mirror of https://github.com/opencv/opencv.git
Merge pull request #1051 from pengx17:2.4_fback_ocl
Roman Donchenko
12 years ago
committed by
OpenCV Buildbot
commit
6bf8f474fa
5 changed files with 1243 additions and 14 deletions
@ -0,0 +1,450 @@ |
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/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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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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// |
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// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved. |
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// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// @Authors |
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// Sen Liu, swjtuls1987@126.com |
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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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// * Redistribution's 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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// * Redistribution's 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 oclMaterials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software 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 the Intel Corporation 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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//M*/ |
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|
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|
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#define tx get_local_id(0) |
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#define ty get_local_id(1) |
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#define bx get_group_id(0) |
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#define bdx get_local_size(0) |
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|
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#define BORDER_SIZE 5 |
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#define MAX_KSIZE_HALF 100 |
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|
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#ifndef polyN |
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#define polyN 5 |
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#endif |
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|
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__kernel void polynomialExpansion(__global float * dst, |
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__global __const float * src, |
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__global __const float * c_g, |
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__global __const float * c_xg, |
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__global __const float * c_xxg, |
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__local float * smem, |
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const float4 ig, |
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const int height, const int width, |
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int dstStep, int srcStep) |
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{ |
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const int y = get_global_id(1); |
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const int x = bx * (bdx - 2*polyN) + tx - polyN; |
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dstStep /= sizeof(*dst); |
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srcStep /= sizeof(*src); |
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int xWarped; |
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__local float *row = smem + tx; |
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if (y < height && y >= 0) |
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{ |
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xWarped = min(max(x, 0), width - 1); |
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row[0] = src[mad24(y, srcStep, xWarped)] * c_g[0]; |
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row[bdx] = 0.f; |
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row[2*bdx] = 0.f; |
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|
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#pragma unroll |
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for (int k = 1; k <= polyN; ++k) |
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{ |
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float t0 = src[mad24(max(y - k, 0), srcStep, xWarped)]; |
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float t1 = src[mad24(min(y + k, height - 1), srcStep, xWarped)]; |
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|
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row[0] += c_g[k] * (t0 + t1); |
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row[bdx] += c_xg[k] * (t1 - t0); |
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row[2*bdx] += c_xxg[k] * (t0 + t1); |
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} |
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} |
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barrier(CLK_LOCAL_MEM_FENCE); |
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if (y < height && y >= 0 && tx >= polyN && tx + polyN < bdx && x < width) |
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{ |
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float b1 = c_g[0] * row[0]; |
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float b3 = c_g[0] * row[bdx]; |
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float b5 = c_g[0] * row[2*bdx]; |
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float b2 = 0, b4 = 0, b6 = 0; |
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#pragma unroll |
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for (int k = 1; k <= polyN; ++k) |
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{ |
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b1 += (row[k] + row[-k]) * c_g[k]; |
