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Commit 0e53c56c authored by Andrey Kamaev's avatar Andrey Kamaev Committed by OpenCV Buildbot
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Merge pull request #744 from pengx17:Branch_2.4_stereobp_ocl

parents 91f6eb7c 917138f5
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modules/ocl/include/opencv2/ocl/ocl.hpp
View file @ 0e53c56c
......@@ -1701,6 +1701,36 @@ namespace cv
private:
oclMat minSSD, leBuf, riBuf;
};
class CV_EXPORTS StereoBeliefPropagation
{
public:
enum { DEFAULT_NDISP = 64 };
enum { DEFAULT_ITERS = 5 };
enum { DEFAULT_LEVELS = 5 };
static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels);
explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP,
int iters = DEFAULT_ITERS,
int levels = DEFAULT_LEVELS,
int msg_type = CV_16S);
StereoBeliefPropagation(int ndisp, int iters, int levels,
float max_data_term, float data_weight,
float max_disc_term, float disc_single_jump,
int msg_type = CV_32F);
void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
void operator()(const oclMat &data, oclMat &disparity);
int ndisp;
int iters;
int levels;
float max_data_term;
float data_weight;
float max_disc_term;
float disc_single_jump;
int msg_type;
private:
oclMat u, d, l, r, u2, d2, l2, r2;
std::vector<oclMat> datas;
oclMat out;
};
}
}
#if defined _MSC_VER && _MSC_VER >= 1200
......
modules/ocl/src/opencl/stereobp.cl 0 → 100644
View file @ 0e53c56c
/*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, 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
// Jia Haipeng, jiahaipeng95@gmail.com
// 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:
//
// * 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 GpuMaterials 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*/
#if defined (DOUBLE_SUPPORT)
#ifdef cl_khr_fp64
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#elif defined (cl_amd_fp64)
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#endif
#endif
#ifdef T_FLOAT
#define T float
#else
#define T short
#endif
///////////////////////////////////////////////////////////////
/////////////////common///////////////////////////////////////
/////////////////////////////////////////////////////////////
T saturate_cast(float v){
#ifdef T_SHORT
return convert_short_sat_rte(v);
#else
return v;
#endif
}
#define FLOAT_MAX 3.402823466e+38f
typedef struct
{
int cndisp;
float cmax_data_term;
float cdata_weight;
float cmax_disc_term;
float cdisc_single_jump;
}con_srtuct_t;
///////////////////////////////////////////////////////////////
////////////////////////// comp data //////////////////////////
///////////////////////////////////////////////////////////////
float pix_diff_1(__global const uchar *ls, __global const uchar *rs)
{
return abs((int)(*ls) - *rs);
}
float pix_diff_3(__global const uchar *ls, __global const uchar *rs)
{
const float tr = 0.299f;
const float tg = 0.587f;
const float tb = 0.114f;
float val;
val = tb * abs((int)ls[0] - rs[0]);
val += tg * abs((int)ls[1] - rs[1]);
val += tr * abs((int)ls[2] - rs[2]);
return val;
}
float pix_diff_4(__global const uchar *ls, __global const uchar *rs)
{
uchar4 l, r;
l = *((__global uchar4 *)ls);
r = *((__global uchar4 *)rs);
const float tr = 0.299f;
const float tg = 0.587f;
const float tb = 0.114f;
float val;
val = tb * abs((int)l.x - r.x);
val += tg * abs((int)l.y - r.y);
val += tr * abs((int)l.z - r.z);
return val;
}
#ifndef CN
#define CN 4
#endif
#define CAT(X,Y) X##Y
#define CAT2(X,Y) CAT(X,Y)
#define PIX_DIFF CAT2(pix_diff_, CN)
