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Merge pull request #897 from bitwangyaoyao:2.4_TVL1

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This commit is contained in:
Vadim Pisarevsky 2013-05-19 00:23:23 +04:00 committed by OpenCV Buildbot
commit aee6a617a6
5 changed files with 1037 additions and 10 deletions
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93
modules/ocl/include/opencv2/ocl/ocl.hpp
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@ -407,6 +407,9 @@ namespace cv
//! computes element-wise product of the two arrays (c = a * b)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void multiply(const oclMat &a, const oclMat &b, oclMat &c, double scale = 1);
//! multiplies matrix to a number (dst = scalar * src)
// supports CV_32FC1 only
CV_EXPORTS void multiply(double scalar, const oclMat &src, oclMat &dst);
//! computes element-wise quotient of the two arrays (c = a / b)
// supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
CV_EXPORTS void divide(const oclMat &a, const oclMat &b, oclMat &c, double scale = 1);
@ -1372,6 +1375,7 @@ namespace cv
private:
oclMat minSSD, leBuf, riBuf;
};
class CV_EXPORTS StereoBeliefPropagation
{
public:
@ -1402,6 +1406,7 @@ namespace cv
std::vector<oclMat> datas;
oclMat out;
};
class CV_EXPORTS StereoConstantSpaceBP
{
public:
@ -1440,6 +1445,94 @@ namespace cv
oclMat temp;
oclMat out;
};
// Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
//
// see reference:
// [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
// [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
class CV_EXPORTS OpticalFlowDual_TVL1_OCL
{
public:
OpticalFlowDual_TVL1_OCL();
void operator ()(const oclMat& I0, const oclMat& I1, oclMat& flowx, oclMat& flowy);
void collectGarbage();
/**
* Time step of the numerical scheme.
*/
double tau;
/**
* Weight parameter for the data term, attachment parameter.
* This is the most relevant parameter, which determines the smoothness of the output.
* The smaller this parameter is, the smoother the solutions we obtain.
* It depends on the range of motions of the images, so its value should be adapted to each image sequence.
*/
double lambda;
/**
* Weight parameter for (u - v)^2, tightness parameter.
* It serves as a link between the attachment and the regularization terms.
* In theory, it should have a small value in order to maintain both parts in correspondence.
* The method is stable for a large range of values of this parameter.
*/
double theta;
/**
* Number of scales used to create the pyramid of images.
*/
int nscales;
/**
* Number of warpings per scale.
* Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
* This is a parameter that assures the stability of the method.
* It also affects the running time, so it is a compromise between speed and accuracy.
*/
int warps;
/**
* Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
* A small value will yield more accurate solutions at the expense of a slower convergence.
*/
double epsilon;
/**
* Stopping criterion iterations number used in the numerical scheme.
*/
int iterations;
bool useInitialFlow;
private:
void procOneScale(const oclMat& I0, const oclMat& I1, oclMat& u1, oclMat& u2);
std::vector<oclMat> I0s;
std::vector<oclMat> I1s;
std::vector<oclMat> u1s;
std::vector<oclMat> u2s;
oclMat I1x_buf;
oclMat I1y_buf;
oclMat I1w_buf;
oclMat I1wx_buf;
oclMat I1wy_buf;
oclMat grad_buf;
oclMat rho_c_buf;
oclMat p11_buf;
oclMat p12_buf;
oclMat p21_buf;
oclMat p22_buf;
oclMat diff_buf;
oclMat norm_buf;
};
}
}
#if defined _MSC_VER && _MSC_VER >= 1200

7
modules/ocl/src/arithm.cpp
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@ -22,6 +22,7 @@
// Jiang Liyuan, jlyuan001.good@163.com
// Rock Li, Rock.Li@amd.com
// Zailong Wu, bullet@yeah.net
// Peng Xiao, pengxiao@outlook.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
