This patch adds ocl kernels to accelerate Dense Inverse Search based optical flow algorithm, it acclerates 3 parts in the algorithm, including 1) Structure tensor elements compute, 2) Patch inverse search, 3) Densification compute. Perf and accuracy test are also added. The perf test shows it is 30% faster than the current implementation. Signed-off-by: Li Peng <peng.li@intel.com>pull/1062/head
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/*
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* By downloading, copying, installing or using the software you agree to this license. |
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* If you do not agree to this license, do not download, install, |
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* copy or use the software. |
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* |
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* |
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* License Agreement |
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* For Open Source Computer Vision Library |
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* (3 - clause BSD License) |
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* |
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* Redistribution and use in source and binary forms, with or without modification, |
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* are permitted provided that the following conditions are met : |
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* |
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* * Redistributions of source code must retain the above copyright notice, |
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* this list of conditions and the following disclaimer. |
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* |
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* * Redistributions in binary form must reproduce the above copyright notice, |
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* this list of conditions and the following disclaimer in the documentation |
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* and / or other materials provided with the distribution. |
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* |
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* * Neither the names of the copyright holders nor the names of the contributors |
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* may be used to endorse or promote products derived from this software |
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* without specific prior written permission. |
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* |
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* This software is provided by the copyright holders and contributors "as is" and |
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* any express or implied warranties, including, but not limited to, the implied |
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* warranties of merchantability and fitness for a particular purpose are disclaimed. |
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* In no event shall copyright holders or contributors be liable for any direct, |
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* indirect, incidental, special, exemplary, or consequential damages |
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* (including, but not limited to, procurement of substitute goods or services; |
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* loss of use, data, or profits; or business interruption) however caused |
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* and on any theory of liability, whether in contract, strict liability, |
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* or tort(including negligence or otherwise) arising in any way out of |
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* the use of this software, even if advised of the possibility of such damage. |
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*/ |
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#include "../perf_precomp.hpp" |
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#include "opencv2/ts/ocl_perf.hpp" |
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using std::tr1::tuple; |
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using std::tr1::get; |
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using namespace perf; |
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using namespace testing; |
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using namespace cv; |
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using namespace cv::optflow; |
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#ifdef HAVE_OPENCL |
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namespace cvtest { |
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namespace ocl { |
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void MakeArtificialExample(UMat &dst_frame1, UMat &dst_frame2); |
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typedef tuple<String, Size> DISParams; |
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typedef TestBaseWithParam<DISParams> DenseOpticalFlow_DIS; |
