Skip to content

O
Opencv

Greenplum / Opencv
大约 1 年 前同步成功

通知 7
Star 0
Fork 0
O
Opencv

体验新版 GitCode，发现更多精彩内容 >>

提交 b9e53ec8 编写于 作者: M Marina Kolpakova
浏览文件
操作

fixed build after r9027

上级 6dc5cd15
隐藏空白更改
内联 并排
Showing with 38 addition and 47 deletion
modules/gpu/include/opencv2/gpu/gpu.hpp
浏览文件 @ b9e53ec8
......@@ -1437,7 +1437,6 @@ public:
int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.1, int minNeighbors = 4,
cv::Size maxObjectSize = cv::Size()/*, Size minSize = Size()*/);
void preallocateIntegralBuffer(cv::Size desired);
Size getClassifierSize() const;
private:
bool read(const FileNode &root);
......
modules/gpu/src/cascadeclassifier.cpp
浏览文件 @ b9e53ec8
......@@ -67,8 +67,7 @@ cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP()
bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const { throw_nogpu(); return true; }
bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string&) { throw_nogpu(); return true; }
Size cv::gpu::CascadeClassifier_GPU_LBP::getClassifierSize() const { throw_nogpu(); return Size(); }
void cv::gpu::CascadeClassifier_GPU_LBP::preallocateIntegralBuffer(cv::Size /*desired*/) { throw_nogpu();}
void cv::gpu::CascadeClassifier_GPU_LBP::initializeBuffers(cv::Size /*frame*/) { throw_nogpu();}
void cv::gpu::CascadeClassifier_GPU_LBP::allocateBuffers(cv::Size /*frame*/) { throw_nogpu();}
int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const cv::gpu::GpuMat& /*image*/, cv::gpu::GpuMat& /*objectsBuf*/,
double /*scaleFactor*/, int /*minNeighbors*/, cv::Size /*maxObjectSize*/){ throw_nogpu(); return 0;}
......@@ -80,8 +79,8 @@ cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP(){}
void cv::gpu::CascadeClassifier_GPU_LBP::allocateBuffers(cv::Size frame)
{
if (frame == cv::Size())
return;
if (frame == cv::Size())
return;
if (resuzeBuffer.empty() || frame.width > resuzeBuffer.cols || frame.height > resuzeBuffer.rows)
{
......@@ -97,17 +96,10 @@ void cv::gpu::CascadeClassifier_GPU_LBP::allocateBuffers(cv::Size frame)
Ncv32u bufSize;
ncvSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) );
integralBuffer.create(1, bufSize, CV_8UC1);
integralBuffer.create(1, bufSize, CV_8UC1);
}
candidates.create(1 , frame.width >> 1, CV_32SC4);
}
void cv::gpu::CascadeClassifier_GPU_LBP::preallocateIntegralBuffer(cv::Size desired)
{
integral.create(desired.width + 1, desired.height + 1, CV_32SC1);
candidates.create(1 , frame.width >> 1, CV_32SC4);
}
bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const { return stage_mat.empty(); }
......@@ -115,8 +107,8 @@ Size cv::gpu::CascadeClassifier_GPU_LBP::getClassifierSize() const { return NxM;
bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string& classifierAsXml)
{
FileStorage fs(classifierAsXml, FileStorage::READ);
return fs.isOpened() ? read(fs.getFirstTopLevelNode()) : false;
FileStorage fs(classifierAsXml, FileStorage::READ);
return fs.isOpened() ? read(fs.getFirstTopLevelNode()) : false;
}
struct Stage
......@@ -129,24 +121,24 @@ struct Stage
// currently only stump based boost classifiers are supported
bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
{
const char *GPU_CC_STAGE_TYPE = "stageType";
const char *GPU_CC_FEATURE_TYPE = "featureType";
const char *GPU_CC_BOOST = "BOOST";
const char *GPU_CC_LBP = "LBP";
const char *GPU_CC_MAX_CAT_COUNT = "maxCatCount";
const char *GPU_CC_HEIGHT = "height";
const char *GPU_CC_WIDTH = "width";
const char *GPU_CC_STAGE_PARAMS = "stageParams";
const char *GPU_CC_MAX_DEPTH = "maxDepth";
const char *GPU_CC_FEATURE_PARAMS = "featureParams";
const char *GPU_CC_STAGES = "stages";
