock_hist_size + tid];
}
__local float products[180];
products[tid] = product;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 90) products[tid] = product = product + products[tid + 90];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 45) products[tid] = product = product + products[tid + 45];
barrier(CLK_LOCAL_MEM_FENCE);
volatile __local float* smem = products;
#ifdef CPU
if (tid < 13) smem[tid] = product = product + smem[tid + 32];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16) smem[tid] = product = product + smem[tid + 16];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<8) smem[tid] = product = product + smem[tid + 8];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<4) smem[tid] = product = product + smem[tid + 4];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<2) smem[tid] = product = product + smem[tid + 2];
barrier(CLK_LOCAL_MEM_FENCE);
#else
if (tid < 13)
{
smem[tid] = product = product + smem[tid + 32];
}
#if WAVE_SIZE < 32
barrier(CLK_LOCAL_MEM_FENCE);
#endif
if (tid < 16)
{
smem[tid] = product = product + smem[tid + 16];
smem[tid] = product = product + smem[tid + 8];
smem[tid] = product = product + smem[tid + 4];
smem[tid] = product = product + smem[tid + 2];
}
#endif
if (tid == 0){
product = product + smem[tid + 1];
labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold);
}
}
__kernel void classify_hists_252_kernel(
const int cdescr_width, const int cdescr_height, const int cblock_hist_size,
const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
__global const float * block_hists, __global const float* coefs,
float free_coef, float threshold, __global uchar* labels)
{
const int tid = get_local_id(0);
const int gidX = get_group_id(0);
const int gidY = get_group_id(1);
__global const float* hist = block_hists + (gidY * win_block_stride_y *
img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
float product = 0.f;
if (tid < cdescr_width)
{
for (int i = 0; i < cdescr_height; i++)
product += coefs[i * cdescr_width + tid] *
hist[i * img_block_width * cblock_hist_size + tid];
}
__local float products[NTHREADS];
products[tid] = product;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 128) products[tid] = product = product + products[tid + 128];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 64) products[tid] = product = product + products[tid + 64];
barrier(CLK_LOCAL_MEM_FENCE);
volatile __local float* smem = products;
#ifdef CPU
if(tid<32) smem[tid] = product = product + smem[tid + 32];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<16) smem[tid] = product = product + smem[tid + 16];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<8) smem[tid] = product = product + smem[tid + 8];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<4) smem[tid] = product = product + smem[tid + 4];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<2) smem[tid] = product = product + smem[tid + 2];
barrier(CLK_LOCAL_MEM_FENCE);
#else
if (tid < 32)
{
smem[tid] = product = product + smem[tid + 32];
#if WAVE_SIZE < 32
} barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16) {
#endif
smem[tid] = product = product + smem[tid + 16];
smem[tid] = product = product + smem[tid + 8];
smem[tid] = product = product + smem[tid + 4];
smem[tid] = product = product + smem[tid + 2];
}
#endif
if (tid == 0){
product = product + smem[tid + 1];
labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold);
}
}
__kernel void classify_hists_kernel(
const int cdescr_size, const int cdescr_width, const int cblock_hist_size,
const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