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OCL_PERF_TEST_P(DenseOpticalFlow_DIS, perf, |
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Combine(Values("PRESET_ULTRAFAST", "PRESET_FAST", "PRESET_MEDIUM"), Values(szVGA, sz720p, sz1080p))) |
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{ |
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DISParams params = GetParam(); |
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// use strings to print preset names in the perf test results:
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String preset_string = get<0>(params); |
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int preset = DISOpticalFlow::PRESET_FAST; |
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if (preset_string == "PRESET_ULTRAFAST") |
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preset = DISOpticalFlow::PRESET_ULTRAFAST; |
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else if (preset_string == "PRESET_FAST") |
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preset = DISOpticalFlow::PRESET_FAST; |
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else if (preset_string == "PRESET_MEDIUM") |
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preset = DISOpticalFlow::PRESET_MEDIUM; |
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Size sz = get<1>(params); |
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UMat frame1(sz, CV_8U); |
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UMat frame2(sz, CV_8U); |
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UMat flow; |
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MakeArtificialExample(frame1, frame2); |
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Ptr<DenseOpticalFlow> algo = createOptFlow_DIS(preset); |
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OCL_TEST_CYCLE_N(10) |
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{ |
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algo->calc(frame1, frame2, flow); |
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} |
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SANITY_CHECK_NOTHING(); |
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} |
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void MakeArtificialExample(UMat &dst_frame1, UMat &dst_frame2) |
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{ |
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int src_scale = 2; |
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int OF_scale = 6; |
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double sigma = dst_frame1.cols / 300; |
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UMat tmp(Size(dst_frame1.cols / (int)pow(2, src_scale), dst_frame1.rows / (int)pow(2, src_scale)), CV_8U); |
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randu(tmp, 0, 255); |
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resize(tmp, dst_frame1, dst_frame1.size(), 0.0, 0.0, INTER_LINEAR); |
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resize(tmp, dst_frame2, dst_frame2.size(), 0.0, 0.0, INTER_LINEAR); |
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Mat displacement_field(Size(dst_frame1.cols / (int)pow(2, OF_scale), dst_frame1.rows / (int)pow(2, OF_scale)), |
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CV_32FC2); |
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randn(displacement_field, 0.0, sigma); |
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resize(displacement_field, displacement_field, dst_frame2.size(), 0.0, 0.0, INTER_CUBIC); |
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for (int i = 0; i < displacement_field.rows; i++) |
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for (int j = 0; j < displacement_field.cols; j++) |
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displacement_field.at<Vec2f>(i, j) += Vec2f((float)j, (float)i); |
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remap(dst_frame2, dst_frame2, displacement_field, Mat(), INTER_LINEAR, BORDER_REPLICATE); |
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} |
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} } // namespace cvtest::ocl
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#endif // HAVE_OPENCL
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// This file is part of OpenCV project. |
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// It is subject to the license terms in the LICENSE file found in the top-level directory |
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// of this distribution and at http://opencv.org/license.html. |
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#define EPS 0.001f |
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#define INF 1E+10F |
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__kernel void dis_precomputeStructureTensor_hor(__global const short *I0x, |
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__global const short *I0y, |
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int patch_size, int patch_stride, |
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int w, int h, int ws, |
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__global float *I0xx_aux_ptr, |