const char *GPU_CC_STAGE_THRESHOLD = "stageThreshold";
const float GPU_THRESHOLD_EPS = 1e-5f;
const char *GPU_CC_WEAK_CLASSIFIERS = "weakClassifiers";
const char *GPU_CC_INTERNAL_NODES = "internalNodes";
const char *GPU_CC_LEAF_VALUES = "leafValues";
const char *GPU_CC_FEATURES = "features";
const char *GPU_CC_RECT = "rect";
const char *GPU_CC_STAGE_TYPE = "stageType";
const char *GPU_CC_FEATURE_TYPE = "featureType";
const char *GPU_CC_BOOST = "BOOST";
const char *GPU_CC_LBP = "LBP";
const char *GPU_CC_MAX_CAT_COUNT = "maxCatCount";
const char *GPU_CC_HEIGHT = "height";
const char *GPU_CC_WIDTH = "width";
const char *GPU_CC_STAGE_PARAMS = "stageParams";
const char *GPU_CC_MAX_DEPTH = "maxDepth";
const char *GPU_CC_FEATURE_PARAMS = "featureParams";
const char *GPU_CC_STAGES = "stages";
const char *GPU_CC_STAGE_THRESHOLD = "stageThreshold";
const float GPU_THRESHOLD_EPS = 1e-5f;
const char *GPU_CC_WEAK_CLASSIFIERS = "weakClassifiers";
const char *GPU_CC_INTERNAL_NODES = "internalNodes";
const char *GPU_CC_LEAF_VALUES = "leafValues";
const char *GPU_CC_FEATURES = "features";
const char *GPU_CC_RECT = "rect";
std::string stageTypeStr = (string)root[GPU_CC_STAGE_TYPE];
CV_Assert(stageTypeStr == GPU_CC_BOOST);
......@@ -300,7 +292,7 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
double scaleFactor, int groupThreshold, cv::Size maxObjectSize /*, Size minSize=Size()*/)
{
CV_Assert(!empty() && scaleFactor > 1 && image.depth() == CV_8U);
const int defaultObjSearchNum = 100;
const float grouping_eps = 0.2;
......@@ -317,10 +309,10 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
maxObjectSize = image.size();
allocateBuffers(image.size());
unsigned int classified = 0;
GpuMat dclassified(1, 1, CV_32S);
cudaSafeCall( cudaMemcpy(dclassified.ptr(), &classified, sizeof(int), cudaMemcpyHostToDevice) );
unsigned int classified = 0;
GpuMat dclassified(1, 1, CV_32S);
cudaSafeCall( cudaMemcpy(dclassified.ptr(), &classified, sizeof(int), cudaMemcpyHostToDevice) );
//int step = 2;
// cv::gpu::device::lbp::bindIntegral(integral);
......@@ -349,10 +341,10 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
gpu::resize(image, scaledImg, scaledImageSize, 0, 0, CV_INTER_LINEAR);
gpu::integralBuffered(scaledImg, scaledIntegral, currBuff);
int step = factor <= 2.f ? 2 : 1;
int step = factor <= 2.f ? 2 : 1;
device::lbp::classifyStumpFixed(integral, integral.step1(), stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat, leaves_mat, subsets_mat, features_mat,
processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, factor, step, subsetSize, candidates, dclassified.ptr<unsigned int>());
processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, factor, step, subsetSize, candidates, dclassified.ptr<unsigned int>());
factor *= scaleFactor;
windowSize = cv::Size(cvRound(NxM.width * factor), cvRound(NxM.height * factor));
......@@ -363,13 +355,13 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
// cv::gpu::device::lbp::unbindIntegral();
if (groupThreshold <= 0 || objects.empty())
return 0;
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
device::lbp::connectedConmonents(candidates, classified, objects, groupThreshold, grouping_eps, dclassified.ptr<unsigned int>());
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
device::lbp::connectedConmonents(candidates, classified, objects, groupThreshold, grouping_eps, dclassified.ptr<unsigned int>());
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
cudaSafeCall( cudaDeviceSynchronize() );
return classified;
}
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或