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Commit 51530d4d authored by Andrey Pavlenko's avatar Andrey Pavlenko Committed by OpenCV Buildbot
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Merge pull request #2543 from apavlenko:24_haar_revert

parents 8541b7bf 3747d264
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Showing with 133 additions and 228 deletions
modules/ocl/doc/image_processing.rst
View file @ 51530d4d
......@@ -65,15 +65,15 @@ ocl::integral
-----------------
Computes an integral image.
.. ocv:function:: void ocl::integral(const oclMat &src, oclMat &sum, oclMat &sqsum, int sdepth=-1)
.. ocv:function:: void ocl::integral(const oclMat &src, oclMat &sum, oclMat &sqsum)
.. ocv:function:: void ocl::integral(const oclMat &src, oclMat &sum, int sdepth=-1)
.. ocv:function:: void ocl::integral(const oclMat &src, oclMat &sum)
:param src: Source image. Only ``CV_8UC1`` images are supported for now.
:param sum: Integral image containing 32-bit unsigned integer or 32-bit floating-point .
:param sum: Integral image containing 32-bit unsigned integer values packed into ``CV_32SC1`` .
:param sqsum: Sqsum values is ``CV_32FC1`` or ``CV_64FC1`` type.
:param sqsum: Sqsum values is ``CV_32FC1`` type.
.. seealso:: :ocv:func:`integral`
......
modules/ocl/include/opencv2/ocl/ocl.hpp
View file @ 51530d4d
......@@ -859,10 +859,10 @@ namespace cv
CV_EXPORTS void warpPerspective(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);
//! computes the integral image and integral for the squared image
// sum will support CV_32S, CV_32F, sqsum - support CV32F, CV_64F
// sum will have CV_32S type, sqsum - CV32F type
// supports only CV_8UC1 source type
CV_EXPORTS void integral(const oclMat &src, oclMat &sum, oclMat &sqsum, int sdepth=-1 );
CV_EXPORTS void integral(const oclMat &src, oclMat &sum, int sdepth=-1 );
CV_EXPORTS void integral(const oclMat &src, oclMat &sum, oclMat &sqsum);
CV_EXPORTS void integral(const oclMat &src, oclMat &sum);
CV_EXPORTS void cornerHarris(const oclMat &src, oclMat &dst, int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
CV_EXPORTS void cornerHarris_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
......@@ -936,7 +936,7 @@ namespace cv
Size m_maxSize;
vector<CvSize> sizev;
vector<float> scalev;
oclMat gimg1, gsum, gsqsum, gsqsum_t;
oclMat gimg1, gsum, gsqsum;
void * buffers;
};
......
modules/ocl/perf/perf_imgproc.cpp
View file @ 51530d4d
......@@ -237,7 +237,7 @@ OCL_PERF_TEST_P(CornerHarrisFixture, CornerHarris,
typedef tuple<Size, MatDepth> IntegralParams;
typedef TestBaseWithParam<IntegralParams> IntegralFixture;
OCL_PERF_TEST_P(IntegralFixture, Integral1, ::testing::Combine(OCL_TEST_SIZES, OCL_PERF_ENUM(CV_32S, CV_32F)))
OCL_PERF_TEST_P(IntegralFixture, DISABLED_Integral1, ::testing::Combine(OCL_TEST_SIZES, OCL_PERF_ENUM(CV_32S, CV_32F)))
{
const IntegralParams params = GetParam();
const Size srcSize = get<0>(params);
......@@ -250,7 +250,7 @@ OCL_PERF_TEST_P(IntegralFixture, Integral1, ::testing::Combine(OCL_TEST_SIZES, O
{
ocl::oclMat oclSrc(src), oclDst;
OCL_TEST_CYCLE() cv::ocl::integral(oclSrc, oclDst, sdepth);
// OCL_TEST_CYCLE() cv::ocl::integral(oclSrc, oclDst, sdepth);
oclDst.download(dst);
......