__kernel void comp_data(__global uchar *left, int left_rows, int left_cols, int left_step,
__global uchar *right, int right_step,
__global T *data, int data_step,
__constant con_srtuct_t *con_st)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y > 0 && y < (left_rows - 1) && x > 0 && x < (left_cols - 1))
{
data_step /= sizeof(T);
const __global uchar* ls = left + y * left_step + x * CN;
const __global uchar* rs = right + y * right_step + x * CN;
__global T *ds = data + y * data_step + x;
const unsigned int disp_step = data_step * left_rows;
for (int disp = 0; disp < con_st -> cndisp; disp++)
{
if (x - disp >= 1)
{
float val = 0;
val = PIX_DIFF(ls, rs - disp * CN);
ds[disp * disp_step] = saturate_cast(fmin(con_st -> cdata_weight * val,
con_st -> cdata_weight * con_st -> cmax_data_term));
}
else
{
ds[disp * disp_step] = saturate_cast(con_st -> cdata_weight * con_st -> cmax_data_term);
}
}
}
}
///////////////////////////////////////////////////////////////
//////////////////////// data step down ///////////////////////
///////////////////////////////////////////////////////////////
__kernel void data_step_down(__global T *src, int src_rows,
__global T *dst, int dst_rows, int dst_cols,
int src_step, int dst_step,
int cndisp)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
src_step /= sizeof(T);
dst_step /= sizeof(T);
for (int d = 0; d < cndisp; ++d)
{
float dst_reg;
dst_reg = src[(d * src_rows + (2*y+0)) * src_step + 2*x+0];
dst_reg += src[(d * src_rows + (2*y+1)) * src_step + 2*x+0];
dst_reg += src[(d * src_rows + (2*y+0)) * src_step + 2*x+1];
dst_reg += src[(d * src_rows + (2*y+1)) * src_step + 2*x+1];
dst[(d * dst_rows + y) * dst_step + x] = saturate_cast(dst_reg);
}
}
}
///////////////////////////////////////////////////////////////
/////////////////// level up messages ////////////////////////
///////////////////////////////////////////////////////////////
__kernel void level_up_message(__global T *src, int src_rows, int src_step,
__global T *dst, int dst_rows, int dst_cols, int dst_step,
int cndisp)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
src_step /= sizeof(T);
dst_step /= sizeof(T);
const int dst_disp_step = dst_step * dst_rows;
const int src_disp_step = src_step * src_rows;
__global T *dstr = dst + y * dst_step + x;
__global const T *srcr = src + (y / 2 * src_step) + (x / 2);
for (int d = 0; d < cndisp; ++d)
dstr[d * dst_disp_step] = srcr[d * src_disp_step];
}
}
///////////////////////////////////////////////////////////////
//////////////////// calc all iterations /////////////////////
///////////////////////////////////////////////////////////////
void calc_min_linear_penalty(__global T * dst, int disp_step,
int cndisp, float cdisc_single_jump)
{
float prev = dst[0];
float cur;
for (int disp = 1; disp < cndisp; ++disp)
{
prev += cdisc_single_jump;
cur = dst[disp_step * disp];
if (prev < cur)
{
cur = prev;
dst[disp_step * disp] = saturate_cast(prev);
}
prev = cur;
}
prev = dst[(cndisp - 1) * disp_step];
for (int disp = cndisp - 2; disp >= 0; disp--)
{
prev += cdisc_single_jump;
cur = dst[disp_step * disp];
if (prev < cur)
{
cur = prev;
dst[disp_step * disp] = saturate_cast(prev);
}
prev = cur;
}
}
void message(const __global T *msg1, const __global T *msg2,
const __global T *msg3, const __global T *data, __global T *dst,
int msg_disp_step, int data_disp_step, int cndisp, float cmax_disc_term, float cdisc_single_jump)
{
float minimum = FLOAT_MAX;
for(int i = 0; i < cndisp; ++i)
{
float dst_reg;
dst_reg = msg1[msg_disp_step * i];
dst_reg += msg2[msg_disp_step * i];
dst_reg += msg3[msg_disp_step * i];