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__global float *I0yy_aux_ptr, |
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__global float *I0xy_aux_ptr, |
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__global float *I0x_aux_ptr, |
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__global float *I0y_aux_ptr) |
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{ |
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int i = get_global_id(0); |
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if (i >= h) return; |
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const __global short *x_row = I0x + i * w; |
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const __global short *y_row = I0y + i * w; |
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float sum_xx = 0.0f, sum_yy = 0.0f, sum_xy = 0.0f, sum_x = 0.0f, sum_y = 0.0f; |
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float8 x_vec = convert_float8(vload8(0, x_row)); |
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float8 y_vec = convert_float8(vload8(0, y_row)); |
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sum_xx = dot(x_vec.lo, x_vec.lo) + dot(x_vec.hi, x_vec.hi); |
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sum_yy = dot(y_vec.lo, y_vec.lo) + dot(y_vec.hi, y_vec.hi); |
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sum_xy = dot(x_vec.lo, y_vec.lo) + dot(x_vec.hi, y_vec.hi); |
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sum_x = dot(x_vec.lo, 1.0f) + dot(x_vec.hi, 1.0f); |
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sum_y = dot(y_vec.lo, 1.0f) + dot(y_vec.hi, 1.0f); |
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I0xx_aux_ptr[i * ws] = sum_xx; |
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I0yy_aux_ptr[i * ws] = sum_yy; |
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I0xy_aux_ptr[i * ws] = sum_xy; |
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I0x_aux_ptr[i * ws] = sum_x; |
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I0y_aux_ptr[i * ws] = sum_y; |
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int js = 1; |
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for (int j = patch_size; j < w; j++) |
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{ |
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short x_val1 = x_row[j]; |
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short x_val2 = x_row[j - patch_size]; |
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short y_val1 = y_row[j]; |
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short y_val2 = y_row[j - patch_size]; |
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sum_xx += (x_val1 * x_val1 - x_val2 * x_val2); |
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sum_yy += (y_val1 * y_val1 - y_val2 * y_val2); |
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sum_xy += (x_val1 * y_val1 - x_val2 * y_val2); |
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sum_x += (x_val1 - x_val2); |
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sum_y += (y_val1 - y_val2); |
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if ((j - patch_size + 1) % patch_stride == 0) |
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{ |
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int index = i * ws + js; |
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I0xx_aux_ptr[index] = sum_xx; |
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I0yy_aux_ptr[index] = sum_yy; |
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I0xy_aux_ptr[index] = sum_xy; |
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I0x_aux_ptr[index] = sum_x; |
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I0y_aux_ptr[index] = sum_y; |
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js++; |
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} |
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} |
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} |
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__kernel void dis_precomputeStructureTensor_ver(__global const float *I0xx_aux_ptr, |
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__global const float *I0yy_aux_ptr, |
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__global const float *I0xy_aux_ptr, |
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__global const float *I0x_aux_ptr, |
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__global const float *I0y_aux_ptr, |
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int patch_size, int patch_stride, |
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int w, int h, int ws, |
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__global float *I0xx_ptr, |
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__global float *I0yy_ptr, |
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__global float *I0xy_ptr, |
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__global float *I0x_ptr, |
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__global float *I0y_ptr) |
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{ |
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int j = get_global_id(0); |