@ -286,6 +287,7 @@ void cv::ocl::multiply(const oclMat &src1, const oclMat &src2, oclMat &dst, doub
else
arithmetic_run<float>(src1, src2, dst, "arithm_mul", &arithm_mul, (void *)(&scalar));
}
void cv::ocl::divide(const oclMat &src1, const oclMat &src2, oclMat &dst, double scalar)
{
@ -468,6 +470,11 @@ void cv::ocl::subtract(const Scalar &src2, const oclMat &src1, oclMat &dst, cons
const char **kernelString = mask.data ? &arithm_add_scalar_mask : &arithm_add_scalar;
arithmetic_scalar( src1, src2, dst, mask, kernelName, kernelString, -1);
}
void cv::ocl::multiply(double scalar, const oclMat &src, oclMat &dst)
{
string kernelName = "arithm_muls";
arithmetic_scalar_run( src, dst, kernelName, &arithm_mul, scalar);
}
void cv::ocl::divide(double scalar, const oclMat &src, oclMat &dst)
{
if(!src.clCxt->supportsFeature(Context::CL_DOUBLE))

407
modules/ocl/src/opencl/tvl1flow.cl Normal file
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@ -0,0 +1,407 @@
/*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
// Jin Ma jin@multicorewareinc.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*/
__kernel void centeredGradientKernel(__global const float* src, int src_col, int src_row, int src_step,
__global float* dx, __global float* dy, int dx_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if((x < src_col)&&(y < src_row))
{
int src_x1 = (x + 1) < (src_col -1)? (x + 1) : (src_col - 1);
int src_x2 = (x - 1) > 0 ? (x -1) : 0;
//if(src[y * src_step + src_x1] == src[y * src_step+ src_x2])
//{
// printf("y = %d\n", y);
// printf("src_x1 = %d\n", src_x1);
// printf("src_x2 = %d\n", src_x2);
//}
dx[y * dx_step+ x] = 0.5f * (src[y * src_step + src_x1] - src[y * src_step+ src_x2]);
int src_y1 = (y+1) < (src_row - 1) ? (y + 1) : (src_row - 1);
int src_y2 = (y - 1) > 0 ? (y - 1) : 0;
dy[y * dx_step+ x] = 0.5f * (src[src_y1 * src_step + x] - src[src_y2 * src_step+ x]);
}
}
float bicubicCoeff(float x_)
{
float x = fabs(x_);
if (x <= 1.0f)
{
return x * x * (1.5f * x - 2.5f) + 1.0f;
}
else if (x < 2.0f)
{
return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
}
else
{
return 0.0f;
}
}
__kernel void warpBackwardKernel(__global const float* I0, int I0_step, int I0_col, int I0_row,
image2d_t tex_I1, image2d_t tex_I1x, image2d_t tex_I1y,
__global const float* u1, int u1_step,
__global const float* u2,
__global float* I1w,
__global float* I1wx, /*int I1wx_step,*/
__global float* I1wy, /*int I1wy_step,*/
__global float* grad, /*int grad_step,*/
__global float* rho,
int I1w_step,
int u2_step,
int u1_offset_x,
int u1_offset_y,
int u2_offset_x,
int u2_offset_y)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
if(x < I0_col&&y < I0_row)
{
//const float u1Val = u1(y, x);
const float u1Val = u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
//const float u2Val = u2(y, x);
const float u2Val = u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
const float wx = x + u1Val;
const float wy = y + u2Val;
const int xmin = ceil(wx - 2.0f);
const int xmax = floor(wx + 2.0f);
const int ymin = ceil(wy - 2.0f);
const int ymax = floor(wy + 2.0f);
float sum = 0.0f;
float sumx = 0.0f;
float sumy = 0.0f;
float wsum = 0.0f;
sampler_t sampleri = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;
for (int cy = ymin; cy <= ymax; ++cy)
{
for (int cx = xmin; cx <= xmax; ++cx)
{
const float w = bicubicCoeff(wx - cx) * bicubicCoeff(wy - cy);
//sum += w * tex2D(tex_I1 , cx, cy);
int2 cood = (int2)(cx, cy);
sum += w * read_imagef(tex_I1, sampleri, cood).x;
//sumx += w * tex2D(tex_I1x, cx, cy);
sumx += w * read_imagef(tex_I1x, sampleri, cood).x;
//sumy += w * tex2D(tex_I1y, cx, cy);
sumy += w * read_imagef(tex_I1y, sampleri, cood).x;
wsum += w;
}
}
const float coeff = 1.0f / wsum;
const float I1wVal = sum * coeff;
const float I1wxVal = sumx * coeff;
