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Commit 966b2f55 authored by Li Peng's avatar Li Peng
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Overhaul bioinspired opencl implementation 

This patch update bioinspired opencl code to make it working
with latest opencv, major changes include:

1. data structure update, e.g. oclMat to UMat.
2. opencl kernel optimization to get it running faster
3. accuracy and perf test update.
Signed-off-by: 's avatarLi Peng <peng.li@intel.com>
parent 86342522
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modules/bioinspired/include/opencv2/bioinspired/retina.hpp
View file @ 966b2f55
...@@ -447,11 +447,6 @@ the log scale that is applied ...@@ -447,11 +447,6 @@ the log scale that is applied
*/ */
CV_EXPORTS_W Ptr<Retina> createRetina(Size inputSize, const bool colorMode, int colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const float reductionFactor=1.0f, const float samplingStrenght=10.0f); CV_EXPORTS_W Ptr<Retina> createRetina(Size inputSize, const bool colorMode, int colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const float reductionFactor=1.0f, const float samplingStrenght=10.0f);
#ifdef HAVE_OPENCV_OCL
Ptr<Retina> createRetina_OCL(Size inputSize);
Ptr<Retina> createRetina_OCL(Size inputSize, const bool colorMode, int colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const float reductionFactor=1.0f, const float samplingStrenght=10.0f);
#endif
//! @} //! @}
//! @} //! @}
......
modules/bioinspired/perf/opencl/perf_retina.cpp deleted 100644 → 0
View file @ 86342522
/*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
// Fangfang Bai, fangfang@multicorewareinc.com
// 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 materials 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 "perf_precomp.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/core/ocl.hpp"
#ifdef HAVE_OPENCV_OCL
#include "opencv2/ocl.hpp"
using namespace std::tr1;
using namespace cv;
using namespace perf;
namespace cvtest {
namespace ocl {
///////////////////////// Retina ////////////////////////
typedef tuple<bool, int, double, double> RetinaParams;
typedef TestBaseWithParam<RetinaParams> RetinaFixture;
#define OCL_TEST_CYCLE() for(; startTimer(), next(); cv::ocl::finish(), stopTimer())
PERF_TEST_P(RetinaFixture, Retina,
::testing::Combine(testing::Bool(), testing::Values((int)cv::bioinspired::RETINA_COLOR_BAYER),
testing::Values(1.0, 0.5), testing::Values(10.0, 5.0)))
{
if (!cv::ocl::haveOpenCL())
throw TestBase::PerfSkipTestException();
RetinaParams params = GetParam();
bool colorMode = get<0>(params), useLogSampling = false;
int colorSamplingMethod = get<1>(params);
double reductionFactor = get<2>(params), samplingStrength = get<3>(params);
Mat input = cv::imread(cvtest::TS::ptr()->get_data_path() + "shared/lena.png", colorMode);
ASSERT_FALSE(input.empty());
Mat gold_parvo, gold_magno;
if (getSelectedImpl() == "plain")
{
Ptr<bioinspired::Retina> gold_retina = bioinspired::createRetina(
input.size(), colorMode, colorSamplingMethod,
useLogSampling, reductionFactor, samplingStrength);
TEST_CYCLE()
{
gold_retina->run(input);
gold_retina->getParvo(gold_parvo);
gold_retina->getMagno(gold_magno);
}
}
else if (getSelectedImpl() == "ocl")
{
cv::ocl::oclMat ocl_input(input), ocl_parvo, ocl_magno;
Ptr<cv::bioinspired::Retina> ocl_retina = cv::bioinspired::createRetina_OCL(
input.size(), colorMode, colorSamplingMethod, useLogSampling,
reductionFactor, samplingStrength);
OCL_TEST_CYCLE()
{
ocl_retina->run(ocl_input);
ocl_retina->getParvo(ocl_parvo);
ocl_retina->getMagno(ocl_magno);
}
}
else
CV_TEST_FAIL_NO_IMPL();
SANITY_CHECK_NOTHING();
}
} } // namespace cvtest::ocl
#endif // HAVE_OPENCV_OCL
modules/bioinspired/perf/opencl/perf_retina.ocl.cpp 0 → 100644
View file @ 966b2f55
#include "../perf_precomp.hpp"
#include "opencv2/ts/ocl_perf.hpp"
using namespace std::tr1;
using namespace cv;
using namespace perf;
namespace cvtest {
namespace ocl {
///////////////////////// Retina ////////////////////////
typedef tuple<bool, int, double, double> RetinaParams;
typedef TestBaseWithParam<RetinaParams> RetinaFixture;
OCL_PERF_TEST_P(RetinaFixture, Retina,
::testing::Combine(testing::Bool(), testing::Values((int)cv::bioinspired::RETINA_COLOR_BAYER),
testing::Values(1.0, 0.5), testing::Values(10.0, 5.0)))
{
RetinaParams params = GetParam();
bool colorMode = get<0>(params), useLogSampling = false;
int colorSamplingMethod = get<1>(params);
float reductionFactor = static_cast<float>(get<2>(params));
float samplingStrength = static_cast<float>(get<3>(params));
Mat input = imread(getDataPath("cv/shared/lena.png"), colorMode);
ASSERT_FALSE(input.empty());
UMat ocl_parvo, ocl_magno;
{
Ptr<cv::bioinspired::Retina> retina = cv::bioinspired::createRetina(
input.size(), colorMode, colorSamplingMethod, useLogSampling,
reductionFactor, samplingStrength);
OCL_TEST_CYCLE()
{
retina->run(input);
retina->getParvo(ocl_parvo);
retina->getMagno(ocl_magno);
}
}
SANITY_CHECK_NOTHING();
}
} } // namespace cvtest::ocl
modules/bioinspired/perf/perf_main.cpp
View file @ 966b2f55
...@@ -42,12 +42,4 @@ ...@@ -42,12 +42,4 @@
#include "perf_precomp.hpp" #include "perf_precomp.hpp"
static const char * impls[] = CV_PERF_TEST_MAIN(bioinspired)
{
#ifdef HAVE_OPENCV_OCL
"ocl",
#endif
"plain"
};
CV_PERF_TEST_MAIN_WITH_IMPLS(ocl, impls, ::perf::TestBase::setModulePerformanceStrategy(::perf::PERF_STRATEGY_SIMPLE))
modules/bioinspired/src/opencl/retina_kernel.cl
View file @ 966b2f55
...@@ -75,72 +75,35 @@ kernel void horizontalCausalFilter_addInput( ...@@ -75,72 +75,35 @@ kernel void horizontalCausalFilter_addInput(
output + mad24(gid, elements_per_row, out_offset / 4); output + mad24(gid, elements_per_row, out_offset / 4);
float res; float res;
float4 in_v4, out_v4, res_v4 = (float4)(0); float4 in_v4, out_v4, sum_v4, res_v4 = (float4)(0);
//vectorize to increase throughput //vectorize to increase throughput
for(int i = 0; i < cols / 4; ++i, iptr += 4, optr += 4) for(int i = 0; i < cols / 4; ++i, iptr += 4, optr += 4)
{ {
in_v4 = vload4(0, iptr); in_v4 = vload4(0, iptr);
out_v4 = vload4(0, optr); out_v4 = vload4(0, optr) * _tau;
sum_v4 = in_v4 + out_v4;
res_v4.x = in_v4.x + _tau * out_v4.x + _a * res_v4.w; res_v4.x = sum_v4.x + _a * res_v4.w;
res_v4.y = in_v4.y + _tau * out_v4.y + _a * res_v4.x; res_v4.y = sum_v4.y + _a * res_v4.x;
res_v4.z = in_v4.z + _tau * out_v4.z + _a * res_v4.y; res_v4.z = sum_v4.z + _a * res_v4.y;
res_v4.w = in_v4.w + _tau * out_v4.w + _a * res_v4.z; res_v4.w = sum_v4.w + _a * res_v4.z;
vstore4(res_v4, 0, optr); vstore4(res_v4, 0, optr);
} }
res = res_v4.w;
// there may be left some
for(int i = 0; i < cols % 4; ++i, ++iptr, ++optr)
{
res = *iptr + _tau * *optr + _a * res;
*optr = res;
}
}
//_horizontalAnticausalFilter optr = output + mad24(gid + 1, elements_per_row, -4 + out_offset / 4);
kernel void horizontalAnticausalFilter( res_v4 = (float4)(0);
global float * output, for(int i = 0; i < elements_per_row / 4; ++i, optr -= 4)
const int cols,
const int rows,
const int elements_per_row,
const int out_offset,
const float _a
)
{
int gid = get_global_id(0);
if(gid >= rows)
{
return;
}
global float * optr = output +
mad24(gid + 1, elements_per_row, - 1 + out_offset / 4);
float4 result_v4 = (float4)(0), out_v4;
float result = 0;
// we assume elements_per_row is multple of WIDTH_MULTIPLE
for(int i = 0; i < WIDTH_MULTIPLE; ++ i, -- optr)
{
if(i >= elements_per_row - cols)
{
result = *optr + _a * result;
}
*optr = result;
}
result_v4.x = result;
optr -= 3;
for(int i = WIDTH_MULTIPLE / 4; i < elements_per_row / 4; ++i, optr -= 4)
{ {
// shift left, `offset` is type `size_t` so it cannot be negative // shift left, `offset` is type `size_t` so it cannot be negative
out_v4 = vload4(0, optr); out_v4 = vload4(0, optr);
result_v4.w = out_v4.w + _a * result_v4.x; res_v4.w = out_v4.w + _a * res_v4.x;
result_v4.z = out_v4.z + _a * result_v4.w; res_v4.z = out_v4.z + _a * res_v4.w;
result_v4.y = out_v4.y + _a * result_v4.z; res_v4.y = out_v4.y + _a * res_v4.z;
result_v4.x = out_v4.x + _a * result_v4.y; res_v4.x = out_v4.x + _a * res_v4.y;
vstore4(result_v4, 0, optr); vstore4(res_v4, 0, optr);
} }
} }
...@@ -151,26 +114,37 @@ kernel void verticalCausalFilter( ...@@ -151,26 +114,37 @@ kernel void verticalCausalFilter(
const int rows, const int rows,
const int elements_per_row, const int elements_per_row,
const int out_offset, const int out_offset,
const float _a const float _a,
const float _gain
) )
{ {
int gid = get_global_id(0); int gid = get_global_id(0) * 2;
if(gid >= cols) if(gid >= cols)
{ {
return; return;
} }
global float * optr = output + gid + out_offset / 4; global float * optr = output + gid + out_offset / 4;
float result = 0; float2 input;
float2 result = (float2)0;
for(int i = 0; i < rows; ++i, optr += elements_per_row) for(int i = 0; i < rows; ++i, optr += elements_per_row)
{ {
result = *optr + _a * result; input = vload2(0, optr);
*optr = result; result = input + _a * result;
vstore2(result, 0, optr);
}
optr = output + (rows - 1) * elements_per_row + gid + out_offset / 4;
result = (float2)0;
for(int i = 0; i < rows; ++i, optr -= elements_per_row)
{
input = vload2(0, optr);
result = input + _a * result;
vstore2(_gain * result, 0, optr);
} }
} }
//_verticalCausalFilter kernel void verticalCausalFilter_multichannel(
kernel void verticalAnticausalFilter_multGain(
global float * output, global float * output,
const int cols, const int cols,
const int rows, const int rows,
...@@ -180,74 +154,69 @@ kernel void verticalAnticausalFilter_multGain( ...@@ -180,74 +154,69 @@ kernel void verticalAnticausalFilter_multGain(
const float _gain const float _gain
) )
{ {
int gid = get_global_id(0); int gid = get_global_id(0) * 2;
if(gid >= cols) if(gid >= cols)
{ {
return; return;
} }
global float * optr = output + (rows - 1) * elements_per_row + gid + out_offset / 4; global float * optr[3];
float result = 0; float2 input[3];
for(int i = 0; i < rows; ++i, optr -= elements_per_row) float2 result[3] = { (float2)0, (float2)0, (float2)0 };
{
result = *optr + _a * result;
*optr = _gain * result;
}
}
//
// end of _spatiotemporalLPfilter
/////////////////////////////////////////////////////////////////////
//////////////// horizontalAnticausalFilter_Irregular //////////////// optr[0] = output + gid + out_offset / 4;
kernel void horizontalAnticausalFilter_Irregular( optr[1] = output + gid + out_offset / 4 + rows * elements_per_row;
global float * output, optr[2] = output + gid + out_offset / 4 + 2 * rows * elements_per_row;
global float * buffer,
const int cols,
const int rows,
const int elements_per_row,
const int out_offset,
const int buffer_offset
)
{
int gid = get_global_id(0);
if(gid >= rows)
{
return;
}
global float * optr = for(int i = 0; i < rows; ++i)
output + mad24(rows - gid, elements_per_row, -1 + out_offset / 4);
global float * bptr =
buffer + mad24(rows - gid, elements_per_row, -1 + buffer_offset / 4);
float4 buf_v4, out_v4, res_v4 = (float4)(0);
float result = 0;
// we assume elements_per_row is multple of WIDTH_MULTIPLE
for(int i = 0; i < WIDTH_MULTIPLE; ++ i, -- optr, -- bptr)
{ {
if(i >= elements_per_row - cols) input[0] = vload2(0, optr[0]);
{ input[1] = vload2(0, optr[1]);
result = *optr + *bptr * result; input[2] = vload2(0, optr[2]);
}
*optr = result; result[0] = input[0] + _a * result[0];
result[1] = input[1] + _a * result[1];
result[2] = input[2] + _a * result[2];
vstore2(result[0], 0, optr[0]);
vstore2(result[1], 0, optr[1]);
vstore2(result[2], 0, optr[2]);
optr[0] += elements_per_row;
optr[1] += elements_per_row;
optr[2] += elements_per_row;
} }
res_v4.x = result;
optr -= 3; optr[0] = output + (rows - 1) * elements_per_row + gid + out_offset / 4;
bptr -= 3; optr[1] = output + (rows - 1) * elements_per_row + gid + out_offset / 4 + rows * elements_per_row;
for(int i = WIDTH_MULTIPLE / 4; i < elements_per_row / 4; ++i, optr -= 4, bptr -= 4) optr[2] = output + (rows - 1) * elements_per_row + gid + out_offset / 4 + 2 * rows * elements_per_row;
result[0] = result[1] = result[2] = (float2)0;
for(int i = 0; i < rows; ++i)
{ {
buf_v4 = vload4(0, bptr); input[0] = vload2(0, optr[0]);
out_v4 = vload4(0, optr); input[1] = vload2(0, optr[1]);
input[2] = vload2(0, optr[2]);
res_v4.w = out_v4.w + buf_v4.w * res_v4.x; result[0] = input[0] + _a * result[0];
res_v4.z = out_v4.z + buf_v4.z * res_v4.w; result[1] = input[1] + _a * result[1];
res_v4.y = out_v4.y + buf_v4.y * res_v4.z; result[2] = input[2] + _a * result[2];
res_v4.x = out_v4.x + buf_v4.x * res_v4.y;
vstore4(res_v4, 0, optr); vstore2(_gain * result[0], 0, optr[0]);
vstore2(_gain * result[1], 0, optr[1]);
vstore2(_gain * result[2], 0, optr[2]);
optr[0] -= elements_per_row;
optr[1] -= elements_per_row;
optr[2] -= elements_per_row;
} }
} }
//
// end of _spatiotemporalLPfilter
/////////////////////////////////////////////////////////////////////
//////////////// verticalCausalFilter_Irregular ////////////////
//////////////// verticalCausalFilter_Irregular //////////////// //////////////// verticalCausalFilter_Irregular ////////////////
kernel void verticalCausalFilter_Irregular( kernel void verticalCausalFilter_Irregular(
global float * output, global float * output,
...@@ -256,22 +225,61 @@ kernel void verticalCausalFilter_Irregular( ...@@ -256,22 +225,61 @@ kernel void verticalCausalFilter_Irregular(
const int rows, const int rows,
const int elements_per_row, const int elements_per_row,
const int out_offset, const int out_offset,
const int buffer_offset const int buffer_offset,
const float gain
) )
{ {
int gid = get_global_id(0); int gid = get_global_id(0) * 2;
if(gid >= cols) if(gid >= cols)
{ {
return; return;
} }
global float * optr = output + gid + out_offset / 4; global float * optr[3];
global float * bptr = buffer + gid + buffer_offset / 4; global float * bptr = buffer + gid + buffer_offset / 4;
float result = 0; float2 result[3] = { (float2)0, (float2)0, (float2)0 };
for(int i = 0; i < rows; ++i, optr += elements_per_row, bptr += elements_per_row) float2 grad, input[3];
{ optr[0] = output + gid + out_offset / 4;
result = *optr + *bptr * result; optr[1] = output + gid + out_offset / 4 + rows * elements_per_row;
*optr = result; optr[2] = output + gid + out_offset / 4 + 2 * rows * elements_per_row;
for(int i = 0; i < rows; ++i, bptr += elements_per_row)
{
input[0] = vload2(0, optr[0]);
input[1] = vload2(0, optr[1]);
input[2] = vload2(0, optr[2]);
grad = vload2(0, bptr);
result[0] = input[0] + grad * result[0];
result[1] = input[1] + grad * result[1];
result[2] = input[2] + grad * result[2];
vstore2(result[0], 0, optr[0]);
vstore2(result[1], 0, optr[1]);
vstore2(result[2], 0, optr[2]);
optr[0] += elements_per_row;
optr[1] += elements_per_row;
optr[2] += elements_per_row;
}
int start_idx = mad24(rows - 1, elements_per_row, gid);
optr[0] = output + start_idx + out_offset / 4;
optr[1] = output + start_idx + out_offset / 4 + rows * elements_per_row;
optr[2] = output + start_idx + out_offset / 4 + 2 * rows * elements_per_row;
bptr = buffer + start_idx + buffer_offset / 4;
result[0] = result[1] = result[2] = (float2)0;
for(int i = 0; i < rows; ++i, bptr -= elements_per_row)
{
input[0] = vload2(0, optr[0]);
input[1] = vload2(0, optr[1]);
input[2] = vload2(0, optr[2]);
grad = vload2(0, bptr);
result[0] = input[0] + grad * result[0];
result[1] = input[1] + grad * result[1];
result[2] = input[2] + grad * result[2];
vstore2(gain * result[0], 0, optr[0]);
vstore2(gain * result[1], 0, optr[1]);
vstore2(gain * result[2], 0, optr[2]);
optr[0] -= elements_per_row;
optr[1] -= elements_per_row;
optr[2] -= elements_per_row;