modules/ocl/perf/perf_match_template.cpp
View file @ 51530d4d
......@@ -109,13 +109,13 @@ OCL_PERF_TEST_P(CV_TM_CCORR_NORMEDFixture, matchTemplate,
oclDst.download(dst);
SANITY_CHECK(dst, 3e-2);
SANITY_CHECK(dst, 2e-2);
}
else if (RUN_PLAIN_IMPL)
{
TEST_CYCLE() cv::matchTemplate(src, templ, dst, CV_TM_CCORR_NORMED);
SANITY_CHECK(dst, 3e-2);
SANITY_CHECK(dst, 2e-2);
}
else
OCL_PERF_ELSE
......
modules/ocl/src/haar.cpp
View file @ 51530d4d
......@@ -747,15 +747,6 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
oclMat gsum(totalheight + 4, gimg.cols + 1, CV_32SC1);
oclMat gsqsum(totalheight + 4, gimg.cols + 1, CV_32FC1);
int sdepth = 0;
if(Context::getContext()->supportsFeature(FEATURE_CL_DOUBLE))
sdepth = CV_64FC1;
else
sdepth = CV_32FC1;
sdepth = CV_MAT_DEPTH(sdepth);
int type = CV_MAKE_TYPE(sdepth, 1);
oclMat gsqsum_t(totalheight + 4, gimg.cols + 1, type);
cl_mem stagebuffer;
cl_mem nodebuffer;
cl_mem candidatebuffer;
......@@ -763,7 +754,6 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
cv::Rect roi, roi2;
cv::Mat imgroi, imgroisq;
cv::ocl::oclMat resizeroi, gimgroi, gimgroisq;
int grp_per_CU = 12;
size_t blocksize = 8;
......@@ -783,7 +773,7 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
roi2 = Rect(0, 0, sz.width - 1, sz.height - 1);
resizeroi = gimg1(roi2);
gimgroi = gsum(roi);
gimgroisq = gsqsum_t(roi);
gimgroisq = gsqsum(roi);
int width = gimgroi.cols - 1 - cascade->orig_window_size.width;
int height = gimgroi.rows - 1 - cascade->orig_window_size.height;
scaleinfo[i].width_height = (width << 16) | height;
......@@ -797,13 +787,8 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
scaleinfo[i].factor = factor;
cv::ocl::resize(gimg, resizeroi, Size(sz.width - 1, sz.height - 1), 0, 0, INTER_LINEAR);
cv::ocl::integral(resizeroi, gimgroi, gimgroisq);
indexy += sz.height;
}
if(gsqsum_t.depth() == CV_64F)
gsqsum_t.convertTo(gsqsum, CV_32FC1);
else
gsqsum = gsqsum_t;
gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
......@@ -1040,12 +1025,7 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
int n_factors = 0;
oclMat gsum;
oclMat gsqsum;
oclMat gsqsum_t;
cv::ocl::integral(gimg, gsum, gsqsum_t);
if(gsqsum_t.depth() == CV_64F)
gsqsum_t.convertTo(gsqsum, CV_32FC1);
else
gsqsum = gsqsum_t;
cv::ocl::integral(gimg, gsum, gsqsum);
CvSize sz;
vector<CvSize> sizev;
vector<float> scalev;
......@@ -1320,16 +1300,12 @@ void cv::ocl::OclCascadeClassifierBuf::detectMultiScale(oclMat &gimg, CV_OUT std
roi2 = Rect(0, 0, sz.width - 1, sz.height - 1);
resizeroi = gimg1(roi2);
gimgroi = gsum(roi);
gimgroisq = gsqsum_t(roi);
gimgroisq = gsqsum(roi);
cv::ocl::resize(gimg, resizeroi, Size(sz.width - 1, sz.height - 1), 0, 0, INTER_LINEAR);
cv::ocl::integral(resizeroi, gimgroi, gimgroisq);
indexy += sz.height;
}
if(gsqsum_t.depth() == CV_64F)
gsqsum_t.convertTo(gsqsum, CV_32FC1);
else
gsqsum = gsqsum_t;
gcascade = (GpuHidHaarClassifierCascade *)(cascade->hid_cascade);
stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
......@@ -1391,11 +1367,7 @@ void cv::ocl::OclCascadeClassifierBuf::detectMultiScale(oclMat &gimg, CV_OUT std
}
else
{
cv::ocl::integral(gimg, gsum, gsqsum_t);
if(gsqsum_t.depth() == CV_64F)
gsqsum_t.convertTo(gsqsum, CV_32FC1);
else
gsqsum = gsqsum_t;
cv::ocl::integral(gimg, gsum, gsqsum);
gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
......@@ -1621,7 +1593,6 @@ void cv::ocl::OclCascadeClassifierBuf::CreateFactorRelatedBufs(
