// Jia Haipeng, jiahaipeng95@gmail.com
// Wu Xinglong, wxl370@126.com
// Wang Yao, bitwangyaoyao@gmail.com
-// Sen Liu, swjtuls1987@126.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
using namespace cv;
using namespace cv::ocl;
-#if 0
-
namespace cv
{
namespace ocl
}
/* these settings affect the quality of detection: change with care */
-#define CV_ADJUST_FEATURES 1
-#define CV_ADJUST_WEIGHTS 0
-
+#define CV_ADJUST_FEATURES 1
+#define CV_ADJUST_WEIGHTS 0
+#define CV_HAAR_FEATURE_MAX 3
typedef int sumtype;
typedef double sqsumtype;
} /* j */
}
}
-
-CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemStorage *storage, double scaleFactor,
- int minNeighbors, int flags, CvSize minSize, CvSize maxSize)
+void OclCascadeClassifier::detectMultiScale(oclMat &gimg, CV_OUT std::vector<cv::Rect>& faces,
+ double scaleFactor, int minNeighbors, int flags,
+ Size minSize, Size maxSize)
+//CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemStorage *storage, double scaleFactor,
+// int minNeighbors, int flags, CvSize minSize, CvSize maxSize)
{
CvHaarClassifierCascade *cascade = oldCascade;
const double GROUP_EPS = 0.2;
- CvSeq *result_seq = 0;
cv::ConcurrentRectVector allCandidates;
std::vector<cv::Rect> rectList;
if( !CV_IS_HAAR_CLASSIFIER(cascade) )
CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier cascade" );
- if( !storage )
- CV_Error( CV_StsNullPtr, "Null storage pointer" );
+ //if( !storage )
+ // CV_Error( CV_StsNullPtr, "Null storage pointer" );
if( CV_MAT_DEPTH(gimg.type()) != CV_8U )
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" );
if( !cascade->hid_cascade )
gpuCreateHidHaarClassifierCascade(cascade, &datasize, &totalclassifier);
- result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage );
+ //result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage );
if( CV_MAT_CN(gimg.type()) > 1 )
{
args.push_back ( std::make_pair(sizeof(cl_mem) , (void *)&pbuffer ));
args.push_back ( std::make_pair(sizeof(cl_mem) , (void *)&correctionbuffer ));
args.push_back ( std::make_pair(sizeof(cl_int) , (void *)&nodenum ));
-
+ const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuRunHaarClassifierCascade_scaled2", globalThreads, localThreads, args, -1, -1, build_options);
candidate = (int *)clEnqueueMapBuffer(qu, candidatebuffer, 1, CL_MAP_READ, 0, 4 * sizeof(int) * outputsz, 0, 0, 0, &status);
else
rweights.resize(rectList.size(), 0);
+ faces.clear();
if( findBiggestObject && rectList.size() )
{
- CvAvgComp result_comp = {{0, 0, 0, 0}, 0};
-
- for( size_t i = 0; i < rectList.size(); i++ )
- {
- cv::Rect r = rectList[i];
- if( r.area() > cv::Rect(result_comp.rect).area() )
- {
- result_comp.rect = r;
- result_comp.neighbors = rweights[i];
- }
- }
- cvSeqPush( result_seq, &result_comp );
- }
- else
- {
- for( size_t i = 0; i < rectList.size(); i++ )
- {
- CvAvgComp c;
- c.rect = rectList[i];
- c.neighbors = rweights[i];
- cvSeqPush( result_seq, &c );
- }
- }
-
- return result_seq;
-}
-
-struct OclBuffers
-{
- cl_mem stagebuffer;
- cl_mem nodebuffer;
- cl_mem candidatebuffer;
- cl_mem scaleinfobuffer;
- cl_mem pbuffer;
- cl_mem correctionbuffer;
- cl_mem newnodebuffer;
-};
-
-struct getRect
-{
- Rect operator()(const CvAvgComp &e) const
- {
- return e.rect;
- }
-};
-
-void cv::ocl::OclCascadeClassifierBuf::detectMultiScale(oclMat &gimg, CV_OUT std::vector<cv::Rect>& faces,
- double scaleFactor, int minNeighbors, int flags,
- Size minSize, Size maxSize)
-{
- int blocksize = 8;
- int grp_per_CU = 12;
- size_t localThreads[3] = { blocksize, blocksize, 1 };
- size_t globalThreads[3] = { grp_per_CU * cv::ocl::Context::getContext()->computeUnits() *localThreads[0],