__global const float * block_hists, __global const float* coefs,
float free_coef, float threshold, __global uchar* labels)
{
const int tid = get_local_id(0);
const int gidX = get_group_id(0);
const int gidY = get_group_id(1);
__global const float* hist = block_hists + (gidY * win_block_stride_y *
img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
float product = 0.f;
for (int i = tid; i < cdescr_size; i += NTHREADS)
{
int offset_y = i / cdescr_width;
int offset_x = i - offset_y * cdescr_width;
product += coefs[i] *
hist[offset_y * img_block_width * cblock_hist_size + offset_x];
}
__local float products[NTHREADS];
products[tid] = product;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 128) products[tid] = product = product + products[tid + 128];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 64) products[tid] = product = product + products[tid + 64];
barrier(CLK_LOCAL_MEM_FENCE);
volatile __local float* smem = products;
#ifdef CPU
if(tid<32) smem[tid] = product = product + smem[tid + 32];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<16) smem[tid] = product = product + smem[tid + 16];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<8) smem[tid] = product = product + smem[tid + 8];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<4) smem[tid] = product = product + smem[tid + 4];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<2) smem[tid] = product = product + smem[tid + 2];
barrier(CLK_LOCAL_MEM_FENCE);
#else
if (tid < 32)
{
smem[tid] = product = product + smem[tid + 32];
#if WAVE_SIZE < 32
} barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16) {
#endif
smem[tid] = product = product + smem[tid + 16];
smem[tid] = product = product + smem[tid + 8];
smem[tid] = product = product + smem[tid + 4];
smem[tid] = product = product + smem[tid + 2];
}
#endif
if (tid == 0){
smem[tid] = product = product + smem[tid + 1];
labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold);
}
}
__kernel void extract_descrs_by_rows_kernel(
const int cblock_hist_size, const int descriptors_quadstep,
const int cdescr_size, const int cdescr_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
__global const float* block_hists, __global float* descriptors)
{
int tid = get_local_id(0);
int gidX = get_group_id(0);
int gidY = get_group_id(1);
__global const float* hist = block_hists + (gidY * win_block_stride_y *
img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
__global float* descriptor = descriptors +
(gidY * get_num_groups(0) + gidX) * descriptors_quadstep;
for (int i = tid; i < cdescr_size; i += NTHREADS)
{
int offset_y = i / cdescr_width;
int offset_x = i - offset_y * cdescr_width;
descriptor[i] = hist[offset_y * img_block_width * cblock_hist_size + offset_x];
}
}
__kernel void extract_descrs_by_cols_kernel(
const int cblock_hist_size, const int descriptors_quadstep, const int cdescr_size,
const int cnblocks_win_x, const int cnblocks_win_y, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
__global const float* block_hists, __global float* descriptors)
{
int tid = get_local_id(0);
int gidX = get_group_id(0);
int gidY = get_group_id(1);
__global const float* hist = block_hists +  (gidY * win_block_stride_y *
img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
__global float* descriptor = descriptors +
(gidY * get_num_groups(0) + gidX) * descriptors_quadstep;
for (int i = tid; i < cdescr_size; i += NTHREADS)
{
int block_idx = i / cblock_hist_size;
int idx_in_block = i - block_idx * cblock_hist_size;
int y = block_idx / cnblocks_win_x;