} }
} }
...@@ -314,41 +322,22 @@ kernel void adaptiveHorizontalCausalFilter_addInput( ...@@ -314,41 +322,22 @@ kernel void adaptiveHorizontalCausalFilter_addInput(
vstore4(res_v4, 0, optr); vstore4(res_v4, 0, optr);
} }
for(int i = 0; i < cols % 4; ++i, ++iptr, ++gptr, ++optr)
{
res_v4.w = *iptr + *gptr * res_v4.w;
*optr = res_v4.w;
}
}
//////////////// _adaptiveVerticalAnticausalFilter_multGain //////////////// optr = output + mad24(gid + 1, elements_per_row, -4 + out_offset / 4);
kernel void adaptiveVerticalAnticausalFilter_multGain( gptr = gradient + mad24(gid + 1, elements_per_row, -4 + grad_offset / 4);
global const float * gradient, res_v4 = (float4)(0);
global float * output,
const int cols,
const int rows,
const int elements_per_row,
const int grad_offset,
const int out_offset,
const float gain
)
{
int gid = get_global_id(0);
if(gid >= cols)
{
return;
}
int start_idx = mad24(rows - 1, elements_per_row, gid); for(int i = 0; i < cols / 4; ++i, gptr -= 4, optr -= 4)
{
grad_v4 = vload4(0, gptr);
out_v4 = vload4(0, optr);
global const float * gptr = gradient + start_idx + grad_offset / 4; res_v4.w = out_v4.w + grad_v4.w * res_v4.x;
global float * optr = output + start_idx + out_offset / 4; res_v4.z = out_v4.z + grad_v4.z * res_v4.w;
res_v4.y = out_v4.y + grad_v4.y * res_v4.z;
res_v4.x = out_v4.x + grad_v4.x * res_v4.y;
float result = 0; vstore4(res_v4, 0, optr);
for(int i = 0; i < rows; ++i, gptr -= elements_per_row, optr -= elements_per_row)
{
result = *optr + *gptr * result;
*optr = gain * result;
} }
} }
...@@ -367,17 +356,18 @@ kernel void localLuminanceAdaptation( ...@@ -367,17 +356,18 @@ kernel void localLuminanceAdaptation(
const float _maxInputValue const float _maxInputValue
) )
{ {
int gidx = get_global_id(0), gidy = get_global_id(1); int gidx = get_global_id(0) * 4, gidy = get_global_id(1);
if(gidx >= cols || gidy >= rows) if(gidx >= cols || gidy >= rows)
{ {
return; return;
} }
int offset = mad24(gidy, elements_per_row, gidx); int offset = mad24(gidy, elements_per_row, gidx);
float4 luma_vec = vload4(0, luma + offset);
float X0 = luma[offset] * _localLuminanceFactor + _localLuminanceAddon; float4 X0 = luma_vec * _localLuminanceFactor + _localLuminanceAddon;
float input_val = input[offset]; float4 input_val = vload4(0, input + offset);
// output of the following line may be different between GPU and CPU // output of the following line may be different between GPU and CPU
output[offset] = (_maxInputValue + X0) * input_val / (input_val + X0 + 0.00000000001f); float4 out_vec = (_maxInputValue + X0) * input_val / (input_val + X0 + 0.00000000001f);
vstore4(out_vec, 0, output + offset);
} }
// end of basicretinafilter // end of basicretinafilter
//------------------------------------------------------ //------------------------------------------------------
...@@ -403,7 +393,7 @@ kernel void amacrineCellsComputing( ...@@ -403,7 +393,7 @@ kernel void amacrineCellsComputing(
const float coeff const float coeff
) )
{ {
int gidx = get_global_id(0), gidy = get_global_id(1); int gidx = get_global_id(0) * 4, gidy = get_global_id(1);
if(gidx >= cols || gidy >= rows) if(gidx >= cols || gidy >= rows)
{ {
return; return;
...@@ -417,13 +407,16 @@ kernel void amacrineCellsComputing( ...@@ -417,13 +407,16 @@ kernel void amacrineCellsComputing(
out_on += offset; out_on += offset;
out_off += offset; out_off += offset;
float magnoXonPixelResult = coeff * (*out_on + *opl_on - *prev_in_on); float4 val_opl_on = vload4(0, opl_on);
*out_on = fmax(magnoXonPixelResult, 0); float4 val_opl_off = vload4(0, opl_off);
float magnoXoffPixelResult = coeff * (*out_off + *opl_off - *prev_in_off);
*out_off = fmax(magnoXoffPixelResult, 0);
*prev_in_on = *opl_on; float4 magnoXonPixelResult = coeff * (vload4(0, out_on) + val_opl_on - vload4(0, prev_in_on));
*prev_in_off = *opl_off; vstore4(fmax(magnoXonPixelResult, 0), 0, out_on);
float4 magnoXoffPixelResult = coeff * (vload4(0, out_off) + val_opl_off - vload4(0, prev_in_off));
vstore4(fmax(magnoXoffPixelResult, 0), 0, out_off);
vstore4(val_opl_on, 0, prev_in_on);
vstore4(val_opl_off, 0, prev_in_off);
} }
///////////////////////////////////////////////////////// /////////////////////////////////////////////////////////
...@@ -457,11 +450,7 @@ kernel void OPL_OnOffWaysComputing( ...@@ -457,11 +450,7 @@ kernel void OPL_OnOffWaysComputing(
parvo_off += offset; parvo_off += offset;
float4 diff = *photo_out - *horiz_out; float4 diff = *photo_out - *horiz_out;
float4 isPositive;// = convert_float4(diff > (float4)(0.0f, 0.0f, 0.0f, 0.0f)); float4 isPositive = convert_float4(abs(diff > (float4)0.0f));
isPositive.x = diff.x > 0.0f;
isPositive.y = diff.y > 0.0f;
isPositive.z = diff.z > 0.0f;
isPositive.w = diff.w > 0.0f;
float4 res_on = isPositive * diff; float4 res_on = isPositive * diff;
float4 res_off = (isPositive - (float4)(1.0f)) * diff; float4 res_off = (isPositive - (float4)(1.0f)) * diff;
...@@ -491,14 +480,19 @@ kernel void runColorMultiplexingBayer( ...@@ -491,14 +480,19 @@ kernel void runColorMultiplexingBayer(
const int elements_per_row const int elements_per_row
) )
{ {
int gidx = get_global_id(0), gidy = get_global_id(1); int gidx = get_global_id(0) * 4, gidy = get_global_id(1);
if(gidx >= cols || gidy >= rows) if(gidx >= cols || gidy >= rows)
{ {
return; return;
} }
int offset = mad24(gidy, elements_per_row, gidx); int offset = mad24(gidy, elements_per_row, gidx);
output[offset] = input[bayerSampleOffset(elements_per_row, rows, gidx, gidy)]; float4 val;
val.x = input[bayerSampleOffset(elements_per_row, rows, gidx + 0, gidy)];
val.y = input[bayerSampleOffset(elements_per_row, rows, gidx + 1, gidy)];
val.z = input[bayerSampleOffset(elements_per_row, rows, gidx + 2, gidy)];
val.w = input[bayerSampleOffset(elements_per_row, rows, gidx + 3, gidy)];
vstore4(val, 0, output + offset);
} }
kernel void runColorDemultiplexingBayer( kernel void runColorDemultiplexingBayer(
...@@ -509,14 +503,18 @@ kernel void runColorDemultiplexingBayer( ...@@ -509,14 +503,18 @@ kernel void runColorDemultiplexingBayer(
const int elements_per_row const int elements_per_row
) )
{ {
int gidx = get_global_id(0), gidy = get_global_id(1); int gidx = get_global_id(0) * 4, gidy = get_global_id(1);
if(gidx >= cols || gidy >= rows) if(gidx >= cols || gidy >= rows)
{ {
return; return;
} }
int offset = mad24(gidy, elements_per_row, gidx); int offset = mad24(gidy, elements_per_row, gidx);
output[bayerSampleOffset(elements_per_row, rows, gidx, gidy)] = input[offset]; float4 val = vload4(0, input + offset);
output[bayerSampleOffset(elements_per_row, rows, gidx + 0, gidy)] = val.x;
output[bayerSampleOffset(elements_per_row, rows, gidx + 1, gidy)] = val.y;
output[bayerSampleOffset(elements_per_row, rows, gidx + 2, gidy)] = val.z;
output[bayerSampleOffset(elements_per_row, rows, gidx + 3, gidy)] = val.w;
} }
kernel void demultiplexAssign( kernel void demultiplexAssign(
...@@ -550,16 +548,16 @@ kernel void normalizeGrayOutputCentredSigmoide( ...@@ -550,16 +548,16 @@ kernel void normalizeGrayOutputCentredSigmoide(
) )
{ {
int gidx = get_global_id(0), gidy = get_global_id(1); int gidx = get_global_id(0) * 4, gidy = get_global_id(1);
if(gidx >= cols || gidy >= rows) if(gidx >= cols || gidy >= rows)
{ {
return; return;
} }
int offset = mad24(gidy, elements_per_row, gidx); int offset = mad24(gidy, elements_per_row, gidx);
float input_val = input[offset]; float4 input_val = vload4(0, input + offset);
output[offset] = meanval + input_val = meanval + (meanval + X0) * (input_val - meanval) / (fabs(input_val - meanval) + X0);
(meanval + X0) * (input_val - meanval) / (fabs(input_val - meanval) + X0); vstore4(input_val, 0, output + offset);
} }
//// normalize by photoreceptors density //// normalize by photoreceptors density
...@@ -575,7 +573,7 @@ kernel void normalizePhotoDensity( ...@@ -575,7 +573,7 @@ kernel void normalizePhotoDensity(
const float pG const float pG
) )
{ {
const int gidx = get_global_id(0), gidy = get_global_id(1); const int gidx = get_global_id(0) * 4, gidy = get_global_id(1);
if(gidx >= cols || gidy >= rows) if(gidx >= cols || gidy >= rows)
{ {
return; return;
...@@ -583,16 +581,19 @@ kernel void normalizePhotoDensity( ...@@ -583,16 +581,19 @@ kernel void normalizePhotoDensity(
const int offset = mad24(gidy, elements_per_row, gidx); const int offset = mad24(gidy, elements_per_row, gidx);
int index = offset; int index = offset;
float Cr = chroma[index] * colorDensity[index]; float4 Cr = vload4(0, chroma + index) * vload4(0, colorDensity + index);
index += elements_per_row * rows; index += elements_per_row * rows;
float Cg = chroma[index] * colorDensity[index]; float4 Cg = vload4(0, chroma + index) * vload4(0, colorDensity + index);
index += elements_per_row * rows; index += elements_per_row * rows;
float Cb = chroma[index] * colorDensity[index]; float4 Cb = vload4(0, chroma + index) * vload4(0, colorDensity + index);
const float luma_res = (Cr + Cg + Cb) * pG; const float4 luma_res = (Cr + Cg + Cb) * pG;
luma[offset] = luma_res; vstore4(luma_res, 0, luma + offset);
demultiplex[bayerSampleOffset(elements_per_row, rows, gidx, gidy)] = float4 res_v4 = vload4(0, multiplex + offset) - luma_res;
multiplex[offset] - luma_res; demultiplex[bayerSampleOffset(elements_per_row, rows, gidx + 0, gidy)] = res_v4.x;
demultiplex[bayerSampleOffset(elements_per_row, rows, gidx + 1, gidy)] = res_v4.y;
demultiplex[bayerSampleOffset(elements_per_row, rows, gidx + 2, gidy)] = res_v4.z;
demultiplex[bayerSampleOffset(elements_per_row, rows, gidx + 3, gidy)] = res_v4.w;
} }
...@@ -629,7 +630,8 @@ kernel void computeGradient( ...@@ -629,7 +630,8 @@ kernel void computeGradient(
const float horiz_grad = 0.5f * h_grad + 0.25f * (h_grad_p + h_grad_n); const float horiz_grad = 0.5f * h_grad + 0.25f * (h_grad_p + h_grad_n);
const float verti_grad = 0.5f * v_grad + 0.25f * (v_grad_p + v_grad_n); const float verti_grad = 0.5f * v_grad + 0.25f * (v_grad_p + v_grad_n);
const bool is_vertical_greater = horiz_grad < verti_grad; const bool is_vertical_greater = (horiz_grad < verti_grad) &&
((verti_grad - horiz_grad) > 1e-5);
gradient[offset + elements_per_row * rows] = is_vertical_greater ? 0.06f : 0.57f; gradient[offset + elements_per_row * rows] = is_vertical_greater ? 0.06f : 0.57f;
gradient[offset ] = is_vertical_greater ? 0.57f : 0.06f; gradient[offset ] = is_vertical_greater ? 0.57f : 0.06f;
...@@ -647,7 +649,7 @@ kernel void substractResidual( ...@@ -647,7 +649,7 @@ kernel void substractResidual(
const float pB const float pB
) )
{ {
const int gidx = get_global_id(0), gidy = get_global_id(1); const int gidx = get_global_id(0) * 4, gidy = get_global_id(1);
if(gidx >= cols || gidy >= rows) if(gidx >= cols || gidy >= rows)
{ {
return; return;
...@@ -658,12 +660,15 @@ kernel void substractResidual( ...@@ -658,12 +660,15 @@ kernel void substractResidual(
mad24(gidy + rows, elements_per_row, gidx), mad24(gidy + rows, elements_per_row, gidx),
mad24(gidy + 2 * rows, elements_per_row, gidx) mad24(gidy + 2 * rows, elements_per_row, gidx)
}; };
float vals[3] = {input[indices[0]], input[indices[1]], input[indices[2]]}; float4 vals[3];
float residu = pR * vals[0] + pG * vals[1] + pB * vals[2]; vals[0] = vload4(0, input + indices[0]);
vals[1] = vload4(0, input + indices[1]);
input[indices[0]] = vals[0] - residu; vals[2] = vload4(0, input + indices[2]);
input[indices[1]] = vals[1] - residu;
input[indices[2]] = vals[2] - residu; float4 residu = pR * vals[0] + pG * vals[1] + pB * vals[2];
vstore4(vals[0] - residu, 0, input + indices[0]);
vstore4(vals[1] - residu, 0, input + indices[1]);
vstore4(vals[2] - residu, 0, input + indices[2]);
} }
///// clipRGBOutput_0_maxInputValue ///// ///// clipRGBOutput_0_maxInputValue /////
...@@ -675,15 +680,15 @@ kernel void clipRGBOutput_0_maxInputValue( ...@@ -675,15 +680,15 @@ kernel void clipRGBOutput_0_maxInputValue(
const float maxVal const float maxVal
) )
{ {
const int gidx = get_global_id(0), gidy = get_global_id(1); const int gidx = get_global_id(0) * 4, gidy = get_global_id(1);
if(gidx >= cols || gidy >= rows) if(gidx >= cols || gidy >= rows)
{ {
return; return;
} }
const int offset = mad24(gidy, elements_per_row, gidx); const int offset = mad24(gidy, elements_per_row, gidx);
float val = input[offset]; float4 val = vload4(0, input + offset);
val = clamp(val, 0.0f, maxVal); val = clamp(val, 0.0f, maxVal);
input[offset] = val; vstore4(val, 0, input + offset);
} }
//// normalizeGrayOutputNearZeroCentreredSigmoide //// //// normalizeGrayOutputNearZeroCentreredSigmoide ////
...@@ -697,15 +702,16 @@ kernel void normalizeGrayOutputNearZeroCentreredSigmoide( ...@@ -697,15 +702,16 @@ kernel void normalizeGrayOutputNearZeroCentreredSigmoide(
const float X0cube const float X0cube
) )
{ {
const int gidx = get_global_id(0), gidy = get_global_id(1); const int gidx = get_global_id(0) * 4, gidy = get_global_id(1);
if(gidx >= cols || gidy >= rows) if(gidx >= cols || gidy >= rows)
{ {
return; return;
} }
const int offset = mad24(gidy, elements_per_row, gidx); const int offset = mad24(gidy, elements_per_row, gidx);
float currentCubeLuminance = input[offset]; float4 currentCubeLuminance = vload4(0, input + offset);
currentCubeLuminance = currentCubeLuminance * currentCubeLuminance * currentCubeLuminance; currentCubeLuminance = currentCubeLuminance * currentCubeLuminance * currentCubeLuminance;
output[offset] = currentCubeLuminance * X0cube / (X0cube + currentCubeLuminance); float4 val = currentCubeLuminance * X0cube / (X0cube + currentCubeLuminance);
vstore4(val, 0, output + offset);
} }
//// centerReductImageLuminance //// //// centerReductImageLuminance ////
...@@ -718,15 +724,16 @@ kernel void centerReductImageLuminance( ...@@ -718,15 +724,16 @@ kernel void centerReductImageLuminance(
const float std_dev const float std_dev
) )
{ {
const int gidx = get_global_id(0), gidy = get_global_id(1); const int gidx = get_global_id(0) * 4, gidy = get_global_id(1);
if(gidx >= cols || gidy >= rows) if(gidx >= cols || gidy >= rows)
{ {
return; return;
} }
const int offset = mad24(gidy, elements_per_row, gidx); const int offset = mad24(gidy, elements_per_row, gidx);
float val = input[offset]; float4 val = vload4(0, input + offset);
input[offset] = (val - mean) / std_dev; val = (val - mean) / std_dev;
vstore4(val, 0, input + offset);
} }
//// inverseValue //// //// inverseValue ////
...@@ -737,13 +744,15 @@ kernel void inverseValue( ...@@ -737,13 +744,15 @@ kernel void inverseValue(
const int elements_per_row const int elements_per_row
) )
{ {
const int gidx = get_global_id(0), gidy = get_global_id(1); const int gidx = get_global_id(0) * 4, gidy = get_global_id(1);
if(gidx >= cols || gidy >= rows) if(gidx >= cols || gidy >= rows)
{ {
return; return;
} }
const int offset = mad24(gidy, elements_per_row, gidx); const int offset = mad24(gidy, elements_per_row, gidx);
input[offset] = 1.f / input[offset]; float4 val = vload4(0, input + offset);
val = 1.f / val;
vstore4(val, 0, input + offset);
} }
#define CV_PI 3.1415926535897932384626433832795 #define CV_PI 3.1415926535897932384626433832795
......
modules/bioinspired/src/precomp.hpp
View file @ 966b2f55
...@@ -48,13 +48,10 @@ ...@@ -48,13 +48,10 @@
#include "opencv2/core/utility.hpp" #include "opencv2/core/utility.hpp"
#include "opencv2/core/private.hpp" #include "opencv2/core/private.hpp"
#include "opencv2/core/ocl.hpp" #include "opencv2/core/ocl.hpp"
#include "opencv2/core/opencl/ocl_defs.hpp"
#include <valarray> #include <valarray>
#ifdef HAVE_OPENCV_OCL
#include "opencv2/ocl/private/util.hpp"
#endif
namespace cv namespace cv
{ {
......