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b4 += (row[k] + row[-k]) * c_xxg[k]; |
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b2 += (row[k] - row[-k]) * c_xg[k]; |
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b3 += (row[k + bdx] + row[-k + bdx]) * c_g[k]; |
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b6 += (row[k + bdx] - row[-k + bdx]) * c_xg[k]; |
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b5 += (row[k + 2*bdx] + row[-k + 2*bdx]) * c_g[k]; |
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} |
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dst[mad24(y, dstStep, xWarped)] = b3*ig.s0; |
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dst[mad24(height + y, dstStep, xWarped)] = b2*ig.s0; |
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dst[mad24(2*height + y, dstStep, xWarped)] = b1*ig.s1 + b5*ig.s2; |
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dst[mad24(3*height + y, dstStep, xWarped)] = b1*ig.s1 + b4*ig.s2; |
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dst[mad24(4*height + y, dstStep, xWarped)] = b6*ig.s3; |
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} |
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} |
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inline int idx_row_low(const int y, const int last_row) |
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{ |
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return abs(y) % (last_row + 1); |
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} |
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inline int idx_row_high(const int y, const int last_row) |
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{ |
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return abs(last_row - abs(last_row - y)) % (last_row + 1); |
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} |
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inline int idx_row(const int y, const int last_row) |
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{ |
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return idx_row_low(idx_row_high(y, last_row), last_row); |
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} |
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inline int idx_col_low(const int x, const int last_col) |
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{ |
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return abs(x) % (last_col + 1); |
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} |
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inline int idx_col_high(const int x, const int last_col) |
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{ |
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return abs(last_col - abs(last_col - x)) % (last_col + 1); |
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} |
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inline int idx_col(const int x, const int last_col) |
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{ |
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return idx_col_low(idx_col_high(x, last_col), last_col); |
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} |
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__kernel void gaussianBlur(__global float * dst, |
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__global const float * src, |
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__global const float * c_gKer, |
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__local float * smem, |
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const int height, const int width, |
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int dstStep, int srcStep, |
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const int ksizeHalf) |
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{ |
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const int y = get_global_id(1); |
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const int x = get_global_id(0); |
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dstStep /= sizeof(*dst); |
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srcStep /= sizeof(*src); |
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__local float *row = smem + ty * (bdx + 2*ksizeHalf); |
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if (y < height) |
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{ |
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// Vertical pass |
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for (int i = tx; i < bdx + 2*ksizeHalf; i += bdx) |
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{ |
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int xExt = (int)(bx * bdx) + i - ksizeHalf; |
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xExt = idx_col(xExt, width - 1); |
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row[i] = src[mad24(y, srcStep, xExt)] * c_gKer[0]; |
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for (int j = 1; j <= ksizeHalf; ++j) |
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row[i] += (src[mad24(idx_row_low(y - j, height - 1), srcStep, xExt)] |
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+ src[mad24(idx_row_high(y + j, height - 1), srcStep, xExt)]) * c_gKer[j]; |
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} |
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} |
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barrier(CLK_LOCAL_MEM_FENCE); |
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if (y < height && y >= 0 && x < width && x >= 0) |
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{ |
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// Horizontal pass |
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row += tx + ksizeHalf; |
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float res = row[0] * c_gKer[0]; |
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for (int i = 1; i <= ksizeHalf; ++i) |
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res += (row[-i] + row[i]) * c_gKer[i]; |
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dst[mad24(y, dstStep, x)] = res; |
