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opencv/modules/ocl/src/stereobp.cpp

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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, 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;
Context* clCtx = Context::getContext();
cl_context clContext = *(cl_context*)(clCtx->getOpenCLContextPtr());
cl_command_queue clCmdQueue = *(cl_command_queue*)(clCtx->getOpenCLCommandQueuePtr());
cl_con_struct = load_constant(clContext, clCmdQueue, (void *)con_struct,
sizeof(con_struct_t));
delete con_struct;
}
static void release_constants()
{
openCLFree(cl_con_struct);
}
/////////////////////////////////////////////////////////////////////////////
///////////////////////////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_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};
char opt[80] = "";
sprintf(opt, "-D %s -D CNDISP=%d", data_type == CV_16S ? "T_SHORT":"T_FLOAT", cndisp);
openCLExecuteKernel(clCxt, &stereobp, kernelName, gt, lt, args, -1, -1, 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);
}