modules/bioinspired/src/retina.cpp
View file @ 966b2f55
...@@ -70,6 +70,7 @@ ...@@ -70,6 +70,7 @@
*/ */
#include "precomp.hpp" #include "precomp.hpp"
#include "retinafilter.hpp" #include "retinafilter.hpp"
#include "retina_ocl.hpp"
#include <cstdio> #include <cstdio>
#include <sstream> #include <sstream>
#include <valarray> #include <valarray>
...@@ -292,11 +293,25 @@ private: ...@@ -292,11 +293,25 @@ private:
bool _convertCvMat2ValarrayBuffer(InputArray inputMatToConvert, std::valarray<float> &outputValarrayMatrix); bool _convertCvMat2ValarrayBuffer(InputArray inputMatToConvert, std::valarray<float> &outputValarrayMatrix);
#ifdef HAVE_OPENCL
ocl::RetinaOCLImpl* _ocl_retina;
bool ocl_run(InputArray inputImage);
bool ocl_getParvo(OutputArray retinaOutput_parvo);
bool ocl_getMagno(OutputArray retinaOutput_magno);
bool ocl_getParvoRAW(OutputArray retinaOutput_parvo);
bool ocl_getMagnoRAW(OutputArray retinaOutput_magno);
#endif
}; };
// smart pointers allocation : // smart pointers allocation :
Ptr<Retina> createRetina(Size inputSize){ return makePtr<RetinaImpl>(inputSize); } Ptr<Retina> createRetina(Size inputSize)
Ptr<Retina> createRetina(Size inputSize, const bool colorMode, int colorSamplingMethod, const bool useRetinaLogSampling, const float reductionFactor, const float samplingStrenght){ {
return makePtr<RetinaImpl>(inputSize);
}
Ptr<Retina> createRetina(Size inputSize, const bool colorMode, int colorSamplingMethod, const bool useRetinaLogSampling, const float reductionFactor, const float samplingStrenght)
{
return makePtr<RetinaImpl>(inputSize, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); return makePtr<RetinaImpl>(inputSize, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght);
} }
...@@ -306,18 +321,34 @@ RetinaImpl::RetinaImpl(const cv::Size inputSz) ...@@ -306,18 +321,34 @@ RetinaImpl::RetinaImpl(const cv::Size inputSz)
{ {
_retinaFilter = 0; _retinaFilter = 0;
_init(inputSz, true, RETINA_COLOR_BAYER, false); _init(inputSz, true, RETINA_COLOR_BAYER, false);
#ifdef HAVE_OPENCL
_ocl_retina = 0;
if (inputSz.width % 4 == 0)
_ocl_retina = new ocl::RetinaOCLImpl(inputSz);
#endif
} }
RetinaImpl::RetinaImpl(const cv::Size inputSz, const bool colorMode, int colorSamplingMethod, const bool useRetinaLogSampling, const float reductionFactor, const float samplingStrenght) RetinaImpl::RetinaImpl(const cv::Size inputSz, const bool colorMode, int colorSamplingMethod, const bool useRetinaLogSampling, const float reductionFactor, const float samplingStrenght)
{ {
_retinaFilter = 0; _retinaFilter = 0;
_init(inputSz, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); _init(inputSz, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght);
#ifdef HAVE_OPENCL
_ocl_retina = 0;
if (inputSz.width % 4 == 0)
_ocl_retina = new ocl::RetinaOCLImpl(inputSz, colorMode, colorSamplingMethod,
useRetinaLogSampling, reductionFactor, samplingStrenght);
#endif
} }
RetinaImpl::~RetinaImpl() RetinaImpl::~RetinaImpl()
{ {
if (_retinaFilter) if (_retinaFilter)
delete _retinaFilter; delete _retinaFilter;
#ifdef HAVE_OPENCL
if (_ocl_retina)
delete _ocl_retina;
#endif
} }
/** /**
...@@ -529,8 +560,18 @@ void RetinaImpl::setupIPLMagnoChannel(const bool normaliseOutput, const float pa ...@@ -529,8 +560,18 @@ void RetinaImpl::setupIPLMagnoChannel(const bool normaliseOutput, const float pa
_retinaParameters.IplMagno.localAdaptintegration_k = localAdaptintegration_k; _retinaParameters.IplMagno.localAdaptintegration_k = localAdaptintegration_k;
} }
#ifdef HAVE_OPENCL
bool RetinaImpl::ocl_run(InputArray inputMatToConvert)
{
_ocl_retina->run(inputMatToConvert);
return true;
}
#endif
void RetinaImpl::run(InputArray inputMatToConvert) void RetinaImpl::run(InputArray inputMatToConvert)
{ {
CV_OCL_RUN((_ocl_retina != 0), ocl_run(inputMatToConvert));
// first convert input image to the compatible format : std::valarray<float> // first convert input image to the compatible format : std::valarray<float>
const bool colorMode = _convertCvMat2ValarrayBuffer(inputMatToConvert.getMat(), _inputBuffer); const bool colorMode = _convertCvMat2ValarrayBuffer(inputMatToConvert.getMat(), _inputBuffer);
// process the retina // process the retina
...@@ -559,8 +600,18 @@ void RetinaImpl::applyFastToneMapping(InputArray inputImage, OutputArray outputT ...@@ -559,8 +600,18 @@ void RetinaImpl::applyFastToneMapping(InputArray inputImage, OutputArray outputT
} }
#ifdef HAVE_OPENCL
bool RetinaImpl::ocl_getParvo(OutputArray retinaOutput_parvo)
{
_ocl_retina->getParvo(retinaOutput_parvo);
return true;
}
#endif
void RetinaImpl::getParvo(OutputArray retinaOutput_parvo) void RetinaImpl::getParvo(OutputArray retinaOutput_parvo)
{ {
CV_OCL_RUN((_ocl_retina != 0) && retinaOutput_parvo.isUMat(), ocl_getParvo(retinaOutput_parvo));
if (_retinaFilter->getColorMode()) if (_retinaFilter->getColorMode())
{ {
// reallocate output buffer (if necessary) // reallocate output buffer (if necessary)
...@@ -572,24 +623,57 @@ void RetinaImpl::getParvo(OutputArray retinaOutput_parvo) ...@@ -572,24 +623,57 @@ void RetinaImpl::getParvo(OutputArray retinaOutput_parvo)
} }
//retinaOutput_parvo/=255.0; //retinaOutput_parvo/=255.0;
} }
#ifdef HAVE_OPENCL
bool RetinaImpl::ocl_getMagno(OutputArray retinaOutput_magno)
{
_ocl_retina->getMagno(retinaOutput_magno);
return true;
}
#endif
void RetinaImpl::getMagno(OutputArray retinaOutput_magno) void RetinaImpl::getMagno(OutputArray retinaOutput_magno)
{ {
CV_OCL_RUN((_ocl_retina != 0) && retinaOutput_magno.isUMat(), ocl_getMagno(retinaOutput_magno));
// reallocate output buffer (if necessary) // reallocate output buffer (if necessary)
_convertValarrayBuffer2cvMat(_retinaFilter->getMovingContours(), _retinaFilter->getOutputNBrows(), _retinaFilter->getOutputNBcolumns(), false, retinaOutput_magno); _convertValarrayBuffer2cvMat(_retinaFilter->getMovingContours(), _retinaFilter->getOutputNBrows(), _retinaFilter->getOutputNBcolumns(), false, retinaOutput_magno);
//retinaOutput_magno/=255.0; //retinaOutput_magno/=255.0;
} }
#ifdef HAVE_OPENCL
bool RetinaImpl::ocl_getMagnoRAW(OutputArray magnoOutputBufferCopy)
{
_ocl_retina->getMagnoRAW(magnoOutputBufferCopy);
return true;
}
#endif
// original API level data accessors : copy buffers if size matches, reallocate if required // original API level data accessors : copy buffers if size matches, reallocate if required
void RetinaImpl::getMagnoRAW(OutputArray magnoOutputBufferCopy){ void RetinaImpl::getMagnoRAW(OutputArray magnoOutputBufferCopy){
CV_OCL_RUN((_ocl_retina != 0) && magnoOutputBufferCopy.isUMat(), ocl_getMagnoRAW(magnoOutputBufferCopy));
// get magno channel header // get magno channel header
const cv::Mat magnoChannel=cv::Mat(getMagnoRAW()); const cv::Mat magnoChannel=cv::Mat(getMagnoRAW());
// copy data // copy data
magnoChannel.copyTo(magnoOutputBufferCopy); magnoChannel.copyTo(magnoOutputBufferCopy);
} }
#ifdef HAVE_OPENCL
bool RetinaImpl::ocl_getParvoRAW(OutputArray parvoOutputBufferCopy)
{
_ocl_retina->getParvoRAW(parvoOutputBufferCopy);
return true;
}
#endif
void RetinaImpl::getParvoRAW(OutputArray parvoOutputBufferCopy){ void RetinaImpl::getParvoRAW(OutputArray parvoOutputBufferCopy){
CV_OCL_RUN((_ocl_retina != 0) && parvoOutputBufferCopy.isUMat(), ocl_getParvoRAW(parvoOutputBufferCopy));
// get parvo channel header // get parvo channel header
const cv::Mat parvoChannel=cv::Mat(getMagnoRAW()); const cv::Mat parvoChannel=cv::Mat(getParvoRAW());
// copy data // copy data
parvoChannel.copyTo(parvoOutputBufferCopy); parvoChannel.copyTo(parvoOutputBufferCopy);
} }
...@@ -649,7 +733,7 @@ void RetinaImpl::_convertValarrayBuffer2cvMat(const std::valarray<float> &grayMa ...@@ -649,7 +733,7 @@ void RetinaImpl::_convertValarrayBuffer2cvMat(const std::valarray<float> &grayMa
for (unsigned int j=0;j<nbColumns;++j) for (unsigned int j=0;j<nbColumns;++j)
{ {
cv::Point2d pixel(j,i); cv::Point2d pixel(j,i);
outMat.at<unsigned char>(pixel)=(unsigned char)*(valarrayPTR++); outMat.at<unsigned char>(pixel)=(unsigned char)cvRound(*(valarrayPTR++));
} }
} }
} }
...@@ -665,9 +749,9 @@ void RetinaImpl::_convertValarrayBuffer2cvMat(const std::valarray<float> &grayMa ...@@ -665,9 +749,9 @@ void RetinaImpl::_convertValarrayBuffer2cvMat(const std::valarray<float> &grayMa
{ {
cv::Point2d pixel(j,i); cv::Point2d pixel(j,i);
cv::Vec3b pixelValues; cv::Vec3b pixelValues;
pixelValues[2]=(unsigned char)*(valarrayPTR); pixelValues[2]=(unsigned char)cvRound(*(valarrayPTR));
pixelValues[1]=(unsigned char)*(valarrayPTR+nbPixels); pixelValues[1]=(unsigned char)cvRound(*(valarrayPTR+nbPixels));
pixelValues[0]=(unsigned char)*(valarrayPTR+doubleNBpixels); pixelValues[0]=(unsigned char)cvRound(*(valarrayPTR+doubleNBpixels));
outMat.at<cv::Vec3b>(pixel)=pixelValues; outMat.at<cv::Vec3b>(pixel)=pixelValues;
} }
...@@ -729,7 +813,15 @@ bool RetinaImpl::_convertCvMat2ValarrayBuffer(InputArray inputMat, std::valarray ...@@ -729,7 +813,15 @@ bool RetinaImpl::_convertCvMat2ValarrayBuffer(InputArray inputMat, std::valarray
return imageNumberOfChannels>1; // return bool : false for gray level image processing, true for color mode return imageNumberOfChannels>1; // return bool : false for gray level image processing, true for color mode
} }
void RetinaImpl::clearBuffers() { _retinaFilter->clearAllBuffers(); } void RetinaImpl::clearBuffers()
{
#ifdef HAVE_OPENCL
if (_ocl_retina != 0)
_ocl_retina->clearBuffers();
#endif
_retinaFilter->clearAllBuffers();
}
void RetinaImpl::activateMovingContoursProcessing(const bool activate) { _retinaFilter->activateMovingContoursProcessing(activate); } void RetinaImpl::activateMovingContoursProcessing(const bool activate) { _retinaFilter->activateMovingContoursProcessing(activate); }
......
modules/bioinspired/src/retina_ocl.cpp
View file @ 966b2f55
...@@ -48,70 +48,34 @@ ...@@ -48,70 +48,34 @@
#include <iostream> #include <iostream>
#include <sstream> #include <sstream>
#ifdef HAVE_OPENCV_OCL #ifdef HAVE_OPENCL
#include "opencl_kernels.hpp" #include "opencl_kernels_bioinspired.hpp"
#define NOT_IMPLEMENTED CV_Error(cv::Error::StsNotImplemented, "Not implemented") #define NOT_IMPLEMENTED CV_Error(cv::Error::StsNotImplemented, "Not implemented")
namespace cv namespace
{ {
static ocl::ProgramEntry retina_kernel = ocl::bioinspired::retina_kernel; template <typename T, size_t N>
inline int sizeOfArray(const T(&)[N])
{
return (int)N;
}
inline void ensureSizeIsEnough(int rows, int cols, int type, cv::UMat &m)
{
m.create(rows, cols, type, m.usageFlags);
}
}
namespace cv
{
namespace bioinspired namespace bioinspired
{ {
namespace ocl namespace ocl
{ {
using namespace cv::ocl; using namespace cv::ocl;
class RetinaOCLImpl : public Retina
{
public:
RetinaOCLImpl(Size getInputSize);
RetinaOCLImpl(Size getInputSize, const bool colorMode, int colorSamplingMethod = RETINA_COLOR_BAYER, const bool useRetinaLogSampling = false, const double reductionFactor = 1.0, const double samplingStrenght = 10.0);
virtual ~RetinaOCLImpl();
Size getInputSize();
Size getOutputSize();
void setup(String retinaParameterFile = "", const bool applyDefaultSetupOnFailure = true);
void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure = true);
void setup(RetinaParameters newParameters);
RetinaOCLImpl::RetinaParameters getParameters();
const String printSetup();
virtual void write( String fs ) const;
virtual void write( FileStorage& fs ) const;
void setupOPLandIPLParvoChannel(const bool colorMode = true, const bool normaliseOutput = true, const float photoreceptorsLocalAdaptationSensitivity = 0.7, const float photoreceptorsTemporalConstant = 0.5, const float photoreceptorsSpatialConstant = 0.53, const float horizontalCellsGain = 0, const float HcellsTemporalConstant = 1, const float HcellsSpatialConstant = 7, const float ganglionCellsSensitivity = 0.7);
void setupIPLMagnoChannel(const bool normaliseOutput = true, const float parasolCells_beta = 0, const float parasolCells_tau = 0, const float parasolCells_k = 7, const float amacrinCellsTemporalCutFrequency = 1.2, const float V0CompressionParameter = 0.95, const float localAdaptintegration_tau = 0, const float localAdaptintegration_k = 7);
void run(InputArray inputImage);
void getParvo(OutputArray retinaOutput_parvo);
void getMagno(OutputArray retinaOutput_magno);
void setColorSaturation(const bool saturateColors = true, const float colorSaturationValue = 4.0);
void clearBuffers();
void activateMovingContoursProcessing(const bool activate);
void activateContoursProcessing(const bool activate);
// unimplemented interfaces:
void applyFastToneMapping(InputArray /*inputImage*/, OutputArray /*outputToneMappedImage*/) { NOT_IMPLEMENTED; }
void getParvoRAW(OutputArray /*retinaOutput_parvo*/) { NOT_IMPLEMENTED; }
void getMagnoRAW(OutputArray /*retinaOutput_magno*/) { NOT_IMPLEMENTED; }
const Mat getMagnoRAW() const { NOT_IMPLEMENTED; return Mat(); }
const Mat getParvoRAW() const { NOT_IMPLEMENTED; return Mat(); }
protected:
RetinaParameters _retinaParameters;
cv::ocl::oclMat _inputBuffer;
RetinaFilter* _retinaFilter;
bool convertToColorPlanes(const cv::ocl::oclMat& input, cv::ocl::oclMat &output);
void convertToInterleaved(const cv::ocl::oclMat& input, bool colorMode, cv::ocl::oclMat &output);
void _init(const Size getInputSize, const bool colorMode, int colorSamplingMethod = RETINA_COLOR_BAYER, const bool useRetinaLogSampling = false, const double reductionFactor = 1.0, const double samplingStrenght = 10.0);
};
RetinaOCLImpl::RetinaOCLImpl(const cv::Size inputSz) RetinaOCLImpl::RetinaOCLImpl(const cv::Size inputSz)
{ {
_retinaFilter = 0; _retinaFilter = 0;
...@@ -133,7 +97,7 @@ RetinaOCLImpl::~RetinaOCLImpl() ...@@ -133,7 +97,7 @@ RetinaOCLImpl::~RetinaOCLImpl()
} }
/** /**
* retreive retina input buffer size * retrieve retina input buffer size
*/ */
Size RetinaOCLImpl::getInputSize() Size RetinaOCLImpl::getInputSize()
{ {
...@@ -141,7 +105,7 @@ Size RetinaOCLImpl::getInputSize() ...@@ -141,7 +105,7 @@ Size RetinaOCLImpl::getInputSize()
} }
/** /**
* retreive retina output buffer size * retrieve retina output buffer size
*/ */
Size RetinaOCLImpl::getOutputSize() Size RetinaOCLImpl::getOutputSize()
{ {
...@@ -154,7 +118,7 @@ void RetinaOCLImpl::setColorSaturation(const bool saturateColors, const float co ...@@ -154,7 +118,7 @@ void RetinaOCLImpl::setColorSaturation(const bool saturateColors, const float co
_retinaFilter->setColorSaturation(saturateColors, colorSaturationValue); _retinaFilter->setColorSaturation(saturateColors, colorSaturationValue);
} }
struct RetinaOCLImpl::RetinaParameters RetinaOCLImpl::getParameters() struct RetinaParameters RetinaOCLImpl::getParameters()
{ {
return _retinaParameters; return _retinaParameters;
} }
...@@ -170,7 +134,7 @@ void RetinaOCLImpl::setup(String retinaParameterFile, const bool applyDefaultSet ...@@ -170,7 +134,7 @@ void RetinaOCLImpl::setup(String retinaParameterFile, const bool applyDefaultSet
} }
catch(Exception &e) catch(Exception &e)
{ {
std::cout << "RetinaOCLImpl::setup: wrong/unappropriate xml parameter file : error report :`n=>" << e.what() << std::endl; std::cout << "RetinaOCLImpl::setup: wrong/inappropriate xml parameter file : error report :`n=>" << e.what() << std::endl;
if (applyDefaultSetupOnFailure) if (applyDefaultSetupOnFailure)
{ {
std::cout << "RetinaOCLImpl::setup: resetting retina with default parameters" << std::endl; std::cout << "RetinaOCLImpl::setup: resetting retina with default parameters" << std::endl;
...@@ -191,7 +155,7 @@ void RetinaOCLImpl::setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFai ...@@ -191,7 +155,7 @@ void RetinaOCLImpl::setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFai
// read parameters file if it exists or apply default setup if asked for // read parameters file if it exists or apply default setup if asked for
if (!fs.isOpened()) if (!fs.isOpened())
{ {
std::cout << "RetinaOCLImpl::setup: provided parameters file could not be open... skeeping configuration" << std::endl; std::cout << "RetinaOCLImpl::setup: provided parameters file could not be open... skipping configuration" << std::endl;
return; return;
// implicit else case : retinaParameterFile could be open (it exists at least) // implicit else case : retinaParameterFile could be open (it exists at least)
} }
...@@ -230,15 +194,15 @@ void RetinaOCLImpl::setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFai ...@@ -230,15 +194,15 @@ void RetinaOCLImpl::setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFai
setupOPLandIPLParvoChannel(); setupOPLandIPLParvoChannel();
setupIPLMagnoChannel(); setupIPLMagnoChannel();
} }
std::cout << "RetinaOCLImpl::setup: wrong/unappropriate xml parameter file : error report :`n=>" << e.what() << std::endl; std::cout << "RetinaOCLImpl::setup: wrong/inappropriate xml parameter file : error report :`n=>" << e.what() << std::endl;
std::cout << "=> keeping current parameters" << std::endl; std::cout << "=> keeping current parameters" << std::endl;
} }
} }
void RetinaOCLImpl::setup(cv::bioinspired::Retina::RetinaParameters newConfiguration) void RetinaOCLImpl::setup(cv::bioinspired::RetinaParameters newConfiguration)
{ {
// simply copy structures // simply copy structures
memcpy(&_retinaParameters, &newConfiguration, sizeof(cv::bioinspired::Retina::RetinaParameters)); memcpy(&_retinaParameters, &newConfiguration, sizeof(cv::bioinspired::RetinaParameters));
// apply setup // apply setup
setupOPLandIPLParvoChannel(_retinaParameters.OPLandIplParvo.colorMode, _retinaParameters.OPLandIplParvo.normaliseOutput, _retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity, _retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant, _retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant, _retinaParameters.OPLandIplParvo.horizontalCellsGain, _retinaParameters.OPLandIplParvo.hcellsTemporalConstant, _retinaParameters.OPLandIplParvo.hcellsSpatialConstant, _retinaParameters.OPLandIplParvo.ganglionCellsSensitivity); setupOPLandIPLParvoChannel(_retinaParameters.OPLandIplParvo.colorMode, _retinaParameters.OPLandIplParvo.normaliseOutput, _retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity, _retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant, _retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant, _retinaParameters.OPLandIplParvo.horizontalCellsGain, _retinaParameters.OPLandIplParvo.hcellsTemporalConstant, _retinaParameters.OPLandIplParvo.hcellsSpatialConstant, _retinaParameters.OPLandIplParvo.ganglionCellsSensitivity);
setupIPLMagnoChannel(_retinaParameters.IplMagno.normaliseOutput, _retinaParameters.IplMagno.parasolCells_beta, _retinaParameters.IplMagno.parasolCells_tau, _retinaParameters.IplMagno.parasolCells_k, _retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency, _retinaParameters.IplMagno.V0CompressionParameter, _retinaParameters.IplMagno.localAdaptintegration_tau, _retinaParameters.IplMagno.localAdaptintegration_k); setupIPLMagnoChannel(_retinaParameters.IplMagno.normaliseOutput, _retinaParameters.IplMagno.parasolCells_beta, _retinaParameters.IplMagno.parasolCells_tau, _retinaParameters.IplMagno.parasolCells_k, _retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency, _retinaParameters.IplMagno.V0CompressionParameter, _retinaParameters.IplMagno.localAdaptintegration_tau, _retinaParameters.IplMagno.localAdaptintegration_k);