const float I1wyVal = sumy * coeff;
I1w[y * I1w_step + x] = I1wVal;
I1wx[y * I1w_step + x] = I1wxVal;
I1wy[y * I1w_step + x] = I1wyVal;
const float Ix2 = I1wxVal * I1wxVal;
const float Iy2 = I1wyVal * I1wyVal;
// store the |Grad(I1)|^2
grad[y * I1w_step + x] = Ix2 + Iy2;
// compute the constant part of the rho function
const float I0Val = I0[y * I0_step + x];
rho[y * I1w_step + x] = I1wVal - I1wxVal * u1Val - I1wyVal * u2Val - I0Val;
}
}
float readImage(__global const float *image, const int x, const int y, const int rows, const int cols, const int elemCntPerRow)
{
int i0 = clamp(x, 0, cols - 1);
int j0 = clamp(y, 0, rows - 1);
int i1 = clamp(x + 1, 0, cols - 1);
int j1 = clamp(y + 1, 0, rows - 1);
return image[j0 * elemCntPerRow + i0];
}
__kernel void warpBackwardKernelNoImage2d(__global const float* I0, int I0_step, int I0_col, int I0_row,
__global const float* tex_I1, __global const float* tex_I1x, __global const float* tex_I1y,
__global const float* u1, int u1_step,
__global const float* u2,
__global float* I1w,
__global float* I1wx, /*int I1wx_step,*/
__global float* I1wy, /*int I1wy_step,*/
__global float* grad, /*int grad_step,*/
__global float* rho,
int I1w_step,
int u2_step,
int I1_step,
int I1x_step)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
if(x < I0_col&&y < I0_row)
{
//const float u1Val = u1(y, x);
const float u1Val = u1[y * u1_step + x];
//const float u2Val = u2(y, x);
const float u2Val = u2[y * u2_step + x];
const float wx = x + u1Val;
const float wy = y + u2Val;
const int xmin = ceil(wx - 2.0f);
const int xmax = floor(wx + 2.0f);
const int ymin = ceil(wy - 2.0f);
const int ymax = floor(wy + 2.0f);
float sum = 0.0f;
float sumx = 0.0f;
float sumy = 0.0f;
float wsum = 0.0f;
for (int cy = ymin; cy <= ymax; ++cy)
{
for (int cx = xmin; cx <= xmax; ++cx)
{
const float w = bicubicCoeff(wx - cx) * bicubicCoeff(wy - cy);
int2 cood = (int2)(cx, cy);
sum += w * readImage(tex_I1, cood.x, cood.y, I0_col, I0_row, I1_step);
sumx += w * readImage(tex_I1x, cood.x, cood.y, I0_col, I0_row, I1x_step);
sumy += w * readImage(tex_I1y, cood.x, cood.y, I0_col, I0_row, I1x_step);
wsum += w;
}
}
const float coeff = 1.0f / wsum;
const float I1wVal = sum * coeff;
const float I1wxVal = sumx * coeff;
const float I1wyVal = sumy * coeff;
I1w[y * I1w_step + x] = I1wVal;
I1wx[y * I1w_step + x] = I1wxVal;
I1wy[y * I1w_step + x] = I1wyVal;
const float Ix2 = I1wxVal * I1wxVal;
const float Iy2 = I1wyVal * I1wyVal;
// store the |Grad(I1)|^2
grad[y * I1w_step + x] = Ix2 + Iy2;
// compute the constant part of the rho function
const float I0Val = I0[y * I0_step + x];
rho[y * I1w_step + x] = I1wVal - I1wxVal * u1Val - I1wyVal * u2Val - I0Val;
}
}
__kernel void estimateDualVariablesKernel(__global const float* u1, int u1_col, int u1_row, int u1_step,
__global const float* u2,
__global float* p11, int p11_step,
__global float* p12,
__global float* p21,
__global float* p22,
const float taut,
int u2_step,
int u1_offset_x,
int u1_offset_y,
int u2_offset_x,
int u2_offset_y)
{
//const int x = blockIdx.x * blockDim.x + threadIdx.x;
//const int y = blockIdx.y * blockDim.y + threadIdx.y;
const int x = get_global_id(0);
const int y = get_global_id(1);
if(x < u1_col && y < u1_row)
{
int src_x1 = (x + 1) < (u1_col - 1) ? (x + 1) : (u1_col - 1);
const float u1x = u1[(y + u1_offset_y) * u1_step + src_x1 + u1_offset_x] - u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
int src_y1 = (y + 1) < (u1_row - 1) ? (y + 1) : (u1_row - 1);
const float u1y = u1[(src_y1 + u1_offset_y) * u1_step + x + u1_offset_x] - u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
int src_x2 = (x + 1) < (u1_col - 1) ? (x + 1) : (u1_col - 1);
const float u2x = u2[(y + u2_offset_y) * u2_step + src_x2 + u2_offset_x] - u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