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if (j >= ws) return; |
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float sum_xx, sum_yy, sum_xy, sum_x, sum_y; |
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sum_xx = sum_yy = sum_xy = sum_x = sum_y = 0.0f; |
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for (int i = 0; i < patch_size; i++) |
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{ |
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sum_xx += I0xx_aux_ptr[i * ws + j]; |
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sum_yy += I0yy_aux_ptr[i * ws + j]; |
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sum_xy += I0xy_aux_ptr[i * ws + j]; |
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sum_x += I0x_aux_ptr[i * ws + j]; |
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sum_y += I0y_aux_ptr[i * ws + j]; |
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} |
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I0xx_ptr[j] = sum_xx; |
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I0yy_ptr[j] = sum_yy; |
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I0xy_ptr[j] = sum_xy; |
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I0x_ptr[j] = sum_x; |
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I0y_ptr[j] = sum_y; |
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int is = 1; |
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for (int i = patch_size; i < h; i++) |
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{ |
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sum_xx += (I0xx_aux_ptr[i * ws + j] - I0xx_aux_ptr[(i - patch_size) * ws + j]); |
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sum_yy += (I0yy_aux_ptr[i * ws + j] - I0yy_aux_ptr[(i - patch_size) * ws + j]); |
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sum_xy += (I0xy_aux_ptr[i * ws + j] - I0xy_aux_ptr[(i - patch_size) * ws + j]); |
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sum_x += (I0x_aux_ptr[i * ws + j] - I0x_aux_ptr[(i - patch_size) * ws + j]); |
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sum_y += (I0y_aux_ptr[i * ws + j] - I0y_aux_ptr[(i - patch_size) * ws + j]); |
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if ((i - patch_size + 1) % patch_stride == 0) |
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{ |
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I0xx_ptr[is * ws + j] = sum_xx; |
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I0yy_ptr[is * ws + j] = sum_yy; |
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I0xy_ptr[is * ws + j] = sum_xy; |
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I0x_ptr[is * ws + j] = sum_x; |
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I0y_ptr[is * ws + j] = sum_y; |
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is++; |
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} |
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} |
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} |
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__kernel void dis_densification(__global const float *sx, __global const float *sy, |
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__global const uchar *i0, __global const uchar *i1, |
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int psz, int pstr, |
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int w, int h, int ws, |
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__global float *ux, __global float *uy) |
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{ |
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int x = get_global_id(0); |
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int y = get_global_id(1); |
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int i, j; |
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if (x >= w || y >= h) return; |
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int start_is, end_is; |
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int start_js, end_js; |
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end_is = min(y / pstr, (h - psz) / pstr); |
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start_is = max(0, y - psz + pstr) / pstr; |
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start_is = min(start_is, end_is); |
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end_js = min(x / pstr, (w - psz) / pstr); |
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start_js = max(0, x - psz + pstr) / pstr; |
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start_js = min(start_js, end_js); |
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float coef, sum_coef = 0.0f; |
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float sum_Ux = 0.0f; |
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float sum_Uy = 0.0f; |
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int i_l, i_u; |
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int j_l, j_u; |
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float i_m, j_m, diff; |
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i = y; |
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j = x; |