...@@ -321,7 +285,7 @@ void RetinaOCLImpl::setupOPLandIPLParvoChannel(const bool colorMode, const bool ...@@ -321,7 +285,7 @@ void RetinaOCLImpl::setupOPLandIPLParvoChannel(const bool colorMode, const bool
_retinaFilter->setParvoGanglionCellsLocalAdaptationSensitivity(ganglionCellsSensitivity); _retinaFilter->setParvoGanglionCellsLocalAdaptationSensitivity(ganglionCellsSensitivity);
_retinaFilter->activateNormalizeParvoOutput_0_maxOutputValue(normaliseOutput); _retinaFilter->activateNormalizeParvoOutput_0_maxOutputValue(normaliseOutput);
// update parameters struture // update parameters structure
_retinaParameters.OPLandIplParvo.colorMode = colorMode; _retinaParameters.OPLandIplParvo.colorMode = colorMode;
_retinaParameters.OPLandIplParvo.normaliseOutput = normaliseOutput; _retinaParameters.OPLandIplParvo.normaliseOutput = normaliseOutput;
...@@ -340,7 +304,7 @@ void RetinaOCLImpl::setupIPLMagnoChannel(const bool normaliseOutput, const float ...@@ -340,7 +304,7 @@ void RetinaOCLImpl::setupIPLMagnoChannel(const bool normaliseOutput, const float
_retinaFilter->setMagnoCoefficientsTable(parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k); _retinaFilter->setMagnoCoefficientsTable(parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k);
_retinaFilter->activateNormalizeMagnoOutput_0_maxOutputValue(normaliseOutput); _retinaFilter->activateNormalizeMagnoOutput_0_maxOutputValue(normaliseOutput);
// update parameters struture // update parameters structure
_retinaParameters.IplMagno.normaliseOutput = normaliseOutput; _retinaParameters.IplMagno.normaliseOutput = normaliseOutput;
_retinaParameters.IplMagno.parasolCells_beta = parasolCells_beta; _retinaParameters.IplMagno.parasolCells_beta = parasolCells_beta;
_retinaParameters.IplMagno.parasolCells_tau = parasolCells_tau; _retinaParameters.IplMagno.parasolCells_tau = parasolCells_tau;
...@@ -353,7 +317,7 @@ void RetinaOCLImpl::setupIPLMagnoChannel(const bool normaliseOutput, const float ...@@ -353,7 +317,7 @@ void RetinaOCLImpl::setupIPLMagnoChannel(const bool normaliseOutput, const float
void RetinaOCLImpl::run(InputArray input) void RetinaOCLImpl::run(InputArray input)
{ {
oclMat &inputMatToConvert = getOclMatRef(input); UMat inputMatToConvert = input.getUMat();
bool colorMode = convertToColorPlanes(inputMatToConvert, _inputBuffer); bool colorMode = convertToColorPlanes(inputMatToConvert, _inputBuffer);
// first convert input image to the compatible format : std::valarray<float> // first convert input image to the compatible format : std::valarray<float>
// process the retina // process the retina
...@@ -365,7 +329,7 @@ void RetinaOCLImpl::run(InputArray input) ...@@ -365,7 +329,7 @@ void RetinaOCLImpl::run(InputArray input)
void RetinaOCLImpl::getParvo(OutputArray output) void RetinaOCLImpl::getParvo(OutputArray output)
{ {
oclMat &retinaOutput_parvo = getOclMatRef(output); UMat &retinaOutput_parvo = output.getUMatRef();
if (_retinaFilter->getColorMode()) if (_retinaFilter->getColorMode())
{ {
// reallocate output buffer (if necessary) // reallocate output buffer (if necessary)
...@@ -380,12 +344,12 @@ void RetinaOCLImpl::getParvo(OutputArray output) ...@@ -380,12 +344,12 @@ void RetinaOCLImpl::getParvo(OutputArray output)
} }
void RetinaOCLImpl::getMagno(OutputArray output) void RetinaOCLImpl::getMagno(OutputArray output)
{ {
oclMat &retinaOutput_magno = getOclMatRef(output); UMat &retinaOutput_magno = output.getUMatRef();
// reallocate output buffer (if necessary) // reallocate output buffer (if necessary)
convertToInterleaved(_retinaFilter->getMovingContours(), false, retinaOutput_magno); convertToInterleaved(_retinaFilter->getMovingContours(), false, retinaOutput_magno);
//retinaOutput_magno/=255.0; //retinaOutput_magno/=255.0;
} }
// private method called by constructirs // private method called by constructors
void RetinaOCLImpl::_init(const cv::Size inputSz, const bool colorMode, int colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) void RetinaOCLImpl::_init(const cv::Size inputSz, const bool colorMode, int colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght)
{ {
// basic error check // basic error check
...@@ -408,22 +372,22 @@ void RetinaOCLImpl::_init(const cv::Size inputSz, const bool colorMode, int colo ...@@ -408,22 +372,22 @@ void RetinaOCLImpl::_init(const cv::Size inputSz, const bool colorMode, int colo
_retinaFilter->clearAllBuffers(); _retinaFilter->clearAllBuffers();
} }
bool RetinaOCLImpl::convertToColorPlanes(const oclMat& input, oclMat &output) bool RetinaOCLImpl::convertToColorPlanes(const UMat& input, UMat &output)
{ {
oclMat convert_input; UMat convert_input;
input.convertTo(convert_input, CV_32F); input.convertTo(convert_input, CV_32F);
if(convert_input.channels() == 3 || convert_input.channels() == 4) if(convert_input.channels() == 3 || convert_input.channels() == 4)
{ {
ocl::ensureSizeIsEnough(int(_retinaFilter->getInputNBrows() * 4), ensureSizeIsEnough(int(_retinaFilter->getInputNBrows() * 4),
int(_retinaFilter->getInputNBcolumns()), CV_32FC1, output); int(_retinaFilter->getInputNBcolumns()), CV_32FC1, output);
oclMat channel_splits[4] = std::vector<UMat> channel_splits;
{ channel_splits.reserve(4);
output(Rect(Point(0, _retinaFilter->getInputNBrows() * 2), getInputSize())), channel_splits.push_back(output(Rect(Point(0, _retinaFilter->getInputNBrows() * 2), getInputSize())));
output(Rect(Point(0, _retinaFilter->getInputNBrows()), getInputSize())), channel_splits.push_back(output(Rect(Point(0, _retinaFilter->getInputNBrows()), getInputSize())));
output(Rect(Point(0, 0), getInputSize())), channel_splits.push_back(output(Rect(Point(0, 0), getInputSize())));
output(Rect(Point(0, _retinaFilter->getInputNBrows() * 3), getInputSize())) channel_splits.push_back(output(Rect(Point(0, _retinaFilter->getInputNBrows() * 3), getInputSize())));
};
ocl::split(convert_input, channel_splits); cv::split(convert_input, channel_splits);
return true; return true;
} }
else if(convert_input.channels() == 1) else if(convert_input.channels() == 1)
...@@ -437,13 +401,13 @@ bool RetinaOCLImpl::convertToColorPlanes(const oclMat& input, oclMat &output) ...@@ -437,13 +401,13 @@ bool RetinaOCLImpl::convertToColorPlanes(const oclMat& input, oclMat &output)
return false; return false;
} }
} }
void RetinaOCLImpl::convertToInterleaved(const oclMat& input, bool colorMode, oclMat &output) void RetinaOCLImpl::convertToInterleaved(const UMat& input, bool colorMode, UMat &output)
{ {
input.convertTo(output, CV_8U); input.convertTo(output, CV_8U);
if(colorMode) if(colorMode)
{ {
int numOfSplits = input.rows / getInputSize().height; int numOfSplits = input.rows / getInputSize().height;
std::vector<oclMat> channel_splits(numOfSplits); std::vector<UMat> channel_splits(numOfSplits);
for(int i = 0; i < static_cast<int>(channel_splits.size()); i ++) for(int i = 0; i < static_cast<int>(channel_splits.size()); i ++)
{ {
channel_splits[i] = channel_splits[i] =
...@@ -472,6 +436,29 @@ void RetinaOCLImpl::activateContoursProcessing(const bool activate) ...@@ -472,6 +436,29 @@ void RetinaOCLImpl::activateContoursProcessing(const bool activate)
_retinaFilter->activateContoursProcessing(activate); _retinaFilter->activateContoursProcessing(activate);
} }
void RetinaOCLImpl::getParvoRAW(OutputArray retinaOutput_parvo)
{
UMat raw_parvo;
if (_retinaFilter->getColorMode())
raw_parvo = _retinaFilter->getColorOutput();
else
raw_parvo = _retinaFilter->getContours();
raw_parvo.copyTo(retinaOutput_parvo);
}
void RetinaOCLImpl::getMagnoRAW(OutputArray retinaOutput_magno)
{
UMat raw_magno = _retinaFilter->getMovingContours();
raw_magno.copyTo(retinaOutput_magno);
}
// unimplemented interfaces:
void RetinaOCLImpl::applyFastToneMapping(InputArray /*inputImage*/, OutputArray /*outputToneMappedImage*/) { NOT_IMPLEMENTED; }
const Mat RetinaOCLImpl::getMagnoRAW() const { NOT_IMPLEMENTED; return Mat(); }
const Mat RetinaOCLImpl::getParvoRAW() const { NOT_IMPLEMENTED; return Mat(); }
/////////////////////////////////////// ///////////////////////////////////////
///////// BasicRetinaFilter /////////// ///////// BasicRetinaFilter ///////////
/////////////////////////////////////// ///////////////////////////////////////
...@@ -534,72 +521,73 @@ void BasicRetinaFilter::setLPfilterParameters(const float beta, const float tau, ...@@ -534,72 +521,73 @@ void BasicRetinaFilter::setLPfilterParameters(const float beta, const float tau,
_filteringCoeficientsTable[1 + tableOffset] = (1.0f - a) * (1.0f - a) * (1.0f - a) * (1.0f - a) / (1.0f + _beta); _filteringCoeficientsTable[1 + tableOffset] = (1.0f - a) * (1.0f - a) * (1.0f - a) * (1.0f - a) / (1.0f + _beta);
_filteringCoeficientsTable[2 + tableOffset] = tau; _filteringCoeficientsTable[2 + tableOffset] = tau;
} }
const oclMat &BasicRetinaFilter::runFilter_LocalAdapdation(const oclMat &inputFrame, const oclMat &localLuminance) const UMat &BasicRetinaFilter::runFilter_LocalAdapdation(const UMat &inputFrame, const UMat &localLuminance)
{ {
_localLuminanceAdaptation(inputFrame, localLuminance, _filterOutput); _localLuminanceAdaptation(inputFrame, localLuminance, _filterOutput);
return _filterOutput; return _filterOutput;
} }
void BasicRetinaFilter::runFilter_LocalAdapdation(const oclMat &inputFrame, const oclMat &localLuminance, oclMat &outputFrame) void BasicRetinaFilter::runFilter_LocalAdapdation(const UMat &inputFrame, const UMat &localLuminance, UMat &outputFrame)
{ {
_localLuminanceAdaptation(inputFrame, localLuminance, outputFrame); _localLuminanceAdaptation(inputFrame, localLuminance, outputFrame);
} }
const oclMat &BasicRetinaFilter::runFilter_LocalAdapdation_autonomous(const oclMat &inputFrame) const UMat &BasicRetinaFilter::runFilter_LocalAdapdation_autonomous(const UMat &inputFrame)
{ {
_spatiotemporalLPfilter(inputFrame, _filterOutput); _spatiotemporalLPfilter(inputFrame, _filterOutput);
_localLuminanceAdaptation(inputFrame, _filterOutput, _filterOutput); _localLuminanceAdaptation(inputFrame, _filterOutput, _filterOutput);
return _filterOutput; return _filterOutput;
} }
void BasicRetinaFilter::runFilter_LocalAdapdation_autonomous(const oclMat &inputFrame, oclMat &outputFrame) void BasicRetinaFilter::runFilter_LocalAdapdation_autonomous(const UMat &inputFrame, UMat &outputFrame)
{ {
_spatiotemporalLPfilter(inputFrame, _filterOutput); _spatiotemporalLPfilter(inputFrame, _filterOutput);
_localLuminanceAdaptation(inputFrame, _filterOutput, outputFrame); _localLuminanceAdaptation(inputFrame, _filterOutput, outputFrame);
} }
void BasicRetinaFilter::_localLuminanceAdaptation(oclMat &inputOutputFrame, const oclMat &localLuminance) void BasicRetinaFilter::_localLuminanceAdaptation(UMat &inputOutputFrame, const UMat &localLuminance)
{ {
_localLuminanceAdaptation(inputOutputFrame, localLuminance, inputOutputFrame, false); _localLuminanceAdaptation(inputOutputFrame, localLuminance, inputOutputFrame, false);
} }
void BasicRetinaFilter::_localLuminanceAdaptation(const oclMat &inputFrame, const oclMat &localLuminance, oclMat &outputFrame, const bool updateLuminanceMean) void BasicRetinaFilter::_localLuminanceAdaptation(const UMat &inputFrame, const UMat &localLuminance, UMat &outputFrame, const bool updateLuminanceMean)
{ {
if (updateLuminanceMean) if (updateLuminanceMean)
{ {
float meanLuminance = saturate_cast<float>(ocl::sum(inputFrame)[0]) / getNBpixels(); float meanLuminance = saturate_cast<float>(cv::sum(inputFrame)[0]) / getNBpixels();
updateCompressionParameter(meanLuminance); updateCompressionParameter(meanLuminance);
} }
int elements_per_row = static_cast<int>(inputFrame.step / inputFrame.elemSize()); int elements_per_row = static_cast<int>(inputFrame.step / inputFrame.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)_NBcols / 4, (size_t)_NBrows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = {16, 16};
size_t globalSize[] = {_NBcols, _NBrows, 1};
size_t localSize[] = {16, 16, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &localLuminance.data)); Kernel kernel("localLuminanceAdaptation", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &inputFrame.data)); kernel.args(ocl::KernelArg::PtrReadOnly(localLuminance),
args.push_back(std::make_pair(sizeof(cl_mem), &outputFrame.data)); ocl::KernelArg::PtrReadOnly(inputFrame),
args.push_back(std::make_pair(sizeof(cl_int), &_NBcols)); ocl::KernelArg::PtrWriteOnly(outputFrame),
args.push_back(std::make_pair(sizeof(cl_int), &_NBrows)); (int)_NBcols, (int)_NBrows, (int)elements_per_row,
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (float)_localLuminanceAddon, (float)_localLuminanceFactor, (float)_maxInputValue);
args.push_back(std::make_pair(sizeof(cl_float), &_localLuminanceAddon)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
args.push_back(std::make_pair(sizeof(cl_float), &_localLuminanceFactor));
args.push_back(std::make_pair(sizeof(cl_float), &_maxInputValue));
openCLExecuteKernel(ctx, &retina_kernel, "localLuminanceAdaptation", globalSize, localSize, args, -1, -1);
} }
const oclMat &BasicRetinaFilter::runFilter_LPfilter(const oclMat &inputFrame, const unsigned int filterIndex) const UMat &BasicRetinaFilter::runFilter_LPfilter(const UMat &inputFrame, const unsigned int filterIndex)
{ {
_spatiotemporalLPfilter(inputFrame, _filterOutput, filterIndex); _spatiotemporalLPfilter(inputFrame, _filterOutput, filterIndex);
return _filterOutput; return _filterOutput;
} }
void BasicRetinaFilter::runFilter_LPfilter(const oclMat &inputFrame, oclMat &outputFrame, const unsigned int filterIndex) void BasicRetinaFilter::runFilter_LPfilter(const UMat &inputFrame, UMat &outputFrame, const unsigned int filterIndex)
{ {
_spatiotemporalLPfilter(inputFrame, outputFrame, filterIndex); _spatiotemporalLPfilter(inputFrame, outputFrame, filterIndex);
} }
void BasicRetinaFilter::_spatiotemporalLPfilter(const oclMat &inputFrame, oclMat &LPfilterOutput, const unsigned int filterIndex) void BasicRetinaFilter::_spatiotemporalLPfilter(const UMat &inputFrame, UMat &LPfilterOutput, const unsigned int filterIndex)
{
_spatiotemporalLPfilter_h(inputFrame, LPfilterOutput, filterIndex);
_spatiotemporalLPfilter_v(LPfilterOutput, 0);
}
void BasicRetinaFilter::_spatiotemporalLPfilter_h(const UMat &inputFrame, UMat &LPfilterOutput, const unsigned int filterIndex)
{ {
unsigned int coefTableOffset = filterIndex * 3; unsigned int coefTableOffset = filterIndex * 3;
...@@ -608,137 +596,88 @@ void BasicRetinaFilter::_spatiotemporalLPfilter(const oclMat &inputFrame, oclMat ...@@ -608,137 +596,88 @@ void BasicRetinaFilter::_spatiotemporalLPfilter(const oclMat &inputFrame, oclMat
_tau = _filteringCoeficientsTable[2 + coefTableOffset]; _tau = _filteringCoeficientsTable[2 + coefTableOffset];
_horizontalCausalFilter_addInput(inputFrame, LPfilterOutput); _horizontalCausalFilter_addInput(inputFrame, LPfilterOutput);
_horizontalAnticausalFilter(LPfilterOutput);
_verticalCausalFilter(LPfilterOutput);
_verticalAnticausalFilter_multGain(LPfilterOutput);
} }
void BasicRetinaFilter::_horizontalCausalFilter_addInput(const oclMat &inputFrame, oclMat &outputFrame) void BasicRetinaFilter::_spatiotemporalLPfilter_v(UMat &LPfilterOutput, const unsigned int multichannel)
{ {
int elements_per_row = static_cast<int>(inputFrame.step / inputFrame.elemSize()); if (multichannel == 0)
_verticalCausalFilter(LPfilterOutput);
Context * ctx = Context::getContext(); else
std::vector<std::pair<size_t, const void *> > args; _verticalCausalFilter_multichannel(LPfilterOutput);
size_t globalSize[] = {_NBrows, 1, 1};
size_t localSize[] = {256, 1, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &inputFrame.data));
args.push_back(std::make_pair(sizeof(cl_mem), &outputFrame.data));
args.push_back(std::make_pair(sizeof(cl_int), &_NBcols));
args.push_back(std::make_pair(sizeof(cl_int), &_NBrows));
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row));
args.push_back(std::make_pair(sizeof(cl_int), &inputFrame.offset));
args.push_back(std::make_pair(sizeof(cl_int), &inputFrame.offset));
args.push_back(std::make_pair(sizeof(cl_float), &_tau));
args.push_back(std::make_pair(sizeof(cl_float), &_a));
openCLExecuteKernel(ctx, &retina_kernel, "horizontalCausalFilter_addInput", globalSize, localSize, args, -1, -1);
}
void BasicRetinaFilter::_horizontalAnticausalFilter(oclMat &outputFrame)
{
int elements_per_row = static_cast<int>(outputFrame.step / outputFrame.elemSize());
Context * ctx = Context::getContext();
std::vector<std::pair<size_t, const void *> > args;
size_t globalSize[] = {_NBrows, 1, 1};
size_t localSize[] = {256, 1, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &outputFrame.data));
args.push_back(std::make_pair(sizeof(cl_int), &_NBcols));
args.push_back(std::make_pair(sizeof(cl_int), &_NBrows));
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row));
args.push_back(std::make_pair(sizeof(cl_int), &outputFrame.offset));
args.push_back(std::make_pair(sizeof(cl_float), &_a));
openCLExecuteKernel(ctx, &retina_kernel, "horizontalAnticausalFilter", globalSize, localSize, args, -1, -1);
} }
void BasicRetinaFilter::_verticalCausalFilter(oclMat &outputFrame) void BasicRetinaFilter::_horizontalCausalFilter_addInput(const UMat &inputFrame, UMat &outputFrame)
{ {
int elements_per_row = static_cast<int>(outputFrame.step / outputFrame.elemSize()); int elements_per_row = static_cast<int>(inputFrame.step / inputFrame.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)_NBrows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 256 };
size_t globalSize[] = {_NBcols, 1, 1};
size_t localSize[] = {256, 1, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &outputFrame.data)); Kernel kernel("horizontalCausalFilter_addInput", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_int), &_NBcols)); kernel.args(ocl::KernelArg::PtrReadOnly(inputFrame),
args.push_back(std::make_pair(sizeof(cl_int), &_NBrows)); ocl::KernelArg::PtrWriteOnly(outputFrame),
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (int)_NBcols, (int)_NBrows, (int)elements_per_row,
args.push_back(std::make_pair(sizeof(cl_int), &outputFrame.offset)); (int)inputFrame.offset, (int)inputFrame.offset,
args.push_back(std::make_pair(sizeof(cl_float), &_a)); (float)_tau, (float)_a);
openCLExecuteKernel(ctx, &retina_kernel, "verticalCausalFilter", globalSize, localSize, args, -1, -1); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
} }
void BasicRetinaFilter::_verticalAnticausalFilter_multGain(oclMat &outputFrame) void BasicRetinaFilter::_verticalCausalFilter(UMat &outputFrame)
{ {
int elements_per_row = static_cast<int>(outputFrame.step / outputFrame.elemSize()); int elements_per_row = static_cast<int>(outputFrame.step / outputFrame.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)_NBcols / 2};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 256 };