dst_reg += data[data_disp_step * i];
if (dst_reg < minimum)
minimum = dst_reg;
dst[msg_disp_step * i] = saturate_cast(dst_reg);
}
calc_min_linear_penalty(dst, msg_disp_step, cndisp, cdisc_single_jump);
minimum += cmax_disc_term;
float sum = 0;
for(int i = 0; i < cndisp; ++i)
{
float dst_reg = dst[msg_disp_step * i];
if (dst_reg > minimum)
{
dst_reg = minimum;
dst[msg_disp_step * i] = saturate_cast(minimum);
}
sum += dst_reg;
}
sum /= cndisp;
for(int i = 0; i < cndisp; ++i)
dst[msg_disp_step * i] -= sum;
}
__kernel void one_iteration(__global T *u, int u_step,
__global T *data, int data_step,
__global T *d, __global T *l, __global T *r,
int t, int cols, int rows,
int cndisp, float cmax_disc_term, float cdisc_single_jump)
{
const int y = get_global_id(1);
const int x = ((get_global_id(0)) << 1) + ((y + t) & 1);
if ((y > 0) && (y < rows - 1) && (x > 0) && (x < cols - 1))
{
u_step /= sizeof(T);
data_step /= sizeof(T);
__global T *us = u + y * u_step + x;
__global T *ds = d + y * u_step + x;
__global T *ls = l + y * u_step + x;
__global T *rs = r + y * u_step + x;
const __global T *dt = data + y * data_step + x;
int msg_disp_step = u_step * rows;
int data_disp_step = data_step * rows;
message(us + u_step, ls + 1, rs - 1, dt, us, msg_disp_step, data_disp_step, cndisp,
cmax_disc_term, cdisc_single_jump);
message(ds - u_step, ls + 1, rs - 1, dt, ds, msg_disp_step, data_disp_step, cndisp,
cmax_disc_term, cdisc_single_jump);
message(us + u_step, ds - u_step, rs - 1, dt, rs, msg_disp_step, data_disp_step, cndisp,
cmax_disc_term, cdisc_single_jump);
message(us + u_step, ds - u_step, ls + 1, dt, ls, msg_disp_step, data_disp_step, cndisp,
cmax_disc_term, cdisc_single_jump);
}
}
///////////////////////////////////////////////////////////////
/////////////////////////// output ////////////////////////////
///////////////////////////////////////////////////////////////
__kernel void output(const __global T *u, int u_step,
const __global T *d, const __global T *l,
const __global T *r, const __global T *data,
__global T *disp, int disp_rows, int disp_cols, int disp_step,
int cndisp)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
if (y > 0 && y < disp_rows - 1 && x > 0 && x < disp_cols - 1)
{
u_step /= sizeof(T);
disp_step /= sizeof(T);
const __global T *us = u + (y + 1) * u_step + x;
const __global T *ds = d + (y - 1) * u_step + x;
const __global T *ls = l + y * u_step + (x + 1);
const __global T *rs = r + y * u_step + (x - 1);
const __global T *dt = data + y * u_step + x;
int disp_steps = disp_rows * u_step;
int best = 0;
float best_val = FLOAT_MAX;
for (int d = 0; d < cndisp; ++d)
{
float val;
val = us[d * disp_steps];
val += ds[d * disp_steps];
val += ls[d * disp_steps];
val += rs[d * disp_steps];
val += dt[d * disp_steps];
if (val < best_val)
{
best_val = val;
best = d;
}
}
(disp + y * disp_step)[x] = convert_short_sat(best);
}
}
modules/ocl/src/stereobp.cpp 0 → 100644
View file @ 0e53c56c
/*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, 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
// Jia Haipeng, jiahaipeng95@gmail.com
// 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:
//
// * 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 <vector>
#include <cstdio>
using namespace cv;
using namespace cv::ocl;
using namespace std;
////////////////////////////////////////////////////////////////////////
///////////////// stereoBP /////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////
namespace cv
{
namespace ocl
{
///////////////////////////OpenCL kernel strings///////////////////////////
