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dextro / libopencv-dev deb
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/mnt/pkg/debpkging/opencv/workdir-opcv-3.2.0/opencv-3.2.0/modules/objdetect/src/detection_based_tracker.cpp
%s: ERROR: OpenCV Exception caught:
'%s'
%s: ERROR: Exception caught:
'%s'
%s: ERROR: UNKNOWN Exception caught
updateTrackedObjects process DetectionBasedTracker workcycleObjectDetector workcycleObjectDetectorFunction SeparateDetectionWork N2cv6detail8PtrOwnerE N2cv21DetectionBasedTrackerE N2cv21DetectionBasedTracker21SeparateDetectionWorkE N2cv6detail12PtrOwnerImplINS_21DetectionBasedTracker21SeparateDetectionWorkENS_14DefaultDeleterIS3_EEEE @@opencv-haar-classifier size stages trees stage_threshold parent next tree %d node %d feature rects tilted left_node left_val right_node right_val Invalid stage %d / %d%d%d%d%d%f%n %s%n %f%d%d%n /mnt/pkg/debpkging/opencv/workdir-opcv-3.2.0/opencv-3.2.0/modules/objdetect/src/haar.cpp header 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 window Trees 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) Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed Sum and tilted_sum must have the same stride (step, widthStep) All integral images must have the same size %s%s%d/AdaBoostCARTHaarClassifier.txt %s/%d/AdaBoostCARTHaarClassifier.txt icvReadHaarClassifier icvCreateHaarClassifierCascade cvLoadHaarClassifierCascade cvHaarDetectObjectsForROC cvRunHaarClassifierCascadeSum icvCreateHidHaarClassifierCascade cvSetImagesForHaarClassifierCascade N2cv36HaarDetectObjects_ScaleImage_InvokerE N2cv38HaarDetectObjects_ScaleCascade_InvokerE N2cv6detail12PtrOwnerImplI5CvMatNS_14DefaultDeleterIS2_EEEE ·Ñ8 % @333333ã?Ù? I@333333Ã?ffffffæ?vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu) /mnt/pkg/debpkging/opencv/workdir-opcv-3.2.0/opencv-3.2.0/modules/objdetect/src/cascadedetect.cpp /mnt/pkg/debpkging/opencv/workdir-opcv-3.2.0/opencv-3.2.0/modules/objdetect/src/cascadedetect.hpp sqsum.u == u && sqsum.size() == s.szi && sqsum.type()==CV_32S -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 LBP origWinSize.height > 0 && origWinSize.width > 0 /mnt/pkg/debpkging/opencv/workdir-opcv-3.2.0/opencv-3.2.0/modules/core/include/opencv2/core/core_c.h rect !cascade.data.stumps.empty() vector::reserve runHaarClassifier runLBPClassifierStumpSimple stageType BOOST featureType HAAR LBP HOG width height featureParams maxCatCount weakClassifiers leafValues internalNodes stageThreshold features getMaskGenerator setMaskGenerator getOldCascade getFeatureType getOriginalWindowSize isOldFormatCascade detectMultiScale detectMultiScale detectMultiScale clipObjects read detectMultiScale Seq detectMultiScale predictCategoricalStump predictOrderedStump setWindow getScaleData Mat computeChannels read Mat partition N2cv21BaseCascadeClassifierE N2cv16FeatureEvaluatorE N2cv21CascadeClassifierImplE N2cv13HaarEvaluatorE N2cv12LBPEvaluatorE N2cv24CascadeClassifierInvokerE N2cv6detail12PtrOwnerImplINS_13HaarEvaluatorENS_14DefaultDeleterIS2_EEEE N2cv6detail12PtrOwnerImplINS_12LBPEvaluatorENS_14DefaultDeleterIS2_EEEE N2cv6detail12PtrOwnerImplI12CvMemStorageNS_14DefaultDeleterIS2_EEEE N2cv6detail12PtrOwnerImplISt6vectorINS_16FeatureEvaluator9ScaleDataESaIS4_EENS_14DefaultDeleterIS6_EEEE N2cv6detail12PtrOwnerImplISt6vectorINS_13HaarEvaluator7FeatureESaIS4_EENS_14DefaultDeleterIS6_EEEE N2cv6detail12PtrOwnerImplISt6vectorINS_13HaarEvaluator10OptFeatureESaIS4_EENS_14DefaultDeleterIS6_EEEE N2cv6detail12PtrOwnerImplISt6vectorINS_12LBPEvaluator7FeatureESaIS4_EENS_14DefaultDeleterIS6_EEEE N2cv6detail12PtrOwnerImplISt6vectorINS_12LBPEvaluator10OptFeatureESaIS4_EENS_14DefaultDeleterIS6_EEEE N2cv6detail12PtrOwnerImplI23CvHaarClassifierCascadeNS_14DefaultDeleterIS2_EEEE N2cv6detail12PtrOwnerImplINS_21CascadeClassifierImplENS_14DefaultDeleterIS2_EEEE ¬Å'7 H7 @ ¹? ? 0@ܹá{ÊÐ?ñh㈵øä> operator<< operator<< operator<< 4#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
#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;
}
}
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