- localThreads[1],
- 1 };
- int outputsz = 256 * globalThreads[0] / localThreads[0];
-
- Init(gimg.rows, gimg.cols, scaleFactor, flags, outputsz, localThreads, minSize, maxSize);
-
- const double GROUP_EPS = 0.2;
-
- cv::ConcurrentRectVector allCandidates;
- std::vector<cv::Rect> rectList;
- std::vector<int> rweights;
-
- CvHaarClassifierCascade *cascade = oldCascade;
- GpuHidHaarClassifierCascade *gcascade;
- GpuHidHaarStageClassifier *stage;
-
- if( CV_MAT_DEPTH(gimg.type()) != CV_8U )
- CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" );
-
- if( CV_MAT_CN(gimg.type()) > 1 )
- {
- oclMat gtemp;
- cvtColor( gimg, gtemp, CV_BGR2GRAY );
- gimg = gtemp;
- }
-
- int *candidate;
- cl_command_queue qu = reinterpret_cast<cl_command_queue>(Context::getContext()->oclCommandQueue());
- if( (flags & CV_HAAR_SCALE_IMAGE) )
- {
- int indexy = 0;
- CvSize sz;
-
- cv::Rect roi, roi2;
- cv::Mat imgroi, imgroisq;
- cv::ocl::oclMat resizeroi, gimgroi, gimgroisq;
-
- for( int i = 0; i < m_loopcount; i++ )
- {
- sz = sizev[i];
- roi = Rect(0, indexy, sz.width, sz.height);
- roi2 = Rect(0, 0, sz.width - 1, sz.height - 1);
- resizeroi = gimg1(roi2);
- gimgroi = gsum(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;
- }
-
- gcascade = (GpuHidHaarClassifierCascade *)(cascade->hid_cascade);
- stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
-
- int startstage = 0;
- int endstage = gcascade->count;
- int startnode = 0;
- int pixelstep = gsum.step / 4;
- int splitstage = 3;
- int splitnode = stage[0].count + stage[1].count + stage[2].count;
- cl_int4 p, pq;
- p.s[0] = gcascade->p0;
- p.s[1] = gcascade->p1;
- p.s[2] = gcascade->p2;
- p.s[3] = gcascade->p3;
- pq.s[0] = gcascade->pq0;
- pq.s[1] = gcascade->pq1;
- pq.s[2] = gcascade->pq2;
- pq.s[3] = gcascade->pq3;
- float correction = gcascade->inv_window_area;
-
- vector<pair<size_t, const void *> > args;
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->stagebuffer ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->scaleinfobuffer ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->nodebuffer ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->candidatebuffer ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&pixelstep ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_loopcount ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitnode ));
- args.push_back ( make_pair(sizeof(cl_int4) , (void *)&p ));
- args.push_back ( make_pair(sizeof(cl_int4) , (void *)&pq ));
- args.push_back ( make_pair(sizeof(cl_float) , (void *)&correction ));
-
- const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
-
- openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascade", globalThreads, localThreads, args, -1, -1, build_options);
-
- candidate = (int *)malloc(4 * sizeof(int) * outputsz);
- memset(candidate, 0, 4 * sizeof(int) * outputsz);
-
- openCLReadBuffer( gsum.clCxt, ((OclBuffers *)buffers)->candidatebuffer, candidate, 4 * sizeof(int)*outputsz );
-
- for(int i = 0; i < outputsz; i++)
- {
- if(candidate[4 * i + 2] != 0)
- {
- allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],
- candidate[4 * i + 2], candidate[4 * i + 3]));
- }
- }
- free((void *)candidate);
- candidate = NULL;
- }
- else
- {
- cv::ocl::integral(gimg, gsum, gsqsum);
-
- gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
-
- int step = gsum.step / 4;
- int startnode = 0;
- int splitstage = 3;
-
- int startstage = 0;
- int endstage = gcascade->count;
-
- vector<pair<size_t, const void *> > args;
