| /* |
| * Copyright (c) 2017 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 = *(mag_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 // (defined CELL_WIDTH && defined CELL_HEIGHT && defined NUM_BINS && defined PHASE_SCALE) |
| |
| #if(defined NUM_CELLS_PER_BLOCK_HEIGHT && defined NUM_BINS_PER_BLOCK_X && defined NUM_BINS_PER_BLOCK && 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 |
| |
| #ifndef L2HYS_NORM |
| #error The value of enum class HOGNormType::L2HYS_NORM has not be passed to the OpenCL kernel |
| #endif |
| |
| #ifndef L1_NORM |
| #error The value of enum class HOGNormType::L1_NORM has not be passed to the OpenCL kernel |
| #endif |
| |
| /** 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 |
| // 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 |
| 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 // (defined NUM_CELLS_PER_BLOCK_HEIGHT && defined NUM_BINS_PER_BLOCK_X && defined NUM_BINS_PER_BLOCK && HOG_NORM_TYPE && defined 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 BLOCK_STRIDE_WIDTH && defined BLOCK_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 |
| * -# -DBLOCK_STRIDE_WIDTH = Block stride for the X direction |
| * -# -DBLOCK_STRIDE_HEIGHT = Block 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) * BLOCK_STRIDE_WIDTH; |
| dst[id].y = get_global_id(1) * BLOCK_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 // defined BIAS && defined NUM_BLOCKS_PER_DESCRIPTOR_Y && defined NUM_BINS_PER_DESCRIPTOR_X && ... |