size_t globalSize[] = {_NBcols, 1, 1};
size_t localSize[] = {256, 1, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &outputFrame.data)); Kernel kernel("verticalCausalFilter", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_int), &_NBcols)); kernel.args(ocl::KernelArg::PtrReadWrite(outputFrame),
args.push_back(std::make_pair(sizeof(cl_int), &_NBrows)); (int)_NBcols, (int)_NBrows, (int)elements_per_row,
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (int)outputFrame.offset, (float)_a, (float)_gain);
args.push_back(std::make_pair(sizeof(cl_int), &outputFrame.offset)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
args.push_back(std::make_pair(sizeof(cl_float), &_a));
args.push_back(std::make_pair(sizeof(cl_float), &_gain));
openCLExecuteKernel(ctx, &retina_kernel, "verticalAnticausalFilter_multGain", globalSize, localSize, args, -1, -1);
} }
void BasicRetinaFilter::_horizontalAnticausalFilter_Irregular(oclMat &outputFrame, const oclMat &spatialConstantBuffer) void BasicRetinaFilter::_verticalCausalFilter_multichannel(UMat &outputFrame)
{ {
int elements_per_row = static_cast<int>(outputFrame.step / outputFrame.elemSize()); int elements_per_row = static_cast<int>(outputFrame.step / outputFrame.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)_NBcols / 2};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 256 };
size_t globalSize[] = {outputFrame.rows, 1, 1};
size_t localSize[] = {256, 1, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &outputFrame.data)); Kernel kernel("verticalCausalFilter_multichannel", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &spatialConstantBuffer.data)); kernel.args(ocl::KernelArg::PtrReadWrite(outputFrame),
args.push_back(std::make_pair(sizeof(cl_int), &outputFrame.cols)); (int)_NBcols, (int)_NBrows, (int)elements_per_row,
args.push_back(std::make_pair(sizeof(cl_int), &outputFrame.rows)); (int)outputFrame.offset, (float)_a, (float)_gain);
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
args.push_back(std::make_pair(sizeof(cl_int), &outputFrame.offset));
args.push_back(std::make_pair(sizeof(cl_int), &spatialConstantBuffer.offset));
openCLExecuteKernel(ctx, &retina_kernel, "horizontalAnticausalFilter_Irregular", globalSize, localSize, args, -1, -1);
} }
// vertical anticausal filter // vertical anticausal filter
void BasicRetinaFilter::_verticalCausalFilter_Irregular(oclMat &outputFrame, const oclMat &spatialConstantBuffer) void BasicRetinaFilter::_verticalCausalFilter_Irregular(UMat &outputFrame, const UMat &spatialConstantBuffer)
{ {
int elements_per_row = static_cast<int>(outputFrame.step / outputFrame.elemSize()); int elements_per_row = static_cast<int>(outputFrame.step / outputFrame.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)outputFrame.cols / 2};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 256 };
size_t globalSize[] = {outputFrame.cols, 1, 1};
size_t localSize[] = {256, 1, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &outputFrame.data)); Kernel kernel("verticalCausalFilter_Irregular", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &spatialConstantBuffer.data)); kernel.args(ocl::KernelArg::PtrReadWrite(outputFrame),
args.push_back(std::make_pair(sizeof(cl_int), &outputFrame.cols)); ocl::KernelArg::PtrReadWrite(spatialConstantBuffer),
args.push_back(std::make_pair(sizeof(cl_int), &outputFrame.rows)); (int)outputFrame.cols, (int)(outputFrame.rows / 3),
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (int)elements_per_row, (int)outputFrame.offset,
args.push_back(std::make_pair(sizeof(cl_int), &outputFrame.offset)); (int)spatialConstantBuffer.offset, (float)_gain);
args.push_back(std::make_pair(sizeof(cl_int), &spatialConstantBuffer.offset)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "verticalCausalFilter_Irregular", globalSize, localSize, args, -1, -1);
} }
void normalizeGrayOutput_0_maxOutputValue(oclMat &inputOutputBuffer, const float maxOutputValue) void normalizeGrayOutput_0_maxOutputValue(UMat &inputOutputBuffer, const float maxOutputValue)
{ {
double min_val, max_val; double min_val, max_val;
ocl::minMax(inputOutputBuffer, &min_val, &max_val); cv::minMaxLoc(inputOutputBuffer, &min_val, &max_val);
float factor = maxOutputValue / static_cast<float>(max_val - min_val); float factor = maxOutputValue / static_cast<float>(max_val - min_val);
float offset = - static_cast<float>(min_val) * factor; float offset = - static_cast<float>(min_val) * factor;
ocl::multiply(factor, inputOutputBuffer, inputOutputBuffer); cv::multiply(factor, inputOutputBuffer, inputOutputBuffer);
ocl::add(inputOutputBuffer, offset, inputOutputBuffer); cv::add(inputOutputBuffer, offset, inputOutputBuffer);
} }
void normalizeGrayOutputCentredSigmoide(const float meanValue, const float sensitivity, oclMat &in, oclMat &out, const float maxValue) void normalizeGrayOutputCentredSigmoide(const float meanValue, const float sensitivity, UMat &in, UMat &out, const float maxValue)
{ {
if (sensitivity == 1.0f) if (sensitivity == 1.0f)
{ {
...@@ -749,63 +688,54 @@ void normalizeGrayOutputCentredSigmoide(const float meanValue, const float sensi ...@@ -749,63 +688,54 @@ void normalizeGrayOutputCentredSigmoide(const float meanValue, const float sensi
float X0 = maxValue / (sensitivity - 1.0f); float X0 = maxValue / (sensitivity - 1.0f);
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)in.cols / 4, (size_t)out.rows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = {16, 16};
size_t globalSize[] = {in.cols, out.rows, 1};
size_t localSize[] = {16, 16, 1};
int elements_per_row = static_cast<int>(out.step / out.elemSize()); int elements_per_row = static_cast<int>(out.step / out.elemSize());
args.push_back(std::make_pair(sizeof(cl_mem), &in.data)); Kernel kernel("normalizeGrayOutputCentredSigmoide", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &out.data)); kernel.args(ocl::KernelArg::PtrReadOnly(in),
args.push_back(std::make_pair(sizeof(cl_int), &in.cols)); ocl::KernelArg::PtrWriteOnly(out),
args.push_back(std::make_pair(sizeof(cl_int), &in.rows)); (int)in.cols, (int)in.rows, (int)elements_per_row,
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (float)meanValue, (float)X0);
args.push_back(std::make_pair(sizeof(cl_float), &meanValue)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
args.push_back(std::make_pair(sizeof(cl_float), &X0));
openCLExecuteKernel(ctx, &retina_kernel, "normalizeGrayOutputCentredSigmoide", globalSize, localSize, args, -1, -1);
} }
void normalizeGrayOutputNearZeroCentreredSigmoide(oclMat &inputPicture, oclMat &outputBuffer, const float sensitivity, const float maxOutputValue) void normalizeGrayOutputNearZeroCentreredSigmoide(UMat &inputPicture, UMat &outputBuffer, const float sensitivity, const float maxOutputValue)
{ {
float X0cube = sensitivity * sensitivity * sensitivity; float X0cube = sensitivity * sensitivity * sensitivity;
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)inputPicture.cols, (size_t)inputPicture.rows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 16, 16 };
size_t globalSize[] = {inputPicture.cols, inputPicture.rows, 1};
size_t localSize[] = {16, 16, 1};
int elements_per_row = static_cast<int>(inputPicture.step / inputPicture.elemSize()); int elements_per_row = static_cast<int>(inputPicture.step / inputPicture.elemSize());
args.push_back(std::make_pair(sizeof(cl_mem), &inputPicture.data));
args.push_back(std::make_pair(sizeof(cl_mem), &outputBuffer.data)); Kernel kernel("normalizeGrayOutputNearZeroCentreredSigmoide", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_int), &inputPicture.cols)); kernel.args(ocl::KernelArg::PtrReadOnly(inputPicture),
args.push_back(std::make_pair(sizeof(cl_int), &inputPicture.rows)); ocl::KernelArg::PtrWriteOnly(outputBuffer),
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (int)inputPicture.cols, (int)inputPicture.rows, (int)elements_per_row,
args.push_back(std::make_pair(sizeof(cl_float), &maxOutputValue)); (float)maxOutputValue, (float)X0cube);
args.push_back(std::make_pair(sizeof(cl_float), &X0cube)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "normalizeGrayOutputNearZeroCentreredSigmoide", globalSize, localSize, args, -1, -1);
} }
void centerReductImageLuminance(oclMat &inputoutput) void centerReductImageLuminance(UMat &inputoutput)
{ {
Scalar mean, stddev; Scalar mean, stddev;
cv::meanStdDev((Mat)inputoutput, mean, stddev); cv::meanStdDev(inputoutput.getMat(ACCESS_READ), mean, stddev);
Context * ctx = Context::getContext(); Context ctx = Context::getDefault();
std::vector<std::pair<size_t, const void *> > args; size_t globalSize[] = {(size_t)inputoutput.cols / 4, (size_t)inputoutput.rows};
size_t globalSize[] = {inputoutput.cols, inputoutput.rows, 1}; size_t localSize[] = {16, 16};
size_t localSize[] = {16, 16, 1};
float f_mean = static_cast<float>(mean[0]); float f_mean = static_cast<float>(mean[0]);
float f_stddev = static_cast<float>(stddev[0]); float f_stddev = static_cast<float>(stddev[0]);
int elements_per_row = static_cast<int>(inputoutput.step / inputoutput.elemSize()); int elements_per_row = static_cast<int>(inputoutput.step / inputoutput.elemSize());
args.push_back(std::make_pair(sizeof(cl_mem), &inputoutput.data));
args.push_back(std::make_pair(sizeof(cl_int), &inputoutput.cols)); Kernel kernel("centerReductImageLuminance", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_int), &inputoutput.rows)); kernel.args(ocl::KernelArg::PtrReadWrite(inputoutput),
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (int)inputoutput.cols, (int)inputoutput.rows, (int)elements_per_row,
args.push_back(std::make_pair(sizeof(cl_float), &f_mean)); (float)f_mean, (float)f_stddev);
args.push_back(std::make_pair(sizeof(cl_float), &f_stddev)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "centerReductImageLuminance", globalSize, localSize, args, -1, -1);
} }
/////////////////////////////////////// ///////////////////////////////////////
...@@ -873,7 +803,7 @@ void ParvoRetinaFilter::setOPLandParvoFiltersParameters(const float beta1, const ...@@ -873,7 +803,7 @@ void ParvoRetinaFilter::setOPLandParvoFiltersParameters(const float beta1, const
setLPfilterParameters(0, tau1, k1, 2); setLPfilterParameters(0, tau1, k1, 2);
} }
const oclMat &ParvoRetinaFilter::runFilter(const oclMat &inputFrame, const bool useParvoOutput) const UMat &ParvoRetinaFilter::runFilter(const UMat &inputFrame, const bool useParvoOutput)
{ {
_spatiotemporalLPfilter(inputFrame, _photoreceptorsOutput); _spatiotemporalLPfilter(inputFrame, _photoreceptorsOutput);
_spatiotemporalLPfilter(_photoreceptorsOutput, _horizontalCellsOutput, 1); _spatiotemporalLPfilter(_photoreceptorsOutput, _horizontalCellsOutput, 1);
...@@ -886,7 +816,7 @@ const oclMat &ParvoRetinaFilter::runFilter(const oclMat &inputFrame, const bool ...@@ -886,7 +816,7 @@ const oclMat &ParvoRetinaFilter::runFilter(const oclMat &inputFrame, const bool
_localLuminanceAdaptation(_parvocellularOutputON, _localAdaptationON); _localLuminanceAdaptation(_parvocellularOutputON, _localAdaptationON);
_spatiotemporalLPfilter(_bipolarCellsOutputOFF, _localAdaptationOFF, 2); _spatiotemporalLPfilter(_bipolarCellsOutputOFF, _localAdaptationOFF, 2);
_localLuminanceAdaptation(_parvocellularOutputOFF, _localAdaptationOFF); _localLuminanceAdaptation(_parvocellularOutputOFF, _localAdaptationOFF);
ocl::subtract(_parvocellularOutputON, _parvocellularOutputOFF, _parvocellularOutputONminusOFF); cv::subtract(_parvocellularOutputON, _parvocellularOutputOFF, _parvocellularOutputONminusOFF);
} }
return _parvocellularOutputONminusOFF; return _parvocellularOutputONminusOFF;
...@@ -895,21 +825,18 @@ void ParvoRetinaFilter::_OPL_OnOffWaysComputing() ...@@ -895,21 +825,18 @@ void ParvoRetinaFilter::_OPL_OnOffWaysComputing()
{ {
int elements_per_row = static_cast<int>(_photoreceptorsOutput.step / _photoreceptorsOutput.elemSize()); int elements_per_row = static_cast<int>(_photoreceptorsOutput.step / _photoreceptorsOutput.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {((size_t)_photoreceptorsOutput.cols + 3) / 4, (size_t)_photoreceptorsOutput.rows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 16, 16 };
size_t globalSize[] = {(_photoreceptorsOutput.cols + 3) / 4, _photoreceptorsOutput.rows, 1};
size_t localSize[] = {16, 16, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &_photoreceptorsOutput.data)); Kernel kernel("OPL_OnOffWaysComputing", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &_horizontalCellsOutput.data)); kernel.args(ocl::KernelArg::PtrReadOnly(_photoreceptorsOutput),
args.push_back(std::make_pair(sizeof(cl_mem), &_bipolarCellsOutputON.data)); ocl::KernelArg::PtrReadOnly(_horizontalCellsOutput),
args.push_back(std::make_pair(sizeof(cl_mem), &_bipolarCellsOutputOFF.data)); ocl::KernelArg::PtrWriteOnly(_bipolarCellsOutputON),
args.push_back(std::make_pair(sizeof(cl_mem), &_parvocellularOutputON.data)); ocl::KernelArg::PtrWriteOnly(_bipolarCellsOutputOFF),
args.push_back(std::make_pair(sizeof(cl_mem), &_parvocellularOutputOFF.data)); ocl::KernelArg::PtrWriteOnly(_parvocellularOutputON),
args.push_back(std::make_pair(sizeof(cl_int), &_photoreceptorsOutput.cols)); ocl::KernelArg::PtrWriteOnly(_parvocellularOutputOFF),
args.push_back(std::make_pair(sizeof(cl_int), &_photoreceptorsOutput.rows)); (int)_photoreceptorsOutput.cols, (int)_photoreceptorsOutput.rows, (int)elements_per_row);
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "OPL_OnOffWaysComputing", globalSize, localSize, args, -1, -1);
} }
/////////////////////////////////////// ///////////////////////////////////////
...@@ -978,31 +905,28 @@ void MagnoRetinaFilter::setCoefficientsTable(const float parasolCells_beta, cons ...@@ -978,31 +905,28 @@ void MagnoRetinaFilter::setCoefficientsTable(const float parasolCells_beta, cons
} }
void MagnoRetinaFilter::_amacrineCellsComputing( void MagnoRetinaFilter::_amacrineCellsComputing(
const oclMat &OPL_ON, const UMat &OPL_ON,
const oclMat &OPL_OFF const UMat &OPL_OFF
) )
{ {
int elements_per_row = static_cast<int>(OPL_ON.step / OPL_ON.elemSize()); int elements_per_row = static_cast<int>(OPL_ON.step / OPL_ON.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)OPL_ON.cols / 4, (size_t)OPL_ON.rows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 16, 16 };
size_t globalSize[] = {OPL_ON.cols, OPL_ON.rows, 1};
size_t localSize[] = {16, 16, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &OPL_ON.data)); Kernel kernel("amacrineCellsComputing", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &OPL_OFF.data)); kernel.args(ocl::KernelArg::PtrReadOnly(OPL_ON),
args.push_back(std::make_pair(sizeof(cl_mem), &_previousInput_ON.data)); ocl::KernelArg::PtrReadOnly(OPL_OFF),
args.push_back(std::make_pair(sizeof(cl_mem), &_previousInput_OFF.data)); ocl::KernelArg::PtrReadWrite(_previousInput_ON),
args.push_back(std::make_pair(sizeof(cl_mem), &_amacrinCellsTempOutput_ON.data)); ocl::KernelArg::PtrReadWrite(_previousInput_OFF),
args.push_back(std::make_pair(sizeof(cl_mem), &_amacrinCellsTempOutput_OFF.data)); ocl::KernelArg::PtrReadWrite(_amacrinCellsTempOutput_ON),
args.push_back(std::make_pair(sizeof(cl_int), &OPL_ON.cols)); ocl::KernelArg::PtrReadWrite(_amacrinCellsTempOutput_OFF),
args.push_back(std::make_pair(sizeof(cl_int), &OPL_ON.rows)); (int)OPL_ON.cols, (int)OPL_ON.rows, (int)elements_per_row,
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (float)_temporalCoefficient);
args.push_back(std::make_pair(sizeof(cl_float), &_temporalCoefficient)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "amacrineCellsComputing", globalSize, localSize, args, -1, -1);
} }
const oclMat &MagnoRetinaFilter::runFilter(const oclMat &OPL_ON, const oclMat &OPL_OFF) const UMat &MagnoRetinaFilter::runFilter(const UMat &OPL_ON, const UMat &OPL_OFF)
{ {
// Compute the high pass temporal filter // Compute the high pass temporal filter
_amacrineCellsComputing(OPL_ON, OPL_OFF); _amacrineCellsComputing(OPL_ON, OPL_OFF);
...@@ -1018,7 +942,7 @@ const oclMat &MagnoRetinaFilter::runFilter(const oclMat &OPL_ON, const oclMat &O ...@@ -1018,7 +942,7 @@ const oclMat &MagnoRetinaFilter::runFilter(const oclMat &OPL_ON, const oclMat &O
_spatiotemporalLPfilter(_magnoXOutputOFF, _localProcessBufferOFF, 1); _spatiotemporalLPfilter(_magnoXOutputOFF, _localProcessBufferOFF, 1);
_localLuminanceAdaptation(_magnoXOutputOFF, _localProcessBufferOFF); _localLuminanceAdaptation(_magnoXOutputOFF, _localProcessBufferOFF);
_magnoYOutput = _magnoXOutputON + _magnoXOutputOFF; add(_magnoXOutputON, _magnoXOutputOFF, _magnoYOutput);
return _magnoYOutput; return _magnoYOutput;
} }
...@@ -1029,7 +953,7 @@ const oclMat &MagnoRetinaFilter::runFilter(const oclMat &OPL_ON, const oclMat &O ...@@ -1029,7 +953,7 @@ const oclMat &MagnoRetinaFilter::runFilter(const oclMat &OPL_ON, const oclMat &O
// define an array of ROI headers of input x // define an array of ROI headers of input x
#define MAKE_OCLMAT_SLICES(x, n) \ #define MAKE_OCLMAT_SLICES(x, n) \
oclMat x##_slices[n];\ UMat x##_slices[n];\
for(int _SLICE_INDEX_ = 0; _SLICE_INDEX_ < n; _SLICE_INDEX_ ++)\ for(int _SLICE_INDEX_ = 0; _SLICE_INDEX_ < n; _SLICE_INDEX_ ++)\
{\ {\
x##_slices[_SLICE_INDEX_] = x(getROI(_SLICE_INDEX_));\ x##_slices[_SLICE_INDEX_] = x(getROI(_SLICE_INDEX_));\
...@@ -1107,20 +1031,17 @@ void RetinaColor::resize(const unsigned int NBrows, const unsigned int NBcolumns ...@@ -1107,20 +1031,17 @@ void RetinaColor::resize(const unsigned int NBrows, const unsigned int NBcolumns
clearAllBuffers(); clearAllBuffers();
} }
static void inverseValue(oclMat &input) static void inverseValue(UMat &input)
{ {
int elements_per_row = static_cast<int>(input.step / input.elemSize()); int elements_per_row = static_cast<int>(input.step / input.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)input.cols / 4, (size_t)input.rows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 16, 16 };
size_t globalSize[] = {input.cols, input.rows, 1};
size_t localSize[] = {16, 16, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &input.data)); Kernel kernel("inverseValue", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_int), &input.cols)); kernel.args(ocl::KernelArg::PtrReadWrite(input),
args.push_back(std::make_pair(sizeof(cl_int), &input.rows)); (int)input.cols, (int)input.rows, (int)elements_per_row);
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "inverseValue", globalSize, localSize, args, -1, -1);
} }
void RetinaColor::_initColorSampling() void RetinaColor::_initColorSampling()
...@@ -1129,76 +1050,69 @@ void RetinaColor::_initColorSampling() ...@@ -1129,76 +1050,69 @@ void RetinaColor::_initColorSampling()
_pR = _pB = 0.25; _pR = _pB = 0.25;
_pG = 0.5; _pG = 0.5;
// filling the mosaic buffer: // filling the mosaic buffer:
_RGBmosaic = 0; Mat tmp_mat(_NBrows * 3, _NBcols, CV_32FC1, Scalar(0));
Mat tmp_mat(_NBrows * 3, _NBcols, CV_32FC1);
float * tmp_mat_ptr = tmp_mat.ptr<float>(); float * tmp_mat_ptr = tmp_mat.ptr<float>();
tmp_mat.setTo(0);