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->stagebuffer ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->scaleinfobuffer ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->newnodebuffer ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->candidatebuffer ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.rows ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.cols ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&step ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_loopcount ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->pbuffer ));
- args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->correctionbuffer ));
- args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_nodenum ));
-
- const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
- openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuRunHaarClassifierCascade_scaled2", globalThreads, localThreads, args, -1, -1, build_options);
-
- candidate = (int *)clEnqueueMapBuffer(qu, ((OclBuffers *)buffers)->candidatebuffer, 1, CL_MAP_READ, 0, 4 * sizeof(int) * outputsz, 0, 0, 0, NULL);
-
- for(int i = 0; i < outputsz; i++)
- {
- if(candidate[4 * i + 2] != 0)
- allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],
- candidate[4 * i + 2], candidate[4 * i + 3]));
- }
- clEnqueueUnmapMemObject(qu, ((OclBuffers *)buffers)->candidatebuffer, candidate, 0, 0, 0);
- }
- rectList.resize(allCandidates.size());
- if(!allCandidates.empty())
- std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
-
- if( minNeighbors != 0 || findBiggestObject )
- groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
- else
- rweights.resize(rectList.size(), 0);
-
- GenResult(faces, rectList, rweights);
-}
-
-void cv::ocl::OclCascadeClassifierBuf::Init(const int rows, const int cols,
- double scaleFactor, int flags,
- const int outputsz, const size_t localThreads[],
- CvSize minSize, CvSize maxSize)
-{
- if(initialized)
- {
- return; // we only allow one time initialization
- }
- CvHaarClassifierCascade *cascade = oldCascade;
-
- if( !CV_IS_HAAR_CLASSIFIER(cascade) )
- CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier cascade" );
-
- if( scaleFactor <= 1 )
- CV_Error( CV_StsOutOfRange, "scale factor must be > 1" );
-
- if( cols < minSize.width || rows < minSize.height )
- CV_Error(CV_StsError, "Image too small");
-
- int datasize=0;
- int totalclassifier=0;
-
- if( !cascade->hid_cascade )
- {
- gpuCreateHidHaarClassifierCascade(cascade, &datasize, &totalclassifier);
- }
-
- if( maxSize.height == 0 || maxSize.width == 0 )
- {
- maxSize.height = rows;
- maxSize.width = cols;
- }
-
- findBiggestObject = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0;
- if( findBiggestObject )
- flags &= ~(CV_HAAR_SCALE_IMAGE | CV_HAAR_DO_CANNY_PRUNING);
-
- CreateBaseBufs(datasize, totalclassifier, flags, outputsz);
- CreateFactorRelatedBufs(rows, cols, flags, scaleFactor, localThreads, minSize, maxSize);
-
- m_scaleFactor = scaleFactor;
- m_rows = rows;
- m_cols = cols;
- m_flags = flags;
- m_minSize = minSize;
- m_maxSize = maxSize;
-
- // initialize nodes
- GpuHidHaarClassifierCascade *gcascade;
- GpuHidHaarStageClassifier *stage;
- GpuHidHaarClassifier *classifier;
- GpuHidHaarTreeNode *node;
- cl_command_queue qu = reinterpret_cast<cl_command_queue>(Context::getContext()->oclCommandQueue());
- if( (flags & CV_HAAR_SCALE_IMAGE) )
- {
- gcascade = (GpuHidHaarClassifierCascade *)(cascade->hid_cascade);
- stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
- classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
- node = (GpuHidHaarTreeNode *)(classifier->node);
-
- gpuSetImagesForHaarClassifierCascade( cascade, 1., gsum.step / 4 );
-
- openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->stagebuffer, 1, 0,