gimg1.release();
gsum.release();
gsqsum.release();
gsqsum_t.release();
}
else if (!(m_flags & CV_HAAR_SCALE_IMAGE) && (flags & CV_HAAR_SCALE_IMAGE))
{
......@@ -1696,16 +1667,6 @@ void cv::ocl::OclCascadeClassifierBuf::CreateFactorRelatedBufs(
gsum.create(totalheight + 4, cols + 1, CV_32SC1);
gsqsum.create(totalheight + 4, cols + 1, CV_32FC1);
int sdepth = 0;
if(Context::getContext()->supportsFeature(FEATURE_CL_DOUBLE))
sdepth = CV_64FC1;
else
sdepth = CV_32FC1;
sdepth = CV_MAT_DEPTH(sdepth);
int type = CV_MAKE_TYPE(sdepth, 1);
gsqsum_t.create(totalheight + 4, cols + 1, type);
scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
for( int i = 0; i < loopcount; i++ )
{
......
modules/ocl/src/imgproc.cpp
View file @ 51530d4d
......@@ -898,7 +898,7 @@ namespace cv
////////////////////////////////////////////////////////////////////////
// integral
void integral(const oclMat &src, oclMat &sum, oclMat &sqsum, int sdepth)
void integral(const oclMat &src, oclMat &sum, oclMat &sqsum)
{
CV_Assert(src.type() == CV_8UC1);
if (!src.clCxt->supportsFeature(ocl::FEATURE_CL_DOUBLE) && src.depth() == CV_64F)
......@@ -907,11 +907,6 @@ namespace cv
return;
}
if( sdepth <= 0 )
sdepth = CV_32S;
sdepth = CV_MAT_DEPTH(sdepth);
int type = CV_MAKE_TYPE(sdepth, 1);
int vlen = 4;
int offset = src.offset / vlen;
int pre_invalid = src.offset % vlen;
......@@ -919,26 +914,17 @@ namespace cv
oclMat t_sum , t_sqsum;
int w = src.cols + 1, h = src.rows + 1;
char build_option[250];
if(Context::getContext()->supportsFeature(ocl::FEATURE_CL_DOUBLE))
{
t_sqsum.create(src.cols, src.rows, CV_64FC1);
sqsum.create(h, w, CV_64FC1);
sprintf(build_option, "-D TYPE=double -D TYPE4=double4 -D convert_TYPE4=convert_double4");
}
else
{
t_sqsum.create(src.cols, src.rows, CV_32FC1);
sqsum.create(h, w, CV_32FC1);
sprintf(build_option, "-D TYPE=float -D TYPE4=float4 -D convert_TYPE4=convert_float4");
}
int depth = src.depth() == CV_8U ? CV_32S : CV_64F;
int type = CV_MAKE_TYPE(depth, 1);
t_sum.create(src.cols, src.rows, type);
sum.create(h, w, type);
int sum_offset = sum.offset / sum.elemSize();
int sqsum_offset = sqsum.offset / sqsum.elemSize();
t_sqsum.create(src.cols, src.rows, CV_32FC1);
sqsum.create(h, w, CV_32FC1);
int sum_offset = sum.offset / vlen;
int sqsum_offset = sqsum.offset / vlen;
vector<pair<size_t , const void *> > args;
args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data ));
......@@ -950,9 +936,8 @@ namespace cv
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.cols ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sqsum.step));
size_t gt[3] = {((vcols + 1) / 2) * 256, 1, 1}, lt[3] = {256, 1, 1};
openCLExecuteKernel(src.clCxt, &imgproc_integral, "integral_cols", gt, lt, args, -1, sdepth, build_option);
openCLExecuteKernel(src.clCxt, &imgproc_integral, "integral_cols", gt, lt, args, -1, depth);
args.clear();
args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sum.data ));
......@@ -962,16 +947,15 @@ namespace cv
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.rows ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.cols ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sqsum.step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&sum.step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&sqsum.step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&sum_offset));
