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Overhaul bioinspired opencl implementation

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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: Li Peng <peng.li@intel.com>
pull/924/head
Li Peng 8 years ago
parent 86342522b0
commit 966b2f55b9
10 changed files with 881 additions and 996 deletions
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  1. 5
      modules/bioinspired/include/opencv2/bioinspired/retina.hpp
  2. 126
      modules/bioinspired/perf/opencl/perf_retina.cpp
  3. 47
      modules/bioinspired/perf/opencl/perf_retina.ocl.cpp
  4. 10
      modules/bioinspired/perf/perf_main.cpp
  5. 411
      modules/bioinspired/src/opencl/retina_kernel.cl
  6. 5
      modules/bioinspired/src/precomp.hpp
  7. 108
      modules/bioinspired/src/retina.cpp
  8. 800
      modules/bioinspired/src/retina_ocl.cpp
  9. 257
      modules/bioinspired/src/retina_ocl.hpp
  10. 100
      modules/bioinspired/test/test_retina_ocl.cpp

5
modules/bioinspired/include/opencv2/bioinspired/retina.hpp
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@ -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
//! @} //! @}
//! @} //! @}

126
modules/bioinspired/perf/opencl/perf_retina.cpp
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@ -1,126 +0,0 @@
/*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

47
modules/bioinspired/perf/opencl/perf_retina.ocl.cpp
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@ -0,0 +1,47 @@
#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

10
modules/bioinspired/perf/perf_main.cpp
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@ -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))

411
modules/bioinspired/src/opencl/retina_kernel.cl
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@ -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(
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(
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, for(int i = 0; i < rows; ++i)
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; input[0] = vload2(0, optr[0]);
} input[1] = vload2(0, optr[1]);
input[2] = vload2(0, optr[2]);
global float * optr = result[0] = input[0] + _a * result[0];
output + mad24(rows - gid, elements_per_row, -1 + out_offset / 4); result[1] = input[1] + _a * result[1];
global float * bptr = result[2] = input[2] + _a * result[2];
buffer + mad24(rows - gid, elements_per_row, -1 + buffer_offset / 4);
float4 buf_v4, out_v4, res_v4 = (float4)(0); vstore2(result[0], 0, optr[0]);
float result = 0; vstore2(result[1], 0, optr[1]);
// we assume elements_per_row is multple of WIDTH_MULTIPLE vstore2(result[2], 0, optr[2]);
for(int i = 0; i < WIDTH_MULTIPLE; ++ i, -- optr, -- bptr)
{ optr[0] += elements_per_row;
if(i >= elements_per_row - cols) optr[1] += elements_per_row;
{ optr[2] += elements_per_row;
result = *optr + *bptr * result;
}
*optr = result;
} }
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(
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(
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(
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(
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(
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(
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(
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(
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(
) )
{ {
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(
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(
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(
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(
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(
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]);
vals[2] = vload4(0, input + indices[2]);
input[indices[0]] = vals[0] - residu; float4 residu = pR * vals[0] + pG * vals[1] + pB * vals[2];
input[indices[1]] = vals[1] - residu; vstore4(vals[0] - residu, 0, input + indices[0]);
input[indices[2]] = vals[2] - residu; 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(
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(
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(
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(
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

5
modules/bioinspired/src/precomp.hpp
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@ -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
{ {

108
modules/bioinspired/src/retina.cpp
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@ -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:
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)
{ {
_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
_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
} }
#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)
} }
//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
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
{ {
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
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); }

800
modules/bioinspired/src/retina_ocl.cpp
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257
modules/bioinspired/src/retina_ocl.hpp
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@ -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
{ {
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:
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:
{ {
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:
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:
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:
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:
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:
{ {
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:
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:
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:
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:
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:
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:
{ {
_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:
{ {
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:
_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:
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:
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__ */

100
modules/bioinspired/test/test_retina_ocl.cpp
Unescape View File

@ -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)
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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