DevMem2D_<int4> objects,\r
unsigned int* classified);\r
\r
- int connectedConmonents(DevMem2D_<int4> candidates, int groupThreshold, float grouping_eps, unsigned int* nclasses);\r
+ int connectedConmonents(DevMem2D_<int4> candidates, DevMem2D_<int4> objects,int groupThreshold, float grouping_eps, unsigned int* nclasses);\r
}\r
}}}\r
\r
else\r
objects.create(1 , defaultObjSearchNum, CV_32SC4);\r
\r
+ GpuMat candidates(1 , defaultObjSearchNum, CV_32SC4);\r
if (maxObjectSize == cv::Size())\r
maxObjectSize = image.size();\r
\r
unsigned int* dclassified;\r
cudaMalloc(&dclassified, sizeof(int));\r
cudaMemcpy(dclassified, classified, sizeof(int), cudaMemcpyHostToDevice);\r
+ int step;\r
\r
for( double factor = 1; ; factor *= scaleFactor )\r
{\r
// continue;\r
\r
cv::gpu::resize(image, scaledImageBuffer, scaledImageSize, 0, 0, CV_INTER_LINEAR);\r
-\r
- integral.create(cv::Size(scaledImageSize.width + 1, scaledImageSize.height + 1), CV_32SC1);\r
cv::gpu::integral(scaledImageBuffer, integral);\r
\r
- int step = (factor <= 2.) + 1;\r
+ step = (factor <= 2.) + 1;\r
\r
cv::gpu::device::lbp::classifyStump(stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat, leaves_mat, subsets_mat, features_mat,\r
- integral, processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, scaleFactor, step, subsetSize, objects, dclassified);\r
+ integral, processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, factor, step, subsetSize, candidates, dclassified);\r
}\r
-\r
- cudaMemcpy(classified, dclassified, sizeof(int), cudaMemcpyDeviceToHost);\r
- GpuMat candidates(1, *classified, objects.type(), objects.ptr());\r
- // std::cout << *classified << " Results: " << cv::Mat(candidates) << std::endl;\r
-\r
if (groupThreshold <= 0 || objects.empty())\r
return 0;\r
- cv::gpu::device::lbp::connectedConmonents(candidates, groupThreshold, grouping_eps, dclassified);\r
+ cv::gpu::device::lbp::connectedConmonents(candidates, objects, groupThreshold, grouping_eps, dclassified);\r
+ cudaMemcpy(classified, dclassified, sizeof(int), cudaMemcpyDeviceToHost);\r
cudaSafeCall( cudaDeviceSynchronize() );\r
- return *classified;\r
+ step = *classified;\r
+ delete[] classified;\r
+ cudaFree(dclassified);\r
+ return step;\r
}\r
\r
// ============ old fashioned haar cascade ==============================================//\r
__global__ void lbp_classify_stump(Stage* stages, int nstages, ClNode* nodes, const float* leaves, const int* subsets, const uchar4* features,
const DevMem2Di integral, int workWidth, int workHeight, int clWidth, int clHeight, float scale, int step, int subsetSize, DevMem2D_<int4> objects, unsigned int* n)
{
- int y = threadIdx.x * scale;
- int x = blockIdx.x * scale;
+ int x = threadIdx.x * step;
+ int y = blockIdx.x * step;
int current_node = 0;
int current_leave = 0;
}
template<typename Pr>
- __global__ void disjoin(int4* candidates, unsigned int n, int groupThreshold, float grouping_eps, unsigned int* nclasses)
+ __global__ void disjoin(int4* candidates, int4* objects, unsigned int n, int groupThreshold, float grouping_eps, unsigned int* nclasses)
{
using cv::gpu::device::VecTraits;
unsigned int tid = threadIdx.x;
__syncthreads();
atomicInc((unsigned int*)labels + cls, n);
- labels[n - 1] = 0;
+ *nclasses = 0;
int active = labels[tid];
if (active)
(n2 > max(3, n1) || n1 < 3) )
break;
}
-
if( j == n)
{
- // printf("founded gpu %d %d %d %d \n", r1[0], r1[1], r1[2], r1[3]);