for (unsigned int index = 0 ; index < getNBpixels(); ++index) for (unsigned int index = 0 ; index < getNBpixels(); ++index)
{ {
tmp_mat_ptr[bayerSampleOffset(index)] = 1.0; tmp_mat_ptr[bayerSampleOffset(index)] = 1.0;
} }
_RGBmosaic.upload(tmp_mat); tmp_mat.copyTo(_RGBmosaic);
// computing photoreceptors local density // computing photoreceptors local density
MAKE_OCLMAT_SLICES(_RGBmosaic, 3); MAKE_OCLMAT_SLICES(_RGBmosaic, 3);
MAKE_OCLMAT_SLICES(_colorLocalDensity, 3); MAKE_OCLMAT_SLICES(_colorLocalDensity, 3);
_colorLocalDensity.setTo(0); _colorLocalDensity.setTo(0);
_spatiotemporalLPfilter(_RGBmosaic_slices[0], _colorLocalDensity_slices[0]); _spatiotemporalLPfilter_h(_RGBmosaic_slices[0], _colorLocalDensity_slices[0]);
_spatiotemporalLPfilter(_RGBmosaic_slices[1], _colorLocalDensity_slices[1]); _spatiotemporalLPfilter_h(_RGBmosaic_slices[1], _colorLocalDensity_slices[1]);
_spatiotemporalLPfilter(_RGBmosaic_slices[2], _colorLocalDensity_slices[2]); _spatiotemporalLPfilter_h(_RGBmosaic_slices[2], _colorLocalDensity_slices[2]);
_spatiotemporalLPfilter_v(_colorLocalDensity, 1);
//_colorLocalDensity = oclMat(_colorLocalDensity.size(), _colorLocalDensity.type(), 1.f) / _colorLocalDensity; //_colorLocalDensity = UMat(_colorLocalDensity.size(), _colorLocalDensity.type(), 1.f) / _colorLocalDensity;
inverseValue(_colorLocalDensity); inverseValue(_colorLocalDensity);
_objectInit = true; _objectInit = true;
} }
static void demultiplex(const oclMat &input, oclMat &ouput) static void demultiplex(const UMat &input, UMat &ouput)
{ {
int elements_per_row = static_cast<int>(input.step / input.elemSize()); int elements_per_row = static_cast<int>(input.step / input.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)input.cols / 4, (size_t)input.rows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 16, 16 };
size_t globalSize[] = {input.cols, input.rows, 1};
size_t localSize[] = {16, 16, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &input.data)); Kernel kernel("runColorDemultiplexingBayer", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &ouput.data)); kernel.args(ocl::KernelArg::PtrReadOnly(input),
args.push_back(std::make_pair(sizeof(cl_int), &input.cols)); ocl::KernelArg::PtrWriteOnly(ouput),
args.push_back(std::make_pair(sizeof(cl_int), &input.rows)); (int)input.cols, (int)input.rows, (int)elements_per_row);
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "runColorDemultiplexingBayer", globalSize, localSize, args, -1, -1);
} }
static void normalizePhotoDensity( static void normalizePhotoDensity(
const oclMat &chroma, const UMat &chroma,
const oclMat &colorDensity, const UMat &colorDensity,
const oclMat &multiplex, const UMat &multiplex,
oclMat &ocl_luma, UMat &ocl_luma,
oclMat &demultiplex, UMat &demultiplex,
const float pG const float pG
) )
{ {
int elements_per_row = static_cast<int>(ocl_luma.step / ocl_luma.elemSize()); int elements_per_row = static_cast<int>(ocl_luma.step / ocl_luma.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)ocl_luma.cols / 4, (size_t)ocl_luma.rows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 16, 16 };
size_t globalSize[] = {ocl_luma.cols, ocl_luma.rows, 1};
size_t localSize[] = {16, 16, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &chroma.data)); Kernel kernel("normalizePhotoDensity", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &colorDensity.data)); kernel.args(ocl::KernelArg::PtrReadOnly(chroma),
args.push_back(std::make_pair(sizeof(cl_mem), &multiplex.data)); ocl::KernelArg::PtrReadOnly(colorDensity),
args.push_back(std::make_pair(sizeof(cl_mem), &ocl_luma.data)); ocl::KernelArg::PtrReadOnly(multiplex),
args.push_back(std::make_pair(sizeof(cl_mem), &demultiplex.data)); ocl::KernelArg::PtrWriteOnly(ocl_luma),
args.push_back(std::make_pair(sizeof(cl_int), &ocl_luma.cols)); ocl::KernelArg::PtrWriteOnly(demultiplex),
args.push_back(std::make_pair(sizeof(cl_int), &ocl_luma.rows)); (int)ocl_luma.cols, (int)ocl_luma.rows, (int)elements_per_row,
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (float)pG);
args.push_back(std::make_pair(sizeof(cl_float), &pG)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "normalizePhotoDensity", globalSize, localSize, args, -1, -1);
} }
static void substractResidual( static void substractResidual(
oclMat &colorDemultiplex, UMat &colorDemultiplex,
float pR, float pR,
float pG, float pG,
float pB float pB
...@@ -1206,43 +1120,35 @@ static void substractResidual( ...@@ -1206,43 +1120,35 @@ static void substractResidual(
{ {
int elements_per_row = static_cast<int>(colorDemultiplex.step / colorDemultiplex.elemSize()); int elements_per_row = static_cast<int>(colorDemultiplex.step / colorDemultiplex.elemSize());
Context * ctx = Context::getContext();
std::vector<std::pair<size_t, const void *> > args;
int rows = colorDemultiplex.rows / 3, cols = colorDemultiplex.cols; int rows = colorDemultiplex.rows / 3, cols = colorDemultiplex.cols;
size_t globalSize[] = {cols, rows, 1}; size_t globalSize[] = {(size_t)cols / 4, (size_t)rows};
size_t localSize[] = {16, 16, 1}; size_t localSize[] = { 16, 16 };
args.push_back(std::make_pair(sizeof(cl_mem), &colorDemultiplex.data)); Kernel kernel("substractResidual", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_int), &cols)); kernel.args(ocl::KernelArg::PtrReadWrite(colorDemultiplex),
args.push_back(std::make_pair(sizeof(cl_int), &rows)); (int)cols, (int)rows, (int)elements_per_row,
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (float)pR, (float)pG, (float)pB);
args.push_back(std::make_pair(sizeof(cl_float), &pR)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
args.push_back(std::make_pair(sizeof(cl_float), &pG));
args.push_back(std::make_pair(sizeof(cl_float), &pB));
openCLExecuteKernel(ctx, &retina_kernel, "substractResidual", globalSize, localSize, args, -1, -1);
} }
static void demultiplexAssign(const oclMat& input, const oclMat& output) static void demultiplexAssign(const UMat& input, const UMat& output)
{ {
// only supports bayer // only supports bayer
int elements_per_row = static_cast<int>(input.step / input.elemSize()); int elements_per_row = static_cast<int>(input.step / input.elemSize());
Context * ctx = Context::getContext();
std::vector<std::pair<size_t, const void *> > args;
int rows = input.rows / 3, cols = input.cols; int rows = input.rows / 3, cols = input.cols;
size_t globalSize[] = {cols, rows, 1}; size_t globalSize[] = {(size_t)cols, (size_t)rows};
size_t localSize[] = {16, 16, 1}; size_t localSize[] = { 16, 16 };
args.push_back(std::make_pair(sizeof(cl_mem), &input.data)); Kernel kernel("demultiplexAssign", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &output.data)); kernel.args(ocl::KernelArg::PtrReadOnly(input),
args.push_back(std::make_pair(sizeof(cl_int), &cols)); ocl::KernelArg::PtrWriteOnly(output),
args.push_back(std::make_pair(sizeof(cl_int), &rows)); (int)cols, (int)rows, (int)elements_per_row);
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "demultiplexAssign", globalSize, localSize, args, -1, -1);
} }
void RetinaColor::runColorDemultiplexing( void RetinaColor::runColorDemultiplexing(
const oclMat &ocl_multiplexed_input, const UMat &ocl_multiplexed_input,
const bool adaptiveFiltering, const bool adaptiveFiltering,
const float maxInputValue const float maxInputValue
) )
...@@ -1262,9 +1168,10 @@ void RetinaColor::runColorDemultiplexing( ...@@ -1262,9 +1168,10 @@ void RetinaColor::runColorDemultiplexing(
CV_Assert(adaptiveFiltering == false); CV_Assert(adaptiveFiltering == false);
} }
_spatiotemporalLPfilter(_demultiplexedTempBuffer_slices[0], _chrominance_slices[0]); _spatiotemporalLPfilter_h(_demultiplexedTempBuffer_slices[0], _chrominance_slices[0]);
_spatiotemporalLPfilter(_demultiplexedTempBuffer_slices[1], _chrominance_slices[1]); _spatiotemporalLPfilter_h(_demultiplexedTempBuffer_slices[1], _chrominance_slices[1]);
_spatiotemporalLPfilter(_demultiplexedTempBuffer_slices[2], _chrominance_slices[2]); _spatiotemporalLPfilter_h(_demultiplexedTempBuffer_slices[2], _chrominance_slices[2]);
_spatiotemporalLPfilter_v(_chrominance, 1);
if (!adaptiveFiltering)// compute the gradient on the luminance if (!adaptiveFiltering)// compute the gradient on the luminance
{ {
...@@ -1277,27 +1184,39 @@ void RetinaColor::runColorDemultiplexing( ...@@ -1277,27 +1184,39 @@ void RetinaColor::runColorDemultiplexing(
// compute the gradient of the luminance // compute the gradient of the luminance
_computeGradient(_luminance, _imageGradient); _computeGradient(_luminance, _imageGradient);
_adaptiveSpatialLPfilter(_RGBmosaic_slices[0], _imageGradient, _chrominance_slices[0]); _adaptiveSpatialLPfilter_h(_RGBmosaic_slices[0], _imageGradient, _chrominance_slices[0]);
_adaptiveSpatialLPfilter(_RGBmosaic_slices[1], _imageGradient, _chrominance_slices[1]); _adaptiveSpatialLPfilter_h(_RGBmosaic_slices[1], _imageGradient, _chrominance_slices[1]);
_adaptiveSpatialLPfilter(_RGBmosaic_slices[2], _imageGradient, _chrominance_slices[2]); _adaptiveSpatialLPfilter_h(_RGBmosaic_slices[2], _imageGradient, _chrominance_slices[2]);
_adaptiveSpatialLPfilter_v(_imageGradient, _chrominance);
_adaptiveSpatialLPfilter(_demultiplexedTempBuffer_slices[0], _imageGradient, _demultiplexedColorFrame_slices[0]); _adaptiveSpatialLPfilter_h(_demultiplexedTempBuffer_slices[0], _imageGradient, _demultiplexedColorFrame_slices[0]);
_adaptiveSpatialLPfilter(_demultiplexedTempBuffer_slices[1], _imageGradient, _demultiplexedColorFrame_slices[1]); _adaptiveSpatialLPfilter_h(_demultiplexedTempBuffer_slices[1], _imageGradient, _demultiplexedColorFrame_slices[1]);
_adaptiveSpatialLPfilter(_demultiplexedTempBuffer_slices[2], _imageGradient, _demultiplexedColorFrame_slices[2]); _adaptiveSpatialLPfilter_h(_demultiplexedTempBuffer_slices[2], _imageGradient, _demultiplexedColorFrame_slices[2]);
_adaptiveSpatialLPfilter_v(_imageGradient, _demultiplexedColorFrame);
_demultiplexedColorFrame /= _chrominance; // per element division divide(_demultiplexedColorFrame, _chrominance, _demultiplexedColorFrame);
substractResidual(_demultiplexedColorFrame, _pR, _pG, _pB); substractResidual(_demultiplexedColorFrame, _pR, _pG, _pB);
runColorMultiplexing(_demultiplexedColorFrame, _tempMultiplexedFrame); runColorMultiplexing(_demultiplexedColorFrame, _tempMultiplexedFrame);
_demultiplexedTempBuffer.setTo(0); _demultiplexedTempBuffer.setTo(0);
_luminance = ocl_multiplexed_input - _tempMultiplexedFrame; subtract(ocl_multiplexed_input, _tempMultiplexedFrame, _luminance);
demultiplexAssign(_demultiplexedColorFrame, _demultiplexedTempBuffer); demultiplexAssign(_demultiplexedColorFrame, _demultiplexedTempBuffer);
for(int i = 0; i < 3; i ++) _spatiotemporalLPfilter_h(_demultiplexedTempBuffer_slices[0], _demultiplexedTempBuffer_slices[0]);
{ _spatiotemporalLPfilter_h(_demultiplexedTempBuffer_slices[1], _demultiplexedTempBuffer_slices[1]);
_spatiotemporalLPfilter(_demultiplexedTempBuffer_slices[i], _demultiplexedTempBuffer_slices[i]); _spatiotemporalLPfilter_h(_demultiplexedTempBuffer_slices[2], _demultiplexedTempBuffer_slices[2]);
_demultiplexedColorFrame_slices[i] = _demultiplexedTempBuffer_slices[i] * _colorLocalDensity_slices[i] + _luminance; _spatiotemporalLPfilter_v(_demultiplexedTempBuffer, 1);
}
multiply(_demultiplexedTempBuffer, _colorLocalDensity, _demultiplexedColorFrame);
std::vector<UMat> m;
UMat _luminance_concat;
m.push_back(_luminance);
m.push_back(_luminance);
m.push_back(_luminance);
vconcat(m, _luminance_concat);
add(_demultiplexedColorFrame, _luminance_concat, _demultiplexedColorFrame);
} }
// eliminate saturated colors by simple clipping values to the input range // eliminate saturated colors by simple clipping values to the input range
clipRGBOutput_0_maxInputValue(_demultiplexedColorFrame, maxInputValue); clipRGBOutput_0_maxInputValue(_demultiplexedColorFrame, maxInputValue);
...@@ -1307,44 +1226,38 @@ void RetinaColor::runColorDemultiplexing( ...@@ -1307,44 +1226,38 @@ void RetinaColor::runColorDemultiplexing(
ocl::normalizeGrayOutputCentredSigmoide(128, maxInputValue, _demultiplexedColorFrame, _demultiplexedColorFrame); ocl::normalizeGrayOutputCentredSigmoide(128, maxInputValue, _demultiplexedColorFrame, _demultiplexedColorFrame);
} }
} }
void RetinaColor::runColorMultiplexing(const oclMat &demultiplexedInputFrame, oclMat &multiplexedFrame) void RetinaColor::runColorMultiplexing(const UMat &demultiplexedInputFrame, UMat &multiplexedFrame)
{ {
int elements_per_row = static_cast<int>(multiplexedFrame.step / multiplexedFrame.elemSize()); int elements_per_row = static_cast<int>(multiplexedFrame.step / multiplexedFrame.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)multiplexedFrame.cols / 4, (size_t)multiplexedFrame.rows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 16, 16 };
size_t globalSize[] = {multiplexedFrame.cols, multiplexedFrame.rows, 1};
size_t localSize[] = {16, 16, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &demultiplexedInputFrame.data)); Kernel kernel("runColorMultiplexingBayer", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &multiplexedFrame.data)); kernel.args(ocl::KernelArg::PtrReadOnly(demultiplexedInputFrame),
args.push_back(std::make_pair(sizeof(cl_int), &multiplexedFrame.cols)); ocl::KernelArg::PtrWriteOnly(multiplexedFrame),
args.push_back(std::make_pair(sizeof(cl_int), &multiplexedFrame.rows)); (int)multiplexedFrame.cols, (int)multiplexedFrame.rows, (int)elements_per_row);
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "runColorMultiplexingBayer", globalSize, localSize, args, -1, -1);
} }
void RetinaColor::clipRGBOutput_0_maxInputValue(oclMat &inputOutputBuffer, const float maxInputValue) void RetinaColor::clipRGBOutput_0_maxInputValue(UMat &inputOutputBuffer, const float maxInputValue)
{ {
// the kernel is equivalent to: // the kernel is equivalent to:
//ocl::threshold(inputOutputBuffer, inputOutputBuffer, maxInputValue, maxInputValue, THRESH_TRUNC); //ocl::threshold(inputOutputBuffer, inputOutputBuffer, maxInputValue, maxInputValue, THRESH_TRUNC);
//ocl::threshold(inputOutputBuffer, inputOutputBuffer, 0, 0, THRESH_TOZERO); //ocl::threshold(inputOutputBuffer, inputOutputBuffer, 0, 0, THRESH_TOZERO);
int elements_per_row = static_cast<int>(inputOutputBuffer.step / inputOutputBuffer.elemSize()); int elements_per_row = static_cast<int>(inputOutputBuffer.step / inputOutputBuffer.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)_NBcols / 4, (size_t)inputOutputBuffer.rows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 16, 16 };
size_t globalSize[] = {_NBcols, inputOutputBuffer.rows, 1};
size_t localSize[] = {16, 16, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &inputOutputBuffer.data)); Kernel kernel("clipRGBOutput_0_maxInputValue", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_int), &_NBcols)); kernel.args(ocl::KernelArg::PtrReadWrite(inputOutputBuffer),
args.push_back(std::make_pair(sizeof(cl_int), &inputOutputBuffer.rows)); (int)_NBcols, (int)inputOutputBuffer.rows, (int)elements_per_row,
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (float)maxInputValue);
args.push_back(std::make_pair(sizeof(cl_float), &maxInputValue)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "clipRGBOutput_0_maxInputValue", globalSize, localSize, args, -1, -1);
} }
void RetinaColor::_adaptiveSpatialLPfilter(const oclMat &inputFrame, const oclMat &gradient, oclMat &outputFrame) void RetinaColor::_adaptiveSpatialLPfilter_h(const UMat &inputFrame, const UMat &gradient, UMat &outputFrame)
{ {
/**********/ /**********/
_gain = (1 - 0.57f) * (1 - 0.57f) * (1 - 0.06f) * (1 - 0.06f); _gain = (1 - 0.57f) * (1 - 0.57f) * (1 - 0.06f) * (1 - 0.06f);
...@@ -1352,69 +1265,41 @@ void RetinaColor::_adaptiveSpatialLPfilter(const oclMat &inputFrame, const oclMa ...@@ -1352,69 +1265,41 @@ void RetinaColor::_adaptiveSpatialLPfilter(const oclMat &inputFrame, const oclMa
// launch the serie of 1D directional filters in order to compute the 2D low pass filter // launch the serie of 1D directional filters in order to compute the 2D low pass filter
// -> horizontal filters work with the first layer of imageGradient // -> horizontal filters work with the first layer of imageGradient
_adaptiveHorizontalCausalFilter_addInput(inputFrame, gradient, outputFrame); _adaptiveHorizontalCausalFilter_addInput(inputFrame, gradient, outputFrame);
_horizontalAnticausalFilter_Irregular(outputFrame, gradient);
// -> horizontal filters work with the second layer of imageGradient
_verticalCausalFilter_Irregular(outputFrame, gradient(getROI(1)));
_adaptiveVerticalAnticausalFilter_multGain(gradient, outputFrame);
} }
void RetinaColor::_adaptiveHorizontalCausalFilter_addInput(const oclMat &inputFrame, const oclMat &gradient, oclMat &outputFrame) void RetinaColor::_adaptiveSpatialLPfilter_v(const UMat &gradient, UMat &outputFrame)
{ {
int elements_per_row = static_cast<int>(inputFrame.step / inputFrame.elemSize()); _verticalCausalFilter_Irregular(outputFrame, gradient(getROI(1)));
Context * ctx = Context::getContext();
std::vector<std::pair<size_t, const void *> > args;
size_t globalSize[] = {_NBrows, 1, 1};
size_t localSize[] = {256, 1, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &inputFrame.data));
args.push_back(std::make_pair(sizeof(cl_mem), &gradient.data));
args.push_back(std::make_pair(sizeof(cl_mem), &outputFrame.data));
args.push_back(std::make_pair(sizeof(cl_int), &_NBcols));
args.push_back(std::make_pair(sizeof(cl_int), &_NBrows));
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row));
args.push_back(std::make_pair(sizeof(cl_int), &inputFrame.offset));
args.push_back(std::make_pair(sizeof(cl_int), &gradient.offset));
args.push_back(std::make_pair(sizeof(cl_int), &outputFrame.offset));
openCLExecuteKernel(ctx, &retina_kernel, "adaptiveHorizontalCausalFilter_addInput", globalSize, localSize, args, -1, -1);
} }
void RetinaColor::_adaptiveVerticalAnticausalFilter_multGain(const oclMat &gradient, oclMat &outputFrame) void RetinaColor::_adaptiveHorizontalCausalFilter_addInput(const UMat &inputFrame, const UMat &gradient, UMat &outputFrame)
{ {
int elements_per_row = static_cast<int>(outputFrame.step / outputFrame.elemSize()); int elements_per_row = static_cast<int>(inputFrame.step / inputFrame.elemSize());
Context * ctx = Context::getContext();
std::vector<std::pair<size_t, const void *> > args;
size_t globalSize[] = {_NBcols, 1, 1};
size_t localSize[] = {256, 1, 1};
int gradOffset = gradient.offset + static_cast<int>(gradient.step * _NBrows); size_t globalSize[] = {(size_t)_NBrows};
size_t localSize[] = { 256 };
args.push_back(std::make_pair(sizeof(cl_mem), &gradient.data)); Kernel kernel("adaptiveHorizontalCausalFilter_addInput", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &outputFrame.data)); kernel.args(ocl::KernelArg::PtrReadOnly(inputFrame),
args.push_back(std::make_pair(sizeof(cl_int), &_NBcols)); ocl::KernelArg::PtrReadOnly(gradient),
args.push_back(std::make_pair(sizeof(cl_int), &_NBrows)); ocl::KernelArg::PtrWriteOnly(outputFrame),
