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LBP features: GPU representation

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This commit is contained in:
Marina Kolpakova
2012-06-26 12:15:19 +00:00
parent 55567582a6
commit a743eca076
4 changed files with 98 additions and 27 deletions
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48
modules/gpu/src/cascadeclassifier.cpp
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@@ -99,10 +99,9 @@ cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP()
void cv::gpu::CascadeClassifier_GPU_LBP::preallocateIntegralBuffer(cv::Size desired)
{
integral.create(desired.width + 1, desired.height + 1, CV_32FC1);
integral.create(desired.width + 1, desired.height + 1, CV_32SC1);
}
bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const
{
return stage_mat.empty();
@@ -132,6 +131,8 @@ bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string& classifierAsXml)
#define GPU_CC_WEAK_CLASSIFIERS "weakClassifiers"
#define GPU_CC_INTERNAL_NODES "internalNodes"
#define GPU_CC_LEAF_VALUES "leafValues"
#define GPU_CC_FEATURES "features"
#define GPU_CC_RECT "rect"
bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
{
@@ -225,6 +226,22 @@ bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
cl_leaves.push_back((float)*iIt);
}
}
fn = root[GPU_CC_FEATURES];
if( fn.empty() )
return false;
std::vector<char> features;
features.reserve(fn.size() * 4);
FileNodeIterator f_it = fn.begin(), f_end = fn.end();
for (; f_it != f_end; ++f_it)
{
FileNode rect = fn[GPU_CC_RECT];
FileNodeIterator r_it = rect.begin();
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
features.push_back(saturate_cast<uchar>((int)*(r_it++)));
}
// copy data structures on gpu
stage_mat = cv::gpu::GpuMat(1, (int)stages.size() * sizeof(Stage), CV_8UC1);
stage_mat.upload(cv::Mat(1, stages.size() * sizeof(Stage), CV_8UC1, &(stages[0]) ));
@@ -241,6 +258,9 @@ bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
subsets_mat = cv::gpu::GpuMat(1, (int)subsets.size(), CV_32SC1);
stage_mat.upload(cv::Mat(subsets));
features_mat = cv::gpu::GpuMat(1, (int)features.size(), CV_8UC1);
features_mat.upload(cv::Mat(features));
return true;
}
@@ -270,22 +290,25 @@ namespace cv { namespace gpu { namespace device
{
namespace lbp
{
void cascadeClassify(const DevMem2Db stages, const DevMem2Di trees, const DevMem2Db nodes, const DevMem2Df leaves, const DevMem2Di subsets,
const DevMem2Db integral, int workWidth, int workHeight, int step, int subsetSize, DevMem2D_<int4> objects, int minNeighbors = 4, cudaStream_t stream = 0);
void cascadeClassify(const DevMem2Db stages, const DevMem2Di trees, const DevMem2Db nodes, const DevMem2Df leaves, const DevMem2Di subsets, const DevMem2Db features,
const DevMem2Di integral, int workWidth, int workHeight, int clWidth, int clHeight, float scale, int step, int subsetSize, DevMem2D_<int4> objects, int minNeighbors = 4, cudaStream_t stream = 0);
}
}}}
int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, GpuMat& scaledImageBuffer, GpuMat& objects, double scaleFactor, int minNeighbors /*, Size minSize=Size()*/)
{
CV_Assert( scaleFactor > 1 && image.depth() == CV_8U );
CV_Assert(empty());
CV_Assert(!empty());
const int defaultObjSearchNum = 100;
if( !objects.empty() && objects.depth() == CV_32S)
objects.reshape(4, 1);
else
objects.create(1 , defaultObjSearchNum, CV_32SC4);
// if( !objects.empty() && objects.depth() == CV_32S)
// objects.reshape(4, 1);
// else
// objects.create(1 , defaultObjSearchNum, CV_32SC4);
// temp solution
objects.create(image.rows, image.cols, CV_32SC4);
scaledImageBuffer.create(image.size(), image.type());
@@ -302,14 +325,13 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
// TODO: min max object sizes cheching
cv::gpu::resize(image, scaledImageBuffer, scaledImageSize, 0, 0, INTER_NEAREST);
//prepare image for evaluation
integral.create(cv::Size(scaledImageSize.width + 1, scaledImageSize.height + 1), CV_32FC1);
integral.create(cv::Size(scaledImageSize.width + 1, scaledImageSize.height + 1), CV_32SC1);
cv::gpu::integral(scaledImageBuffer, integral);
int step = (factor <= 2.) + 1;
int stripCount = 1, stripSize = processingRectSize.height;
cv::gpu::device::lbp::cascadeClassify(stage_mat, trees_mat, nodes_mat, leaves_mat, subsets_mat,
integral, processingRectSize.width, processingRectSize.height, step, subsetSize, objects, minNeighbors);
cv::gpu::device::lbp::cascadeClassify(stage_mat, trees_mat, nodes_mat, leaves_mat, subsets_mat, features_mat,
integral, processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, scaleFactor, step, subsetSize, objects, minNeighbors);
}
// TODO: reject levels