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} |
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} |
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__constant float c_border[BORDER_SIZE + 1] = { 0.14f, 0.14f, 0.4472f, 0.4472f, 0.4472f, 1.f }; |
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__kernel void updateMatrices(__global float * M, |
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__global const float * flowx, __global const float * flowy, |
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__global const float * R0, __global const float * R1, |
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const int height, const int width, |
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int mStep, int xStep, int yStep, int R0Step, int R1Step) |
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{ |
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const int y = get_global_id(1); |
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const int x = get_global_id(0); |
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mStep /= sizeof(*M); |
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xStep /= sizeof(*flowx); |
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yStep /= sizeof(*flowy); |
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R0Step /= sizeof(*R0); |
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R1Step /= sizeof(*R1); |
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if (y < height && y >= 0 && x < width && x >= 0) |
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{ |
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float dx = flowx[mad24(y, xStep, x)]; |
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float dy = flowy[mad24(y, yStep, x)]; |
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float fx = x + dx; |
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float fy = y + dy; |
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int x1 = convert_int(floor(fx)); |
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int y1 = convert_int(floor(fy)); |
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fx -= x1; |
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fy -= y1; |
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float r2, r3, r4, r5, r6; |
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if (x1 >= 0 && y1 >= 0 && x1 < width - 1 && y1 < height - 1) |
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{ |
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float a00 = (1.f - fx) * (1.f - fy); |
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float a01 = fx * (1.f - fy); |
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float a10 = (1.f - fx) * fy; |
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float a11 = fx * fy; |
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r2 = a00 * R1[mad24(y1, R1Step, x1)] + |
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a01 * R1[mad24(y1, R1Step, x1 + 1)] + |
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a10 * R1[mad24(y1 + 1, R1Step, x1)] + |
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a11 * R1[mad24(y1 + 1, R1Step, x1 + 1)]; |
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r3 = a00 * R1[mad24(height + y1, R1Step, x1)] + |
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a01 * R1[mad24(height + y1, R1Step, x1 + 1)] + |
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a10 * R1[mad24(height + y1 + 1, R1Step, x1)] + |
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a11 * R1[mad24(height + y1 + 1, R1Step, x1 + 1)]; |
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r4 = a00 * R1[mad24(2*height + y1, R1Step, x1)] + |
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a01 * R1[mad24(2*height + y1, R1Step, x1 + 1)] + |
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a10 * R1[mad24(2*height + y1 + 1, R1Step, x1)] + |
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a11 * R1[mad24(2*height + y1 + 1, R1Step, x1 + 1)]; |
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r5 = a00 * R1[mad24(3*height + y1, R1Step, x1)] + |
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a01 * R1[mad24(3*height + y1, R1Step, x1 + 1)] + |
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a10 * R1[mad24(3*height + y1 + 1, R1Step, x1)] + |
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a11 * R1[mad24(3*height + y1 + 1, R1Step, x1 + 1)]; |
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r6 = a00 * R1[mad24(4*height + y1, R1Step, x1)] + |
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a01 * R1[mad24(4*height + y1, R1Step, x1 + 1)] + |
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a10 * R1[mad24(4*height + y1 + 1, R1Step, x1)] + |
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a11 * R1[mad24(4*height + y1 + 1, R1Step, x1 + 1)]; |
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r4 = (R0[mad24(2*height + y, R0Step, x)] + r4) * 0.5f; |
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r5 = (R0[mad24(3*height + y, R0Step, x)] + r5) * 0.5f; |
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r6 = (R0[mad24(4*height + y, R0Step, x)] + r6) * 0.25f; |
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} |
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else |
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{ |
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r2 = r3 = 0.f; |
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r4 = R0[mad24(2*height + y, R0Step, x)]; |
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r5 = R0[mad24(3*height + y, R0Step, x)]; |
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r6 = R0[mad24(4*height + y, R0Step, x)] * 0.5f; |
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} |
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r2 = (R0[mad24(y, R0Step, x)] - r2) * 0.5f; |
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r3 = (R0[mad24(height + y, R0Step, x)] - r3) * 0.5f; |
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r2 += r4*dy + r6*dx; |
