/* * Copyright (c) 2017-2018 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "helpers.h" #include "types.h" #if defined(CELL_WIDTH) && defined(CELL_HEIGHT) && defined(NUM_BINS) && defined(PHASE_SCALE) /** This OpenCL kernel computes the HOG orientation binning * * @attention The following variables must be passed at compile time: * * -# -DCELL_WIDTH = Width of the cell * -# -DCELL_HEIGHT = height of the cell * -# -DNUM_BINS = Number of bins for each cell * -# -DPHASE_SCALE = Scale factor used to evaluate the index of the local HOG * * @note Each work-item computes a single cell * * @param[in] mag_ptr Pointer to the source image which stores the magnitude of the gradient for each pixel. Supported data types: S16 * @param[in] mag_stride_x Stride of the magnitude image in X dimension (in bytes) * @param[in] mag_step_x mag_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] mag_stride_y Stride of the magnitude image in Y dimension (in bytes) * @param[in] mag_step_y mag_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] mag_offset_first_element_in_bytes The offset of the first element in the magnitude image * @param[in] phase_ptr Pointer to the source image which stores the phase of the gradient for each pixel. Supported data types: U8 * @param[in] phase_stride_x Stride of the phase image in X dimension (in bytes) * @param[in] phase_step_x phase_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] phase_stride_y Stride of the the phase image in Y dimension (in bytes) * @param[in] phase_step_y phase_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] phase_offset_first_element_in_bytes The offset of the first element in the the phase image * @param[out] dst_ptr Pointer to the destination image which stores the local HOG for each cell Supported data types: F32. Number of channels supported: equal to the number of histogram bins per cell * @param[in] dst_stride_x Stride of the destination image in X dimension (in bytes) * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination image in Y dimension (in bytes) * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination image */ __kernel void hog_orientation_binning(IMAGE_DECLARATION(mag), IMAGE_DECLARATION(phase), IMAGE_DECLARATION(dst)) { float bins[NUM_BINS] = { 0 }; // Compute address for the magnitude and phase images Image mag = CONVERT_TO_IMAGE_STRUCT(mag); Image phase = CONVERT_TO_IMAGE_STRUCT(phase); __global uchar *mag_row_ptr = mag.ptr; __global uchar *phase_row_ptr = phase.ptr; for(int yc = 0; yc < CELL_HEIGHT; ++yc) { int xc = 0; for(; xc <= (CELL_WIDTH - 4); xc += 4) { // Load magnitude and phase values const float4 mag_f32 = convert_float4(vload4(0, (__global short *)mag_row_ptr + xc)); float4 phase_f32 = convert_float4(vload4(0, phase_row_ptr + xc)); // Scale phase: phase * scale + 0.5f phase_f32 = (float4)0.5f + phase_f32 * (float4)PHASE_SCALE; // Compute histogram index. int4 hidx_s32 = convert_int4(phase_f32); // Compute magnitude weights (w0 and w1) const float4 hidx_f32 = convert_float4(hidx_s32); // w1 = phase_f32 - hidx_s32 const float4 w1_f32 = phase_f32 - hidx_f32; // w0 = 1.0 - w1 const