args.push_back( make_pair( sizeof(cl_int) , (void *)&sqsum_offset));
size_t gt2[3] = {t_sum.cols * 32, 1, 1}, lt2[3] = {256, 1, 1};
openCLExecuteKernel(src.clCxt, &imgproc_integral, "integral_rows", gt2, lt2, args, -1, sdepth, build_option);
openCLExecuteKernel(src.clCxt, &imgproc_integral, "integral_rows", gt2, lt2, args, -1, depth);
}
void integral(const oclMat &src, oclMat &sum, int sdepth)
void integral(const oclMat &src, oclMat &sum)
{
CV_Assert(src.type() == CV_8UC1);
int vlen = 4;
......@@ -979,13 +963,10 @@ namespace cv
int pre_invalid = src.offset % vlen;
int vcols = (pre_invalid + src.cols + vlen - 1) / vlen;
if( sdepth <= 0 )
sdepth = CV_32S;
sdepth = CV_MAT_DEPTH(sdepth);
int type = CV_MAKE_TYPE(sdepth, 1);
oclMat t_sum;
int w = src.cols + 1, h = src.rows + 1;
int depth = src.depth() == CV_8U ? CV_32S : CV_32F;
int type = CV_MAKE_TYPE(depth, 1);
t_sum.create(src.cols, src.rows, type);
sum.create(h, w, type);
......@@ -1001,7 +982,7 @@ namespace cv
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step ));
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step));
size_t gt[3] = {((vcols + 1) / 2) * 256, 1, 1}, lt[3] = {256, 1, 1};
openCLExecuteKernel(src.clCxt, &imgproc_integral_sum, "integral_sum_cols", gt, lt, args, -1, sdepth);
openCLExecuteKernel(src.clCxt, &imgproc_integral_sum, "integral_sum_cols", gt, lt, args, -1, depth);
args.clear();
args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sum.data ));
......@@ -1012,7 +993,7 @@ namespace cv
args.push_back( make_pair( sizeof(cl_int) , (void *)&sum.step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&sum_offset));
size_t gt2[3] = {t_sum.cols * 32, 1, 1}, lt2[3] = {256, 1, 1};
openCLExecuteKernel(src.clCxt, &imgproc_integral_sum, "integral_sum_rows", gt2, lt2, args, -1, sdepth);
openCLExecuteKernel(src.clCxt, &imgproc_integral_sum, "integral_sum_rows", gt2, lt2, args, -1, depth);
}
/////////////////////// corner //////////////////////////////
......
modules/ocl/src/match_template.cpp
View file @ 51530d4d
......@@ -245,15 +245,12 @@ namespace cv
void matchTemplate_CCORR_NORMED(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf)
{
cv::ocl::oclMat temp;
matchTemplate_CCORR(image, templ, result, buf);
buf.image_sums.resize(1);
buf.image_sqsums.resize(1);
integral(image.reshape(1), buf.image_sums[0], temp);
if(temp.depth() == CV_64F)
temp.convertTo(buf.image_sqsums[0], CV_32FC1);
else
buf.image_sqsums[0] = temp;
integral(image.reshape(1), buf.image_sums[0], buf.image_sqsums[0]);
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];
Context *clCxt = image.clCxt;
......@@ -419,12 +416,7 @@ namespace cv
{
buf.image_sums.resize(1);
buf.image_sqsums.resize(1);
cv::ocl::oclMat temp;
integral(image, buf.image_sums[0], temp);
if(temp.depth() == CV_64F)
temp.convertTo(buf.image_sqsums[0], CV_32FC1);
else
buf.image_sqsums[0] = temp;
integral(image, buf.image_sums[0], buf.image_sqsums[0]);
templ_sum[0] = (float)sum(templ)[0];
......@@ -460,14 +452,10 @@ namespace cv
templ_sum *= scale;
buf.image_sums.resize(buf.images.size());
buf.image_sqsums.resize(buf.images.size());
cv::ocl::oclMat temp;
for(int i = 0; i < image.oclchannels(); i ++)
{
integral(buf.images[i], buf.image_sums[i], temp);
if(temp.depth() == CV_64F)
temp.convertTo(buf.image_sqsums[i], CV_32FC1);
else
buf.image_sqsums[i] = temp;
integral(buf.images[i], buf.image_sums[i], buf.image_sqsums[i]);
}
switch(image.oclchannels())
......