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/* Iterate through all the patches that overlap the current location (i,j) */ |
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for (int is = start_is; is <= end_is; is++) |
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for (int js = start_js; js <= end_js; js++) |
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{ |
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float sx_val = sx[is * ws + js]; |
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float sy_val = sy[is * ws + js]; |
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uchar2 i1_vec1, i1_vec2; |
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j_m = min(max(j + sx_val, 0.0f), w - 1.0f - EPS); |
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i_m = min(max(i + sy_val, 0.0f), h - 1.0f - EPS); |
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j_l = (int)j_m; |
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j_u = j_l + 1; |
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i_l = (int)i_m; |
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i_u = i_l + 1; |
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i1_vec1 = vload2(0, i1 + i_u * w + j_l); |
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i1_vec2 = vload2(0, i1 + i_l * w + j_l); |
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diff = (j_m - j_l) * (i_m - i_l) * i1_vec1.y + |
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(j_u - j_m) * (i_m - i_l) * i1_vec1.x + |
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(j_m - j_l) * (i_u - i_m) * i1_vec2.y + |
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(j_u - j_m) * (i_u - i_m) * i1_vec2.x - i0[i * w + j]; |
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coef = 1 / max(1.0f, fabs(diff)); |
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sum_Ux += coef * sx_val; |
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sum_Uy += coef * sy_val; |
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sum_coef += coef; |
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} |
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ux[i * w + j] = sum_Ux / sum_coef; |
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uy[i * w + j] = sum_Uy / sum_coef; |
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} |
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#define INIT_BILINEAR_WEIGHTS(Ux, Uy) \ |
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i_I1 = min(max(i + Uy + bsz, i_lower_limit), i_upper_limit); \ |
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j_I1 = min(max(j + Ux + bsz, j_lower_limit), j_upper_limit); \ |
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\ |
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w11 = (i_I1 - floor(i_I1)) * (j_I1 - floor(j_I1)); \ |
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w10 = (i_I1 - floor(i_I1)) * (floor(j_I1) + 1 - j_I1); \ |
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w01 = (floor(i_I1) + 1 - i_I1) * (j_I1 - floor(j_I1)); \ |
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w00 = (floor(i_I1) + 1 - i_I1) * (floor(j_I1) + 1 - j_I1); |
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float computeSSDMeanNorm(const __global uchar *I0_ptr, const __global uchar *I1_ptr, |
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int I0_stride, int I1_stride, |
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float w00, float w01, float w10, float w11, int patch_sz, int i) |
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{ |
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float sum_diff = 0.0f, sum_diff_sq = 0.0f; |
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int n = patch_sz * patch_sz; |
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uchar8 I1_vec1, I1_vec2, I0_vec; |
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uchar I1_val1, I1_val2; |
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I0_vec = vload8(0, I0_ptr + i * I0_stride); |
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I1_vec1 = vload8(0, I1_ptr + i * I1_stride); |
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I1_vec2 = vload8(0, I1_ptr + (i + 1) * I1_stride); |
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I1_val1 = I1_ptr[i * I1_stride + 8]; |
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I1_val2 = I1_ptr[(i + 1) * I1_stride + 8]; |
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float8 vec = w00 * convert_float8(I1_vec1) + w01 * convert_float8((uchar8)(I1_vec1.s123, I1_vec1.s4567, I1_val1)) + |
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w10 * convert_float8(I1_vec2) + w11 * convert_float8((uchar8)(I1_vec2.s123, I1_vec2.s4567, I1_val2)) - |
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convert_float8(I0_vec); |
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sum_diff = (dot(vec.lo, 1.0) + dot(vec.hi, 1.0)); |
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sum_diff_sq = (dot(vec.lo, vec.lo) + dot(vec.hi, vec.hi)); |
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sum_diff = sub_group_reduce_add(sum_diff); |
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sum_diff_sq = sub_group_reduce_add(sum_diff_sq); |
