Enabled Intel-specific optimizations for HOG detector.

modules/objdetect/src/hog.cpp

@@ -1085,8 +1085,8 @@ static bool ocl_compute_gradients_8UC1(int height, int width, InputArray _img, f
     size_t globalThreads[3] = { width, height, 1 };
     char correctGamma = (correct_gamma) ? 1 : 0;
     int grad_quadstep = (int)grad.step >> 3;
-    int qangle_step_shift = 0;
-    int qangle_step = (int)qangle.step >> (1 + qangle_step_shift);
+    int qangle_elem_size = CV_ELEM_SIZE1(qangle.type());
+    int qangle_step = (int)qangle.step / (2 * qangle_elem_size);
 
     int idx = 0;
     idx = k.set(idx, height);
@@ -1137,9 +1137,9 @@ static bool ocl_compute_hists(int nbins, int block_stride_x, int block_stride_y,
     int img_block_height = (height - CELLS_PER_BLOCK_Y * CELL_HEIGHT + block_stride_y)/block_stride_y;
     int blocks_total = img_block_width * img_block_height;
 
-    int qangle_step_shift = 0;
+    int qangle_elem_size = CV_ELEM_SIZE1(qangle.type());
     int grad_quadstep = (int)grad.step >> 2;
-    int qangle_step = (int)qangle.step >> qangle_step_shift;
+    int qangle_step = (int)qangle.step / qangle_elem_size;
 
     int blocks_in_group = 4;
     size_t localThreads[3] = { blocks_in_group * 24, 2, 1 };
@@ -1316,11 +1316,12 @@ static bool ocl_extract_descrs_by_cols(int win_height, int win_width, int block_
 static bool ocl_compute(InputArray _img, Size win_stride, std::vector<float>& _descriptors, int descr_format, Size blockSize,
                         Size cellSize, int nbins, Size blockStride, Size winSize, float sigma, bool gammaCorrection, double L2HysThreshold)
 {
-     Size imgSize = _img.size();
+    Size imgSize = _img.size();
     Size effect_size = imgSize;
 
     UMat grad(imgSize, CV_32FC2);
-    UMat qangle(imgSize, CV_8UC2);
+    int qangle_type = ocl::Device::getDefault().isIntel() ? CV_32SC2 : CV_8UC2;
+    UMat qangle(imgSize, qangle_type);
 
     const size_t block_hist_size = getBlockHistogramSize(blockSize, cellSize, nbins);
     const Size blocks_per_img = numPartsWithin(imgSize, blockSize, blockStride);
@@ -1720,7 +1721,8 @@ static bool ocl_detect(InputArray img, std::vector<Point> &hits, double hit_thre
     Size imgSize = img.size();
     Size effect_size = imgSize;
     UMat grad(imgSize, CV_32FC2);
-    UMat qangle(imgSize, CV_8UC2);
+    int qangle_type = ocl::Device::getDefault().isIntel() ? CV_32SC2 : CV_8UC2;
+    UMat qangle(imgSize, qangle_type);
 
     const size_t block_hist_size = getBlockHistogramSize(blockSize, cellSize, nbins);
     const Size blocks_per_img = numPartsWithin(imgSize, blockSize, blockStride);

modules/objdetect/src/opencl/objdetect_hog.cl

@@ -50,6 +50,14 @@
 #define NTHREADS 256
 #define CV_PI_F 3.1415926535897932384626433832795f
 
+#ifdef INTEL_DEVICE
+#define QANGLE_TYPE     int
+#define QANGLE_TYPE2    int2
+#else
+#define QANGLE_TYPE     uchar
+#define QANGLE_TYPE2    uchar2
+#endif
+
 //----------------------------------------------------------------------------
 // Histogram computation
 // 12 threads for a cell, 12x4 threads per block
@@ -59,7 +67,7 @@ __kernel void compute_hists_lut_kernel(
     const int cnbins, const int cblock_hist_size, const int img_block_width,
     const int blocks_in_group, const int blocks_total,
     const int grad_quadstep, const int qangle_step,
-    __global const float* grad, __global const uchar* qangle,
+    __global const float* grad, __global const QANGLE_TYPE* qangle,
     __global const float* gauss_w_lut,
     __global float* block_hists, __local float* smem)
 {
@@ -86,7 +94,7 @@ __kernel void compute_hists_lut_kernel(
 
     __global const float* grad_ptr = (gid < blocks_total) ?
         grad + offset_y * grad_quadstep + (offset_x << 1) : grad;
-    __global const uchar* qangle_ptr = (gid < blocks_total) ?
+    __global const QANGLE_TYPE* qangle_ptr = (gid < blocks_total) ?
         qangle + offset_y * qangle_step + (offset_x << 1) : qangle;
 
     __local float* hist = hists + 12 * (cell_y * CELLS_PER_BLOCK_Y + cell_x) +
@@ -101,7 +109,7 @@ __kernel void compute_hists_lut_kernel(
     for (int dist_y = dist_y_begin; dist_y < dist_y_begin + 12; ++dist_y)
     {
         float2 vote = (float2) (grad_ptr[0], grad_ptr[1]);
-        uchar2 bin = (uchar2) (qangle_ptr[0], qangle_ptr[1]);
+        QANGLE_TYPE2 bin = (QANGLE_TYPE2) (qangle_ptr[0], qangle_ptr[1]);
 
         grad_ptr += grad_quadstep;
         qangle_ptr += qangle_step;
@@ -558,7 +566,7 @@ __kernel void extract_descrs_by_cols_kernel(
 __kernel void compute_gradients_8UC4_kernel(
     const int height, const int width,
     const int img_step, const int grad_quadstep, const int qangle_step,
-    const __global uchar4 * img, __global float * grad, __global uchar * qangle,
+    const __global uchar4 * img, __global float * grad, __global QANGLE_TYPE * qangle,
     const float angle_scale, const char correct_gamma, const int cnbins)
 {
     const int x = get_global_id(0);
@@ -660,7 +668,7 @@ __kernel void compute_gradients_8UC4_kernel(
 __kernel void compute_gradients_8UC1_kernel(
     const int height, const int width,
     const int img_step, const int grad_quadstep, const int qangle_step,
-    __global const uchar * img, __global float * grad, __global uchar * qangle,
+    __global const uchar * img, __global float * grad, __global QANGLE_TYPE * qangle,
     const float angle_scale, const char correct_gamma, const int cnbins)
 {
     const int x = get_global_id(0);