int x = block_idx - y * cnblocks_win_x;
descriptor[(x * cnblocks_win_y + y) * cblock_hist_size + idx_in_block] =
hist[(y * img_block_width  + x) * cblock_hist_size + idx_in_block];
}
}
__kernel void compute_gradients_8UC4_kernel(
const int height, const int width,
const int img_step, const int grad_quadstep, const int qangle_step,
const __global uchar4 * img, __global float * grad, __global QANGLE_TYPE * qangle,
const float angle_scale, const char correct_gamma, const int cnbins)
{
const int x = get_global_id(0);
const int tid = get_local_id(0);
const int gSizeX = get_local_size(0);
const int gidY = get_group_id(1);
__global const uchar4* row = img + gidY * img_step;
__local float sh_row[(NTHREADS + 2) * 3];
uchar4 val;
if (x < width)
val = row[x];
else
val = row[width - 2];
sh_row[tid + 1] = val.x;
sh_row[tid + 1 + (NTHREADS + 2)] = val.y;
sh_row[tid + 1 + 2 * (NTHREADS + 2)] = val.z;
if (tid == 0)
{
val = row[max(x - 1, 1)];
sh_row[0] = val.x;
sh_row[(NTHREADS + 2)] = val.y;
sh_row[2 * (NTHREADS + 2)] = val.z;
}
if (tid == gSizeX - 1)
{
val = row[min(x + 1, width - 2)];
sh_row[gSizeX + 1] = val.x;
sh_row[gSizeX + 1 + (NTHREADS + 2)] = val.y;
sh_row[gSizeX + 1 + 2 * (NTHREADS + 2)] = val.z;
}
barrier(CLK_LOCAL_MEM_FENCE);
if (x < width)
{
float4 a = (float4) (sh_row[tid], sh_row[tid + (NTHREADS + 2)],
sh_row[tid + 2 * (NTHREADS + 2)], 0);
float4 b = (float4) (sh_row[tid + 2], sh_row[tid + 2 + (NTHREADS + 2)],
sh_row[tid + 2 + 2 * (NTHREADS + 2)], 0);
float4 dx;
if (correct_gamma == 1)
dx = sqrt(b) - sqrt(a);
else
dx = b - a;
float4 dy = (float4) 0.f;
if (gidY > 0 && gidY < height - 1)
{
a = convert_float4(img[(gidY - 1) * img_step + x].xyzw);
b = convert_float4(img[(gidY + 1) * img_step + x].xyzw);
if (correct_gamma == 1)
dy = sqrt(b) - sqrt(a);
else
dy = b - a;
}
float4 mag = hypot(dx, dy);
float best_dx = dx.x;
float best_dy = dy.x;
float mag0 = mag.x;
if (mag0 < mag.y)
{
best_dx = dx.y;
best_dy = dy.y;
mag0 = mag.y;
}
if (mag0 < mag.z)
{
best_dx = dx.z;
best_dy = dy.z;
mag0 = mag.z;
}
float ang = (atan2(best_dy, best_dx) + CV_PI_F) * angle_scale - 0.5f;
int hidx = (int)floor(ang);
ang -= hidx;
hidx = (hidx + cnbins) % cnbins;
qangle[(gidY * qangle_step + x) << 1] = hidx;
qangle[((gidY * qangle_step + x) << 1) + 1] = (hidx + 1) % cnbins;
grad[(gidY * grad_quadstep + x) << 1] = mag0 * (1.f - ang);
grad[((gidY * grad_quadstep + x) << 1) + 1] = mag0 * ang;
}
}
__kernel void compute_gradients_8UC1_kernel(
const int height, const int width,
const int img_step, const int grad_quadstep, const int qangle_step,
__global const uchar * img, __global float * grad, __global QANGLE_TYPE * qangle,
const float angle_scale, const char correct_gamma, const int cnbins)
{
const int x = get_global_id(0);
const int tid = get_local_id(0);
const int gSizeX = get_local_size(0);
const int gidY = get_group_id(1);
__global const uchar* row = img + gidY * img_step;
__local float sh_row[NTHREADS + 2];
if (x < width)
sh_row[tid + 1] = row[x];
else
sh_row[tid + 1] = row[width - 2];
if (tid == 0)
sh_row[0] = row[max(x - 1, 1)];
if (tid == gSizeX - 1)
sh_row[gSizeX + 1] = row[min(x + 1, width - 2)];
barrier(CLK_LOCAL_MEM_FENCE);
if (x < width)
{
float dx;
if (correct_gamma == 1)
dx = sqrt(sh_row[tid + 2]) - sqrt(sh_row[tid]);
else
dx = sh_row[tid + 2] - sh_row[tid];
float dy = 0.f;
if (gidY > 0 && gidY < height - 1)
{
float a = (float) img[ (gidY + 1) * img_step + x ];
float b = (float) img[ (gidY - 1) * img_step + x ];
if (correct_gamma == 1)
dy = sqrt(a) - sqrt(b);
else
dy = a - b;
}
float mag = hypot(dx, dy);