extern const char *stereobp;
}
}
namespace cv
{
namespace ocl
{
namespace stereoBP
{
//////////////////////////////////////////////////////////////////////////
//////////////////////////////common////////////////////////////////////
////////////////////////////////////////////////////////////////////////
typedef struct
{
int cndisp;
float cmax_data_term;
float cdata_weight;
float cmax_disc_term;
float cdisc_single_jump;
} con_struct_t;
cl_mem cl_con_struct = NULL;
static void load_constants(int ndisp, float max_data_term, float data_weight,
float max_disc_term, float disc_single_jump)
{
con_struct_t *con_struct = new con_struct_t;
con_struct -> cndisp = ndisp;
con_struct -> cmax_data_term = max_data_term;
con_struct -> cdata_weight = data_weight;
con_struct -> cmax_disc_term = max_disc_term;
con_struct -> cdisc_single_jump = disc_single_jump;
cl_con_struct = load_constant(*((cl_context*)getoclContext()), *((cl_command_queue*)getoclCommandQueue()), (void *)con_struct,
sizeof(con_struct_t));
delete con_struct;
}
static void release_constants()
{
openCLFree(cl_con_struct);
}
static inline int divUp(int total, int grain)
{
return (total + grain - 1) / grain;
}
/////////////////////////////////////////////////////////////////////////////
///////////////////////////comp data////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////
static void comp_data_call(const oclMat &left, const oclMat &right, oclMat &data, int /*disp*/,
float /*cmax_data_term*/, float /*cdata_weight*/)
{
Context *clCxt = left.clCxt;
int channels = left.oclchannels();
int data_type = data.type();
string kernelName = "comp_data";
vector<pair<size_t , const void *> > args;
args.push_back( make_pair( sizeof(cl_mem) , (void *)&left.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&left.rows));
args.push_back( make_pair( sizeof(cl_int) , (void *)&left.cols));
args.push_back( make_pair( sizeof(cl_int) , (void *)&left.step));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&right.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&right.step));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&data.step));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&cl_con_struct));
size_t gt[3] = {left.cols, left.rows, 1}, lt[3] = {16, 16, 1};
const int OPT_SIZE = 50;
char cn_opt [OPT_SIZE] = "";
sprintf( cn_opt, "%s -D CN=%d",
(data_type == CV_16S ? "-D T_SHORT":"-D T_FLOAT"),
channels
);
openCLExecuteKernel(clCxt, &stereobp, kernelName, gt, lt, args, -1, -1, cn_opt);
}
///////////////////////////////////////////////////////////////////////////////////
/////////////////////////data set down////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////
static void data_step_down_call(int dst_cols, int dst_rows, int src_rows,
const oclMat &src, oclMat &dst, int disp)
{
Context *clCxt = src.clCxt;
int data_type = src.type();
string kernelName = "data_step_down";
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_rows));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_rows));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_cols));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&disp));
size_t gt[3] = {dst_cols, dst_rows, 1}, lt[3] = {16, 16, 1};
const char* t_opt = data_type == CV_16S ? "-D T_SHORT":"-D T_FLOAT";
openCLExecuteKernel(clCxt, &stereobp, kernelName, gt, lt, args, -1, -1, t_opt);
}
/////////////////////////////////////////////////////////////////////////////////
///////////////////////////live up message////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////