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return sum_diff_sq - sum_diff * sum_diff / n; |
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} |
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__kernel void dis_patch_inverse_search_fwd_1(__global const float *Ux_ptr, __global const float *Uy_ptr, |
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__global const uchar *I0_ptr, __global const uchar *I1_ptr, |
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int border_size, int patch_size, int patch_stride, |
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int w, int h, int ws, int hs, int pyr_level, |
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__global float *Sx_ptr, __global float *Sy_ptr) |
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{ |
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int id = get_global_id(0); |
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int is = id / 8; |
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if (id >= (hs * 8)) return; |
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int i = is * patch_stride; |
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int j = 0; |
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int psz = patch_size; |
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int psz2 = psz / 2; |
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int w_ext = w + 2 * border_size; |
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int bsz = border_size; |
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float i_lower_limit = bsz - psz + 1.0f; |
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float i_upper_limit = bsz + h - 1.0f; |
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float j_lower_limit = bsz - psz + 1.0f; |
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float j_upper_limit = bsz + w - 1.0f; |
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float i_I1, j_I1, w00, w01, w10, w11; |
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float prev_Ux = Ux_ptr[(i + psz2) * w + j + psz2]; |
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float prev_Uy = Uy_ptr[(i + psz2) * w + j + psz2]; |
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Sx_ptr[is * ws] = prev_Ux; |
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Sy_ptr[is * ws] = prev_Uy; |
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j += patch_stride; |
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int sid = get_sub_group_local_id(); |
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for (int js = 1; js < ws; js++, j += patch_stride) |
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{ |
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float min_SSD, cur_SSD; |
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float Ux = Ux_ptr[(i + psz2) * w + j + psz2]; |
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float Uy = Uy_ptr[(i + psz2) * w + j + psz2]; |
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INIT_BILINEAR_WEIGHTS(Ux, Uy); |
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min_SSD = computeSSDMeanNorm(I0_ptr + i * w + j, I1_ptr + (int)i_I1 * w_ext + (int)j_I1, |
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w, w_ext, w00, w01, w10, w11, psz, sid); |
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INIT_BILINEAR_WEIGHTS(prev_Ux, prev_Uy); |
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cur_SSD = computeSSDMeanNorm(I0_ptr + i * w + j, I1_ptr + (int)i_I1 * w_ext + (int)j_I1, |
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w, w_ext, w00, w01, w10, w11, psz, sid); |
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if (cur_SSD < min_SSD) |
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{ |
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Ux = prev_Ux; |
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Uy = prev_Uy; |
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} |
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prev_Ux = Ux; |
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prev_Uy = Uy; |
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Sx_ptr[is * ws + js] = Ux; |
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Sy_ptr[is * ws + js] = Uy; |
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} |
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} |
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float3 processPatchMeanNorm(const __global uchar *I0_ptr, const __global uchar *I1_ptr, |
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const __global short *I0x_ptr, const __global short *I0y_ptr, |
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int I0_stride, int I1_stride, float w00, float w01, float w10, |
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float w11, int patch_sz, float x_grad_sum, float y_grad_sum) |
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{ |
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float sum_diff = 0.0, sum_diff_sq = 0.0; |
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float sum_I0x_mul = 0.0, sum_I0y_mul = 0.0; |
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int n = patch_sz * patch_sz; |