float ang = (atan2(dy, dx) + CV_PI_F) * angle_scale - 0.5f;
int hidx = (int)floor(ang);
ang -= hidx;
hidx = (hidx + cnbins) % cnbins;
qangle[ (gidY * qangle_step + x) << 1 ]     = hidx;
qangle[ ((gidY * qangle_step + x) << 1) + 1 ] = (hidx + 1) % cnbins;
grad[ (gidY * grad_quadstep + x) << 1 ]       = mag * (1.f - ang);
grad[ ((gidY * grad_quadstep + x) << 1) + 1 ]   = mag * ang;
}
}
bc57f4f75fb81bae73bfe73cc4ca15e4#ifdef HAAR
typedef struct __attribute__((aligned(4))) OptHaarFeature
{
int4 ofs[3] __attribute__((aligned (4)));
float4 weight __attribute__((aligned (4)));
}
OptHaarFeature;
#endif
#ifdef LBP
typedef struct __attribute__((aligned(4))) OptLBPFeature
{
int16 ofs __attribute__((aligned (4)));
}
OptLBPFeature;
#endif
typedef struct __attribute__((aligned(4))) Stump
{
float4 st __attribute__((aligned (4)));
}
Stump;
typedef struct __attribute__((aligned(4))) Node
{
int4 n __attribute__((aligned (4)));
}
Node;
typedef struct __attribute__((aligned (4))) Stage
{
int first __attribute__((aligned (4)));
int ntrees __attribute__((aligned (4)));
float threshold __attribute__((aligned (4)));
}
Stage;
typedef struct __attribute__((aligned (4))) ScaleData
{
float scale __attribute__((aligned (4)));
int szi_width __attribute__((aligned (4)));
int szi_height __attribute__((aligned (4)));
int layer_ofs __attribute__((aligned (4)));
int ystep __attribute__((aligned (4)));
}
ScaleData;
#ifndef SUM_BUF_SIZE
#define SUM_BUF_SIZE 0
#endif
#ifndef NODE_COUNT
#define NODE_COUNT 1
#endif
#ifdef HAAR
__kernel __attribute__((reqd_work_group_size(LOCAL_SIZE_X,LOCAL_SIZE_Y,1)))
void runHaarClassifier(
int nscales, __global const ScaleData* scaleData,
__global const int* sum,
int _sumstep, int sumoffset,
__global const OptHaarFeature* optfeatures,
__global const Stage* stages,
__global const Node* nodes,
__global const float* leaves0,
volatile __global int* facepos,
int4 normrect, int sqofs, int2 windowsize)
{
int lx = get_local_id(0);
int ly = get_local_id(1);
int groupIdx = get_group_id(0);
int i, ngroups = get_global_size(0)/LOCAL_SIZE_X;
int scaleIdx, tileIdx, stageIdx;
int sumstep = (int)(_sumstep/sizeof(int));
int4 nofs0 = (int4)(mad24(normrect.y, sumstep, normrect.x),
mad24(normrect.y, sumstep, normrect.x + normrect.z),
mad24(normrect.y + normrect.w, sumstep, normrect.x),
mad24(normrect.y + normrect.w, sumstep, normrect.x + normrect.z));
int normarea = normrect.z * normrect.w;
float invarea = 1.f/normarea;
int lidx = ly*LOCAL_SIZE_X + lx;
#if SUM_BUF_SIZE > 0
int4 nofs = (int4)(mad24(normrect.y, SUM_BUF_STEP, normrect.x),
mad24(normrect.y, SUM_BUF_STEP, normrect.x + normrect.z),
mad24(normrect.y + normrect.w, SUM_BUF_STEP, normrect.x),
mad24(normrect.y + normrect.w, SUM_BUF_STEP, normrect.x + normrect.z));
#else
int4 nofs = nofs0;
#endif
#define LOCAL_SIZE (LOCAL_SIZE_X*LOCAL_SIZE_Y)
__local int lstore[SUM_BUF_SIZE + LOCAL_SIZE*5/2+1];
#if SUM_BUF_SIZE > 0
__local int* ibuf = lstore;
__local int* lcount = ibuf + SUM_BUF_SIZE;
#else
__local int* lcount = lstore;
#endif
__local float* lnf = (__local float*)(lcount + 1);
__local float* lpartsum = lnf + LOCAL_SIZE;
__local short* lbuf = (__local short*)(lpartsum + LOCAL_SIZE);
for( scaleIdx = nscales-1; scaleIdx >= 0; scaleIdx-- )
{
__global const ScaleData* s = scaleData + scaleIdx;
int ystep = s->ystep;
int2 worksize = (int2)(max(s->szi_width - windowsize.x, 0), max(s->szi_height - windowsize.y, 0));
int2 ntiles = (int2)((worksize.x + LOCAL_SIZE_X-1)/LOCAL_SIZE_X,
(worksize.y + LOCAL_SIZE_Y-1)/LOCAL_SIZE_Y);
int totalTiles = ntiles.x*ntiles.y;