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); (int)_NBcols, (int)_NBrows, (int)elements_per_row, (int)inputFrame.offset,
args.push_back(std::make_pair(sizeof(cl_int), &gradOffset)); (int)gradient.offset, (int)outputFrame.offset);
args.push_back(std::make_pair(sizeof(cl_int), &outputFrame.offset)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
args.push_back(std::make_pair(sizeof(cl_float), &_gain));
openCLExecuteKernel(ctx, &retina_kernel, "adaptiveVerticalAnticausalFilter_multGain", globalSize, localSize, args, -1, -1);
} }
void RetinaColor::_computeGradient(const oclMat &luminance, oclMat &gradient)
void RetinaColor::_computeGradient(const UMat &luminance, UMat &gradient)
{ {
int elements_per_row = static_cast<int>(luminance.step / luminance.elemSize()); int elements_per_row = static_cast<int>(luminance.step / luminance.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)_NBcols, (size_t)_NBrows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 16, 16 };
size_t globalSize[] = {_NBcols, _NBrows, 1};
size_t localSize[] = {16, 16, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &luminance.data)); Kernel kernel("computeGradient", ocl::bioinspired::retina_kernel_oclsrc);
args.push_back(std::make_pair(sizeof(cl_mem), &gradient.data)); kernel.args(ocl::KernelArg::PtrReadOnly(luminance),
args.push_back(std::make_pair(sizeof(cl_int), &_NBcols)); ocl::KernelArg::PtrWriteOnly(gradient),
args.push_back(std::make_pair(sizeof(cl_int), &_NBrows)); (int)_NBcols, (int)_NBrows, (int)elements_per_row);
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row)); kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
openCLExecuteKernel(ctx, &retina_kernel, "computeGradient", globalSize, localSize, args, -1, -1);
} }
/////////////////////////////////////// ///////////////////////////////////////
...@@ -1500,7 +1385,7 @@ void RetinaFilter::setGlobalParameters(const float OPLspatialResponse1, const fl ...@@ -1500,7 +1385,7 @@ void RetinaFilter::setGlobalParameters(const float OPLspatialResponse1, const fl
_setInitPeriodCount(); _setInitPeriodCount();
} }
bool RetinaFilter::checkInput(const oclMat &input, const bool) bool RetinaFilter::checkInput(const UMat &input, const bool)
{ {
BasicRetinaFilter *inputTarget = &_photoreceptorsPrefilter; BasicRetinaFilter *inputTarget = &_photoreceptorsPrefilter;
...@@ -1518,7 +1403,7 @@ bool RetinaFilter::checkInput(const oclMat &input, const bool) ...@@ -1518,7 +1403,7 @@ bool RetinaFilter::checkInput(const oclMat &input, const bool)
} }
// main function that runs the filter for a given input frame // main function that runs the filter for a given input frame
bool RetinaFilter::runFilter(const oclMat &imageInput, const bool useAdaptiveFiltering, const bool processRetinaParvoMagnoMapping, const bool useColorMode, const bool inputIsColorMultiplexed) bool RetinaFilter::runFilter(const UMat &imageInput, const bool useAdaptiveFiltering, const bool processRetinaParvoMagnoMapping, const bool useColorMode, const bool inputIsColorMultiplexed)
{ {
// preliminary check // preliminary check
bool processSuccess = true; bool processSuccess = true;
...@@ -1537,8 +1422,8 @@ bool RetinaFilter::runFilter(const oclMat &imageInput, const bool useAdaptiveFil ...@@ -1537,8 +1422,8 @@ bool RetinaFilter::runFilter(const oclMat &imageInput, const bool useAdaptiveFil
_useColorMode = useColorMode; _useColorMode = useColorMode;
oclMat selectedPhotoreceptorsLocalAdaptationInput = imageInput; UMat selectedPhotoreceptorsLocalAdaptationInput = imageInput;
oclMat selectedPhotoreceptorsColorInput = imageInput; UMat selectedPhotoreceptorsColorInput = imageInput;
//********** Following is input data specific photoreceptors processing //********** Following is input data specific photoreceptors processing
if (useColorMode && (!inputIsColorMultiplexed)) // not multiplexed color input case if (useColorMode && (!inputIsColorMultiplexed)) // not multiplexed color input case
...@@ -1592,7 +1477,7 @@ bool RetinaFilter::runFilter(const oclMat &imageInput, const bool useAdaptiveFil ...@@ -1592,7 +1477,7 @@ bool RetinaFilter::runFilter(const oclMat &imageInput, const bool useAdaptiveFil
return processSuccess; return processSuccess;
} }
const oclMat &RetinaFilter::getContours() const UMat &RetinaFilter::getContours()
{ {
if (_useColorMode) if (_useColorMode)
{ {
...@@ -1605,8 +1490,8 @@ const oclMat &RetinaFilter::getContours() ...@@ -1605,8 +1490,8 @@ const oclMat &RetinaFilter::getContours()
} }
void RetinaFilter::_processRetinaParvoMagnoMapping() void RetinaFilter::_processRetinaParvoMagnoMapping()
{ {
oclMat parvo = _ParvoRetinaFilter.getOutput(); UMat parvo = _ParvoRetinaFilter.getOutput();
oclMat magno = _MagnoRetinaFilter.getOutput(); UMat magno = _MagnoRetinaFilter.getOutput();
int halfRows = parvo.rows / 2; int halfRows = parvo.rows / 2;
int halfCols = parvo.cols / 2; int halfCols = parvo.cols / 2;
...@@ -1614,30 +1499,19 @@ void RetinaFilter::_processRetinaParvoMagnoMapping() ...@@ -1614,30 +1499,19 @@ void RetinaFilter::_processRetinaParvoMagnoMapping()
int elements_per_row = static_cast<int>(parvo.step / parvo.elemSize()); int elements_per_row = static_cast<int>(parvo.step / parvo.elemSize());
Context * ctx = Context::getContext(); size_t globalSize[] = {(size_t)parvo.cols, (size_t)parvo.rows};
std::vector<std::pair<size_t, const void *> > args; size_t localSize[] = { 16, 16 };
size_t globalSize[] = {parvo.cols, parvo.rows, 1};
size_t localSize[] = {16, 16, 1};
args.push_back(std::make_pair(sizeof(cl_mem), &parvo.data));
args.push_back(std::make_pair(sizeof(cl_mem), &magno.data));
args.push_back(std::make_pair(sizeof(cl_int), &parvo.cols));
args.push_back(std::make_pair(sizeof(cl_int), &parvo.rows));
args.push_back(std::make_pair(sizeof(cl_int), &halfCols));
args.push_back(std::make_pair(sizeof(cl_int), &halfRows));
args.push_back(std::make_pair(sizeof(cl_int), &elements_per_row));
args.push_back(std::make_pair(sizeof(cl_float), &minDistance));
openCLExecuteKernel(ctx, &retina_kernel, "processRetinaParvoMagnoMapping", globalSize, localSize, args, -1, -1);
}
} /* namespace ocl */
Ptr<Retina> createRetina_OCL(Size getInputSize){ return makePtr<ocl::RetinaOCLImpl>(getInputSize); } Kernel kernel("processRetinaParvoMagnoMapping", ocl::bioinspired::retina_kernel_oclsrc);
Ptr<Retina> createRetina_OCL(Size getInputSize, const bool colorMode, int colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) kernel.args(ocl::KernelArg::PtrReadOnly(parvo),
{ ocl::KernelArg::PtrReadOnly(magno),
return makePtr<ocl::RetinaOCLImpl>(getInputSize, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); (int)parvo.cols, (int)parvo.rows, (int)halfCols,
(int)halfRows, (int)elements_per_row, (float)minDistance);
kernel.run(sizeOfArray(globalSize), globalSize, localSize, false);
} }
} /* namespace ocl */
} /* namespace bioinspired */ } /* namespace bioinspired */
} /* namespace cv */ } /* namespace cv */
#endif /* #ifdef HAVE_OPENCV_OCL */ #endif /* #ifdef HAVE_OPENCL */
modules/bioinspired/src/retina_ocl.hpp
View file @ 966b2f55
...@@ -47,8 +47,9 @@ ...@@ -47,8 +47,9 @@
#define __OCL_RETINA_HPP__ #define __OCL_RETINA_HPP__
#include "precomp.hpp" #include "precomp.hpp"
#include "opencv2/bioinspired/retina.hpp"
#ifdef HAVE_OPENCV_OCL #ifdef HAVE_OPENCL
// please refer to c++ headers for API comments // please refer to c++ headers for API comments
namespace cv namespace cv
...@@ -57,10 +58,10 @@ namespace bioinspired ...@@ -57,10 +58,10 @@ namespace bioinspired
{ {
namespace ocl namespace ocl
{ {
void normalizeGrayOutputCentredSigmoide(const float meanValue, const float sensitivity, cv::ocl::oclMat &in, cv::ocl::oclMat &out, const float maxValue = 255.f); void normalizeGrayOutputCentredSigmoide(const float meanValue, const float sensitivity, UMat &in, UMat &out, const float maxValue = 255.f);
void normalizeGrayOutput_0_maxOutputValue(cv::ocl::oclMat &inputOutputBuffer, const float maxOutputValue = 255.0); void normalizeGrayOutput_0_maxOutputValue(UMat &inputOutputBuffer, const float maxOutputValue = 255.0);
void normalizeGrayOutputNearZeroCentreredSigmoide(cv::ocl::oclMat &inputPicture, cv::ocl::oclMat &outputBuffer, const float sensitivity = 40, const float maxOutputValue = 255.0f); void normalizeGrayOutputNearZeroCentreredSigmoide(UMat &inputPicture, UMat &outputBuffer, const float sensitivity = 40, const float maxOutputValue = 255.0f);
void centerReductImageLuminance(cv::ocl::oclMat &inputOutputBuffer); void centerReductImageLuminance(UMat &inputOutputBuffer);
class BasicRetinaFilter class BasicRetinaFilter
{ {
...@@ -81,13 +82,13 @@ public: ...@@ -81,13 +82,13 @@ public:
clearSecondaryBuffer(); clearSecondaryBuffer();
} }
void resize(const unsigned int NBrows, const unsigned int NBcolumns); void resize(const unsigned int NBrows, const unsigned int NBcolumns);
const cv::ocl::oclMat &runFilter_LPfilter(const cv::ocl::oclMat &inputFrame, const unsigned int filterIndex = 0); const UMat &runFilter_LPfilter(const UMat &inputFrame, const unsigned int filterIndex = 0);
void runFilter_LPfilter(const cv::ocl::oclMat &inputFrame, cv::ocl::oclMat &outputFrame, const unsigned int filterIndex = 0); void runFilter_LPfilter(const UMat &inputFrame, UMat &outputFrame, const unsigned int filterIndex = 0);
void runFilter_LPfilter_Autonomous(cv::ocl::oclMat &inputOutputFrame, const unsigned int filterIndex = 0); void runFilter_LPfilter_Autonomous(UMat &inputOutputFrame, const unsigned int filterIndex = 0);
const cv::ocl::oclMat &runFilter_LocalAdapdation(const cv::ocl::oclMat &inputOutputFrame, const cv::ocl::oclMat &localLuminance); const UMat &runFilter_LocalAdapdation(const UMat &inputOutputFrame, const UMat &localLuminance);
void runFilter_LocalAdapdation(const cv::ocl::oclMat &inputFrame, const cv::ocl::oclMat &localLuminance, cv::ocl::oclMat &outputFrame); void runFilter_LocalAdapdation(const UMat &inputFrame, const UMat &localLuminance, UMat &outputFrame);
const cv::ocl::oclMat &runFilter_LocalAdapdation_autonomous(const cv::ocl::oclMat &inputFrame); const UMat &runFilter_LocalAdapdation_autonomous(const UMat &inputFrame);
void runFilter_LocalAdapdation_autonomous(const cv::ocl::oclMat &inputFrame, cv::ocl::oclMat &outputFrame); void runFilter_LocalAdapdation_autonomous(const UMat &inputFrame, UMat &outputFrame);
void setLPfilterParameters(const float beta, const float tau, const float k, const unsigned int filterIndex = 0); void setLPfilterParameters(const float beta, const float tau, const float k, const unsigned int filterIndex = 0);
inline void setV0CompressionParameter(const float v0, const float maxInputValue, const float) inline void setV0CompressionParameter(const float v0, const float maxInputValue, const float)
{ {
...@@ -122,7 +123,7 @@ public: ...@@ -122,7 +123,7 @@ public:
{ {
return _v0 / _maxInputValue; return _v0 / _maxInputValue;
} }
inline const cv::ocl::oclMat &getOutput() const inline const UMat &getOutput() const
{ {
return _filterOutput; return _filterOutput;
} }
...@@ -166,8 +167,8 @@ protected: ...@@ -166,8 +167,8 @@ protected:
unsigned int _halfNBrows; unsigned int _halfNBrows;
unsigned int _halfNBcolumns; unsigned int _halfNBcolumns;
cv::ocl::oclMat _filterOutput; UMat _filterOutput;
cv::ocl::oclMat _localBuffer; UMat _localBuffer;
std::valarray <float>_filteringCoeficientsTable; std::valarray <float>_filteringCoeficientsTable;
float _v0; float _v0;
...@@ -180,19 +181,19 @@ protected: ...@@ -180,19 +181,19 @@ protected:
float _tau; float _tau;
float _gain; float _gain;
void _spatiotemporalLPfilter(const cv::ocl::oclMat &inputFrame, cv::ocl::oclMat &LPfilterOutput, const unsigned int coefTableOffset = 0); void _spatiotemporalLPfilter(const UMat &inputFrame, UMat &LPfilterOutput, const unsigned int coefTableOffset = 0);
float _squaringSpatiotemporalLPfilter(const cv::ocl::oclMat &inputFrame, cv::ocl::oclMat &outputFrame, const unsigned int filterIndex = 0); void _spatiotemporalLPfilter_h(const UMat &inputFrame, UMat &LPfilterOutput, const unsigned int coefTableOffset = 0);
void _spatiotemporalLPfilter_Irregular(const cv::ocl::oclMat &inputFrame, cv::ocl::oclMat &outputFrame, const unsigned int filterIndex = 0); void _spatiotemporalLPfilter_v(UMat &LPfilterOutput, const unsigned int multichannel = 0);
void _localSquaringSpatioTemporalLPfilter(const cv::ocl::oclMat &inputFrame, cv::ocl::oclMat &LPfilterOutput, const unsigned int *integrationAreas, const unsigned int filterIndex = 0); float _squaringSpatiotemporalLPfilter(const UMat &inputFrame, UMat &outputFrame, const unsigned int filterIndex = 0);
void _localLuminanceAdaptation(const cv::ocl::oclMat &inputFrame, const cv::ocl::oclMat &localLuminance, cv::ocl::oclMat &outputFrame, const bool updateLuminanceMean = true); void _spatiotemporalLPfilter_Irregular(const UMat &inputFrame, UMat &outputFrame, const unsigned int filterIndex = 0);
void _localLuminanceAdaptation(cv::ocl::oclMat &inputOutputFrame, const cv::ocl::oclMat &localLuminance); void _localSquaringSpatioTemporalLPfilter(const UMat &inputFrame, UMat &LPfilterOutput, const unsigned int *integrationAreas, const unsigned int filterIndex = 0);
void _localLuminanceAdaptationPosNegValues(const cv::ocl::oclMat &inputFrame, const cv::ocl::oclMat &localLuminance, float *outputFrame); void _localLuminanceAdaptation(const UMat &inputFrame, const UMat &localLuminance, UMat &outputFrame, const bool updateLuminanceMean = true);
void _horizontalCausalFilter_addInput(const cv::ocl::oclMat &inputFrame, cv::ocl::oclMat &outputFrame); void _localLuminanceAdaptation(UMat &inputOutputFrame, const UMat &localLuminance);
void _horizontalAnticausalFilter(cv::ocl::oclMat &outputFrame); void _localLuminanceAdaptationPosNegValues(const UMat &inputFrame, const UMat &localLuminance, float *outputFrame);
void _verticalCausalFilter(cv::ocl::oclMat &outputFrame); void _horizontalCausalFilter_addInput(const UMat &inputFrame, UMat &outputFrame);
void _horizontalAnticausalFilter_Irregular(cv::ocl::oclMat &outputFrame, const cv::ocl::oclMat &spatialConstantBuffer); void _verticalCausalFilter(UMat &outputFrame);
void _verticalCausalFilter_Irregular(cv::ocl::oclMat &outputFrame, const cv::ocl::oclMat &spatialConstantBuffer); void _verticalCausalFilter_multichannel(UMat &outputFrame);
void _verticalAnticausalFilter_multGain(cv::ocl::oclMat &outputFrame); void _verticalCausalFilter_Irregular(UMat &outputFrame, const UMat &spatialConstantBuffer);
}; };
class MagnoRetinaFilter: public BasicRetinaFilter class MagnoRetinaFilter: public BasicRetinaFilter
...@@ -204,17 +205,17 @@ public: ...@@ -204,17 +205,17 @@ public:
void resize(const unsigned int NBrows, const unsigned int NBcolumns); void resize(const unsigned int NBrows, const unsigned int NBcolumns);
void setCoefficientsTable(const float parasolCells_beta, const float parasolCells_tau, const float parasolCells_k, const float amacrinCellsTemporalCutFrequency, const float localAdaptIntegration_tau, const float localAdaptIntegration_k); void setCoefficientsTable(const float parasolCells_beta, const float parasolCells_tau, const float parasolCells_k, const float amacrinCellsTemporalCutFrequency, const float localAdaptIntegration_tau, const float localAdaptIntegration_k);
const cv::ocl::oclMat &runFilter(const cv::ocl::oclMat &OPL_ON, const cv::ocl::oclMat &OPL_OFF); const UMat &runFilter(const UMat &OPL_ON, const UMat &OPL_OFF);
inline const cv::ocl::oclMat &getMagnoON() const inline const UMat &getMagnoON() const
{ {
return _magnoXOutputON; return _magnoXOutputON;
} }
inline const cv::ocl::oclMat &getMagnoOFF() const inline const UMat &getMagnoOFF() const
{ {
return _magnoXOutputOFF; return _magnoXOutputOFF;
} }
inline const cv::ocl::oclMat &getMagnoYsaturated() const inline const UMat &getMagnoYsaturated() const
{ {
return _magnoYsaturated; return _magnoYsaturated;
} }
...@@ -227,19 +228,19 @@ public: ...@@ -227,19 +228,19 @@ public:
return this->_filteringCoeficientsTable[2]; return this->_filteringCoeficientsTable[2];
} }
private: private:
cv::ocl::oclMat _previousInput_ON; UMat _previousInput_ON;
cv::ocl::oclMat _previousInput_OFF; UMat _previousInput_OFF;
cv::ocl::oclMat _amacrinCellsTempOutput_ON; UMat _amacrinCellsTempOutput_ON;
cv::ocl::oclMat _amacrinCellsTempOutput_OFF; UMat _amacrinCellsTempOutput_OFF;
cv::ocl::oclMat _magnoXOutputON; UMat _magnoXOutputON;
cv::ocl::oclMat _magnoXOutputOFF; UMat _magnoXOutputOFF;
cv::ocl::oclMat _localProcessBufferON; UMat _localProcessBufferON;
cv::ocl::oclMat _localProcessBufferOFF; UMat _localProcessBufferOFF;
cv::ocl::oclMat _magnoYOutput; UMat _magnoYOutput;
cv::ocl::oclMat _magnoYsaturated; UMat _magnoYsaturated;
float _temporalCoefficient; float _temporalCoefficient;
void _amacrineCellsComputing(const cv::ocl::oclMat &OPL_ON, const cv::ocl::oclMat &OPL_OFF); void _amacrineCellsComputing(const UMat &OPL_ON, const UMat &OPL_OFF);
}; };
class ParvoRetinaFilter: public BasicRetinaFilter class ParvoRetinaFilter: public BasicRetinaFilter
...@@ -255,34 +256,34 @@ public: ...@@ -255,34 +256,34 @@ public:
{ {
BasicRetinaFilter::setLPfilterParameters(0, tau, k, 2); BasicRetinaFilter::setLPfilterParameters(0, tau, k, 2);
} }
const cv::ocl::oclMat &runFilter(const cv::ocl::oclMat &inputFrame, const bool useParvoOutput = true); const UMat &runFilter(const UMat &inputFrame, const bool useParvoOutput = true);
inline const cv::ocl::oclMat &getPhotoreceptorsLPfilteringOutput() const inline const UMat &getPhotoreceptorsLPfilteringOutput() const
{ {
return _photoreceptorsOutput; return _photoreceptorsOutput;
} }
inline const cv::ocl::oclMat &getHorizontalCellsOutput() const inline const UMat &getHorizontalCellsOutput() const
{ {
return _horizontalCellsOutput; return _horizontalCellsOutput;
} }
inline const cv::ocl::oclMat &getParvoON() const inline const UMat &getParvoON() const
{ {
return _parvocellularOutputON; return _parvocellularOutputON;
} }
inline const cv::ocl::oclMat &getParvoOFF() const inline const UMat &getParvoOFF() const
{ {
return _parvocellularOutputOFF; return _parvocellularOutputOFF;
} }
inline const cv::ocl::oclMat &getBipolarCellsON() const inline const UMat &getBipolarCellsON() const
{ {
return _bipolarCellsOutputON; return _bipolarCellsOutputON;
} }
inline const cv::ocl::oclMat &getBipolarCellsOFF() const inline const UMat &getBipolarCellsOFF() const
{ {
return _bipolarCellsOutputOFF; return _bipolarCellsOutputOFF;
} }
...@@ -297,15 +298,15 @@ public: ...@@ -297,15 +298,15 @@ public:
return this->_filteringCoeficientsTable[5]; return this->_filteringCoeficientsTable[5];
} }
private: private:
cv::ocl::oclMat _photoreceptorsOutput; UMat _photoreceptorsOutput;
cv::ocl::oclMat _horizontalCellsOutput; UMat _horizontalCellsOutput;
cv::ocl::oclMat _parvocellularOutputON; UMat _parvocellularOutputON;
cv::ocl::oclMat _parvocellularOutputOFF; UMat _parvocellularOutputOFF;
cv::ocl::oclMat _bipolarCellsOutputON; UMat _bipolarCellsOutputON;
cv::ocl::oclMat _bipolarCellsOutputOFF; UMat _bipolarCellsOutputOFF;
cv::ocl::oclMat _localAdaptationOFF; UMat _localAdaptationOFF;