- sizeof(GpuHidHaarStageClassifier) * gcascade->count,
- stage, 0, NULL, NULL));
-
- openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->nodebuffer, 1, 0,
- m_nodenum * sizeof(GpuHidHaarTreeNode),
- node, 0, NULL, NULL));
- }
- else
- {
- gpuSetHaarClassifierCascade(cascade);
-
- gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
- stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
- classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
- node = (GpuHidHaarTreeNode *)(classifier->node);
-
- openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->nodebuffer, 1, 0,
- m_nodenum * sizeof(GpuHidHaarTreeNode),
- node, 0, NULL, NULL));
-
- cl_int4 *p = (cl_int4 *)malloc(sizeof(cl_int4) * m_loopcount);
- float *correction = (float *)malloc(sizeof(float) * m_loopcount);
- double factor;
- for(int i = 0; i < m_loopcount; i++)
- {
- factor = scalev[i];
- int equRect_x = (int)(factor * gcascade->p0 + 0.5);
- int equRect_y = (int)(factor * gcascade->p1 + 0.5);
- int equRect_w = (int)(factor * gcascade->p3 + 0.5);
- int equRect_h = (int)(factor * gcascade->p2 + 0.5);
- p[i].s[0] = equRect_x;
- p[i].s[1] = equRect_y;
- p[i].s[2] = equRect_x + equRect_w;
- p[i].s[3] = equRect_y + equRect_h;
- correction[i] = 1. / (equRect_w * equRect_h);
- int startnodenum = m_nodenum * i;
- float factor2 = (float)factor;
-
- vector<pair<size_t, const void *> > args1;
- args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->nodebuffer ));
- args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->newnodebuffer ));
- args1.push_back ( make_pair(sizeof(cl_float) , (void *)&factor2 ));
- args1.push_back ( make_pair(sizeof(cl_float) , (void *)&correction[i] ));
- args1.push_back ( make_pair(sizeof(cl_int) , (void *)&startnodenum ));
-
- size_t globalThreads2[3] = {m_nodenum, 1, 1};
-
- openCLExecuteKernel(Context::getContext(), &haarobjectdetect_scaled2, "gpuscaleclassifier", globalThreads2, NULL/*localThreads2*/, args1, -1, -1);
- }
- openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->stagebuffer, 1, 0, sizeof(GpuHidHaarStageClassifier)*gcascade->count, stage, 0, NULL, NULL));
- openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->pbuffer, 1, 0, sizeof(cl_int4)*m_loopcount, p, 0, NULL, NULL));
- openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->correctionbuffer, 1, 0, sizeof(cl_float)*m_loopcount, correction, 0, NULL, NULL));
-
- free(p);
- free(correction);
- }
- initialized = true;
-}
-
-void cv::ocl::OclCascadeClassifierBuf::CreateBaseBufs(const int datasize, const int totalclassifier,
- const int flags, const int outputsz)
-{
- if (!initialized)
- {
- buffers = malloc(sizeof(OclBuffers));
-
- size_t tempSize =
- sizeof(GpuHidHaarStageClassifier) * ((GpuHidHaarClassifierCascade *)oldCascade->hid_cascade)->count;
- m_nodenum = (datasize - sizeof(GpuHidHaarClassifierCascade) - tempSize - sizeof(GpuHidHaarClassifier) * totalclassifier)
- / sizeof(GpuHidHaarTreeNode);
-
- ((OclBuffers *)buffers)->stagebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, tempSize);
- ((OclBuffers *)buffers)->nodebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, m_nodenum * sizeof(GpuHidHaarTreeNode));
- }
-
- if (initialized
- && ((m_flags & CV_HAAR_SCALE_IMAGE) ^ (flags & CV_HAAR_SCALE_IMAGE)))
- {
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->candidatebuffer));
- }
-
- if (flags & CV_HAAR_SCALE_IMAGE)
- {
- ((OclBuffers *)buffers)->candidatebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(),
- CL_MEM_WRITE_ONLY,
- 4 * sizeof(int) * outputsz);
- }
- else
- {
- ((OclBuffers *)buffers)->candidatebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(),