for( tileIdx = groupIdx; tileIdx < totalTiles; tileIdx += ngroups )
{
int ix0 = (tileIdx % ntiles.x)*LOCAL_SIZE_X;
int iy0 = (tileIdx / ntiles.x)*LOCAL_SIZE_Y;
int ix = lx, iy = ly;
__global const int* psum0 = sum + mad24(iy0, sumstep, ix0) + s->layer_ofs;
__global const int* psum1 = psum0 + mad24(iy, sumstep, ix);
if( ix0 >= worksize.x || iy0 >= worksize.y )
continue;
#if SUM_BUF_SIZE > 0
for( i = lidx*4; i < SUM_BUF_SIZE; i += LOCAL_SIZE_X*LOCAL_SIZE_Y*4 )
{
int dy = i/SUM_BUF_STEP, dx = i - dy*SUM_BUF_STEP;
vstore4(vload4(0, psum0 + mad24(dy, sumstep, dx)), 0, ibuf+i);
}
#endif
if( lidx == 0 )
lcount[0] = 0;
barrier(CLK_LOCAL_MEM_FENCE);
if( ix0 + ix < worksize.x && iy0 + iy < worksize.y )
{
#if NODE_COUNT==1
__global const Stump* stump = (__global const Stump*)nodes;
#else
__global const Node* node = nodes;
__global const float* leaves = leaves0;
#endif
#if SUM_BUF_SIZE > 0
__local const int* psum = ibuf + mad24(iy, SUM_BUF_STEP, ix);
#else
__global const int* psum = psum1;
#endif
__global const int* psqsum = (__global const int*)(psum1 + sqofs);
float sval = (psum[nofs.x] - psum[nofs.y] - psum[nofs.z] + psum[nofs.w])*invarea;
float sqval = (psqsum[nofs0.x] - psqsum[nofs0.y] - psqsum[nofs0.z] + psqsum[nofs0.w])*invarea;
float nf = (float)normarea * sqrt(max(sqval - sval * sval, 0.f));
nf = nf > 0 ? nf : 1.f;
for( stageIdx = 0; stageIdx < SPLIT_STAGE; stageIdx++ )
{
int ntrees = stages[stageIdx].ntrees;
float s = 0.f;
#if NODE_COUNT==1
for( i = 0; i < ntrees; i++ )
{
float4 st = stump[i].st;
__global const OptHaarFeature* f = optfeatures + as_int(st.x);
float4 weight = f->weight;
int4 ofs = f->ofs[0];
sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x;
ofs = f->ofs[1];
sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval);
if( weight.z > 0 )
{
ofs = f->ofs[2];
sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval);
}
s += (sval < st.y*nf) ? st.z : st.w;
}
stump += ntrees;
#else
for( i = 0; i < ntrees; i++, node += NODE_COUNT, leaves += NODE_COUNT+1 )
{
int idx = 0;
do
{
int4 n = node[idx].n;
__global const OptHaarFeature* f = optfeatures + n.x;
float4 weight = f->weight;
int4 ofs = f->ofs[0];
sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x;
ofs = f->ofs[1];
sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval);
if( weight.z > 0 )
{
ofs = f->ofs[2];
sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval);
}
idx = (sval < as_float(n.y)*nf) ? n.z : n.w;
}
while(idx > 0);
s += leaves[-idx];
}
#endif
if( s < stages[stageIdx].threshold )
break;
}
if( stageIdx == SPLIT_STAGE && (ystep == 1 || ((ix | iy) & 1) == 0) )
{
int count = atomic_inc(lcount);
lbuf[count] = (int)(ix | (iy << 8));
lnf[count] = nf;
}
}
for( stageIdx = SPLIT_STAGE; stageIdx < N_STAGES; stageIdx++ )
{
barrier(CLK_LOCAL_MEM_FENCE);
int nrects = lcount[0];
if( nrects == 0 )
break;
barrier(CLK_LOCAL_MEM_FENCE);
if( lidx == 0 )
lcount[0] = 0;
{
#if NODE_COUNT == 1
__global const Stump* stump = (__global const Stump*)nodes + stages[stageIdx].first;
#else
__global const Node* node = nodes + stages[stageIdx].first*NODE_COUNT;
__global const float* leaves = leaves0 + stages[stageIdx].first*(NODE_COUNT+1);
#endif
int nparts = LOCAL_SIZE / nrects;
int ntrees = stages[stageIdx].ntrees;
int ntrees_p = (ntrees + nparts - 1)/nparts;
int nr = lidx / nparts;
int partidx = -1, idxval = 0;
float partsum = 0.f, nf = 0.f;
if( nr < nrects )
{
partidx = lidx % nparts;
idxval = lbuf[nr];
nf = lnf[nr];
{
int ntrees0 = ntrees_p*partidx;
int ntrees1 = min(ntrees0 + ntrees_p, ntrees);
int ix1 = idxval & 255, iy1 = idxval >> 8;
#if SUM_BUF_SIZE > 0