cv::ocl::oclMat _localAdaptationON; UMat _localAdaptationON;
cv::ocl::oclMat _parvocellularOutputONminusOFF; UMat _parvocellularOutputONminusOFF;
void _OPL_OnOffWaysComputing(); void _OPL_OnOffWaysComputing();
}; };
class RetinaColor: public BasicRetinaFilter class RetinaColor: public BasicRetinaFilter
...@@ -316,12 +317,12 @@ public: ...@@ -316,12 +317,12 @@ public:
void clearAllBuffers(); void clearAllBuffers();
void resize(const unsigned int NBrows, const unsigned int NBcolumns); void resize(const unsigned int NBrows, const unsigned int NBcolumns);
inline void runColorMultiplexing(const cv::ocl::oclMat &inputRGBFrame) inline void runColorMultiplexing(const UMat &inputRGBFrame)
{ {
runColorMultiplexing(inputRGBFrame, _multiplexedFrame); runColorMultiplexing(inputRGBFrame, _multiplexedFrame);
} }
void runColorMultiplexing(const cv::ocl::oclMat &demultiplexedInputFrame, cv::ocl::oclMat &multiplexedFrame); void runColorMultiplexing(const UMat &demultiplexedInputFrame, UMat &multiplexedFrame);
void runColorDemultiplexing(const cv::ocl::oclMat &multiplexedColorFrame, const bool adaptiveFiltering = false, const float maxInputValue = 255.0); void runColorDemultiplexing(const UMat &multiplexedColorFrame, const bool adaptiveFiltering = false, const float maxInputValue = 255.0);
void setColorSaturation(const bool saturateColors = true, const float colorSaturationValue = 4.0) void setColorSaturation(const bool saturateColors = true, const float colorSaturationValue = 4.0)
{ {
...@@ -334,29 +335,29 @@ public: ...@@ -334,29 +335,29 @@ public:
setLPfilterParameters(beta, tau, k); setLPfilterParameters(beta, tau, k);
} }
bool applyKrauskopfLMS2Acr1cr2Transform(cv::ocl::oclMat &result); bool applyKrauskopfLMS2Acr1cr2Transform(UMat &result);
bool applyLMS2LabTransform(cv::ocl::oclMat &result); bool applyLMS2LabTransform(UMat &result);
inline const cv::ocl::oclMat &getMultiplexedFrame() const inline const UMat &getMultiplexedFrame() const
{ {
return _multiplexedFrame; return _multiplexedFrame;
} }
inline const cv::ocl::oclMat &getDemultiplexedColorFrame() const inline const UMat &getDemultiplexedColorFrame() const
{ {
return _demultiplexedColorFrame; return _demultiplexedColorFrame;
} }
inline const cv::ocl::oclMat &getLuminance() const inline const UMat &getLuminance() const
{ {
return _luminance; return _luminance;
} }
inline const cv::ocl::oclMat &getChrominance() const inline const UMat &getChrominance() const
{ {
return _chrominance; return _chrominance;
} }
void clipRGBOutput_0_maxInputValue(cv::ocl::oclMat &inputOutputBuffer, const float maxOutputValue = 255.0); void clipRGBOutput_0_maxInputValue(UMat &inputOutputBuffer, const float maxOutputValue = 255.0);
void normalizeRGBOutput_0_maxOutputValue(const float maxOutputValue = 255.0); void normalizeRGBOutput_0_maxOutputValue(const float maxOutputValue = 255.0);
inline void setDemultiplexedColorFrame(const cv::ocl::oclMat &demultiplexedImage) inline void setDemultiplexedColorFrame(const UMat &demultiplexedImage)
{ {
_demultiplexedColorFrame = demultiplexedImage; _demultiplexedColorFrame = demultiplexedImage;
} }
...@@ -372,26 +373,26 @@ protected: ...@@ -372,26 +373,26 @@ protected:
int _samplingMethod; int _samplingMethod;
bool _saturateColors; bool _saturateColors;
float _colorSaturationValue; float _colorSaturationValue;
cv::ocl::oclMat _luminance; UMat _luminance;
cv::ocl::oclMat _multiplexedFrame; UMat _multiplexedFrame;
cv::ocl::oclMat _RGBmosaic; UMat _RGBmosaic;
cv::ocl::oclMat _tempMultiplexedFrame; UMat _tempMultiplexedFrame;
cv::ocl::oclMat _demultiplexedTempBuffer; UMat _demultiplexedTempBuffer;
cv::ocl::oclMat _demultiplexedColorFrame; UMat _demultiplexedColorFrame;
cv::ocl::oclMat _chrominance; UMat _chrominance;
cv::ocl::oclMat _colorLocalDensity; UMat _colorLocalDensity;
cv::ocl::oclMat _imageGradient; UMat _imageGradient;
float _pR, _pG, _pB; float _pR, _pG, _pB;
bool _objectInit; bool _objectInit;
void _initColorSampling(); void _initColorSampling();
void _adaptiveSpatialLPfilter(const cv::ocl::oclMat &inputFrame, const cv::ocl::oclMat &gradient, cv::ocl::oclMat &outputFrame); void _adaptiveSpatialLPfilter_h(const UMat &inputFrame, const UMat &gradient, UMat &outputFrame);
void _adaptiveHorizontalCausalFilter_addInput(const cv::ocl::oclMat &inputFrame, const cv::ocl::oclMat &gradient, cv::ocl::oclMat &outputFrame); void _adaptiveSpatialLPfilter_v(const UMat &gradient, UMat &outputFrame);
void _adaptiveVerticalAnticausalFilter_multGain(const cv::ocl::oclMat &gradient, cv::ocl::oclMat &outputFrame); void _adaptiveHorizontalCausalFilter_addInput(const UMat &inputFrame, const UMat &gradient, UMat &outputFrame);
void _computeGradient(const cv::ocl::oclMat &luminance, cv::ocl::oclMat &gradient); void _computeGradient(const UMat &luminance, UMat &gradient);
void _normalizeOutputs_0_maxOutputValue(void); void _normalizeOutputs_0_maxOutputValue(void);
void _applyImageColorSpaceConversion(const cv::ocl::oclMat &inputFrame, cv::ocl::oclMat &outputFrame, const float *transformTable); void _applyImageColorSpaceConversion(const UMat &inputFrame, UMat &outputFrame, const float *transformTable);
}; };
class RetinaFilter class RetinaFilter
{ {
...@@ -401,8 +402,8 @@ public: ...@@ -401,8 +402,8 @@ public:
void clearAllBuffers(); void clearAllBuffers();
void resize(const unsigned int NBrows, const unsigned int NBcolumns); void resize(const unsigned int NBrows, const unsigned int NBcolumns);
bool checkInput(const cv::ocl::oclMat &input, const bool colorMode); bool checkInput(const UMat &input, const bool colorMode);
bool runFilter(const cv::ocl::oclMat &imageInput, const bool useAdaptiveFiltering = true, const bool processRetinaParvoMagnoMapping = false, const bool useColorMode = false, const bool inputIsColorMultiplexed = false); bool runFilter(const UMat &imageInput, const bool useAdaptiveFiltering = true, const bool processRetinaParvoMagnoMapping = false, const bool useColorMode = false, const bool inputIsColorMultiplexed = false);
void setGlobalParameters(const float OPLspatialResponse1 = 0.7, const float OPLtemporalresponse1 = 1, const float OPLassymetryGain = 0, const float OPLspatialResponse2 = 5, const float OPLtemporalresponse2 = 1, const float LPfilterSpatialResponse = 5, const float LPfilterGain = 0, const float LPfilterTemporalresponse = 0, const float MovingContoursExtractorCoefficient = 5, const bool normalizeParvoOutput_0_maxOutputValue = false, const bool normalizeMagnoOutput_0_maxOutputValue = false, const float maxOutputValue = 255.0, const float maxInputValue = 255.0, const float meanValue = 128.0); void setGlobalParameters(const float OPLspatialResponse1 = 0.7, const float OPLtemporalresponse1 = 1, const float OPLassymetryGain = 0, const float OPLspatialResponse2 = 5, const float OPLtemporalresponse2 = 1, const float LPfilterSpatialResponse = 5, const float LPfilterGain = 0, const float LPfilterTemporalresponse = 0, const float MovingContoursExtractorCoefficient = 5, const bool normalizeParvoOutput_0_maxOutputValue = false, const bool normalizeMagnoOutput_0_maxOutputValue = false, const float maxOutputValue = 255.0, const float maxInputValue = 255.0, const float meanValue = 128.0);
...@@ -467,16 +468,16 @@ public: ...@@ -467,16 +468,16 @@ public:
{ {
_colorEngine.setColorSaturation(saturateColors, colorSaturationValue); _colorEngine.setColorSaturation(saturateColors, colorSaturationValue);
} }
inline const cv::ocl::oclMat &getLocalAdaptation() const inline const UMat &getLocalAdaptation() const
{ {
return _photoreceptorsPrefilter.getOutput(); return _photoreceptorsPrefilter.getOutput();
} }
inline const cv::ocl::oclMat &getPhotoreceptors() const inline const UMat &getPhotoreceptors() const
{ {
return _ParvoRetinaFilter.getPhotoreceptorsLPfilteringOutput(); return _ParvoRetinaFilter.getPhotoreceptorsLPfilteringOutput();
} }
inline const cv::ocl::oclMat &getHorizontalCells() const inline const UMat &getHorizontalCells() const
{ {
return _ParvoRetinaFilter.getHorizontalCellsOutput(); return _ParvoRetinaFilter.getHorizontalCellsOutput();
} }
...@@ -484,20 +485,20 @@ public: ...@@ -484,20 +485,20 @@ public:
{ {
return _useParvoOutput; return _useParvoOutput;
} }
bool getParvoFoveaResponse(cv::ocl::oclMat &parvoFovealResponse); bool getParvoFoveaResponse(UMat &parvoFovealResponse);
inline void activateContoursProcessing(const bool useParvoOutput) inline void activateContoursProcessing(const bool useParvoOutput)
{ {
_useParvoOutput = useParvoOutput; _useParvoOutput = useParvoOutput;
} }
const cv::ocl::oclMat &getContours(); const UMat &getContours();
inline const cv::ocl::oclMat &getContoursON() const inline const UMat &getContoursON() const
{ {
return _ParvoRetinaFilter.getParvoON(); return _ParvoRetinaFilter.getParvoON();
} }
inline const cv::ocl::oclMat &getContoursOFF() const inline const UMat &getContoursOFF() const
{ {
return _ParvoRetinaFilter.getParvoOFF(); return _ParvoRetinaFilter.getParvoOFF();
} }
...@@ -512,41 +513,41 @@ public: ...@@ -512,41 +513,41 @@ public:
_useMagnoOutput = useMagnoOutput; _useMagnoOutput = useMagnoOutput;
} }
inline const cv::ocl::oclMat &getMovingContours() const inline const UMat &getMovingContours() const
{ {
return _MagnoRetinaFilter.getOutput(); return _MagnoRetinaFilter.getOutput();
} }
inline const cv::ocl::oclMat &getMovingContoursSaturated() const inline const UMat &getMovingContoursSaturated() const
{ {
return _MagnoRetinaFilter.getMagnoYsaturated(); return _MagnoRetinaFilter.getMagnoYsaturated();
} }
inline const cv::ocl::oclMat &getMovingContoursON() const inline const UMat &getMovingContoursON() const
{ {
return _MagnoRetinaFilter.getMagnoON(); return _MagnoRetinaFilter.getMagnoON();
} }
inline const cv::ocl::oclMat &getMovingContoursOFF() const inline const UMat &getMovingContoursOFF() const
{ {
return _MagnoRetinaFilter.getMagnoOFF(); return _MagnoRetinaFilter.getMagnoOFF();
} }
inline const cv::ocl::oclMat &getRetinaParvoMagnoMappedOutput() const inline const UMat &getRetinaParvoMagnoMappedOutput() const
{ {
return _retinaParvoMagnoMappedFrame; return _retinaParvoMagnoMappedFrame;
} }
inline const cv::ocl::oclMat &getParvoContoursChannel() const inline const UMat &getParvoContoursChannel() const
{ {
return _colorEngine.getLuminance(); return _colorEngine.getLuminance();
} }
inline const cv::ocl::oclMat &getParvoChrominance() const inline const UMat &getParvoChrominance() const
{ {
return _colorEngine.getChrominance(); return _colorEngine.getChrominance();
} }
inline const cv::ocl::oclMat &getColorOutput() const inline const UMat &getColorOutput() const
{ {
return _colorEngine.getDemultiplexedColorFrame(); return _colorEngine.getDemultiplexedColorFrame();
} }
...@@ -609,7 +610,7 @@ private: ...@@ -609,7 +610,7 @@ private:
unsigned int _ellapsedFramesSinceLastReset; unsigned int _ellapsedFramesSinceLastReset;
unsigned int _globalTemporalConstant; unsigned int _globalTemporalConstant;
cv::ocl::oclMat _retinaParvoMagnoMappedFrame; UMat _retinaParvoMagnoMappedFrame;
BasicRetinaFilter _photoreceptorsPrefilter; BasicRetinaFilter _photoreceptorsPrefilter;
ParvoRetinaFilter _ParvoRetinaFilter; ParvoRetinaFilter _ParvoRetinaFilter;
MagnoRetinaFilter _MagnoRetinaFilter; MagnoRetinaFilter _MagnoRetinaFilter;
...@@ -623,12 +624,60 @@ private: ...@@ -623,12 +624,60 @@ private:
void _setInitPeriodCount(); void _setInitPeriodCount();
void _processRetinaParvoMagnoMapping(); void _processRetinaParvoMagnoMapping();
void _runGrayToneMapping(const cv::ocl::oclMat &grayImageInput, cv::ocl::oclMat &grayImageOutput , const float PhotoreceptorsCompression = 0.6, const float ganglionCellsCompression = 0.6); void _runGrayToneMapping(const UMat &grayImageInput, UMat &grayImageOutput , const float PhotoreceptorsCompression = 0.6, const float ganglionCellsCompression = 0.6);
};
class RetinaOCLImpl : public Retina
{
public:
RetinaOCLImpl(Size getInputSize);
RetinaOCLImpl(Size getInputSize, const bool colorMode, int colorSamplingMethod = RETINA_COLOR_BAYER, const bool useRetinaLogSampling = false, const double reductionFactor = 1.0, const double samplingStrenght = 10.0);
virtual ~RetinaOCLImpl();
Size getInputSize();
Size getOutputSize();
void setup(String retinaParameterFile = "", const bool applyDefaultSetupOnFailure = true);
void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure = true);
void setup(RetinaParameters newParameters);
RetinaParameters getParameters();
const String printSetup();
virtual void write(String fs) const;
virtual void write(FileStorage& fs) const;
void setupOPLandIPLParvoChannel(const bool colorMode = true, const bool normaliseOutput = true, const float photoreceptorsLocalAdaptationSensitivity = 0.7, const float photoreceptorsTemporalConstant = 0.5, const float photoreceptorsSpatialConstant = 0.53, const float horizontalCellsGain = 0, const float HcellsTemporalConstant = 1, const float HcellsSpatialConstant = 7, const float ganglionCellsSensitivity = 0.7);
void setupIPLMagnoChannel(const bool normaliseOutput = true, const float parasolCells_beta = 0, const float parasolCells_tau = 0, const float parasolCells_k = 7, const float amacrinCellsTemporalCutFrequency = 1.2, const float V0CompressionParameter = 0.95, const float localAdaptintegration_tau = 0, const float localAdaptintegration_k = 7);
void run(InputArray inputImage);
void getParvo(OutputArray retinaOutput_parvo);
void getMagno(OutputArray retinaOutput_magno);
void setColorSaturation(const bool saturateColors = true, const float colorSaturationValue = 4.0);
void clearBuffers();
void activateMovingContoursProcessing(const bool activate);
void activateContoursProcessing(const bool activate);
// unimplemented interfaces:
void applyFastToneMapping(InputArray /*inputImage*/, OutputArray /*outputToneMappedImage*/);
void getParvoRAW(OutputArray /*retinaOutput_parvo*/);
void getMagnoRAW(OutputArray /*retinaOutput_magno*/);
const Mat getMagnoRAW() const;
const Mat getParvoRAW() const;
protected:
RetinaParameters _retinaParameters;
UMat _inputBuffer;
RetinaFilter* _retinaFilter;
bool convertToColorPlanes(const UMat& input, UMat &output);
void convertToInterleaved(const UMat& input, bool colorMode, UMat &output);
void _init(const Size getInputSize, const bool colorMode, int colorSamplingMethod = RETINA_COLOR_BAYER, const bool useRetinaLogSampling = false, const double reductionFactor = 1.0, const double samplingStrenght = 10.0);
}; };
} /* namespace ocl */ } /* namespace ocl */
} /* namespace bioinspired */ } /* namespace bioinspired */
} /* namespace cv */ } /* namespace cv */
#endif /* HAVE_OPENCV_OCL */ #endif /* HAVE_OPENCL */
#endif /* __OCL_RETINA_HPP__ */ #endif /* __OCL_RETINA_HPP__ */
modules/bioinspired/test/test_retina_ocl.cpp
View file @ 966b2f55
...@@ -44,87 +44,39 @@ ...@@ -44,87 +44,39 @@
//M*/ //M*/
#include "test_precomp.hpp" #include "test_precomp.hpp"
#include "opencv2/opencv_modules.hpp" #include "opencv2/ts/ocl_test.hpp"
#include "opencv2/bioinspired.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/core/ocl.hpp" // cv::ocl::haveOpenCL #ifdef HAVE_OPENCL
#if defined(HAVE_OPENCV_OCL)
#include "opencv2/ocl.hpp"
#define RETINA_ITERATIONS 5 #define RETINA_ITERATIONS 5
static double checkNear(const cv::Mat &m1, const cv::Mat &m2) namespace cvtest {
{ namespace ocl {
return cv::norm(m1, m2, cv::NORM_INF);
}
#define PARAM_TEST_CASE(name, ...) struct name : testing::TestWithParam< std::tr1::tuple< __VA_ARGS__ > >
#define GET_PARAM(k) std::tr1::get< k >(GetParam())
static int oclInit = false;
static int oclAvailable = false;
PARAM_TEST_CASE(Retina_OCL, bool, int, bool, double, double) PARAM_TEST_CASE(Retina_OCL, bool, int, bool, double, double)
{ {
bool colorMode; bool colorMode;
int colorSamplingMethod; int colorSamplingMethod;
bool useLogSampling; bool useLogSampling;
double reductionFactor; float reductionFactor;
double samplingStrength; float samplingStrength;
virtual void SetUp() virtual void SetUp()
{ {
colorMode = GET_PARAM(0); colorMode = GET_PARAM(0);
colorSamplingMethod = GET_PARAM(1); colorSamplingMethod = GET_PARAM(1);
useLogSampling = GET_PARAM(2); useLogSampling = GET_PARAM(2);
reductionFactor = GET_PARAM(3); reductionFactor = static_cast<float>(GET_PARAM(3));
samplingStrength = GET_PARAM(4); samplingStrength = static_cast<float>(GET_PARAM(4));
if (!oclInit)
{
if (cv::ocl::haveOpenCL())
{
try
{
const cv::ocl::DeviceInfo& dev = cv::ocl::Context::getContext()->getDeviceInfo();
std::cout << "Device name:" << dev.deviceName << std::endl;
oclAvailable = true;
}
catch (...)
{
std::cout << "Device name: N/A" << std::endl;
}
}
oclInit = true;
}
} }
}; };
TEST_P(Retina_OCL, Accuracy) OCL_TEST_P(Retina_OCL, Accuracy)
{ {
if (!oclAvailable)
{
std::cout << "SKIP test" << std::endl;
return;
}
using namespace cv;
Mat input = imread(cvtest::TS::ptr()->get_data_path() + "shared/lena.png", colorMode); Mat input = imread(cvtest::TS::ptr()->get_data_path() + "shared/lena.png", colorMode);
CV_Assert(!input.empty()); CV_Assert(!input.empty());
ocl::oclMat ocl_input(input);
Ptr<bioinspired::Retina> ocl_retina = bioinspired::createRetina_OCL( Ptr<bioinspired::Retina> retina = bioinspired::createRetina(
input.size(),
colorMode,
colorSamplingMethod,
useLogSampling,
reductionFactor,
samplingStrength);
Ptr<bioinspired::Retina> gold_retina = bioinspired::createRetina(
input.size(), input.size(),
colorMode, colorMode,
colorSamplingMethod, colorSamplingMethod,
...@@ -134,31 +86,35 @@ TEST_P(Retina_OCL, Accuracy) ...@@ -134,31 +86,35 @@ TEST_P(Retina_OCL, Accuracy)
Mat gold_parvo; Mat gold_parvo;
Mat gold_magno; Mat gold_magno;
ocl::oclMat ocl_parvo; UMat ocl_parvo;
ocl::oclMat ocl_magno; UMat ocl_magno;
for(int i = 0; i < RETINA_ITERATIONS; i ++) for(int i = 0; i < RETINA_ITERATIONS; i ++)
{ {
ocl_retina->run(ocl_input); OCL_OFF(retina->run(input));
gold_retina->run(input); OCL_OFF(retina->getParvo(gold_parvo));
OCL_OFF(retina->getMagno(gold_magno));
OCL_OFF(retina->clearBuffers());
gold_retina->getParvo(gold_parvo); OCL_ON(retina->run(input));
gold_retina->getMagno(gold_magno); OCL_ON(retina->getParvo(ocl_parvo));
OCL_ON(retina->getMagno(ocl_magno));
OCL_ON(retina->clearBuffers());
ocl_retina->getParvo(ocl_parvo); int eps = 1;
ocl_retina->getMagno(ocl_magno);
int eps = colorMode ? 2 : 1; EXPECT_MAT_NEAR(gold_parvo, ocl_parvo, eps);
EXPECT_MAT_NEAR(gold_magno, ocl_magno, eps);
EXPECT_LE(checkNear(gold_parvo, (Mat)ocl_parvo), eps);
EXPECT_LE(checkNear(gold_magno, (Mat)ocl_magno), eps);
} }
} }
INSTANTIATE_TEST_CASE_P(Contrib, Retina_OCL, testing::Combine( OCL_INSTANTIATE_TEST_CASE_P(Contrib, Retina_OCL, testing::Combine(
testing::Bool(), testing::Bool(),
testing::Values((int)cv::bioinspired::RETINA_COLOR_BAYER), testing::Values((int)cv::bioinspired::RETINA_COLOR_BAYER),
testing::Values(false/*,true*/), testing::Values(false/*,true*/),
testing::Values(1.0, 0.5), testing::Values(1.0, 0.5),
testing::Values(10.0, 5.0))); testing::Values(10.0, 5.0)));
#endif
} } // namespace cvtest::ocl
#endif // HAVE_OPENCL
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