float4 w0_f32 = (float4)1.0f - w1_f32; // Calculate the weights for splitting vote const float4 mag_w0_f32 = mag_f32 * w0_f32; const float4 mag_w1_f32 = mag_f32 * w1_f32; // Weighted vote between 2 bins // Check if the histogram index is equal to NUM_BINS. If so, replace the index with 0 hidx_s32 = select(hidx_s32, (int4)0, hidx_s32 == (int4)(NUM_BINS)); // Bin 0 bins[hidx_s32.s0] += mag_w0_f32.s0; bins[hidx_s32.s1] += mag_w0_f32.s1; bins[hidx_s32.s2] += mag_w0_f32.s2; bins[hidx_s32.s3] += mag_w0_f32.s3; hidx_s32 += (int4)1; // Check if the histogram index is equal to NUM_BINS. If so, replace the index with 0 hidx_s32 = select(hidx_s32, (int4)0, hidx_s32 == (int4)(NUM_BINS)); // Bin1 bins[hidx_s32.s0] += mag_w1_f32.s0; bins[hidx_s32.s1] += mag_w1_f32.s1; bins[hidx_s32.s2] += mag_w1_f32.s2; bins[hidx_s32.s3] += mag_w1_f32.s3; } // Left over computation for(; xc < CELL_WIDTH; xc++) { const float mag_value = *((__global short *)mag_row_ptr + xc); const float phase_value = *(phase_row_ptr + xc) * (float)PHASE_SCALE + 0.5f; const float w1 = phase_value - floor(phase_value); // The quantised phase is the histogram index [0, NUM_BINS - 1] // Check limit of histogram index. If hidx == NUM_BINS, hidx = 0 const uint hidx = (uint)(phase_value) % NUM_BINS; // Weighted vote between 2 bins bins[hidx] += mag_value * (1.0f - w1); bins[(hidx + 1) % NUM_BINS] += mag_value * w1; } // Point to the next row of magnitude and phase images mag_row_ptr += mag_stride_y; phase_row_ptr += phase_stride_y; } // Compute address for the destination image Image dst = CONVERT_TO_IMAGE_STRUCT(dst); // Store the local HOG in the global memory int xc = 0; for(; xc <= (NUM_BINS - 4); xc += 4) { float4 values = vload4(0, bins + xc); vstore4(values, 0, ((__global float *)dst.ptr) + xc); } // Left over stores for(; xc < NUM_BINS; ++xc) { ((__global float *)dst.ptr)[xc] = bins[xc]; } } #endif /* CELL_WIDTH and CELL_HEIGHT and NUM_BINS and PHASE_SCALE */ #if defined(NUM_CELLS_PER_BLOCK_HEIGHT) && defined(NUM_BINS_PER_BLOCK_X) && defined(NUM_BINS_PER_BLOCK) && defined(HOG_NORM_TYPE) && defined(L2_HYST_THRESHOLD) #ifndef L2_NORM #error The value of enum class HOGNormType::L2_NORM has not be passed to the OpenCL kernel #endif /* not L2_NORM */ #ifndef L2HYS_NORM #error The value of enum class HOGNormType::L2HYS_NORM has not be passed to the OpenCL kernel #endif /* not L2HYS_NORM */ #ifndef L1_NORM #error The value of enum class HOGNormType::L1_NORM has not be passed to the OpenCL kernel #endif /* not L1_NORM */ /** This OpenCL kernel computes the HOG block normalization * * @attention The following variables must be passed at compile time: * * -# -DNUM_CELLS_PER_BLOCK_HEIGHT = Number of cells for each block * -# -DNUM_BINS_PER_BLOCK_X = Number of bins for each block along the X direction * -# -DNUM_BINS_PER_BLOCK = Number of bins for each block * -# -DHOG_NORM_TYPE = Normalization type * -# -DL2_HYST_THRESHOLD = Threshold used for L2HYS_NORM normalization method * -# -DL2_NORM = Value of the enum class HOGNormType::L2_NORM * -# -DL2HYS_NORM = Value of the enum class HOGNormType::L2HYS_NORM * -# -DL1_NORM = Value of the enum