__local const int* psum = ibuf + mad24(iy1, SUM_BUF_STEP, ix1);
#else
__global const int* psum = psum0 + mad24(iy1, sumstep, ix1);
#endif
#if NODE_COUNT == 1
for( i = ntrees0; i < ntrees1; i++ )
{
float4 st = stump[i].st;
__global const OptHaarFeature* f = optfeatures + as_int(st.x);
float4 weight = f->weight;
int4 ofs = f->ofs[0];
float sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x;
ofs = f->ofs[1];
sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval);
if( fabs(weight.z) > 0 )
{
ofs = f->ofs[2];
sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval);
}
partsum += (sval < st.y*nf) ? st.z : st.w;
}
#else
for( i = ntrees0; i < ntrees1; i++ )
{
int idx = 0;
do
{
int4 n = node[i*2 + idx].n;
__global const OptHaarFeature* f = optfeatures + n.x;
float4 weight = f->weight;
int4 ofs = f->ofs[0];
float sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x;
ofs = f->ofs[1];
sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval);
if( weight.z > 0 )
{
ofs = f->ofs[2];
sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval);
}
idx = (sval < as_float(n.y)*nf) ? n.z : n.w;
}
while(idx > 0);
partsum += leaves[i*3-idx];
}
#endif
}
}
lpartsum[lidx] = partsum;
barrier(CLK_LOCAL_MEM_FENCE);
if( partidx == 0 )
{
float s = lpartsum[nr*nparts];
for( i = 1; i < nparts; i++ )
s += lpartsum[i + nr*nparts];
if( s >= stages[stageIdx].threshold )
{
int count = atomic_inc(lcount);
lbuf[count] = idxval;
lnf[count] = nf;
}
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if( stageIdx == N_STAGES )
{
int nrects = lcount[0];
if( lidx < nrects )
{
int nfaces = atomic_inc(facepos);
if( nfaces < MAX_FACES )
{
volatile __global int* face = facepos + 1 + nfaces*3;
int val = lbuf[lidx];
face[0] = scaleIdx;
face[1] = ix0 + (val & 255);
face[2] = iy0 + (val >> 8);
}
}
}
}
}
}
#endif
#ifdef LBP
#undef CALC_SUM_OFS_
#define CALC_SUM_OFS_(p0, p1, p2, p3, ptr) \
((ptr)[p0] - (ptr)[p1] - (ptr)[p2] + (ptr)[p3])
__kernel void runLBPClassifierStumpSimple(
int nscales, __global const ScaleData* scaleData,
__global const int* sum,
int _sumstep, int sumoffset,
__global const OptLBPFeature* optfeatures,
__global const Stage* stages,
__global const Stump* stumps,
__global const int* bitsets,
int bitsetSize,
volatile __global int* facepos,
int2 windowsize)
{
int lx = get_local_id(0);
int ly = get_local_id(1);
int local_size_x = get_local_size(0);
int local_size_y = get_local_size(1);
int groupIdx = get_group_id(1)*get_num_groups(0) + get_group_id(0);
int ngroups = get_num_groups(0)*get_num_groups(1);
int scaleIdx, tileIdx, stageIdx;
int sumstep = (int)(_sumstep/sizeof(int));
for( scaleIdx = nscales-1; scaleIdx >= 0; scaleIdx-- )
{
__global const ScaleData* s = scaleData + scaleIdx;
int ystep = s->ystep;
int2 worksize = (int2)(max(s->szi_width - windowsize.x, 0), max(s->szi_height - windowsize.y, 0));
int2 ntiles = (int2)((worksize.x/ystep + local_size_x-1)/local_size_x,
(worksize.y/ystep + local_size_y-1)/local_size_y);
int totalTiles = ntiles.x*ntiles.y;
for( tileIdx = groupIdx; tileIdx < totalTiles; tileIdx += ngroups )
{
int iy = mad24((tileIdx / ntiles.x), local_size_y, ly) * ystep;
int ix = mad24((tileIdx % ntiles.x), local_size_x, lx) * ystep;
if( ix < worksize.x && iy < worksize.y )
{
__global const int* p = sum + mad24(iy, sumstep, ix) + s->layer_ofs;
__global const Stump* stump = stumps;
__global const int* bitset = bitsets;
for( stageIdx = 0; stageIdx < N_STAGES; stageIdx++ )
{
int i, ntrees = stages[stageIdx].ntrees;
float s = 0.f;
for( i = 0; i < ntrees; i++, stump++, bitset += bitsetSize )
{
float4 st = stump->st;