static void level_up_message_call(int dst_cols, int dst_rows, int src_rows,
oclMat &src, oclMat &dst, int ndisp)
{
Context *clCxt = src.clCxt;
int data_type = src.type();
string kernelName = "level_up_message";
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_rows));
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_rows));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst_cols));
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&ndisp));
size_t gt[3] = {dst_cols, dst_rows, 1}, lt[3] = {16, 16, 1};
const char* t_opt = data_type == CV_16S ? "-D T_SHORT":"-D T_FLOAT";
openCLExecuteKernel(clCxt, &stereobp, kernelName, gt, lt, args, -1, -1, t_opt);
}
static void level_up_messages_calls(int dst_idx, int dst_cols, int dst_rows, int src_rows,
oclMat *mus, oclMat *mds, oclMat *mls, oclMat *mrs,
int ndisp)
{
int src_idx = (dst_idx + 1) & 1;
level_up_message_call(dst_cols, dst_rows, src_rows,
mus[src_idx], mus[dst_idx], ndisp);
level_up_message_call(dst_cols, dst_rows, src_rows,
mds[src_idx], mds[dst_idx], ndisp);
level_up_message_call(dst_cols, dst_rows, src_rows,
mls[src_idx], mls[dst_idx], ndisp);
level_up_message_call(dst_cols, dst_rows, src_rows,
mrs[src_idx], mrs[dst_idx], ndisp);
}
//////////////////////////////////////////////////////////////////////////////////
//////////////////////////////cals_all_iterations_call///////////////////////////
/////////////////////////////////////////////////////////////////////////////////
static void calc_all_iterations_call(int cols, int rows, oclMat &u, oclMat &d,
oclMat &l, oclMat &r, oclMat &data,
int t, int cndisp, float cmax_disc_term,
float cdisc_single_jump)
{
Context *clCxt = l.clCxt;
int data_type = u.type();
string kernelName = "one_iteration";
vector<pair<size_t , const void *> > args;
args.push_back( make_pair( sizeof(cl_mem) , (void *)&u.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&u.step));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&data.step));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&d.data));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&l.data));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&r.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&t));
args.push_back( make_pair( sizeof(cl_int) , (void *)&cols));
args.push_back( make_pair( sizeof(cl_int) , (void *)&rows));
args.push_back( make_pair( sizeof(cl_int) , (void *)&cndisp));
args.push_back( make_pair( sizeof(cl_float) , (void *)&cmax_disc_term));
args.push_back( make_pair( sizeof(cl_float) , (void *)&cdisc_single_jump));
size_t gt[3] = {cols, rows, 1}, lt[3] = {16, 16, 1};
const char* t_opt = data_type == CV_16S ? "-D T_SHORT":"-D T_FLOAT";
openCLExecuteKernel(clCxt, &stereobp, kernelName, gt, lt, args, -1, -1, t_opt);
}
static void calc_all_iterations_calls(int cols, int rows, int iters, oclMat &u,
oclMat &d, oclMat &l, oclMat &r,
oclMat &data, int cndisp, float cmax_disc_term,
float cdisc_single_jump)
{
for(int t = 0; t < iters; ++t)
calc_all_iterations_call(cols, rows, u, d, l, r, data, t, cndisp,
cmax_disc_term, cdisc_single_jump);
}
///////////////////////////////////////////////////////////////////////////////
///////////////////////output///////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
static void output_call(const oclMat &u, const oclMat &d, const oclMat l, const oclMat &r,
const oclMat &data, oclMat &disp, int ndisp)
{
Context *clCxt = u.clCxt;
int data_type = u.type();
string kernelName = "output";
vector<pair<size_t , const void *> > args;