int src_y2 = (y + 1) < (u1_row - 1) ? (y + 1) : (u1_row - 1);
const float u2y = u2[(src_y2 + u2_offset_y) * u2_step + x + u2_offset_x] - u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
const float g1 = hypot(u1x, u1y);
const float g2 = hypot(u2x, u2y);
const float ng1 = 1.0f + taut * g1;
const float ng2 = 1.0f + taut * g2;
p11[y * p11_step + x] = (p11[y * p11_step + x] + taut * u1x) / ng1;
p12[y * p11_step + x] = (p12[y * p11_step + x] + taut * u1y) / ng1;
p21[y * p11_step + x] = (p21[y * p11_step + x] + taut * u2x) / ng2;
p22[y * p11_step + x] = (p22[y * p11_step + x] + taut * u2y) / ng2;
}
}
float divergence(__global const float* v1, __global const float* v2, int y, int x, int v1_step, int v2_step)
{
if (x > 0 && y > 0)
{
const float v1x = v1[y * v1_step + x] - v1[y * v1_step + x - 1];
const float v2y = v2[y * v2_step + x] - v2[(y - 1) * v2_step + x];
return v1x + v2y;
}
else
{
if (y > 0)
return v1[y * v1_step + 0] + v2[y * v2_step + 0] - v2[(y - 1) * v2_step + 0];
else
{
if (x > 0)
return v1[0 * v1_step + x] - v1[0 * v1_step + x - 1] + v2[0 * v2_step + x];
else
return v1[0 * v1_step + 0] + v2[0 * v2_step + 0];
}
}
}
__kernel void estimateUKernel(__global const float* I1wx, int I1wx_col, int I1wx_row, int I1wx_step,
__global const float* I1wy, /*int I1wy_step,*/
__global const float* grad, /*int grad_step,*/
__global const float* rho_c, /*int rho_c_step,*/
__global const float* p11, /*int p11_step,*/
__global const float* p12, /*int p12_step,*/
__global const float* p21, /*int p21_step,*/
__global const float* p22, /*int p22_step,*/
__global float* u1, int u1_step,
__global float* u2,
__global float* error, const float l_t, const float theta, int u2_step,
int u1_offset_x,
int u1_offset_y,
int u2_offset_x,
int u2_offset_y)
{
//const int x = blockIdx.x * blockDim.x + threadIdx.x;
//const int y = blockIdx.y * blockDim.y + threadIdx.y;
int x = get_global_id(0);
int y = get_global_id(1);
if(x < I1wx_col && y < I1wx_row)
{
const float I1wxVal = I1wx[y * I1wx_step + x];
const float I1wyVal = I1wy[y * I1wx_step + x];
const float gradVal = grad[y * I1wx_step + x];
const float u1OldVal = u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
const float u2OldVal = u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
const float rho = rho_c[y * I1wx_step + x] + (I1wxVal * u1OldVal + I1wyVal * u2OldVal);
// estimate the values of the variable (v1, v2) (thresholding operator TH)
float d1 = 0.0f;
float d2 = 0.0f;
if (rho < -l_t * gradVal)
{
d1 = l_t * I1wxVal;
d2 = l_t * I1wyVal;
}
else if (rho > l_t * gradVal)
{
d1 = -l_t * I1wxVal;
d2 = -l_t * I1wyVal;
}
else if (gradVal > 1.192092896e-07f)
{
const float fi = -rho / gradVal;
d1 = fi * I1wxVal;
d2 = fi * I1wyVal;
}
const float v1 = u1OldVal + d1;
const float v2 = u2OldVal + d2;
// compute the divergence of the dual variable (p1, p2)
const float div_p1 = divergence(p11, p12, y, x, I1wx_step, I1wx_step);
const float div_p2 = divergence(p21, p22, y, x, I1wx_step, I1wx_step);
// estimate the values of the optical flow (u1, u2)
const float u1NewVal = v1 + theta * div_p1;
const float u2NewVal = v2 + theta * div_p2;
u1[(y + u1_offset_y) * u1_step + x + u1_offset_x] = u1NewVal;
u2[(y + u2_offset_y) * u2_step + x + u2_offset_x] = u2NewVal;
const float n1 = (u1OldVal - u1NewVal) * (u1OldVal - u1NewVal);
const float n2 = (u2OldVal - u2NewVal) * (u2OldVal - u2NewVal);
error[y * I1wx_step + x] = n1 + n2;
}
}

475
modules/ocl/src/tvl1flow.cpp Normal file
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@ -0,0 +1,475 @@
/*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
// Jin Ma, jin@multicorewareinc.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"
using namespace std;
using namespace cv;
using namespace cv::ocl;
namespace cv
{
namespace ocl
{
///////////////////////////OpenCL kernel strings///////////////////////////