__global const OptLBPFeature* f = optfeatures + as_int(st.x);
int16 ofs = f->ofs;
int cval = CALC_SUM_OFS_( ofs.s5, ofs.s6, ofs.s9, ofs.sa, p );
int mask, idx = (CALC_SUM_OFS_( ofs.s0, ofs.s1, ofs.s4, ofs.s5, p ) >= cval ? 4 : 0);
idx |= (CALC_SUM_OFS_( ofs.s1, ofs.s2, ofs.s5, ofs.s6, p ) >= cval ? 2 : 0);
idx |= (CALC_SUM_OFS_( ofs.s2, ofs.s3, ofs.s6, ofs.s7, p ) >= cval ? 1 : 0);
mask = (CALC_SUM_OFS_( ofs.s6, ofs.s7, ofs.sa, ofs.sb, p ) >= cval ? 16 : 0);
mask |= (CALC_SUM_OFS_( ofs.sa, ofs.sb, ofs.se, ofs.sf, p ) >= cval ? 8 : 0);
mask |= (CALC_SUM_OFS_( ofs.s9, ofs.sa, ofs.sd, ofs.se, p ) >= cval ? 4 : 0);
mask |= (CALC_SUM_OFS_( ofs.s8, ofs.s9, ofs.sc, ofs.sd, p ) >= cval ? 2 : 0);
mask |= (CALC_SUM_OFS_( ofs.s4, ofs.s5, ofs.s8, ofs.s9, p ) >= cval ? 1 : 0);
s += (bitset[idx] & (1 << mask)) ? st.z : st.w;
}
if( s < stages[stageIdx].threshold )
break;
}
if( stageIdx == N_STAGES )
{
int nfaces = atomic_inc(facepos);
if( nfaces < MAX_FACES )
{
volatile __global int* face = facepos + 1 + nfaces*3;
face[0] = scaleIdx;
face[1] = ix;
face[2] = iy;
}
}
}
}
}
}
__kernel __attribute__((reqd_work_group_size(LOCAL_SIZE_X,LOCAL_SIZE_Y,1)))
void runLBPClassifierStump(
int nscales, __global const ScaleData* scaleData,
__global const int* sum,
int _sumstep, int sumoffset,
__global const OptLBPFeature* optfeatures,
__global const Stage* stages,
__global const Stump* stumps,
__global const int* bitsets,
int bitsetSize,
volatile __global int* facepos,
int2 windowsize)
{
int lx = get_local_id(0);
int ly = get_local_id(1);
int groupIdx = get_group_id(0);
int i, ngroups = get_global_size(0)/LOCAL_SIZE_X;
int scaleIdx, tileIdx, stageIdx;
int sumstep = (int)(_sumstep/sizeof(int));
int lidx = ly*LOCAL_SIZE_X + lx;
#define LOCAL_SIZE (LOCAL_SIZE_X*LOCAL_SIZE_Y)
__local int lstore[SUM_BUF_SIZE + LOCAL_SIZE*3/2+1];
#if SUM_BUF_SIZE > 0
__local int* ibuf = lstore;
__local int* lcount = ibuf + SUM_BUF_SIZE;
#else
__local int* lcount = lstore;
#endif
__local float* lpartsum = (__local float*)(lcount + 1);
__local short* lbuf = (__local short*)(lpartsum + LOCAL_SIZE);
for( scaleIdx = nscales-1; scaleIdx >= 0; scaleIdx-- )
{
__global const ScaleData* s = scaleData + scaleIdx;
int ystep = s->ystep;
int2 worksize = (int2)(max(s->szi_width - windowsize.x, 0), max(s->szi_height - windowsize.y, 0));
int2 ntiles = (int2)((worksize.x + LOCAL_SIZE_X-1)/LOCAL_SIZE_X,
(worksize.y + LOCAL_SIZE_Y-1)/LOCAL_SIZE_Y);
int totalTiles = ntiles.x*ntiles.y;
for( tileIdx = groupIdx; tileIdx < totalTiles; tileIdx += ngroups )
{
int ix0 = (tileIdx % ntiles.x)*LOCAL_SIZE_X;
int iy0 = (tileIdx / ntiles.x)*LOCAL_SIZE_Y;
int ix = lx, iy = ly;
__global const int* psum0 = sum + mad24(iy0, sumstep, ix0) + s->layer_ofs;
if( ix0 >= worksize.x || iy0 >= worksize.y )
continue;
#if SUM_BUF_SIZE > 0
for( i = lidx*4; i < SUM_BUF_SIZE; i += LOCAL_SIZE_X*LOCAL_SIZE_Y*4 )
{
int dy = i/SUM_BUF_STEP, dx = i - dy*SUM_BUF_STEP;
vstore4(vload4(0, psum0 + mad24(dy, sumstep, dx)), 0, ibuf+i);
}
barrier(CLK_LOCAL_MEM_FENCE);
#endif
if( lidx == 0 )
lcount[0] = 0;
barrier(CLK_LOCAL_MEM_FENCE);
if( ix0 + ix < worksize.x && iy0 + iy < worksize.y )
{
__global const Stump* stump = stumps;
__global const int* bitset = bitsets;
#if SUM_BUF_SIZE > 0
__local const int* p = ibuf + mad24(iy, SUM_BUF_STEP, ix);
#else
__global const int* p = psum0 + mad24(iy, sumstep, ix);
#endif
for( stageIdx = 0; stageIdx < SPLIT_STAGE; stageIdx++ )
{
int ntrees = stages[stageIdx].ntrees;
float s = 0.f;
for( i = 0; i < ntrees; i++, stump++, bitset += bitsetSize )
{
float4 st = stump->st;