args.push_back( make_pair( sizeof(cl_mem) , (void *)&u.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&u.step));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&d.data));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&l.data));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&r.data));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&data.data));
args.push_back( make_pair( sizeof(cl_mem) , (void *)&disp.data));
args.push_back( make_pair( sizeof(cl_int) , (void *)&disp.rows));
args.push_back( make_pair( sizeof(cl_int) , (void *)&disp.cols));
args.push_back( make_pair( sizeof(cl_int) , (void *)&disp.step));
args.push_back( make_pair( sizeof(cl_int) , (void *)&ndisp));
size_t gt[3] = {disp.cols, disp.rows, 1}, lt[3] = {16, 16, 1};
const char* t_opt = data_type == CV_16S ? "-D T_SHORT":"-D T_FLOAT";
openCLExecuteKernel(clCxt, &stereobp, kernelName, gt, lt, args, -1, -1, t_opt);
}
}
}
}
namespace
{
const float DEFAULT_MAX_DATA_TERM = 10.0f;
const float DEFAULT_DATA_WEIGHT = 0.07f;
const float DEFAULT_MAX_DISC_TERM = 1.7f;
const float DEFAULT_DISC_SINGLE_JUMP = 1.0f;
}
void cv::ocl::StereoBeliefPropagation::estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels)
{
ndisp = width / 4;
if ((ndisp & 1) != 0)
ndisp++;
int mm = ::max(width, height);
iters = mm / 100 + 2;
levels = (int)(::log(static_cast<double>(mm)) + 1) * 4 / 5;
if (levels == 0) levels++;
}
cv::ocl::StereoBeliefPropagation::StereoBeliefPropagation(int ndisp_, int iters_, int levels_, int msg_type_)
: ndisp(ndisp_), iters(iters_), levels(levels_),
max_data_term(DEFAULT_MAX_DATA_TERM), data_weight(DEFAULT_DATA_WEIGHT),
max_disc_term(DEFAULT_MAX_DISC_TERM), disc_single_jump(DEFAULT_DISC_SINGLE_JUMP),
msg_type(msg_type_), datas(levels_)
{
}
cv::ocl::StereoBeliefPropagation::StereoBeliefPropagation(int ndisp_, int iters_, int levels_, float max_data_term_, float data_weight_, float max_disc_term_, float disc_single_jump_, int msg_type_)
: ndisp(ndisp_), iters(iters_), levels(levels_),
max_data_term(max_data_term_), data_weight(data_weight_),
max_disc_term(max_disc_term_), disc_single_jump(disc_single_jump_),
msg_type(msg_type_), datas(levels_)
{
}
namespace
{
class StereoBeliefPropagationImpl
{
public:
StereoBeliefPropagationImpl(StereoBeliefPropagation &rthis_,
oclMat &u_, oclMat &d_, oclMat &l_, oclMat &r_,
oclMat &u2_, oclMat &d2_, oclMat &l2_, oclMat &r2_,
vector<oclMat> &datas_, oclMat &out_)
: rthis(rthis_), u(u_), d(d_), l(l_), r(r_), u2(u2_), d2(d2_), l2(l2_), r2(r2_), datas(datas_), out(out_),
zero(Scalar::all(0)), scale(rthis_.msg_type == CV_32F ? 1.0f : 10.0f)
{
CV_Assert(0 < rthis.ndisp && 0 < rthis.iters && 0 < rthis.levels);
CV_Assert(rthis.msg_type == CV_32F || rthis.msg_type == CV_16S);
CV_Assert(rthis.msg_type == CV_32F || (1 << (rthis.levels - 1)) * scale * rthis.max_data_term < numeric_limits<short>::max());
}
void operator()(const oclMat &left, const oclMat &right, oclMat &disp)
{
CV_Assert(left.size() == right.size() && left.type() == right.type());
CV_Assert(left.type() == CV_8UC1 || left.type() == CV_8UC3 || left.type() == CV_8UC4);
rows = left.rows;
cols = left.cols;
int divisor = (int)pow(2.f, rthis.levels - 1.0f);
int lowest_cols = cols / divisor;
int lowest_rows = rows / divisor;
const int min_image_dim_size = 2;
CV_Assert(min(lowest_cols, lowest_rows) > min_image_dim_size);
init();
datas[0].create(rows * rthis.ndisp, cols, rthis.msg_type);
datas[0].setTo(Scalar_<short>::all(0));
cv::ocl::stereoBP::comp_data_call(left, right, datas[0], rthis.ndisp, rthis.max_data_term, scale * rthis.data_weight);
calcBP(disp);
}
void operator()(const oclMat &data, oclMat &disp)
{