extern const char* tvl1flow;
}
}
cv::ocl::OpticalFlowDual_TVL1_OCL::OpticalFlowDual_TVL1_OCL()
{
tau = 0.25;
lambda = 0.15;
theta = 0.3;
nscales = 5;
warps = 5;
epsilon = 0.01;
iterations = 300;
useInitialFlow = false;
}
void cv::ocl::OpticalFlowDual_TVL1_OCL::operator()(const oclMat& I0, const oclMat& I1, oclMat& flowx, oclMat& flowy)
{
CV_Assert( I0.type() == CV_8UC1 || I0.type() == CV_32FC1 );
CV_Assert( I0.size() == I1.size() );
CV_Assert( I0.type() == I1.type() );
CV_Assert( !useInitialFlow || (flowx.size() == I0.size() && flowx.type() == CV_32FC1 && flowy.size() == flowx.size() && flowy.type() == flowx.type()) );
CV_Assert( nscales > 0 );
// allocate memory for the pyramid structure
I0s.resize(nscales);
I1s.resize(nscales);
u1s.resize(nscales);
u2s.resize(nscales);
//I0s_step == I1s_step
I0.convertTo(I0s[0], CV_32F, I0.depth() == CV_8U ? 1.0 : 255.0);
I1.convertTo(I1s[0], CV_32F, I1.depth() == CV_8U ? 1.0 : 255.0);
if (!useInitialFlow)
{
flowx.create(I0.size(), CV_32FC1);
flowy.create(I0.size(), CV_32FC1);
}
//u1s_step != u2s_step
u1s[0] = flowx;
u2s[0] = flowy;
I1x_buf.create(I0.size(), CV_32FC1);
I1y_buf.create(I0.size(), CV_32FC1);
I1w_buf.create(I0.size(), CV_32FC1);
I1wx_buf.create(I0.size(), CV_32FC1);
I1wy_buf.create(I0.size(), CV_32FC1);
grad_buf.create(I0.size(), CV_32FC1);
rho_c_buf.create(I0.size(), CV_32FC1);
p11_buf.create(I0.size(), CV_32FC1);
p12_buf.create(I0.size(), CV_32FC1);
p21_buf.create(I0.size(), CV_32FC1);
p22_buf.create(I0.size(), CV_32FC1);
diff_buf.create(I0.size(), CV_32FC1);
// create the scales
for (int s = 1; s < nscales; ++s)
{
ocl::pyrDown(I0s[s - 1], I0s[s]);
ocl::pyrDown(I1s[s - 1], I1s[s]);
if (I0s[s].cols < 16 || I0s[s].rows < 16)
{
nscales = s;
break;
}
if (useInitialFlow)
{
ocl::pyrDown(u1s[s - 1], u1s[s]);
ocl::pyrDown(u2s[s - 1], u2s[s]);
//ocl::multiply(u1s[s], Scalar::all(0.5), u1s[s]);
multiply(0.5, u1s[s], u1s[s]);
//ocl::multiply(u2s[s], Scalar::all(0.5), u2s[s]);
multiply(0.5, u1s[s], u2s[s]);
}
}
// pyramidal structure for computing the optical flow
for (int s = nscales - 1; s >= 0; --s)
{
// compute the optical flow at the current scale
procOneScale(I0s[s], I1s[s], u1s[s], u2s[s]);
// if this was the last scale, finish now
if (s == 0)
break;
// otherwise, upsample the optical flow
// zoom the optical flow for the next finer scale
ocl::resize(u1s[s], u1s[s - 1], I0s[s - 1].size());
ocl::resize(u2s[s], u2s[s - 1], I0s[s - 1].size());
// scale the optical flow with the appropriate zoom factor
multiply(2, u1s[s - 1], u1s[s - 1]);
multiply(2, u2s[s - 1], u2s[s - 1]);
}
}
namespace ocl_tvl1flow
{
void centeredGradient(const oclMat &src, oclMat &dx, oclMat &dy);
void warpBackward(const oclMat &I0, const oclMat &I1, oclMat &I1x, oclMat &I1y,
oclMat &u1, oclMat &u2, oclMat &I1w, oclMat &I1wx, oclMat &I1wy,
oclMat &grad, oclMat &rho);
void estimateU(oclMat &I1wx, oclMat &I1wy, oclMat &grad,
oclMat &rho_c, oclMat &p11, oclMat &p12,
oclMat &p21, oclMat &p22, oclMat &u1,
oclMat &u2, oclMat &error, float l_t, float theta);
void estimateDualVariables(oclMat &u1, oclMat &u2,
oclMat &p11, oclMat &p12, oclMat &p21, oclMat &p22, float taut);
}
void cv::ocl::OpticalFlowDual_TVL1_OCL::procOneScale(const oclMat &I0, const oclMat &I1, oclMat &u1, oclMat &u2)
{
using namespace ocl_tvl1flow;
const double scaledEpsilon = epsilon * epsilon * I0.size().area();
CV_DbgAssert( I1.size() == I0.size() );
CV_DbgAssert( I1.type() == I0.type() );
CV_DbgAssert( u1.empty() || u1.size() == I0.size() );
CV_DbgAssert( u2.size() == u1.size() );
if (u1.empty())
{
u1.create(I0.size(), CV_32FC1);
u1.setTo(Scalar::all(0));
u2.create(I0.size(), CV_32FC1);
u2.setTo(Scalar::all(0));
}
oclMat I1x = I1x_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat I1y = I1y_buf(Rect(0, 0, I0.cols, I0.rows));