modules/ocl/src/opencl/haarobjectdetect.cl
View file @ 51530d4d
......@@ -62,13 +62,13 @@ typedef struct __attribute__((aligned (128) )) GpuHidHaarTreeNode
GpuHidHaarTreeNode;
//typedef struct __attribute__((aligned (32))) GpuHidHaarClassifier
//{
// int count __attribute__((aligned (4)));
// GpuHidHaarTreeNode* node __attribute__((aligned (8)));
// float* alpha __attribute__((aligned (8)));
//}
//GpuHidHaarClassifier;
typedef struct __attribute__((aligned (32))) GpuHidHaarClassifier
{
int count __attribute__((aligned (4)));
GpuHidHaarTreeNode* node __attribute__((aligned (8)));
float* alpha __attribute__((aligned (8)));
}
GpuHidHaarClassifier;
typedef struct __attribute__((aligned (64))) GpuHidHaarStageClassifier
......@@ -84,22 +84,22 @@ typedef struct __attribute__((aligned (64))) GpuHidHaarStageClassifier
GpuHidHaarStageClassifier;
//typedef struct __attribute__((aligned (64))) GpuHidHaarClassifierCascade
//{
// int count __attribute__((aligned (4)));
// int is_stump_based __attribute__((aligned (4)));
// int has_tilted_features __attribute__((aligned (4)));
// int is_tree __attribute__((aligned (4)));
// int pq0 __attribute__((aligned (4)));
// int pq1 __attribute__((aligned (4)));
// int pq2 __attribute__((aligned (4)));
// int pq3 __attribute__((aligned (4)));
// int p0 __attribute__((aligned (4)));
// int p1 __attribute__((aligned (4)));
// int p2 __attribute__((aligned (4)));
// int p3 __attribute__((aligned (4)));
// float inv_window_area __attribute__((aligned (4)));
//} GpuHidHaarClassifierCascade;
typedef struct __attribute__((aligned (64))) GpuHidHaarClassifierCascade
{
int count __attribute__((aligned (4)));
int is_stump_based __attribute__((aligned (4)));
int has_tilted_features __attribute__((aligned (4)));
int is_tree __attribute__((aligned (4)));
int pq0 __attribute__((aligned (4)));
int pq1 __attribute__((aligned (4)));
int pq2 __attribute__((aligned (4)));
int pq3 __attribute__((aligned (4)));
int p0 __attribute__((aligned (4)));
int p1 __attribute__((aligned (4)));
int p2 __attribute__((aligned (4)));
int p3 __attribute__((aligned (4)));
float inv_window_area __attribute__((aligned (4)));
} GpuHidHaarClassifierCascade;
#ifdef PACKED_CLASSIFIER
......@@ -196,12 +196,10 @@ __kernel void gpuRunHaarClassifierCascadePacked(
for(int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++ )
{// iterate until candidate is valid
float stage_sum = 0.0f;
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier));
int lcl_off = (yl*DATA_SIZE_X)+(xl);
int stagecount = stageinfo->count;
float stagethreshold = stageinfo->threshold;
for(int nodeloop = 0; nodeloop < stagecount; nodecounter++,nodeloop++ )
int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
float stagethreshold = as_float(stageinfo.y);
int lcl_off = (lid_y*DATA_SIZE_X)+(lid_x);
for(int nodeloop = 0; nodeloop < stageinfo.x; nodecounter++,nodeloop++ )
{
// simple macro to extract shorts from int
#define M0(_t) ((_t)&0xFFFF)
......@@ -357,17 +355,14 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
variance_norm_factor = variance_norm_factor * correction - mean * mean;
variance_norm_factor = variance_norm_factor >=0.f ? sqrt(variance_norm_factor) : 1.f;
for(int stageloop = start_stage; (stageloop < split_stage) && result; stageloop++ )
for(int stageloop = start_stage; (stageloop < split_stage) && result; stageloop++ )
{
float stage_sum = 0.f;
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
float stagethreshold = stageinfo->threshold;
for(int nodeloop = 0; nodeloop < stagecount; )