__global const OptLBPFeature* f = optfeatures + as_int(st.x);
int16 ofs = f->ofs;
int cval = CALC_SUM_OFS_( ofs.s5, ofs.s6, ofs.s9, ofs.sa, p );
int mask, idx = (CALC_SUM_OFS_( ofs.s0, ofs.s1, ofs.s4, ofs.s5, p ) >= cval ? 4 : 0);
idx |= (CALC_SUM_OFS_( ofs.s1, ofs.s2, ofs.s5, ofs.s6, p ) >= cval ? 2 : 0);
idx |= (CALC_SUM_OFS_( ofs.s2, ofs.s3, ofs.s6, ofs.s7, p ) >= cval ? 1 : 0);
mask = (CALC_SUM_OFS_( ofs.s6, ofs.s7, ofs.sa, ofs.sb, p ) >= cval ? 16 : 0);
mask |= (CALC_SUM_OFS_( ofs.sa, ofs.sb, ofs.se, ofs.sf, p ) >= cval ? 8 : 0);
mask |= (CALC_SUM_OFS_( ofs.s9, ofs.sa, ofs.sd, ofs.se, p ) >= cval ? 4 : 0);
mask |= (CALC_SUM_OFS_( ofs.s8, ofs.s9, ofs.sc, ofs.sd, p ) >= cval ? 2 : 0);
mask |= (CALC_SUM_OFS_( ofs.s4, ofs.s5, ofs.s8, ofs.s9, p ) >= cval ? 1 : 0);
s += (bitset[idx] & (1 << mask)) ? st.z : st.w;
}
if( s < stages[stageIdx].threshold )
break;
}
if( stageIdx == SPLIT_STAGE && (ystep == 1 || ((ix | iy) & 1) == 0) )
{
int count = atomic_inc(lcount);
lbuf[count] = (int)(ix | (iy << 8));
}
}
for( stageIdx = SPLIT_STAGE; stageIdx < N_STAGES; stageIdx++ )
{
int nrects = lcount[0];
barrier(CLK_LOCAL_MEM_FENCE);
if( nrects == 0 )
break;
if( lidx == 0 )
lcount[0] = 0;
{
__global const Stump* stump = stumps + stages[stageIdx].first;
__global const int* bitset = bitsets + stages[stageIdx].first*bitsetSize;
int nparts = LOCAL_SIZE / nrects;
int ntrees = stages[stageIdx].ntrees;
int ntrees_p = (ntrees + nparts - 1)/nparts;
int nr = lidx / nparts;
int partidx = -1, idxval = 0;
float partsum = 0.f, nf = 0.f;
if( nr < nrects )
{
partidx = lidx % nparts;
idxval = lbuf[nr];
{
int ntrees0 = ntrees_p*partidx;
int ntrees1 = min(ntrees0 + ntrees_p, ntrees);
int ix1 = idxval & 255, iy1 = idxval >> 8;
#if SUM_BUF_SIZE > 0
__local const int* p = ibuf + mad24(iy1, SUM_BUF_STEP, ix1);
#else
__global const int* p = psum0 + mad24(iy1, sumstep, ix1);
#endif
for( i = ntrees0; i < ntrees1; i++ )
{
float4 st = stump[i].st;
__global const OptLBPFeature* f = optfeatures + as_int(st.x);
int16 ofs = f->ofs;
#define CALC_SUM_OFS_(p0, p1, p2, p3, ptr) \
((ptr)[p0] - (ptr)[p1] - (ptr)[p2] + (ptr)[p3])
int cval = CALC_SUM_OFS_( ofs.s5, ofs.s6, ofs.s9, ofs.sa, p );
int mask, idx = (CALC_SUM_OFS_( ofs.s0, ofs.s1, ofs.s4, ofs.s5, p ) >= cval ? 4 : 0);
idx |= (CALC_SUM_OFS_( ofs.s1, ofs.s2, ofs.s5, ofs.s6, p ) >= cval ? 2 : 0);
idx |= (CALC_SUM_OFS_( ofs.s2, ofs.s3, ofs.s6, ofs.s7, p ) >= cval ? 1 : 0);
mask = (CALC_SUM_OFS_( ofs.s6, ofs.s7, ofs.sa, ofs.sb, p ) >= cval ? 16 : 0);
mask |= (CALC_SUM_OFS_( ofs.sa, ofs.sb, ofs.se, ofs.sf, p ) >= cval ? 8 : 0);
mask |= (CALC_SUM_OFS_( ofs.s9, ofs.sa, ofs.sd, ofs.se, p ) >= cval ? 4 : 0);
mask |= (CALC_SUM_OFS_( ofs.s8, ofs.s9, ofs.sc, ofs.sd, p ) >= cval ? 2 : 0);
mask |= (CALC_SUM_OFS_( ofs.s4, ofs.s5, ofs.s8, ofs.s9, p ) >= cval ? 1 : 0);
partsum += (bitset[i*bitsetSize + idx] & (1 << mask)) ? st.z : st.w;
}
}
}
lpartsum[lidx] = partsum;
barrier(CLK_LOCAL_MEM_FENCE);
if( partidx == 0 )
{
float s = lpartsum[nr*nparts];
for( i = 1; i < nparts; i++ )
s += lpartsum[i + nr*nparts];
if( s >= stages[stageIdx].threshold )
{
int count = atomic_inc(lcount);
lbuf[count] = idxval;
}
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if( stageIdx == N_STAGES )
{
int nrects = lcount[0];
if( lidx < nrects )
{
int nfaces = atomic_inc(facepos);
if( nfaces < MAX_FACES )
{
volatile __global int* face = facepos + 1 + nfaces*3;
int val = lbuf[lidx];
face[0] = scaleIdx;
face[1] = ix0 + (val & 255);
face[2] = iy0 + (val >> 8);
}
}
}
}
}
}
#endif
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