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uchar8 I1_vec1, I1_vec2; |
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uchar I1_val1, I1_val2; |
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for (int i = 0; i < 8; i++) |
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{ |
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uchar8 I0_vec = vload8(0, I0_ptr + i * I0_stride); |
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I1_vec1 = (i == 0) ? vload8(0, I1_ptr + i * I1_stride) : I1_vec2; |
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I1_vec2 = vload8(0, I1_ptr + (i + 1) * I1_stride); |
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I1_val1 = (i == 0) ? I1_ptr[i * I1_stride + patch_sz] : I1_val2; |
||||
I1_val2 = I1_ptr[(i + 1) * I1_stride + patch_sz]; |
||||
|
||||
float8 vec = w00 * convert_float8(I1_vec1) + w01 * convert_float8((uchar8)(I1_vec1.s123, I1_vec1.s4567, I1_val1)) + |
||||
w10 * convert_float8(I1_vec2) + w11 * convert_float8((uchar8)(I1_vec2.s123, I1_vec2.s4567, I1_val2)) - |
||||
convert_float8(I0_vec); |
||||
|
||||
sum_diff += (dot(vec.lo, 1.0) + dot(vec.hi, 1.0)); |
||||
sum_diff_sq += (dot(vec.lo, vec.lo) + dot(vec.hi, vec.hi)); |
||||
|
||||
short8 I0x_vec = vload8(0, I0x_ptr + i * I0_stride); |
||||
short8 I0y_vec = vload8(0, I0y_ptr + i * I0_stride); |
||||
|
||||
sum_I0x_mul += dot(vec.lo, convert_float4(I0x_vec.lo)); |
||||
sum_I0x_mul += dot(vec.hi, convert_float4(I0x_vec.hi)); |
||||
sum_I0y_mul += dot(vec.lo, convert_float4(I0y_vec.lo)); |
||||
sum_I0y_mul += dot(vec.hi, convert_float4(I0y_vec.hi)); |
||||
} |
||||
|
||||
float dst_dUx = sum_I0x_mul - sum_diff * x_grad_sum / n; |
||||
float dst_dUy = sum_I0y_mul - sum_diff * y_grad_sum / n; |
||||
float SSD = sum_diff_sq - sum_diff * sum_diff / n; |
||||
|
||||
return (float3)(SSD, dst_dUx, dst_dUy); |
||||
} |
||||
|
||||
__kernel void dis_patch_inverse_search_fwd_2(__global const float *Ux_ptr, __global const float *Uy_ptr, |
||||
__global const uchar *I0_ptr, __global const uchar *I1_ptr, |
||||
__global const short *I0x_ptr, __global const short *I0y_ptr, |
||||
__global const float *xx_ptr, __global const float *yy_ptr, |
||||
__global const float *xy_ptr, |
||||
__global const float *x_ptr, __global const float *y_ptr, |
||||
int border_size, int patch_size, int patch_stride, |
||||
int w, int h, int ws, int hs, int num_inner_iter, int pyr_level, |
||||
__global float *Sx_ptr, __global float *Sy_ptr) |
||||
{ |
||||
int js = get_global_id(0); |
||||
int is = get_global_id(1); |
||||
int i = is * patch_stride; |
||||
int j = js * patch_stride; |
||||
int psz = patch_size; |
||||
int psz2 = psz / 2; |
||||
int w_ext = w + 2 * border_size; |
||||
int bsz = border_size; |
||||
int index = is * ws + js; |
||||
|
||||
if (js >= ws || is >= hs) return; |
||||
|
||||
float Ux = Sx_ptr[index]; |
||||
float Uy = Sy_ptr[index]; |
||||
float cur_Ux = Ux; |
||||
float cur_Uy = Uy; |
||||
float cur_xx = xx_ptr[index]; |
||||
float cur_yy = yy_ptr[index]; |
||||
float cur_xy = xy_ptr[index]; |
||||
float detH = cur_xx * cur_yy - cur_xy * cur_xy; |
||||
|
||||
if (fabs(detH) < EPS) detH = EPS; |
||||
|
||||
float invH11 = cur_yy / detH; |
||||
float invH12 = -cur_xy / detH; |
||||
float invH22 = cur_xx / detH; |
||||
float prev_SSD = INF, SSD; |
||||
float x_grad_sum = x_ptr[index]; |
||||
float y_grad_sum = y_ptr[index]; |
||||
|
||||
float i_lower_limit = bsz - psz + 1.0f; |
||||
float i_upper_limit = bsz + h - 1.0f; |
||||
float j_lower_limit = bsz - psz + 1.0f; |
||||
float j_upper_limit = bsz + w - 1.0f; |
||||
float dUx, dUy, i_I1, j_I1, w00, w01, w10, w11, dx, dy; |
||||
float3 res; |
||||
|
||||
for (int t = 0; t < num_inner_iter; t++) |
||||
{ |
||||
INIT_BILINEAR_WEIGHTS(cur_Ux, cur_Uy); |
||||
res = processPatchMeanNorm(I0_ptr + i * w + j, |
||||
I1_ptr + (int)i_I1 * w_ext + (int)j_I1, I0x_ptr + i * w + j, |
||||
I0y_ptr + i * w + j, w, w_ext, w00, w01, w10, w11, psz, |
||||
x_grad_sum, y_grad_sum); |
||||
|
||||
SSD = res.x; |
||||
dUx = res.y; |
||||
dUy = res.z; |
||||
dx = invH11 * dUx + invH12 * dUy; |
||||
dy = invH12 * dUx + invH22 * dUy; |
||||
|
||||
cur_Ux -= dx; |
||||
cur_Uy -= dy; |
||||
|
||||
if (SSD >= prev_SSD) |
||||
break; |
||||
prev_SSD = SSD; |
||||
} |
||||
|
||||
float2 vec = (float2)(cur_Ux - Ux, cur_Uy - Uy); |
||||
if (dot(vec, vec) <= (float)(psz * psz)) |
||||
{ |
||||
Sx_ptr[index] = cur_Ux; |
||||
Sy_ptr[index] = cur_Uy; |
||||
} |
||||
} |
||||
|
||||
__kernel void dis_patch_inverse_search_bwd_1(__global const uchar *I0_ptr, __global const uchar *I1_ptr, |
||||
int border_size, int patch_size, int patch_stride, |
||||
int w, int h, int ws, int hs, int pyr_level, |
||||
__global float *Sx_ptr, __global float *Sy_ptr) |
||||
{ |
||||
int id = get_global_id(0); |
||||
int is = id / 8; |
||||
if (id >= (hs * 8)) return; |
||||
|
||||
is = (hs - 1 - is); |
||||
int i = is * patch_stride; |
||||
int j = (ws - 2) * patch_stride; |
||||
int psz = patch_size; |
||||
int psz2 = psz / 2; |
||||
int w_ext = w + 2 * border_size; |
||||
int bsz = border_size; |
||||
|
||||
float i_lower_limit = bsz - psz + 1.0f; |
||||
float i_upper_limit = bsz + h - 1.0f; |
||||
float j_lower_limit = bsz - psz + 1.0f; |
||||