int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
float stagethreshold = as_float(stageinfo.y);
for(int nodeloop = 0; nodeloop < stageinfo.x; )
{
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*)
(((__global uchar*)nodeptr) + nodecounter * sizeof(GpuHidHaarTreeNode));
__global GpuHidHaarTreeNode* currentnodeptr = (nodeptr + nodecounter);
int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
......@@ -423,7 +418,7 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
#endif
}
result = (stage_sum >= stagethreshold) ? 1 : 0;
result = (stage_sum >= stagethreshold);
}
if(factor < 2)
{
......@@ -452,17 +447,14 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
lclcount[0]=0;
barrier(CLK_LOCAL_MEM_FENCE);
//int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
float stagethreshold = stageinfo->threshold;
int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
float stagethreshold = as_float(stageinfo.y);
int perfscale = queuecount > 4 ? 3 : 2;
int queuecount_loop = (queuecount + (1<<perfscale)-1) >> perfscale;
int lcl_compute_win = lcl_sz >> perfscale;
int lcl_compute_win_id = (lcl_id >>(6-perfscale));
int lcl_loops = (stagecount + lcl_compute_win -1) >> (6-perfscale);
int lcl_loops = (stageinfo.x + lcl_compute_win -1) >> (6-perfscale);
int lcl_compute_id = lcl_id - (lcl_compute_win_id << (6-perfscale));
for(int queueloop=0; queueloop<queuecount_loop; queueloop++)
{
......@@ -477,10 +469,10 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
float part_sum = 0.f;
const int stump_factor = STUMP_BASED ? 1 : 2;
int root_offset = 0;
for(int lcl_loop=0; lcl_loop<lcl_loops && tempnodecounter<stagecount;)
for(int lcl_loop=0; lcl_loop<lcl_loops && tempnodecounter<stageinfo.x;)
{
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*)
(((__global uchar*)nodeptr) + sizeof(GpuHidHaarTreeNode) * ((nodecounter + tempnodecounter) * stump_factor + root_offset));
__global GpuHidHaarTreeNode* currentnodeptr =
nodeptr + (nodecounter + tempnodecounter) * stump_factor + root_offset;
int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
......@@ -557,7 +549,7 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
queuecount = lclcount[0];
barrier(CLK_LOCAL_MEM_FENCE);
nodecounter += stagecount;
nodecounter += stageinfo.x;
}//end for(int stageloop = splitstage; stageloop< endstage && queuecount>0;stageloop++)
if(lcl_id<queuecount)
......
modules/ocl/src/opencl/haarobjectdetect_scaled2.cl
View file @ 51530d4d
......@@ -59,13 +59,13 @@ typedef struct __attribute__((aligned(128))) GpuHidHaarTreeNode
int right __attribute__((aligned(4)));
}
GpuHidHaarTreeNode;
//typedef struct __attribute__((aligned(32))) GpuHidHaarClassifier
//{
// int count __attribute__((aligned(4)));
// GpuHidHaarTreeNode *node __attribute__((aligned(8)));
// float *alpha __attribute__((aligned(8)));
//}
//GpuHidHaarClassifier;
typedef struct __attribute__((aligned(32))) GpuHidHaarClassifier
{
int count __attribute__((aligned(4)));
GpuHidHaarTreeNode *node __attribute__((aligned(8)));
float *alpha __attribute__((aligned(8)));
}
GpuHidHaarClassifier;
typedef struct __attribute__((aligned(64))) GpuHidHaarStageClassifier
{
int count __attribute__((aligned(4)));
......@@ -77,29 +77,29 @@ typedef struct __attribute__((aligned(64))) GpuHidHaarStageClassifier
int reserved3 __attribute__((aligned(8)));
}
GpuHidHaarStageClassifier;
//typedef struct __attribute__((aligned(64))) GpuHidHaarClassifierCascade
//{
// int count __attribute__((aligned(4)));
// int is_stump_based __attribute__((aligned(4)));