centeredGradient(I1, I1x, I1y);
oclMat I1w = I1w_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat I1wx = I1wx_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat I1wy = I1wy_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat grad = grad_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat rho_c = rho_c_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat p11 = p11_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat p12 = p12_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat p21 = p21_buf(Rect(0, 0, I0.cols, I0.rows));
oclMat p22 = p22_buf(Rect(0, 0, I0.cols, I0.rows));
p11.setTo(Scalar::all(0));
p12.setTo(Scalar::all(0));
p21.setTo(Scalar::all(0));
p22.setTo(Scalar::all(0));
oclMat diff = diff_buf(Rect(0, 0, I0.cols, I0.rows));
const float l_t = static_cast<float>(lambda * theta);
const float taut = static_cast<float>(tau / theta);
for (int warpings = 0; warpings < warps; ++warpings)
{
warpBackward(I0, I1, I1x, I1y, u1, u2, I1w, I1wx, I1wy, grad, rho_c);
double error = numeric_limits<double>::max();
for (int n = 0; error > scaledEpsilon && n < iterations; ++n)
{
estimateU(I1wx, I1wy, grad, rho_c, p11, p12, p21, p22,
u1, u2, diff, l_t, static_cast<float>(theta));
error = ocl::sum(diff)[0];
estimateDualVariables(u1, u2, p11, p12, p21, p22, taut);
}
}
}
void cv::ocl::OpticalFlowDual_TVL1_OCL::collectGarbage()
{
I0s.clear();
I1s.clear();
u1s.clear();
u2s.clear();
I1x_buf.release();
I1y_buf.release();
I1w_buf.release();
I1wx_buf.release();
I1wy_buf.release();
grad_buf.release();
rho_c_buf.release();
p11_buf.release();
p12_buf.release();
p21_buf.release();
p22_buf.release();
diff_buf.release();
norm_buf.release();
}
void ocl_tvl1flow::centeredGradient(const oclMat &src, oclMat &dx, oclMat &dy)
{
Context *clCxt = src.clCxt;
size_t localThreads[3] = {32, 8, 1};
size_t globalThreads[3] = {src.cols, src.rows, 1};
int srcElementSize = src.elemSize();
int src_step = src.step/srcElementSize;
int dElememntSize = dx.elemSize();
int dx_step = dx.step/dElememntSize;
string kernelName = "centeredGradientKernel";
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void*)&src.data));
args.push_back( make_pair( sizeof(cl_int), (void*)&src.cols));
args.push_back( make_pair( sizeof(cl_int), (void*)&src.rows));
args.push_back( make_pair( sizeof(cl_int), (void*)&src_step));
args.push_back( make_pair( sizeof(cl_mem), (void*)&dx.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&dy.data));
args.push_back( make_pair( sizeof(cl_int), (void*)&dx_step));
openCLExecuteKernel(clCxt, &tvl1flow, kernelName, globalThreads, localThreads, args, -1, -1);
}
void ocl_tvl1flow::estimateDualVariables(oclMat &u1, oclMat &u2, oclMat &p11, oclMat &p12, oclMat &p21, oclMat &p22, float taut)
{
Context *clCxt = u1.clCxt;
size_t localThread[] = {32, 8, 1};
size_t globalThread[] =
{
u1.cols,
u1.rows,
1
};
int u1_element_size = u1.elemSize();
int u1_step = u1.step/u1_element_size;
int u2_element_size = u2.elemSize();
int u2_step = u2.step/u2_element_size;
int p11_element_size = p11.elemSize();
int p11_step = p11.step/p11_element_size;
int u1_offset_y = u1.offset/u1.step;
int u1_offset_x = u1.offset%u1.step;
u1_offset_x = u1_offset_x/u1.elemSize();
int u2_offset_y = u2.offset/u2.step;
int u2_offset_x = u2.offset%u2.step;
u2_offset_x = u2_offset_x/u2.elemSize();
string kernelName = "estimateDualVariablesKernel";
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void*)&u1.data));
args.push_back( make_pair( sizeof(cl_int), (void*)&u1.cols));
args.push_back( make_pair( sizeof(cl_int), (void*)&u1.rows));
args.push_back( make_pair( sizeof(cl_int), (void*)&u1_step));
args.push_back( make_pair( sizeof(cl_mem), (void*)&u2.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&p11.data));
args.push_back( make_pair( sizeof(cl_int), (void*)&p11_step));