float j_upper_limit = bsz + w - 1.0f; |
||||
float i_I1, j_I1, w00, w01, w10, w11; |
||||
|
||||
int sid = get_sub_group_local_id(); |
||||
for (int js = (ws - 2); js > -1; js--, j -= patch_stride) |
||||
{ |
||||
float min_SSD, cur_SSD; |
||||
float2 Ux = vload2(0, Sx_ptr + is * ws + js); |
||||
float2 Uy = vload2(0, Sy_ptr + is * ws + js); |
||||
|
||||
INIT_BILINEAR_WEIGHTS(Ux.x, Uy.x); |
||||
min_SSD = computeSSDMeanNorm(I0_ptr + i * w + j, I1_ptr + (int)i_I1 * w_ext + (int)j_I1, |
||||
w, w_ext, w00, w01, w10, w11, psz, sid); |
||||
|
||||
INIT_BILINEAR_WEIGHTS(Ux.y, Uy.y); |
||||
cur_SSD = computeSSDMeanNorm(I0_ptr + i * w + j, I1_ptr + (int)i_I1 * w_ext + (int)j_I1, |
||||
w, w_ext, w00, w01, w10, w11, psz, sid); |
||||
if (cur_SSD < min_SSD) |
||||
{ |
||||
Sx_ptr[is * ws + js] = Ux.y; |
||||
Sy_ptr[is * ws + js] = Uy.y; |
||||
} |
||||
} |
||||
} |
||||
|
||||
__kernel void dis_patch_inverse_search_bwd_2(__global const uchar *I0_ptr, __global const uchar *I1_ptr, |
||||
__global const short *I0x_ptr, __global const short *I0y_ptr, |
||||
__global const float *xx_ptr, __global const float *yy_ptr, |
||||
__global const float *xy_ptr, |
||||
__global const float *x_ptr, __global const float *y_ptr, |
||||
int border_size, int patch_size, int patch_stride, |
||||
int w, int h, int ws, int hs, int num_inner_iter, |
||||
__global float *Sx_ptr, __global float *Sy_ptr) |
||||
{ |
||||
int js = get_global_id(0); |
||||
int is = get_global_id(1); |
||||
if (js >= ws || is >= hs) return; |
||||
|
||||
js = (ws - 1 - js); |
||||
is = (hs - 1 - is); |
||||
|
||||
int j = js * patch_stride; |
||||
int i = is * patch_stride; |
||||
int psz = patch_size; |
||||
int psz2 = psz / 2; |
||||
int w_ext = w + 2 * border_size; |
||||
int bsz = border_size; |
||||
int index = is * ws + js; |
||||
|
||||
float Ux = Sx_ptr[index]; |
||||
float Uy = Sy_ptr[index]; |
||||
float cur_Ux = Ux; |
||||
float cur_Uy = Uy; |
||||
float cur_xx = xx_ptr[index]; |
||||
float cur_yy = yy_ptr[index]; |
||||
float cur_xy = xy_ptr[index]; |
||||
float detH = cur_xx * cur_yy - cur_xy * cur_xy; |
||||
|
||||
if (fabs(detH) < EPS) detH = EPS; |
||||
|
||||
float invH11 = cur_yy / detH; |
||||
float invH12 = -cur_xy / detH; |
||||
float invH22 = cur_xx / detH; |
||||
float prev_SSD = INF, SSD; |
||||
float x_grad_sum = x_ptr[index]; |
||||
float y_grad_sum = y_ptr[index]; |
||||
|
||||
float i_lower_limit = bsz - psz + 1.0f; |
||||
float i_upper_limit = bsz + h - 1.0f; |
||||
float j_lower_limit = bsz - psz + 1.0f; |
||||
float j_upper_limit = bsz + w - 1.0f; |
||||
float dUx, dUy, i_I1, j_I1, w00, w01, w10, w11, dx, dy; |
||||
float3 res; |
||||
|
||||
for (int t = 0; t < num_inner_iter; t++) |
||||
{ |
||||
INIT_BILINEAR_WEIGHTS(cur_Ux, cur_Uy); |
||||
res = processPatchMeanNorm(I0_ptr + i * w + j, |
||||
I1_ptr + (int)i_I1 * w_ext + (int)j_I1, I0x_ptr + i * w + j, |
||||
I0y_ptr + i * w + j, w, w_ext, w00, w01, w10, w11, psz, |
||||
x_grad_sum, y_grad_sum); |
||||
|
||||
SSD = res.x; |
||||
dUx = res.y; |
||||
dUy = res.z; |
||||
dx = invH11 * dUx + invH12 * dUy; |
||||
dy = invH12 * dUx + invH22 * dUy; |
||||
|
||||
cur_Ux -= dx; |
||||
cur_Uy -= dy; |
||||
|
||||
if (SSD >= prev_SSD) |
||||
break; |
||||
prev_SSD = SSD; |
||||
} |
||||
|
||||
float2 vec = (float2)(cur_Ux - Ux, cur_Uy - Uy); |
||||
if ((dot(vec, vec)) <= (float)(psz * psz)) |
||||
{ |
||||
Sx_ptr[index] = cur_Ux; |
||||
Sy_ptr[index] = cur_Uy; |
||||
} |
||||
} |
||||
|
@ -0,0 +1,100 @@ |
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// Intel License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "../test_precomp.hpp" |
||||
#include "opencv2/ts/ocl_test.hpp" |
||||
|
||||
#ifdef HAVE_OPENCL |
||||
|
||||
using namespace cv; |
||||
using namespace optflow; |
||||
|
||||
namespace cvtest { |
||||
namespace ocl { |
||||
|
||||
PARAM_TEST_CASE(OCL_DenseOpticalFlow_DIS, int) |
||||
{ |
||||
int preset; |
||||
|
||||
virtual void SetUp() |
||||
{ |
||||
preset = GET_PARAM(0); |
||||
} |
||||
}; |
||||
|
||||
OCL_TEST_P(OCL_DenseOpticalFlow_DIS, Mat) |
||||
{ |
||||
Mat frame1, frame2, GT; |
||||
|
||||
frame1 = imread(TS::ptr()->get_data_path() + "optflow/RubberWhale1.png"); |
||||
frame2 = imread(TS::ptr()->get_data_path() + "optflow/RubberWhale2.png"); |
||||
|
||||
CV_Assert(!frame1.empty() && !frame2.empty()); |
||||
|
||||
cvtColor(frame1, frame1, COLOR_BGR2GRAY); |
||||
cvtColor(frame2, frame2, COLOR_BGR2GRAY); |
||||
|
||||
Ptr<DenseOpticalFlow> algo; |
||||
|
||||
// iterate over presets:
|
||||
for (int i = 0; i < test_loop_times; i++) |
||||
{ |
||||
Mat flow; |
||||
UMat ocl_flow; |
||||
|
||||
algo = createOptFlow_DIS(preset); |
||||
OCL_OFF(algo->calc(frame1, frame2, flow)); |
||||
OCL_ON(algo->calc(frame1, frame2, ocl_flow)); |
||||
ASSERT_EQ(flow.rows, ocl_flow.rows); |
||||
ASSERT_EQ(flow.cols, ocl_flow.cols); |
||||
|
||||
EXPECT_MAT_SIMILAR(flow, ocl_flow, 6e-3); |
||||
} |
||||
} |
||||
|
||||
OCL_INSTANTIATE_TEST_CASE_P(Contrib, OCL_DenseOpticalFlow_DIS, |
||||
Values(DISOpticalFlow::PRESET_ULTRAFAST, |
||||
DISOpticalFlow::PRESET_FAST, |
||||
DISOpticalFlow::PRESET_MEDIUM)); |
||||
|
||||
} } // namespace cvtest::ocl
|
||||
|
||||
#endif // HAVE_OPENCL
|
Loading…
Reference in new issue