// int has_tilted_features __attribute__((aligned(4)));
// int is_tree __attribute__((aligned(4)));
// int pq0 __attribute__((aligned(4)));
// int pq1 __attribute__((aligned(4)));
// int pq2 __attribute__((aligned(4)));
// int pq3 __attribute__((aligned(4)));
// int p0 __attribute__((aligned(4)));
// int p1 __attribute__((aligned(4)));
// int p2 __attribute__((aligned(4)));
// int p3 __attribute__((aligned(4)));
// float inv_window_area __attribute__((aligned(4)));
//} GpuHidHaarClassifierCascade;
typedef struct __attribute__((aligned(64))) GpuHidHaarClassifierCascade
{
int count __attribute__((aligned(4)));
int is_stump_based __attribute__((aligned(4)));
int has_tilted_features __attribute__((aligned(4)));
int is_tree __attribute__((aligned(4)));
int pq0 __attribute__((aligned(4)));
int pq1 __attribute__((aligned(4)));
int pq2 __attribute__((aligned(4)));
int pq3 __attribute__((aligned(4)));
int p0 __attribute__((aligned(4)));
int p1 __attribute__((aligned(4)));
int p2 __attribute__((aligned(4)));
int p3 __attribute__((aligned(4)));
float inv_window_area __attribute__((aligned(4)));
} GpuHidHaarClassifierCascade;
__kernel void gpuRunHaarClassifierCascade_scaled2(
global GpuHidHaarStageClassifier *stagecascadeptr_,
global GpuHidHaarStageClassifier *stagecascadeptr,
global int4 *info,
global GpuHidHaarTreeNode *nodeptr_,
global GpuHidHaarTreeNode *nodeptr,
global const int *restrict sum,
global const float *restrict sqsum,
global const float *restrict sqsum,
global int4 *candidate,
const int rows,
const int cols,
......@@ -132,7 +132,8 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
int max_idx = rows * cols - 1;
for (int scalei = 0; scalei < loopcount; scalei++)
{
int4 scaleinfo1 = info[scalei];
int4 scaleinfo1;
scaleinfo1 = info[scalei];
int grpnumperline = (scaleinfo1.y & 0xffff0000) >> 16;
int totalgrp = scaleinfo1.y & 0xffff;
float factor = as_float(scaleinfo1.w);
......@@ -173,18 +174,15 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
for (int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++)
{
float stage_sum = 0.f;
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
(((__global uchar*)stagecascadeptr_)+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
int stagecount = stagecascadeptr[stageloop].count;
for (int nodeloop = 0; nodeloop < stagecount;)
{
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*)
(((__global uchar*)nodeptr_) + nodecounter * sizeof(GpuHidHaarTreeNode));
__global GpuHidHaarTreeNode *currentnodeptr = (nodeptr + nodecounter);
int4 info1 = *(__global int4 *)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4 *)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4 *)(&(currentnodeptr->p[2][0]));
float4 w = *(__global float4 *)(&(currentnodeptr->weight[0]));
float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0]));
float3 alpha3 = *(__global float3 *)(&(currentnodeptr->alpha[0]));
float nodethreshold = w.w * variance_norm_factor;
info1.x += p_offset;
......@@ -206,7 +204,7 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
sum[clamp(mad24(info3.w, step, info3.x), 0, max_idx)]
+ sum[clamp(mad24(info3.w, step, info3.z), 0, max_idx)]) * w.z;
bool passThres = (classsum >= nodethreshold) ? 1 : 0;
bool passThres = classsum >= nodethreshold;
#if STUMP_BASED
stage_sum += passThres ? alpha3.y : alpha3.x;
......@@ -236,8 +234,7 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
}
#endif
}
result = (stage_sum >= stageinfo->threshold) ? 1 : 0;
result = (int)(stage_sum >= stagecascadeptr[stageloop].threshold);
}