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r3 += r6*dy + r5*dx; |
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float scale = |
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c_border[min(x, BORDER_SIZE)] * |
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c_border[min(y, BORDER_SIZE)] * |
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c_border[min(width - x - 1, BORDER_SIZE)] * |
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c_border[min(height - y - 1, BORDER_SIZE)]; |
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r2 *= scale; |
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r3 *= scale; |
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r4 *= scale; |
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r5 *= scale; |
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r6 *= scale; |
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M[mad24(y, mStep, x)] = r4*r4 + r6*r6; |
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M[mad24(height + y, mStep, x)] = (r4 + r5)*r6; |
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M[mad24(2*height + y, mStep, x)] = r5*r5 + r6*r6; |
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M[mad24(3*height + y, mStep, x)] = r4*r2 + r6*r3; |
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M[mad24(4*height + y, mStep, x)] = r6*r2 + r5*r3; |
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} |
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} |
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__kernel void boxFilter5(__global float * dst, |
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__global const float * src, |
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__local float * smem, |
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const int height, const int width, |
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int dstStep, int srcStep, |
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const int ksizeHalf) |
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{ |
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const int y = get_global_id(1); |
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const int x = get_global_id(0); |
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const float boxAreaInv = 1.f / ((1 + 2*ksizeHalf) * (1 + 2*ksizeHalf)); |
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const int smw = bdx + 2*ksizeHalf; // shared memory "width" |
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__local float *row = smem + 5 * ty * smw; |
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dstStep /= sizeof(*dst); |
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srcStep /= sizeof(*src); |
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if (y < height) |
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{ |
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// Vertical pass |
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for (int i = tx; i < bdx + 2*ksizeHalf; i += bdx) |
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{ |
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int xExt = (int)(bx * bdx) + i - ksizeHalf; |
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xExt = min(max(xExt, 0), width - 1); |
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#pragma unroll |
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for (int k = 0; k < 5; ++k) |
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row[k*smw + i] = src[mad24(k*height + y, srcStep, xExt)]; |
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for (int j = 1; j <= ksizeHalf; ++j) |
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#pragma unroll |
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for (int k = 0; k < 5; ++k) |
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row[k*smw + i] += |
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src[mad24(k*height + max(y - j, 0), srcStep, xExt)] + |
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src[mad24(k*height + min(y + j, height - 1), srcStep, xExt)]; |
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} |
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} |
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barrier(CLK_LOCAL_MEM_FENCE); |
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if (y < height && y >= 0 && x < width && x >= 0) |
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{ |
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// Horizontal pass |
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row += tx + ksizeHalf; |
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float res[5]; |
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#pragma unroll |
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for (int k = 0; k < 5; ++k) |
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res[k] = row[k*smw]; |
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for (int i = 1; i <= ksizeHalf; ++i) |
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#pragma unroll |
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for (int k = 0; k < 5; ++k) |
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res[k] += row[k*smw - i] + row[k*smw + i]; |
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#pragma unroll |
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for (int k = 0; k < 5; ++k) |
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dst[mad24(k*height + y, dstStep, x)] = res[k] * boxAreaInv; |
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} |
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} |
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__kernel void updateFlow(__global float4 * flowx, __global float4 * flowy, |
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__global const float4 * M, |
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const int height, const int width, |
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int xStep, int yStep, int mStep) |
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{ |
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const int y = get_global_id(1); |