CV_Assert((data.type() == rthis.msg_type) && (data.rows % rthis.ndisp == 0));
rows = data.rows / rthis.ndisp;
cols = data.cols;
int divisor = (int)pow(2.f, rthis.levels - 1.0f);
int lowest_cols = cols / divisor;
int lowest_rows = rows / divisor;
const int min_image_dim_size = 2;
CV_Assert(min(lowest_cols, lowest_rows) > min_image_dim_size);
init();
datas[0] = data;
calcBP(disp);
}
private:
void init()
{
u.create(rows * rthis.ndisp, cols, rthis.msg_type);
d.create(rows * rthis.ndisp, cols, rthis.msg_type);
l.create(rows * rthis.ndisp, cols, rthis.msg_type);
r.create(rows * rthis.ndisp, cols, rthis.msg_type);
if (rthis.levels & 1)
{
//can clear less area
u = zero;
d = zero;
l = zero;
r = zero;
}
if (rthis.levels > 1)
{
int less_rows = (rows + 1) / 2;
int less_cols = (cols + 1) / 2;
u2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
d2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
l2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
r2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
if ((rthis.levels & 1) == 0)
{
u2 = zero;
d2 = zero;
l2 = zero;
r2 = zero;
}
}
cv::ocl::stereoBP::load_constants(rthis.ndisp, rthis.max_data_term, scale * rthis.data_weight,
scale * rthis.max_disc_term, scale * rthis.disc_single_jump);
datas.resize(rthis.levels);
cols_all.resize(rthis.levels);
rows_all.resize(rthis.levels);
cols_all[0] = cols;
rows_all[0] = rows;
}
void calcBP(oclMat &disp)
{
using namespace cv::ocl::stereoBP;
for (int i = 1; i < rthis.levels; ++i)
{
cols_all[i] = (cols_all[i - 1] + 1) / 2;
rows_all[i] = (rows_all[i - 1] + 1) / 2;
datas[i].create(rows_all[i] * rthis.ndisp, cols_all[i], rthis.msg_type);
datas[i].setTo(Scalar_<short>::all(0));
data_step_down_call(cols_all[i], rows_all[i], rows_all[i - 1],
datas[i - 1], datas[i], rthis.ndisp);
}
oclMat mus[] = {u, u2};
oclMat mds[] = {d, d2};
oclMat mrs[] = {r, r2};
oclMat mls[] = {l, l2};
int mem_idx = (rthis.levels & 1) ? 0 : 1;
for (int i = rthis.levels - 1; i >= 0; --i)
{
// for lower level we have already computed messages by setting to zero
if (i != rthis.levels - 1)
level_up_messages_calls(mem_idx, cols_all[i], rows_all[i], rows_all[i + 1],
mus, mds, mls, mrs, rthis.ndisp);
calc_all_iterations_calls(cols_all[i], rows_all[i], rthis.iters, mus[mem_idx],
mds[mem_idx], mls[mem_idx], mrs[mem_idx], datas[i],
rthis.ndisp, scale * rthis.max_disc_term,
scale * rthis.disc_single_jump);
mem_idx = (mem_idx + 1) & 1;
}
if (disp.empty())
disp.create(rows, cols, CV_16S);
out = ((disp.type() == CV_16S) ? disp : (out.create(rows, cols, CV_16S), out));
out = zero;
output_call(u, d, l, r, datas.front(), out, rthis.ndisp);
if (disp.type() != CV_16S)
out.convertTo(disp, disp.type());
release_constants();
}
StereoBeliefPropagationImpl& operator=(const StereoBeliefPropagationImpl&);
StereoBeliefPropagation &rthis;
oclMat &u;
oclMat &d;
oclMat &l;
oclMat &r;
oclMat &u2;
oclMat &d2;
oclMat &l2;
oclMat &r2;
vector<oclMat> &datas;
oclMat &out;
const Scalar zero;
const float scale;
int rows, cols;
vector<int> cols_all, rows_all;
};
}
void cv::ocl::StereoBeliefPropagation::operator()(const oclMat &left, const oclMat &right, oclMat &disp)
{
::StereoBeliefPropagationImpl impl(*this, u, d, l, r, u2, d2, l2, r2, datas, out);
impl(left, right, disp);
}
void cv::ocl::StereoBeliefPropagation::operator()(const oclMat &data, oclMat &disp)
{
::StereoBeliefPropagationImpl impl(*this, u, d, l, r, u2, d2, l2, r2, datas, out);
impl(data, disp);
}
modules/ocl/test/test_calib3d.cpp
View file @ 0e53c56c
......@@ -15,7 +15,7 @@
// Third party copyrights are property of their respective owners.