class HOGNormType::L1_NORM * * @note Each work-item computes a single block * * @param[in] src_ptr Pointer to the source image which stores the local HOG. Supported data types: F32. Number of channels supported: equal to the number of histogram bins per cell * @param[in] src_stride_x Stride of the source image in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image * @param[out] dst_ptr Pointer to the destination image which stores the normlized HOG Supported data types: F32. Number of channels supported: equal to the number of histogram bins per block * @param[in] dst_stride_x Stride of the destination image in X dimension (in bytes) * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination image in Y dimension (in bytes) * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination image */ __kernel void hog_block_normalization(IMAGE_DECLARATION(src), IMAGE_DECLARATION(dst)) { float sum = 0.0f; float4 sum_f32 = (float4)(0.0f); // Compute address for the source and destination tensor Image src = CONVERT_TO_IMAGE_STRUCT(src); Image dst = CONVERT_TO_IMAGE_STRUCT(dst); for(size_t yc = 0; yc < NUM_CELLS_PER_BLOCK_HEIGHT; ++yc) { const __global float *hist_ptr = (__global float *)(src.ptr + yc * src_stride_y); int xc = 0; for(; xc <= (NUM_BINS_PER_BLOCK_X - 16); xc += 16) { const float4 val0 = vload4(0, hist_ptr + xc + 0); const float4 val1 = vload4(0, hist_ptr + xc + 4); const float4 val2 = vload4(0, hist_ptr + xc + 8); const float4 val3 = vload4(0, hist_ptr + xc + 12); #if(HOG_NORM_TYPE == L2_NORM) || (HOG_NORM_TYPE == L2HYS_NORM) // Compute val^2 for L2_NORM or L2HYS_NORM sum_f32 += val0 * val0; sum_f32 += val1 * val1; sum_f32 += val2 * val2; sum_f32 += val3 * val3; #else /* (HOG_NORM_TYPE == L2_NORM) || (HOG_NORM_TYPE == L2HYS_NORM) */ // Compute |val| for L1_NORM sum_f32 += fabs(val0); sum_f32 += fabs(val1); sum_f32 += fabs(val2); sum_f32 += fabs(val3); #endif /* (HOG_NORM_TYPE == L2_NORM) || (HOG_NORM_TYPE == L2HYS_NORM) */ // Store linearly the input values un-normalized in the output image. These values will be reused for the normalization. // This approach will help us to be cache friendly in the next for loop where the normalization will be done because all the values // will be accessed consecutively vstore4(val0, 0, ((__global float *)dst.ptr) + xc + 0 + yc * NUM_BINS_PER_BLOCK_X); vstore4(val1, 0, ((__global float *)dst.ptr) + xc + 4 + yc * NUM_BINS_PER_BLOCK_X); vstore4(val2, 0, ((__global float *)dst.ptr) + xc + 8 + yc * NUM_BINS_PER_BLOCK_X); vstore4(val3, 0, ((__global float *)dst.ptr) + xc + 12 + yc * NUM_BINS_PER_BLOCK_X); } // Compute left over for(; xc < NUM_BINS_PER_BLOCK_X; ++xc) { const float val = hist_ptr[xc]; #if(HOG_NORM_TYPE == L2_NORM) || (HOG_NORM_TYPE == L2HYS_NORM) sum += val * val; #else /* (HOG_NORM_TYPE == L2_NORM) || (HOG_NORM_TYPE == L2HYS_NORM) */ sum += fabs(val); #endif /* (HOG_NORM_TYPE == L2_NORM) || (HOG_NORM_TYPE == L2HYS_NORM) */ ((__global float *)dst.ptr)[xc + 0 + yc * NUM_BINS_PER_BLOCK_X] = val; } } sum += dot(sum_f32, (float4)1.0f); float scale = 1.0f / (sqrt(sum) + NUM_BINS_PER_BLOCK * 0.1f); #if(HOG_NORM_TYPE == L2HYS_NORM) // Reset sum sum_f32 = (float4)0.0f; sum = 0.0f; int k = 0; for(; k <= NUM_BINS_PER_BLOCK - 16; k += 16) { float4 val0 = vload4(0, ((__global float *)dst.ptr) + k + 0); float4 val1 = vload4(0, ((__global float *)dst.ptr) + k + 4); float4 val2 = vload4(0, ((__global float *)dst.ptr) + k + 8); float4 val3 = vload4(0, ((__global float *)dst.ptr) + k + 12); // Scale val val0 = val0 * (float4)scale; val1 = val1 * (float4)scale; val2 = val2 * (float4)scale; val3 = val3 * (float4)scale; // Clip val if over _threshold_l2hys val0 = fmin(val0, (float4)L2_HYST_THRESHOLD); val1 = fmin(val1, (float4)L2_HYST_THRESHOLD); val2 = fmin(val2, (float4)L2_HYST_THRESHOLD); val3 = fmin(val3, (float4)L2_HYST_THRESHOLD); // Compute val^2 sum_f32 += val0 * val0; sum_f32 += val1 * val1; sum_f32 += val2 * val2; sum_f32 += val3 * val3; vstore4(val0, 0, ((__global float *)dst.ptr) + k + 0); vstore4(val1, 0, ((__global float *)dst.ptr) + k + 4); vstore4(val2, 0, ((__global float *)dst.ptr) + k + 8); vstore4(val3, 0, ((__global float *)dst.ptr) + k + 12); } // Compute left over for(; k < NUM_BINS_PER_BLOCK; ++k) { float val = ((__global float *)dst.ptr)[k] * scale; // Clip scaled input_value if over L2_HYST_THRESHOLD val = fmin(val, (float)L2_HYST_THRESHOLD); sum += val * val; ((__global float *)dst.ptr)[k] = val; } sum += dot(sum_f32, (float4)1.0f); // We use the same constants of OpenCV scale = 1.0f / (sqrt(sum) + 1e-3f); #endif /* (HOG_NORM_TYPE == L2HYS_NORM) */ int i = 0; for(; i <= (NUM_BINS_PER_BLOCK - 16); i += 16) { float4 val0 = vload4(0, ((__global float *)dst.ptr) + i + 0); float4 val1 = vload4(0, ((__global float *)dst.ptr) + i + 4); float4 val2 = vload4(0, ((__global float *)dst.ptr) + i + 8); float4 val3 = vload4(0, ((__global float *)dst.ptr) + i + 12); // Multiply val by the normalization scale factor val0 = val0 * (float4)scale; val1 = val1 * (float4)scale; val2 = val2 * (float4)scale; val3 = val3 * (float4)scale; vstore4(val0, 0, ((__global float *)dst.ptr) + i + 0); vstore4(val1, 0, ((__global float *)dst.ptr) + i + 4); vstore4(val2, 0, ((__global float *)dst.ptr) + i + 8); vstore4(val3, 0, ((__global float *)dst.ptr) + i + 12); } for(; i < NUM_BINS_PER_BLOCK; ++i) { ((__global float *)dst.ptr)[i] *= scale; } } #endif /* NUM_CELLS_PER_BLOCK_HEIGHT and NUM_BINS_PER_BLOCK_X and NUM_BINS_PER_BLOCK and HOG_NORM_TYPE and L2_HYST_THRESHOLD */ #if defined(NUM_BLOCKS_PER_DESCRIPTOR_Y) && defined(NUM_BINS_PER_DESCRIPTOR_X) && defined(THRESHOLD) && defined(MAX_NUM_DETECTION_WINDOWS) && defined(IDX_CLASS) && defined(DETECTION_WINDOW_STRIDE_WIDTH) && defined(DETECTION_WINDOW_STRIDE_HEIGHT) && defined(DETECTION_WINDOW_WIDTH) && defined(DETECTION_WINDOW_HEIGHT) /** This OpenCL kernel computes the HOG detector using linear SVM * * @attention The following variables must be passed at compile time: * * -# -DNUM_BLOCKS_PER_DESCRIPTOR_Y = Number of blocks per descriptor along the Y direction * -# -DNUM_BINS_PER_DESCRIPTOR_X = Number of bins per descriptor along the X direction * -# -DTHRESHOLD = Threshold for the distance between features and SVM classifying plane * -# -DMAX_NUM_DETECTION_WINDOWS = Maximum