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const int x = get_global_id(0); |
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|
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xStep /= sizeof(*flowx); |
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yStep /= sizeof(*flowy); |
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mStep /= sizeof(*M); |
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if (y < height && y >= 0 && x < width && x >= 0) |
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{ |
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float4 g11 = M[mad24(y, mStep, x)]; |
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float4 g12 = M[mad24(height + y, mStep, x)]; |
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float4 g22 = M[mad24(2*height + y, mStep, x)]; |
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float4 h1 = M[mad24(3*height + y, mStep, x)]; |
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float4 h2 = M[mad24(4*height + y, mStep, x)]; |
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float4 detInv = (float4)(1.f) / (g11*g22 - g12*g12 + (float4)(1e-3f)); |
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flowx[mad24(y, xStep, x)] = (g11*h2 - g12*h1) * detInv; |
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flowy[mad24(y, yStep, x)] = (g22*h1 - g12*h2) * detInv; |
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} |
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} |
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|
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__kernel void gaussianBlur5(__global float * dst, |
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__global const float * src, |
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__global const float * c_gKer, |
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__local float * smem, |
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const int height, const int width, |
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int dstStep, int srcStep, |
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const int ksizeHalf) |
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{ |
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const int y = get_global_id(1); |
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const int x = get_global_id(0); |
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|
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const int smw = bdx + 2*ksizeHalf; // shared memory "width" |
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__local volatile float *row = smem + 5 * ty * smw; |
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|
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dstStep /= sizeof(*dst); |
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srcStep /= sizeof(*src); |
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|
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if (y < height) |
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{ |
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// Vertical pass |
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for (int i = tx; i < bdx + 2*ksizeHalf; i += bdx) |
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{ |
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int xExt = (int)(bx * bdx) + i - ksizeHalf; |
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xExt = idx_col(xExt, width - 1); |
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|
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#pragma unroll |
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for (int k = 0; k < 5; ++k) |
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row[k*smw + i] = src[mad24(k*height + y, srcStep, xExt)] * c_gKer[0]; |
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|
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for (int j = 1; j <= ksizeHalf; ++j) |
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#pragma unroll |
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for (int k = 0; k < 5; ++k) |
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row[k*smw + i] += |
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(src[mad24(k*height + idx_row_low(y - j, height - 1), srcStep, xExt)] + |
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src[mad24(k*height + idx_row_high(y + j, height - 1), srcStep, xExt)]) * c_gKer[j]; |
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} |
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} |
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|
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barrier(CLK_LOCAL_MEM_FENCE); |
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|
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if (y < height && y >= 0 && x < width && x >= 0) |
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{ |
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// Horizontal pass |
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|
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row += tx + ksizeHalf; |
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float res[5]; |
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|
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#pragma unroll |
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for (int k = 0; k < 5; ++k) |
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res[k] = row[k*smw] * c_gKer[0]; |
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|
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for (int i = 1; i <= ksizeHalf; ++i) |
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#pragma unroll |
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for (int k = 0; k < 5; ++k) |
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res[k] += (row[k*smw - i] + row[k*smw + i]) * c_gKer[i]; |
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|
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#pragma unroll |
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for (int k = 0; k < 5; ++k) |
