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LBP classifier: tracking of count of detected objects was moved in cascadeclassifier.cpp

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This commit is contained in:
Marina Kolpakova 2012-07-04 04:51:00 +00:00
parent 0ee2662da6
commit a53f0f397e
2 changed files with 31 additions and 30 deletions
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17
modules/gpu/src/cascadeclassifier.cpp
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@ -273,7 +273,7 @@ namespace cv { namespace gpu { namespace device
{ {
namespace lbp namespace lbp
{ {
int classifyStump(const DevMem2Db mstages, classifyStump(const DevMem2Db mstages,
const int nstages, const int nstages,
const DevMem2Di mnodes, const DevMem2Di mnodes,
const DevMem2Df mleaves, const DevMem2Df mleaves,
@ -287,7 +287,8 @@ namespace cv { namespace gpu { namespace device
float scale, float scale,
int step, int step,
int subsetSize, int subsetSize,
DevMem2D_<int4> objects); DevMem2D_<int4> objects,
unsigned int* classified);
} }
}}} }}}
@ -308,6 +309,11 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
maxObjectSize = image.size(); maxObjectSize = image.size();
scaledImageBuffer.create(image.rows + 1, image.cols + 1, CV_8U); scaledImageBuffer.create(image.rows + 1, image.cols + 1, CV_8U);
unsigned int* classified = new unsigned int[1];
*classified = 0;
unsigned int* dclassified;
cudaMalloc(&dclassified, sizeof(int));
cudaMemcpy(dclassified, classified, sizeof(int), cudaMemcpyHostToDevice);
for( double factor = 1; ; factor *= scaleFactor ) for( double factor = 1; ; factor *= scaleFactor )
{ {
@ -331,10 +337,11 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
int step = (factor <= 2.) + 1; int step = (factor <= 2.) + 1;
int res = cv::gpu::device::lbp::classifyStump(stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat, leaves_mat, subsets_mat, features_mat, cv::gpu::device::lbp::classifyStump(stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat, leaves_mat, subsets_mat, features_mat,
integral, processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, scaleFactor, step, subsetSize, objects); integral, processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, scaleFactor, step, subsetSize, objects, dclassified);
std::cout << res << "Results: " << cv::Mat(objects).row(0).colRange(0, res) << std::endl;
} }
cudaMemcpy(classified, dclassified, sizeof(int), cudaMemcpyDeviceToHost);
std::cout << *classified << "Results: " << cv::Mat(objects).row(0).colRange(0, *classified) << std::endl;
// TODO: reject levels // TODO: reject levels
return 0; return 0;

18
modules/gpu/src/cuda/lbp.cu
View File

@ -51,8 +51,6 @@ namespace cv { namespace gpu { namespace device
{ {
int y = threadIdx.x * scale; int y = threadIdx.x * scale;
int x = blockIdx.x * scale; int x = blockIdx.x * scale;
*n = 0;
int i = 0;
int current_node = 0; int current_node = 0;
int current_leave = 0; int current_leave = 0;
@ -77,7 +75,6 @@ namespace cv { namespace gpu { namespace device
current_leave += 2; current_leave += 2;
} }
i = s;
if (sum < stage.threshold) if (sum < stage.threshold)
return; return;
} }
@ -88,29 +85,26 @@ namespace cv { namespace gpu { namespace device
rect.z = roundf(clWidth); rect.z = roundf(clWidth);
rect.w = roundf(clHeight); rect.w = roundf(clHeight);
int res = atomicInc(n, 1000); int res = atomicInc(n, 100);
objects(0, res) = rect; objects(0, res) = rect;
} }
int classifyStump(const DevMem2Db mstages, const int nstages, const DevMem2Di mnodes, const DevMem2Df mleaves, const DevMem2Di msubsets, const DevMem2Db mfeatures, classifyStump(const DevMem2Db mstages, const int nstages, const DevMem2Di mnodes, const DevMem2Df mleaves, const DevMem2Di msubsets, const DevMem2Db mfeatures,
const DevMem2Di integral, const int workWidth, const int workHeight, const int clWidth, const int clHeight, float scale, int step, int subsetSize, const DevMem2Di integral, const int workWidth, const int workHeight, const int clWidth, const int clHeight, float scale, int step, int subsetSize,
DevMem2D_<int4> objects) DevMem2D_<int4> objects, unsigned int* classified)
{ {
int blocks = ceilf(workHeight / (float)step); int blocks = ceilf(workHeight / (float)step);
int threads = ceilf(workWidth / (float)step); int threads = ceilf(workWidth / (float)step);
printf("blocks %d, threads %d\n", blocks, threads); // printf("blocks %d, threads %d\n", blocks, threads);
Stage* stages = (Stage*)(mstages.ptr()); Stage* stages = (Stage*)(mstages.ptr());
ClNode* nodes = (ClNode*)(mnodes.ptr()); ClNode* nodes = (ClNode*)(mnodes.ptr());
const float* leaves = mleaves.ptr(); const float* leaves = mleaves.ptr();
const int* subsets = msubsets.ptr(); const int* subsets = msubsets.ptr();
const uchar4* features = (uchar4*)(mfeatures.ptr()); const uchar4* features = (uchar4*)(mfeatures.ptr());
unsigned int * n, *h_n = new unsigned int[1];
cudaMalloc(&n, sizeof(int));
lbp_classify_stump<<<blocks, threads>>>(stages, nstages, nodes, leaves, subsets, features, integral, lbp_classify_stump<<<blocks, threads>>>(stages, nstages, nodes, leaves, subsets, features, integral,
workWidth, workHeight, clWidth, clHeight, scale, step, subsetSize, objects, n); workWidth, workHeight, clWidth, clHeight, scale, step, subsetSize, objects, classified);
cudaMemcpy(h_n, n, sizeof(int), cudaMemcpyDeviceToHost);
return *h_n;
} }
} }
}}} }}}