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -284,14 +281,11 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
}
}
}
__kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuHidHaarTreeNode *newnode, float scale, float weight_scale, const int nodenum)
__kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuHidHaarTreeNode *newnode, float scale, float weight_scale, int nodenum)
{
const int counter = get_global_id(0);
int counter = get_global_id(0);
int tr_x[3], tr_y[3], tr_h[3], tr_w[3], i = 0;
GpuHidHaarTreeNode t1 = *(__global GpuHidHaarTreeNode*)
(((__global uchar*)orinode) + counter * sizeof(GpuHidHaarTreeNode));
__global GpuHidHaarTreeNode* pNew = (__global GpuHidHaarTreeNode*)
(((__global uchar*)newnode) + (counter + nodenum) * sizeof(GpuHidHaarTreeNode));
GpuHidHaarTreeNode t1 = *(orinode + counter);
#pragma unroll
for (i = 0; i < 3; i++)
......@@ -303,21 +297,22 @@ __kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuH
}
t1.weight[0] = -(t1.weight[1] * tr_h[1] * tr_w[1] + t1.weight[2] * tr_h[2] * tr_w[2]) / (tr_h[0] * tr_w[0]);
counter += nodenum;
#pragma unroll
for (i = 0; i < 3; i++)
{
pNew->p[i][0] = tr_x[i];
pNew->p[i][1] = tr_y[i];
pNew->p[i][2] = tr_x[i] + tr_w[i];
pNew->p[i][3] = tr_y[i] + tr_h[i];
pNew->weight[i] = t1.weight[i] * weight_scale;
newnode[counter].p[i][0] = tr_x[i];
newnode[counter].p[i][1] = tr_y[i];
newnode[counter].p[i][2] = tr_x[i] + tr_w[i];
newnode[counter].p[i][3] = tr_y[i] + tr_h[i];
newnode[counter].weight[i] = t1.weight[i] * weight_scale;
}
pNew->left = t1.left;
pNew->right = t1.right;
pNew->threshold = t1.threshold;
pNew->alpha[0] = t1.alpha[0];
pNew->alpha[1] = t1.alpha[1];
pNew->alpha[2] = t1.alpha[2];
newnode[counter].left = t1.left;
newnode[counter].right = t1.right;
newnode[counter].threshold = t1.threshold;
newnode[counter].alpha[0] = t1.alpha[0];
newnode[counter].alpha[1] = t1.alpha[1];
newnode[counter].alpha[2] = t1.alpha[2];
}
modules/ocl/src/opencl/imgproc_integral.cl
View file @ 51530d4d
This diff is collapsed.
modules/ocl/test/test_imgproc.cpp
View file @ 51530d4d
......@@ -295,33 +295,23 @@ OCL_TEST_P(CornerHarris, Mat)
//////////////////////////////////integral/////////////////////////////////////////////////
struct Integral :
public ImgprocTestBase
{
int sdepth;
typedef ImgprocTestBase Integral;
virtual void SetUp()
{
type = GET_PARAM(0);
blockSize = GET_PARAM(1);
sdepth = GET_PARAM(2);
useRoi = GET_PARAM(3);
}
};
OCL_TEST_P(Integral, Mat1)
{
for (int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
ocl::integral(gsrc_roi, gdst_roi, sdepth);
integral(src_roi, dst_roi, sdepth);
ocl::integral(gsrc_roi, gdst_roi);
integral(src_roi, dst_roi);
Near();
}
}
OCL_TEST_P(Integral, Mat2)
// TODO wrong output type
OCL_TEST_P(Integral, DISABLED_Mat2)
{
Mat dst1;
ocl::oclMat gdst1;
......@@ -330,12 +320,10 @@ OCL_TEST_P(Integral, Mat2)
{
random_roi();
integral(src_roi, dst_roi, dst1, sdepth);
ocl::integral(gsrc_roi, gdst_roi, gdst1, sdepth);
integral(src_roi, dst1, dst_roi);
ocl::integral(gsrc_roi, gdst1, gdst_roi);
Near();
if(gdst1.clCxt->supportsFeature(ocl::FEATURE_CL_DOUBLE))
EXPECT_MAT_NEAR(dst1, Mat(gdst1), 0.);
}
}
......@@ -575,7 +563,7 @@ INSTANTIATE_TEST_CASE_P(Imgproc, CornerHarris, Combine(
INSTANTIATE_TEST_CASE_P(Imgproc, Integral, Combine(
Values((MatType)CV_8UC1), // TODO does not work with CV_32F, CV_64F
Values(0), // not used
Values((MatType)CV_32SC1, (MatType)CV_32FC1),
Values(0), // not used
Bool()));
INSTANTIATE_TEST_CASE_P(Imgproc, Threshold, Combine(
......
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