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dst[mad24(k*height + y, dstStep, x)] = res[k]; |
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} |
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} |
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@ -0,0 +1,540 @@ |
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/*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.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// @Authors
|
||||
// Sen Liu, swjtuls1987@126.com
|
||||
//
|
||||
// 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 oclMaterials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders 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 "precomp.hpp" |
||||
#include "opencv2/video/tracking.hpp" |
||||
|
||||
using namespace std; |
||||
using namespace cv; |
||||
using namespace cv::ocl; |
||||
|
||||
#define MIN_SIZE 32 |
||||
|
||||
namespace cv |
||||
{ |
||||
namespace ocl |
||||
{ |
||||
///////////////////////////OpenCL kernel strings///////////////////////////
|
||||
extern const char *optical_flow_farneback; |
||||
} |
||||
} |
||||
|
||||
namespace cv { |
||||
namespace ocl { |
||||
namespace optflow_farneback |
||||
{ |
||||
oclMat g; |
||||
oclMat xg; |
||||
oclMat xxg; |
||||
oclMat gKer; |
||||
|
||||
float ig[4]; |
||||
|
||||
inline int divUp(int total, int grain) |
||||
{ |
||||
return (total + grain - 1) / grain; |
||||
} |
||||
|
||||
inline void setGaussianBlurKernel(const float *c_gKer, int ksizeHalf) |
||||
{ |
||||
cv::Mat t_gKer(1, ksizeHalf + 1, CV_32FC1, const_cast<float *>(c_gKer)); |
||||
gKer.upload(t_gKer); |
||||
} |
||||
|
||||
static void gaussianBlurOcl(const oclMat &src, int ksizeHalf, oclMat &dst) |
||||
{ |
||||
string kernelName("gaussianBlur"); |
||||
size_t localThreads[3] = { 256, 1, 1 }; |
||||
size_t globalThreads[3] = { divUp(src.cols, localThreads[0]) * localThreads[0], src.rows, 1 }; |
||||
int smem_size = (localThreads[0] + 2*ksizeHalf) * sizeof(float); |
||||
|
||||
CV_Assert(dst.size() == src.size()); |
||||
std::vector< std::pair<size_t, const void *> > args; |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&dst.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&src.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&gKer.data)); |
||||
args.push_back(std::make_pair(smem_size, (void *)NULL)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&dst.rows)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&dst.cols)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&dst.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&src.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&ksizeHalf)); |
||||
|
||||
openCLExecuteKernel(Context::getContext(), &optical_flow_farneback, kernelName, |
||||
globalThreads, localThreads, args, -1, -1); |
||||
} |
||||
|
||||
static void polynomialExpansionOcl(const oclMat &src, int polyN, oclMat &dst) |
||||
{ |
||||
string kernelName("polynomialExpansion"); |
||||
size_t localThreads[3] = { 256, 1, 1 }; |
||||
size_t globalThreads[3] = { divUp(src.cols, localThreads[0] - 2*polyN) * localThreads[0], src.rows, 1 }; |
||||
int smem_size = 3 * localThreads[0] * sizeof(float); |
||||
|
||||
std::vector< std::pair<size_t, const void *> > args; |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&dst.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&src.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&g.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&xg.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&xxg.data)); |
||||
args.push_back(std::make_pair(smem_size, (void *)NULL)); |
||||
args.push_back(std::make_pair(sizeof(cl_float4), (void *)&ig)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&src.rows)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&src.cols)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&dst.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&src.step)); |
||||
|
||||
char opt [128]; |
||||
sprintf(opt, "-D polyN=%d", polyN); |
||||
|
||||
openCLExecuteKernel(Context::getContext(), &optical_flow_farneback, kernelName, |
||||
globalThreads, localThreads, args, -1, -1, opt); |
||||
} |
||||
|
||||
static void updateMatricesOcl(const oclMat &flowx, const oclMat &flowy, const oclMat &R0, const oclMat &R1, oclMat &M) |
||||
{ |
||||
string kernelName("updateMatrices"); |
||||
size_t localThreads[3] = { 32, 8, 1 }; |
||||
size_t globalThreads[3] = { divUp(flowx.cols, localThreads[0]) * localThreads[0], |
||||
divUp(flowx.rows, localThreads[1]) * localThreads[1], |
||||
1 |
||||
}; |
||||
|
||||
std::vector< std::pair<size_t, const void *> > args; |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&M.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&flowx.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&flowy.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&R0.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&R1.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&flowx.rows)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&flowx.cols)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&M.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&flowx.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&flowy.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&R0.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&R1.step)); |
||||
|
||||
openCLExecuteKernel(Context::getContext(), &optical_flow_farneback, kernelName, |
||||
globalThreads, localThreads, args, -1, -1); |
||||
} |
||||
|
||||
static void boxFilter5Ocl(const oclMat &src, int ksizeHalf, oclMat &dst) |
||||
{ |
||||
string kernelName("boxFilter5"); |
||||
int height = src.rows / 5; |
||||
size_t localThreads[3] = { 256, 1, 1 }; |
||||
size_t globalThreads[3] = { divUp(src.cols, localThreads[0]) * localThreads[0], height, 1 }; |
||||
int smem_size = (localThreads[0] + 2*ksizeHalf) * 5 * sizeof(float); |
||||
|
||||
std::vector< std::pair<size_t, const void *> > args; |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&dst.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&src.data)); |
||||