- CL_MEM_WRITE_ONLY | CL_MEM_ALLOC_HOST_PTR,
- 4 * sizeof(int) * outputsz);
- }
-}
-
-void cv::ocl::OclCascadeClassifierBuf::CreateFactorRelatedBufs(
- const int rows, const int cols, const int flags,
- const double scaleFactor, const size_t localThreads[],
- CvSize minSize, CvSize maxSize)
-{
- if (initialized)
- {
- if ((m_flags & CV_HAAR_SCALE_IMAGE) && !(flags & CV_HAAR_SCALE_IMAGE))
- {
- gimg1.release();
- gsum.release();
- gsqsum.release();
- }
- else if (!(m_flags & CV_HAAR_SCALE_IMAGE) && (flags & CV_HAAR_SCALE_IMAGE))
- {
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
- }
- else if ((m_flags & CV_HAAR_SCALE_IMAGE) && (flags & CV_HAAR_SCALE_IMAGE))
- {
- if (fabs(m_scaleFactor - scaleFactor) < 1e-6
- && (rows == m_rows && cols == m_cols)
- && (minSize.width == m_minSize.width)
- && (minSize.height == m_minSize.height)
- && (maxSize.width == m_maxSize.width)
- && (maxSize.height == m_maxSize.height))
- {
- return;
- }
- }
- else
- {
- if (fabs(m_scaleFactor - scaleFactor) < 1e-6
- && (rows == m_rows && cols == m_cols)
- && (minSize.width == m_minSize.width)
- && (minSize.height == m_minSize.height)
- && (maxSize.width == m_maxSize.width)
- && (maxSize.height == m_maxSize.height))
- {
- return;
- }
- else
- {
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
- }
- }
- }
-
- int loopcount;
- int indexy = 0;
- int totalheight = 0;
- double factor;
- Rect roi;
- CvSize sz;
- CvSize winSize0 = oldCascade->orig_window_size;
- detect_piramid_info *scaleinfo;
- cl_command_queue qu = reinterpret_cast<cl_command_queue>(Context::getContext()->oclCommandQueue());
- if (flags & CV_HAAR_SCALE_IMAGE)
- {
- for(factor = 1.f;; factor *= scaleFactor)
- {
- CvSize winSize = { cvRound(winSize0.width * factor), cvRound(winSize0.height * factor) };
- sz.width = cvRound( cols / factor ) + 1;
- sz.height = cvRound( rows / factor ) + 1;
- CvSize sz1 = { sz.width - winSize0.width - 1, sz.height - winSize0.height - 1 };
-
- if( sz1.width <= 0 || sz1.height <= 0 )
- break;
- if( winSize.width > maxSize.width || winSize.height > maxSize.height )
- break;
- if( winSize.width < minSize.width || winSize.height < minSize.height )
- continue;
-
- totalheight += sz.height;
- sizev.push_back(sz);
- scalev.push_back(static_cast<float>(factor));
- }
-
- loopcount = sizev.size();
- gimg1.create(rows, cols, CV_8UC1);
- gsum.create(totalheight + 4, cols + 1, CV_32SC1);
- gsqsum.create(totalheight + 4, cols + 1, CV_32FC1);
-
- scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
- for( int i = 0; i < loopcount; i++ )
- {
- sz = sizev[i];
- roi = Rect(0, indexy, sz.width, sz.height);
- int width = sz.width - 1 - oldCascade->orig_window_size.width;
- int height = sz.height - 1 - oldCascade->orig_window_size.height;
- int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
- int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
-
- ((detect_piramid_info *)scaleinfo)[i].width_height = (width << 16) | height;
- ((detect_piramid_info *)scaleinfo)[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
- ((detect_piramid_info *)scaleinfo)[i].imgoff = gsum(roi).offset >> 2;
- ((detect_piramid_info *)scaleinfo)[i].factor = scalev[i];
-
- indexy += sz.height;
- }
- }
- else
- {
- for(factor = 1;
- cvRound(factor * winSize0.width) < cols - 10 && cvRound(factor * winSize0.height) < rows - 10;
- factor *= scaleFactor)
- {
- CvSize winSize = { cvRound( winSize0.width * factor ), cvRound( winSize0.height * factor ) };
- if( winSize.width < minSize.width || winSize.height < minSize.height )
- {
- continue;
- }
- sizev.push_back(winSize);
- scalev.push_back(factor);
- }
-
- loopcount = scalev.size();