number of possible detection windows. It is equal to the size of the DetectioWindow array * -# -DIDX_CLASS = Index of the class to detect * -# -DDETECTION_WINDOW_STRIDE_WIDTH = Detection window stride for the X direction * -# -DDETECTION_WINDOW_STRIDE_HEIGHT = Detection window stride for the Y direction * -# -DDETECTION_WINDOW_WIDTH = Width of the detection window * -# -DDETECTION_WINDOW_HEIGHT = Height of the detection window * * @note Each work-item computes a single detection window * * @param[in] src_ptr Pointer to the source image which stores the local HOG. Supported data types: F32. Number of channels supported: equal to the number of histogram bins per cell * @param[in] src_stride_x Stride of the source image in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image * @param[in] hog_descriptor Pointer to HOG descriptor. Supported data types: F32 * @param[out] dst Pointer to DetectionWindow array * @param[out] num_detection_windows Number of objects detected */ __kernel void hog_detector(IMAGE_DECLARATION(src), __global float *hog_descriptor, __global DetectionWindow *dst, __global uint *num_detection_windows) { // Check if the DetectionWindow array is full if(*num_detection_windows >= MAX_NUM_DETECTION_WINDOWS) { return; } Image src = CONVERT_TO_IMAGE_STRUCT(src); const int src_step_y_f32 = src_stride_y / sizeof(float); // Init score_f32 with 0 float4 score_f32 = (float4)0.0f; // Init score with 0 float score = 0.0f; __global float *src_row_ptr = (__global float *)src.ptr; // Compute Linear SVM for(int yb = 0; yb < NUM_BLOCKS_PER_DESCRIPTOR_Y; ++yb, src_row_ptr += src_step_y_f32) { int xb = 0; const int offset_y = yb * NUM_BINS_PER_DESCRIPTOR_X; for(; xb < (int)NUM_BINS_PER_DESCRIPTOR_X - 8; xb += 8) { // Load descriptor values float4 a0_f32 = vload4(0, src_row_ptr + xb + 0); float4 a1_f32 = vload4(0, src_row_ptr + xb + 4); float4 b0_f32 = vload4(0, hog_descriptor + xb + 0 + offset_y); float4 b1_f32 = vload4(0, hog_descriptor + xb + 4 + offset_y); // Multiply accumulate score_f32 += a0_f32 * b0_f32; score_f32 += a1_f32 * b1_f32; } for(; xb < NUM_BINS_PER_DESCRIPTOR_X; ++xb) { const float a = src_row_ptr[xb]; const float b = hog_descriptor[xb + offset_y]; score += a * b; } } score += dot(score_f32, (float4)1.0f); // Add the bias. The bias is located at the position (descriptor_size() - 1) // (descriptor_size - 1) = NUM_BINS_PER_DESCRIPTOR_X * NUM_BLOCKS_PER_DESCRIPTOR_Y score += hog_descriptor[NUM_BINS_PER_DESCRIPTOR_X * NUM_BLOCKS_PER_DESCRIPTOR_Y]; if(score > (float)THRESHOLD) { int id = atomic_inc(num_detection_windows); if(id < MAX_NUM_DETECTION_WINDOWS) { dst[id].x = get_global_id(0) * DETECTION_WINDOW_STRIDE_WIDTH; dst[id].y = get_global_id(1) * DETECTION_WINDOW_STRIDE_HEIGHT; dst[id].width = DETECTION_WINDOW_WIDTH; dst[id].height = DETECTION_WINDOW_HEIGHT; dst[id].idx_class = IDX_CLASS; dst[id].score = score; } } } #endif /* NUM_BLOCKS_PER_DESCRIPTOR_Y && NUM_BINS_PER_DESCRIPTOR_X && THRESHOLD && MAX_NUM_DETECTION_WINDOWS && IDX_CLASS && * DETECTION_WINDOW_STRIDE_WIDTH && DETECTION_WINDOW_STRIDE_HEIGHT && DETECTION_WINDOW_WIDTH && DETECTION_WINDOW_HEIGHT */