args.push_back(std::make_pair(smem_size, (void *)NULL)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&height)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&src.cols)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&dst.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&src.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&ksizeHalf)); |
||||
|
||||
openCLExecuteKernel(Context::getContext(), &optical_flow_farneback, kernelName, |
||||
globalThreads, localThreads, args, -1, -1); |
||||
} |
||||
|
||||
static void updateFlowOcl(const oclMat &M, oclMat &flowx, oclMat &flowy) |
||||
{ |
||||
string kernelName("updateFlow"); |
||||
int cols = divUp(flowx.cols, 4); |
||||
size_t localThreads[3] = { 32, 8, 1 }; |
||||
size_t globalThreads[3] = { divUp(cols, localThreads[0]) * localThreads[0], |
||||
divUp(flowx.rows, localThreads[1]) * localThreads[0], |
||||
1 |
||||
}; |
||||
|
||||
std::vector< std::pair<size_t, const void *> > args; |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&flowx.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&flowy.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&M.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&flowx.rows)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&cols)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&flowx.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&flowy.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&M.step)); |
||||
|
||||
openCLExecuteKernel(Context::getContext(), &optical_flow_farneback, kernelName, |
||||
globalThreads, localThreads, args, -1, -1); |
||||
} |
||||
|
||||
static void gaussianBlur5Ocl(const oclMat &src, int ksizeHalf, oclMat &dst) |
||||
{ |
||||
string kernelName("gaussianBlur5"); |
||||
int height = src.rows / 5; |
||||
int width = src.cols; |
||||
size_t localThreads[3] = { 256, 1, 1 }; |
||||
size_t globalThreads[3] = { divUp(width, localThreads[0]) * localThreads[0], height, 1 }; |
||||
int smem_size = (localThreads[0] + 2*ksizeHalf) * 5 * sizeof(float); |
||||
|
||||
std::vector< std::pair<size_t, const void *> > args; |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&dst.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&src.data)); |
||||
args.push_back(std::make_pair(sizeof(cl_mem), (void *)&gKer.data)); |
||||
args.push_back(std::make_pair(smem_size, (void *)NULL)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&height)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&width)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&dst.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&src.step)); |
||||
args.push_back(std::make_pair(sizeof(cl_int), (void *)&ksizeHalf)); |
||||
|
||||
openCLExecuteKernel(Context::getContext(), &optical_flow_farneback, kernelName, |
||||
globalThreads, localThreads, args, -1, -1); |
||||
} |
||||
} |
||||
} |
||||
} // namespace cv { namespace ocl { namespace optflow_farneback
|
||||
|
||||
static oclMat allocMatFromBuf(int rows, int cols, int type, oclMat &mat) |
||||
{ |
||||
if (!mat.empty() && mat.type() == type && mat.rows >= rows && mat.cols >= cols) |
||||
return mat(Rect(0, 0, cols, rows)); |
||||
return mat = oclMat(rows, cols, type); |
||||
} |
||||
|
||||
cv::ocl::FarnebackOpticalFlow::FarnebackOpticalFlow() |
||||
{ |
||||
numLevels = 5; |
||||
pyrScale = 0.5; |
||||
fastPyramids = false; |
||||
winSize = 13; |
||||
numIters = 10; |
||||
polyN = 5; |
||||
polySigma = 1.1; |
||||
flags = 0; |
||||
} |
||||
|
||||
void cv::ocl::FarnebackOpticalFlow::releaseMemory() |
||||
{ |
||||
frames_[0].release(); |
||||
frames_[1].release(); |
||||
pyrLevel_[0].release(); |
||||
pyrLevel_[1].release(); |
||||
M_.release(); |
||||
bufM_.release(); |
||||
R_[0].release(); |
||||
R_[1].release(); |
||||
blurredFrame_[0].release(); |
||||
blurredFrame_[1].release(); |
||||
pyramid0_.clear(); |
||||
pyramid1_.clear(); |
||||
} |
||||
|
||||
void cv::ocl::FarnebackOpticalFlow::prepareGaussian( |
||||
int n, double sigma, float *g, float *xg, float *xxg, |
||||
double &ig11, double &ig03, double &ig33, double &ig55) |
||||
{ |
||||
double s = 0.; |
||||
for (int x = -n; x <= n; x++) |
||||
{ |
||||
g[x] = (float)std::exp(-x*x/(2*sigma*sigma)); |
||||
s += g[x]; |
||||
} |
||||
|
||||
s = 1./s; |
||||
for (int x = -n; x <= n; x++) |
||||
{ |
||||
g[x] = (float)(g[x]*s); |
||||
xg[x] = (float)(x*g[x]); |
||||
xxg[x] = (float)(x*x*g[x]); |
||||
} |
||||
|
||||
Mat_<double> G(6, 6); |
||||
G.setTo(0); |
||||
|
||||
for (int y = -n; y <= n; y++) |
||||
{ |
||||
for (int x = -n; x <= n; x++) |
||||
{ |
||||
G(0,0) += g[y]*g[x]; |
||||
G(1,1) += g[y]*g[x]*x*x; |
||||
G(3,3) += g[y]*g[x]*x*x*x*x; |
||||
G(5,5) += g[y]*g[x]*x*x*y*y; |
||||
} |
||||
} |
||||
|
||||
//G[0][0] = 1.;
|
||||
G(2,2) = G(0,3) = G(0,4) = G(3,0) = G(4,0) = G(1,1); |
||||
G(4,4) = G(3,3); |
||||
G(3,4) = G(4,3) = G(5,5); |
||||
|
||||
// invG:
|
||||
// [ x e e ]
|
||||
// [ y ]
|
||||
// [ y ]
|
||||
// [ e z ]
|
||||
// [ e z ]
|
||||
// [ u ]
|
||||
Mat_<double> invG = G.inv(DECOMP_CHOLESKY); |
||||
|
||||
ig11 = invG(1,1); |
||||
ig03 = invG(0,3); |
||||
ig33 = invG(3,3); |
||||
ig55 = invG(5,5); |
||||
} |
||||
|
||||
void cv::ocl::FarnebackOpticalFlow::setPolynomialExpansionConsts(int n, double sigma) |
||||
{ |
||||
vector<float> buf(n*6 + 3); |
||||
float* g = &buf[0] + n; |
||||
float* xg = g + n*2 + 1; |
||||
float* xxg = xg + n*2 + 1; |
||||
|
||||
if (sigma < FLT_EPSILON) |
||||
sigma = n*0.3; |
||||
|
||||
double ig11, ig03, ig33, ig55; |
||||
prepareGaussian(n, sigma, g, xg, xxg, ig11, ig03, ig33, ig55); |
||||
|
||||
cv::Mat t_g(1, n + 1, CV_32FC1, g); |
||||
cv::Mat t_xg(1, n + 1, CV_32FC1, xg); |
||||
cv::Mat t_xxg(1, n + 1, CV_32FC1, xxg); |
||||
|
||||
optflow_farneback::g.upload(t_g); |
||||
optflow_farneback::xg.upload(t_xg); |
||||
optflow_farneback::xxg.upload(t_xxg); |
||||
|
||||
optflow_farneback::ig[0] = static_cast<float>(ig11); |
||||
optflow_farneback::ig[1] = static_cast<float>(ig03); |
||||
optflow_farneback::ig[2] = static_cast<float>(ig33); |
||||
optflow_farneback::ig[3] = static_cast<float>(ig55); |
||||
} |
||||
|
||||