args.push_back( make_pair( sizeof(cl_mem), (void*)&p12.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&p21.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&p22.data));
args.push_back( make_pair( sizeof(cl_float), (void*)&taut));
args.push_back( make_pair( sizeof(cl_int), (void*)&u2_step));
args.push_back( make_pair( sizeof(cl_int), (void*)&u1_offset_x));
args.push_back( make_pair( sizeof(cl_int), (void*)&u1_offset_y));
args.push_back( make_pair( sizeof(cl_int), (void*)&u2_offset_x));
args.push_back( make_pair( sizeof(cl_int), (void*)&u2_offset_y));
openCLExecuteKernel(clCxt, &tvl1flow, kernelName, globalThread, localThread, args, -1, -1);
}
void ocl_tvl1flow::estimateU(oclMat &I1wx, oclMat &I1wy, oclMat &grad,
oclMat &rho_c, oclMat &p11, oclMat &p12,
oclMat &p21, oclMat &p22, oclMat &u1,
oclMat &u2, oclMat &error, float l_t, float theta)
{
Context* clCxt = I1wx.clCxt;
size_t localThread[] = {32, 8, 1};
size_t globalThread[] =
{
I1wx.cols,
I1wx.rows,
1
};
int I1wx_element_size = I1wx.elemSize();
int I1wx_step = I1wx.step/I1wx_element_size;
int u1_element_size = u1.elemSize();
int u1_step = u1.step/u1_element_size;
int u2_element_size = u2.elemSize();
int u2_step = u2.step/u2_element_size;
int u1_offset_y = u1.offset/u1.step;
int u1_offset_x = u1.offset%u1.step;
u1_offset_x = u1_offset_x/u1.elemSize();
int u2_offset_y = u2.offset/u2.step;
int u2_offset_x = u2.offset%u2.step;
u2_offset_x = u2_offset_x/u2.elemSize();
string kernelName = "estimateUKernel";
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void*)&I1wx.data));
args.push_back( make_pair( sizeof(cl_int), (void*)&I1wx.cols));
args.push_back( make_pair( sizeof(cl_int), (void*)&I1wx.rows));
args.push_back( make_pair( sizeof(cl_int), (void*)&I1wx_step));
args.push_back( make_pair( sizeof(cl_mem), (void*)&I1wy.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&grad.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&rho_c.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&p11.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&p12.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&p21.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&p22.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&u1.data));
args.push_back( make_pair( sizeof(cl_int), (void*)&u1_step));
args.push_back( make_pair( sizeof(cl_mem), (void*)&u2.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&error.data));
args.push_back( make_pair( sizeof(cl_float), (void*)&l_t));
args.push_back( make_pair( sizeof(cl_float), (void*)&theta));
args.push_back( make_pair( sizeof(cl_int), (void*)&u2_step));
args.push_back( make_pair( sizeof(cl_int), (void*)&u1_offset_x));
args.push_back( make_pair( sizeof(cl_int), (void*)&u1_offset_y));
args.push_back( make_pair( sizeof(cl_int), (void*)&u2_offset_x));
args.push_back( make_pair( sizeof(cl_int), (void*)&u2_offset_y));
openCLExecuteKernel(clCxt, &tvl1flow, kernelName, globalThread, localThread, args, -1, -1);
}
void ocl_tvl1flow::warpBackward(const oclMat &I0, const oclMat &I1, oclMat &I1x, oclMat &I1y, oclMat &u1, oclMat &u2, oclMat &I1w, oclMat &I1wx, oclMat &I1wy, oclMat &grad, oclMat &rho)
{
Context* clCxt = I0.clCxt;
const bool isImgSupported = support_image2d(clCxt);
CV_Assert(isImgSupported);
int u1ElementSize = u1.elemSize();
int u1Step = u1.step/u1ElementSize;
int u2ElementSize = u2.elemSize();
int u2Step = u2.step/u2ElementSize;
int I0ElementSize = I0.elemSize();
int I0Step = I0.step/I0ElementSize;
int I1w_element_size = I1w.elemSize();
int I1w_step = I1w.step/I1w_element_size;
int u1_offset_y = u1.offset/u1.step;
int u1_offset_x = u1.offset%u1.step;
u1_offset_x = u1_offset_x/u1.elemSize();
int u2_offset_y = u2.offset/u2.step;
int u2_offset_x = u2.offset%u2.step;
u2_offset_x = u2_offset_x/u2.elemSize();
size_t localThread[] = {32, 8, 1};
size_t globalThread[] =