- if(loopcount == 0)
- {
- loopcount = 1;
- sizev.push_back(minSize);
- scalev.push_back( min(cvRound(minSize.width / winSize0.width), cvRound(minSize.height / winSize0.height)) );
- }
-
- ((OclBuffers *)buffers)->pbuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY,
- sizeof(cl_int4) * loopcount);
- ((OclBuffers *)buffers)->correctionbuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY,
- sizeof(cl_float) * loopcount);
- ((OclBuffers *)buffers)->newnodebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_WRITE,
- loopcount * m_nodenum * sizeof(GpuHidHaarTreeNode));
-
- scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
- for( int i = 0; i < loopcount; i++ )
- {
- sz = sizev[i];
- factor = scalev[i];
- int ystep = cvRound(std::max(2., factor));
- int width = (cols - 1 - sz.width + ystep - 1) / ystep;
- int height = (rows - 1 - sz.height + ystep - 1) / ystep;
- int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
- int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
-
- ((detect_piramid_info *)scaleinfo)[i].width_height = (width << 16) | height;
- ((detect_piramid_info *)scaleinfo)[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
- ((detect_piramid_info *)scaleinfo)[i].imgoff = 0;
- ((detect_piramid_info *)scaleinfo)[i].factor = factor;
- }
- }
-
- if (loopcount != m_loopcount)
- {
- if (initialized)
- {
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->scaleinfobuffer));
- }
- ((OclBuffers *)buffers)->scaleinfobuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
- }
-
- openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->scaleinfobuffer, 1, 0,
- sizeof(detect_piramid_info)*loopcount,
- scaleinfo, 0, NULL, NULL));
- free(scaleinfo);
-
- m_loopcount = loopcount;
-}
-
-void cv::ocl::OclCascadeClassifierBuf::GenResult(CV_OUT std::vector<cv::Rect>& faces,
- const std::vector<cv::Rect> &rectList,
- const std::vector<int> &rweights)
-{
- MemStorage tempStorage(cvCreateMemStorage(0));
- CvSeq *result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), tempStorage );
-
- if( findBiggestObject && rectList.size() )
- {
- CvAvgComp result_comp = {CvRect(), 0};
-
+ Rect result_comp(0, 0, 0, 0);
for( size_t i = 0; i < rectList.size(); i++ )
{
cv::Rect r = rectList[i];
- if( r.area() > cv::Rect(result_comp.rect).area() )
+ if( r.area() > result_comp.area() )
{
- result_comp.rect = r;
- result_comp.neighbors = rweights[i];
+ result_comp = r;
}
}
- cvSeqPush( result_seq, &result_comp );
+ faces.push_back(result_comp);
}
else
{
- for( size_t i = 0; i < rectList.size(); i++ )
- {
- CvAvgComp c;
- c.rect = rectList[i];
- c.neighbors = rweights[i];
- cvSeqPush( result_seq, &c );
- }
+ faces = rectList;
}
-
- vector<CvAvgComp> vecAvgComp;
- Seq<CvAvgComp>(result_seq).copyTo(vecAvgComp);
- faces.resize(vecAvgComp.size());
- std::transform(vecAvgComp.begin(), vecAvgComp.end(), faces.begin(), getRect());
}
-
-void cv::ocl::OclCascadeClassifierBuf::release()
-{
- if(initialized)
- {
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->stagebuffer));
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->scaleinfobuffer));
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->nodebuffer));
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->candidatebuffer));
-
- if( (m_flags & CV_HAAR_SCALE_IMAGE) )
- {
- cvFree(&oldCascade->hid_cascade);
- }
- else
- {
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
- openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
- }
-
- free(buffers);
- buffers = NULL;
- initialized = false;
- }
-}
-
-#ifndef _MAX_PATH
-#define _MAX_PATH 1024
-#endif
-
-#endif
projects / profile / ivi / opencv.git / commitdiff