void cv::ocl::FarnebackOpticalFlow::updateFlow_boxFilter( |
||||
const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat &flowy, |
||||
oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices) |
||||
{ |
||||
optflow_farneback::boxFilter5Ocl(M, blockSize/2, bufM); |
||||
|
||||
swap(M, bufM); |
||||
|
||||
finish(); |
||||
|
||||
optflow_farneback::updateFlowOcl(M, flowx, flowy); |
||||
|
||||
if (updateMatrices) |
||||
optflow_farneback::updateMatricesOcl(flowx, flowy, R0, R1, M); |
||||
} |
||||
|
||||
|
||||
void cv::ocl::FarnebackOpticalFlow::updateFlow_gaussianBlur( |
||||
const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat& flowy, |
||||
oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices) |
||||
{ |
||||
optflow_farneback::gaussianBlur5Ocl(M, blockSize/2, bufM); |
||||
|
||||
swap(M, bufM); |
||||
|
||||
optflow_farneback::updateFlowOcl(M, flowx, flowy); |
||||
|
||||
if (updateMatrices) |
||||
optflow_farneback::updateMatricesOcl(flowx, flowy, R0, R1, M); |
||||
} |
||||
|
||||
|
||||
void cv::ocl::FarnebackOpticalFlow::operator ()( |
||||
const oclMat &frame0, const oclMat &frame1, oclMat &flowx, oclMat &flowy) |
||||
{ |
||||
CV_Assert(frame0.channels() == 1 && frame1.channels() == 1); |
||||
CV_Assert(frame0.size() == frame1.size()); |
||||
CV_Assert(polyN == 5 || polyN == 7); |
||||
CV_Assert(!fastPyramids || std::abs(pyrScale - 0.5) < 1e-6); |
||||
|
||||
Size size = frame0.size(); |
||||
oclMat prevFlowX, prevFlowY, curFlowX, curFlowY; |
||||
|
||||
flowx.create(size, CV_32F); |
||||
flowy.create(size, CV_32F); |
||||
oclMat flowx0 = flowx; |
||||
oclMat flowy0 = flowy; |
||||
|
||||
// Crop unnecessary levels
|
||||
double scale = 1; |
||||
int numLevelsCropped = 0; |
||||
for (; numLevelsCropped < numLevels; numLevelsCropped++) |
||||
{ |
||||
scale *= pyrScale; |
||||
if (size.width*scale < MIN_SIZE || size.height*scale < MIN_SIZE) |
||||
break; |
||||
} |
||||
|
||||
frame0.convertTo(frames_[0], CV_32F); |
||||
frame1.convertTo(frames_[1], CV_32F); |
||||
|
||||
if (fastPyramids) |
||||
{ |
||||
// Build Gaussian pyramids using pyrDown()
|
||||
pyramid0_.resize(numLevelsCropped + 1); |
||||
pyramid1_.resize(numLevelsCropped + 1); |
||||
pyramid0_[0] = frames_[0]; |
||||
pyramid1_[0] = frames_[1]; |
||||
for (int i = 1; i <= numLevelsCropped; ++i) |
||||
{ |
||||
pyrDown(pyramid0_[i - 1], pyramid0_[i]); |
||||
pyrDown(pyramid1_[i - 1], pyramid1_[i]); |
||||
} |
||||
} |
||||
|
||||
setPolynomialExpansionConsts(polyN, polySigma); |
||||
|
||||
for (int k = numLevelsCropped; k >= 0; k--) |
||||
{ |
||||
scale = 1; |
||||
for (int i = 0; i < k; i++) |
||||
scale *= pyrScale; |
||||
|
||||
double sigma = (1./scale - 1) * 0.5; |
||||
int smoothSize = cvRound(sigma*5) | 1; |
||||
smoothSize = std::max(smoothSize, 3); |
||||
|
||||
int width = cvRound(size.width*scale); |
||||
int height = cvRound(size.height*scale); |
||||
|
||||
if (fastPyramids) |
||||
{ |
||||
width = pyramid0_[k].cols; |
||||
height = pyramid0_[k].rows; |
||||
} |
||||
|
||||
if (k > 0) |
||||
{ |
||||
curFlowX.create(height, width, CV_32F); |
||||
curFlowY.create(height, width, CV_32F); |
||||
} |
||||
else |
||||
{ |
||||
curFlowX = flowx0; |
||||
curFlowY = flowy0; |
||||
} |
||||
|
||||
if (!prevFlowX.data) |
||||
{ |
||||
if (flags & cv::OPTFLOW_USE_INITIAL_FLOW) |
||||
{ |
||||
resize(flowx0, curFlowX, Size(width, height), 0, 0, INTER_LINEAR); |
||||
resize(flowy0, curFlowY, Size(width, height), 0, 0, INTER_LINEAR); |
||||
multiply(scale, curFlowX, curFlowX); |
||||
multiply(scale, curFlowY, curFlowY); |
||||
} |
||||
else |
||||
{ |
||||
curFlowX.setTo(0); |
||||
curFlowY.setTo(0); |
||||
} |
||||
} |
||||
else |
||||
{ |
||||
resize(prevFlowX, curFlowX, Size(width, height), 0, 0, INTER_LINEAR); |
||||
resize(prevFlowY, curFlowY, Size(width, height), 0, 0, INTER_LINEAR); |
||||
multiply(1./pyrScale, curFlowX, curFlowX); |
||||
multiply(1./pyrScale, curFlowY, curFlowY); |
||||
} |
||||
|
||||
oclMat M = allocMatFromBuf(5*height, width, CV_32F, M_); |
||||
oclMat bufM = allocMatFromBuf(5*height, width, CV_32F, bufM_); |
||||
oclMat R[2] = |
||||
{ |
||||
allocMatFromBuf(5*height, width, CV_32F, R_[0]), |
||||
allocMatFromBuf(5*height, width, CV_32F, R_[1]) |
||||
}; |
||||
|
||||
if (fastPyramids) |
||||
{ |
||||
optflow_farneback::polynomialExpansionOcl(pyramid0_[k], polyN, R[0]); |
||||
optflow_farneback::polynomialExpansionOcl(pyramid1_[k], polyN, R[1]); |
||||
} |
||||
else |
||||
{ |
||||
oclMat blurredFrame[2] = |
||||
{ |
||||
allocMatFromBuf(size.height, size.width, CV_32F, blurredFrame_[0]), |
||||
allocMatFromBuf(size.height, size.width, CV_32F, blurredFrame_[1]) |
||||
}; |
||||
oclMat pyrLevel[2] = |
||||
{ |
||||
allocMatFromBuf(height, width, CV_32F, pyrLevel_[0]), |
||||
allocMatFromBuf(height, width, CV_32F, pyrLevel_[1]) |
||||
}; |
||||
|
||||
Mat g = getGaussianKernel(smoothSize, sigma, CV_32F); |
||||
optflow_farneback::setGaussianBlurKernel(g.ptr<float>(smoothSize/2), smoothSize/2); |
||||
|
||||
for (int i = 0; i < 2; i++) |
||||
{ |
||||
optflow_farneback::gaussianBlurOcl(frames_[i], smoothSize/2, blurredFrame[i]); |
||||
resize(blurredFrame[i], pyrLevel[i], Size(width, height), INTER_LINEAR); |
||||
optflow_farneback::polynomialExpansionOcl(pyrLevel[i], polyN, R[i]); |
||||
} |
||||
} |
||||
|
||||
optflow_farneback::updateMatricesOcl(curFlowX, curFlowY, R[0], R[1], M); |
||||
|
||||
if (flags & OPTFLOW_FARNEBACK_GAUSSIAN) |
||||
{ |
||||
Mat g = getGaussianKernel(winSize, winSize/2*0.3f, CV_32F); |
||||
optflow_farneback::setGaussianBlurKernel(g.ptr<float>(winSize/2), winSize/2); |
||||
} |
||||
for (int i = 0; i < numIters; i++) |
||||
{ |
||||
if (flags & OPTFLOW_FARNEBACK_GAUSSIAN) |
||||
updateFlow_gaussianBlur(R[0], R[1], curFlowX, curFlowY, M, bufM, winSize, i < numIters-1); |
||||
else |
||||
updateFlow_boxFilter(R[0], R[1], curFlowX, curFlowY, M, bufM, winSize, i < numIters-1); |
||||
} |
||||
|
||||
prevFlowX = curFlowX; |
||||
prevFlowY = curFlowY; |
||||
} |
||||
|
||||
flowx = curFlowX; |
||||
flowy = curFlowY; |
||||
} |
||||
Loading…
Reference in new issue