//
// @Authors
// 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:
......@@ -63,12 +63,12 @@ PARAM_TEST_CASE(StereoMatchBM, int, int)
}
};
TEST_P(StereoMatchBM, Accuracy)
TEST_P(StereoMatchBM, Regression)
{
Mat left_image = readImage(workdir + "../ocl/aloe-L.png", IMREAD_GRAYSCALE);
Mat right_image = readImage(workdir + "../ocl/aloe-R.png", IMREAD_GRAYSCALE);
Mat disp_gold = readImage(workdir + "../ocl/aloe-disp.png", IMREAD_GRAYSCALE);
Mat left_image = readImage("stereobm/aloe-L.png", IMREAD_GRAYSCALE);
Mat right_image = readImage("stereobm/aloe-R.png", IMREAD_GRAYSCALE);
Mat disp_gold = readImage("stereobm/aloe-disp.png", IMREAD_GRAYSCALE);
ocl::oclMat d_left, d_right;
ocl::oclMat d_disp(left_image.size(), CV_8U);
Mat disp;
......@@ -88,7 +88,50 @@ TEST_P(StereoMatchBM, Accuracy)
EXPECT_MAT_SIMILAR(disp_gold, disp, 1e-3);
}
INSTANTIATE_TEST_CASE_P(GPU_Calib3D, StereoMatchBM, testing::Combine(testing::Values(128),
INSTANTIATE_TEST_CASE_P(OCL_Calib3D, StereoMatchBM, testing::Combine(testing::Values(128),
testing::Values(19)));
PARAM_TEST_CASE(StereoMatchBP, int, int, int, float, float, float, float)
{
int ndisp_;
int iters_;
int levels_;
float max_data_term_;
float data_weight_;
float max_disc_term_;
float disc_single_jump_;
virtual void SetUp()
{
ndisp_ = GET_PARAM(0);
iters_ = GET_PARAM(1);
levels_ = GET_PARAM(2);
max_data_term_ = GET_PARAM(3);
data_weight_ = GET_PARAM(4);
max_disc_term_ = GET_PARAM(5);
disc_single_jump_ = GET_PARAM(6);
}
};
TEST_P(StereoMatchBP, Regression)
{
Mat left_image = readImage("stereobp/aloe-L.png");
Mat right_image = readImage("stereobp/aloe-R.png");
Mat disp_gold = readImage("stereobp/aloe-disp.png", IMREAD_GRAYSCALE);
ocl::oclMat d_left, d_right;
ocl::oclMat d_disp;
Mat disp;
ASSERT_FALSE(left_image.empty());
ASSERT_FALSE(right_image.empty());
ASSERT_FALSE(disp_gold.empty());
d_left.upload(left_image);
d_right.upload(right_image);
ocl::StereoBeliefPropagation bp(ndisp_, iters_, levels_, max_data_term_, data_weight_,
max_disc_term_, disc_single_jump_, CV_16S);
bp(d_left, d_right, d_disp);
d_disp.download(disp);
disp.convertTo(disp, disp_gold.depth());
EXPECT_MAT_NEAR(disp_gold, disp, 0.0, "");
}
INSTANTIATE_TEST_CASE_P(OCL_Calib3D, StereoMatchBP, testing::Combine(testing::Values(64),
testing::Values(8),testing::Values(2),testing::Values(25.0f),
testing::Values(0.1f),testing::Values(15.0f),testing::Values(1.0f)));
#endif // HAVE_OPENCL
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