{
I0.cols,
I0.rows,
1
};
cl_mem I1_tex;
cl_mem I1x_tex;
cl_mem I1y_tex;
I1_tex = bindTexture(I1);
I1x_tex = bindTexture(I1x);
I1y_tex = bindTexture(I1y);
string kernelName = "warpBackwardKernel";
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void*)&I0.data));
args.push_back( make_pair( sizeof(cl_int), (void*)&I0Step));
args.push_back( make_pair( sizeof(cl_int), (void*)&I0.cols));
args.push_back( make_pair( sizeof(cl_int), (void*)&I0.rows));
args.push_back( make_pair( sizeof(cl_mem), (void*)&I1_tex));
args.push_back( make_pair( sizeof(cl_mem), (void*)&I1x_tex));
args.push_back( make_pair( sizeof(cl_mem), (void*)&I1y_tex));
args.push_back( make_pair( sizeof(cl_mem), (void*)&u1.data));
args.push_back( make_pair( sizeof(cl_int), (void*)&u1Step));
args.push_back( make_pair( sizeof(cl_mem), (void*)&u2.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&I1w.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&I1wx.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&I1wy.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&grad.data));
args.push_back( make_pair( sizeof(cl_mem), (void*)&rho.data));
args.push_back( make_pair( sizeof(cl_int), (void*)&I1w_step));
args.push_back( make_pair( sizeof(cl_int), (void*)&u2Step));
args.push_back( make_pair( sizeof(cl_int), (void*)&u1_offset_x));
args.push_back( make_pair( sizeof(cl_int), (void*)&u1_offset_y));
args.push_back( make_pair( sizeof(cl_int), (void*)&u2_offset_x));
args.push_back( make_pair( sizeof(cl_int), (void*)&u2_offset_y));
openCLExecuteKernel(clCxt, &tvl1flow, kernelName, globalThread, localThread, args, -1, -1);
}

65
modules/ocl/test/test_pyrlk.cpp → modules/ocl/test/test_optflow.cpp
View File

@ -1,4 +1,4 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
@ -7,12 +7,16 @@
// copy or use the software.
//
//
// Intel License Agreement
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
//
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
@ -21,9 +25,9 @@
//
// * 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.
// and/or other oclMaterials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// * 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
@ -51,6 +55,47 @@ using namespace testing;
using namespace std;
extern string workdir;
//////////////////////////////////////////////////////////////////////////
PARAM_TEST_CASE(TVL1, bool)
{
bool useRoi;
virtual void SetUp()
{
useRoi = GET_PARAM(0);
}
};
TEST_P(TVL1, Accuracy)
{
cv::Mat frame0 = readImage(workdir + "../gpu/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage(workdir + "../gpu/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
cv::ocl::OpticalFlowDual_TVL1_OCL d_alg;
cv::RNG &rng = TS::ptr()->get_rng();
cv::Mat flowx = randomMat(rng, frame0.size(), CV_32FC1, 0, 0, useRoi);
cv::Mat flowy = randomMat(rng, frame0.size(), CV_32FC1, 0, 0, useRoi);
cv::ocl::oclMat d_flowx(flowx), d_flowy(flowy);
d_alg(oclMat(frame0), oclMat(frame1), d_flowx, d_flowy);
cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1();
cv::Mat flow;
alg->calc(frame0, frame1, flow);
cv::Mat gold[2];
cv::split(flow, gold);
EXPECT_MAT_SIMILAR(gold[0], d_flowx, 3e-3);
EXPECT_MAT_SIMILAR(gold[1], d_flowy, 3e-3);
}
INSTANTIATE_TEST_CASE_P(OCL_Video, TVL1, Values(true, false));
/////////////////////////////////////////////////////////////////////////////////////////////////
// PyrLKOpticalFlow
PARAM_TEST_CASE(Sparse, bool, bool)
{
@ -60,7 +105,7 @@ PARAM_TEST_CASE(Sparse, bool, bool)
virtual void SetUp()
{
UseSmart = GET_PARAM(0);
useGray = GET_PARAM(0);
useGray = GET_PARAM(1);
}
};
@ -147,9 +192,9 @@ TEST_P(Sparse, Mat)
}
INSTANTIATE_TEST_CASE_P(Video, Sparse, Combine(
Values(false, true),
Values(false)));
INSTANTIATE_TEST_CASE_P(OCL_Video, Sparse, Combine(
Values(false, true),
Values(false, true)));
#endif // HAVE_OPENCL