- candidates[atomicInc((unsigned int*)labels + n -1, n)] = VecTraits<int4>::make(r1[0], r1[1], r1[2], r1[3]);
+ objects[atomicInc(nclasses, n)] = VecTraits<int4>::make(r1[0], r1[1], r1[2], r1[3]);
}
}
}
workWidth, workHeight, clWidth, clHeight, scale, step, subsetSize, objects, classified);
}
- int connectedConmonents(DevMem2D_<int4> candidates, int groupThreshold, float grouping_eps, unsigned int* nclasses)
+ int connectedConmonents(DevMem2D_<int4> candidates, DevMem2D_<int4> objects, int groupThreshold, float grouping_eps, unsigned int* nclasses)
{
int threads = candidates.cols;
int smem_amount = threads * sizeof(int) + threads * sizeof(int4);
- disjoin<InSameComponint><<<1, threads, smem_amount>>>((int4*)candidates.ptr(), candidates.cols, groupThreshold, grouping_eps, nclasses);
+ disjoin<InSameComponint><<<1, threads, smem_amount>>>((int4*)candidates.ptr(), (int4*)objects.ptr(), candidates.cols, groupThreshold, grouping_eps, nclasses);
return 0;
}
}
struct InSameComponint
{
public:
- __device__ __forceinline__ InSameComponint(float _eps) : eps(_eps * 0.5) {}
+ __device__ __forceinline__ InSameComponint(float _eps) : eps(_eps) {}
__device__ __forceinline__ InSameComponint(const InSameComponint& other) : eps(other.eps) {}
__device__ __forceinline__ bool operator()(const int4& r1, const int4& r2) const
{
- double delta = eps * (min(r1.z, r2.z) + min(r1.w, r2.w));
+ float delta = eps * (min(r1.z, r2.z) + min(r1.w, r2.w)) * 0.5;
return abs(r1.x - r2.x) <= delta && abs(r1.y - r2.y) <= delta
&& abs(r1.x + r1.z - r2.x - r2.z) <= delta && abs(r1.y + r1.w - r2.y - r2.w) <= delta;
testing::Values<int>(0)\r
));\r
\r
+PARAM_TEST_CASE(LBP_classify, cv::gpu::DeviceInfo, int)\r
+{\r
+ cv::gpu::DeviceInfo devInfo;\r
+\r
+ virtual void SetUp()\r
+ {\r
+ devInfo = GET_PARAM(0);\r
+ cv::gpu::setDevice(devInfo.deviceID());\r
+ }\r
+};\r
+\r
+TEST_P(LBP_classify, Accuracy)\r
+{\r
+ std::string classifierXmlPath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/lbpcascade_frontalface.xml";\r
+ std::string imagePath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/er.png";\r
+\r
+ cv::CascadeClassifier cpuClassifier(classifierXmlPath);\r
+ ASSERT_FALSE(cpuClassifier.empty());\r
+\r
+ cv::Mat image = cv::imread(imagePath);\r
+ image = image.colRange(0, image.cols / 2);\r
+ cv::Mat grey;\r
+ cvtColor(image, grey, CV_BGR2GRAY);\r
+ ASSERT_FALSE(image.empty());\r
+\r
+ std::vector<cv::Rect> rects;\r
+ cpuClassifier.detectMultiScale(grey, rects);\r
+ cv::Mat markedImage = image.clone();\r
+\r
+ std::vector<cv::Rect>::iterator it = rects.begin();\r
+ for (; it != rects.end(); ++it)\r
+ cv::rectangle(markedImage, *it, cv::Scalar(255, 0, 0, 255));\r
+\r
+ cv::gpu::CascadeClassifier_GPU_LBP gpuClassifier;\r
+ ASSERT_TRUE(gpuClassifier.load(classifierXmlPath));\r
+ cv::gpu::GpuMat gpu_rects, buffer;\r
+ cv::gpu::GpuMat tested(grey);\r
+ int count = gpuClassifier.detectMultiScale(tested, buffer, gpu_rects);\r
+\r
+ cv::Mat gpu_f(gpu_rects);\r
+ int* gpu_faces = (int*)gpu_f.ptr();\r
+ for (int i = 0; i < count; i++)\r
+ {\r
+ cv::Rect r(gpu_faces[i * 4],gpu_faces[i * 4 + 1],gpu_faces[i * 4 + 2],gpu_faces[i * 4 + 3]);\r
+ cv::rectangle(markedImage, r , cv::Scalar(0, 0, 255, 255));\r
+ }\r
+}\r
+\r
+INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, LBP_classify, testing::Combine(\r
+ ALL_DEVICES,\r
+ testing::Values<int>(0)\r
+ ));\r
+\r
} // namespace\r