提交 572cfc99 编写于 作者: R Roman Donchenko 提交者: OpenCV Buildbot

Merge pull request #973 from pengx17:2.4_oclclahe

......@@ -483,6 +483,23 @@ namespace cv
CV_EXPORTS void calcHist(const oclMat &mat_src, oclMat &mat_hist);
//! only 8UC1 and 256 bins is supported now
CV_EXPORTS void equalizeHist(const oclMat &mat_src, oclMat &mat_dst);
//! only 8UC1 is supported now
class CV_EXPORTS CLAHE
{
public:
virtual void apply(const oclMat &src, oclMat &dst) = 0;
virtual void setClipLimit(double clipLimit) = 0;
virtual double getClipLimit() const = 0;
virtual void setTilesGridSize(Size tileGridSize) = 0;
virtual Size getTilesGridSize() const = 0;
virtual void collectGarbage() = 0;
};
CV_EXPORTS Ptr<cv::ocl::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
//! bilateralFilter
// supports 8UC1 8UC4
CV_EXPORTS void bilateralFilter(const oclMat& src, oclMat& dst, int d, double sigmaColor, double sigmaSpave, int borderType=BORDER_DEFAULT);
......
......@@ -921,4 +921,51 @@ PERFTEST(remap)
}
}
}
\ No newline at end of file
}
///////////// CLAHE ////////////////////////
PERFTEST(CLAHE)
{
Mat src, dst, ocl_dst;
cv::ocl::oclMat d_src, d_dst;
int all_type[] = {CV_8UC1};
std::string type_name[] = {"CV_8UC1"};
double clipLimit = 40.0;
cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE(clipLimit);
cv::Ptr<cv::ocl::CLAHE> d_clahe = cv::ocl::createCLAHE(clipLimit);
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
{
SUBTEST << size << 'x' << size << "; " << type_name[j] ;
gen(src, size, size, all_type[j], 0, 256);
CPU_ON;
clahe->apply(src, dst);
CPU_OFF;
d_src.upload(src);
WARMUP_ON;
d_clahe->apply(d_src, d_dst);
WARMUP_OFF;
ocl_dst = d_dst;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
GPU_ON;
d_clahe->apply(d_src, d_dst);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
d_clahe->apply(d_src, d_dst);
d_dst.download(dst);
GPU_FULL_OFF;
}
}
}
......@@ -25,6 +25,7 @@
// Xu Pang, pangxu010@163.com
// Wu Zailong, bullet@yeah.net
// Wenju He, wenju@multicorewareinc.com
// Sen Liu, swjtuls1987@126.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
......@@ -80,6 +81,7 @@ namespace cv
extern const char *imgproc_calcHarris;
extern const char *imgproc_calcMinEigenVal;
extern const char *imgproc_convolve;
extern const char *imgproc_clahe;
////////////////////////////////////OpenCL call wrappers////////////////////////////
template <typename T> struct index_and_sizeof;
......@@ -1511,6 +1513,189 @@ namespace cv
openCLExecuteKernel(clCxt, &imgproc_histogram, kernelName, globalThreads, localThreads, args, -1, -1);
LUT(mat_src, lut, mat_dst);
}
////////////////////////////////////////////////////////////////////////
// CLAHE
namespace clahe
{
inline int divUp(int total, int grain)
{
return (total + grain - 1) / grain * grain;
}
static void calcLut(const oclMat &src, oclMat &dst,
const int tilesX, const int tilesY, const cv::Size tileSize,
const int clipLimit, const float lutScale)
{
cl_int2 tile_size;
tile_size.s[0] = tileSize.width;
tile_size.s[1] = tileSize.height;
std::vector<pair<size_t , const void *> > args;
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&src.data ));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&dst.data ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.step ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&dst.step ));
args.push_back( std::make_pair( sizeof(cl_int2), (void *)&tile_size ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&tilesX ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&clipLimit ));
args.push_back( std::make_pair( sizeof(cl_float), (void *)&lutScale ));
String kernelName = "calcLut";
size_t localThreads[3] = { 32, 8, 1 };
size_t globalThreads[3] = { tilesX * localThreads[0], tilesY * localThreads[1], 1 };
bool is_cpu = queryDeviceInfo<IS_CPU_DEVICE, bool>();
if (is_cpu)
{
openCLExecuteKernel(Context::getContext(), &imgproc_clahe, kernelName, globalThreads, localThreads, args, -1, -1, (char*)" -D CPU");
}
else
{
cl_kernel kernel = openCLGetKernelFromSource(Context::getContext(), &imgproc_clahe, kernelName);
int wave_size = queryDeviceInfo<WAVEFRONT_SIZE, int>(kernel);
openCLSafeCall(clReleaseKernel(kernel));
static char opt[20] = {0};
sprintf(opt, " -D WAVE_SIZE=%d", wave_size);
openCLExecuteKernel(Context::getContext(), &imgproc_clahe, kernelName, globalThreads, localThreads, args, -1, -1, opt);
}
}
static void transform(const oclMat &src, oclMat &dst, const oclMat &lut,
const int tilesX, const int tilesY, const cv::Size tileSize)
{
cl_int2 tile_size;
tile_size.s[0] = tileSize.width;
tile_size.s[1] = tileSize.height;
std::vector<pair<size_t , const void *> > args;
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&src.data ));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&dst.data ));
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&lut.data ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.step ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&dst.step ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&lut.step ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.cols ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.rows ));
args.push_back( std::make_pair( sizeof(cl_int2), (void *)&tile_size ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&tilesX ));
args.push_back( std::make_pair( sizeof(cl_int), (void *)&tilesY ));
String kernelName = "transform";
size_t localThreads[3] = { 32, 8, 1 };
size_t globalThreads[3] = { divUp(src.cols, localThreads[0]), divUp(src.rows, localThreads[1]), 1 };
openCLExecuteKernel(Context::getContext(), &imgproc_clahe, kernelName, globalThreads, localThreads, args, -1, -1);
}
}
namespace
{
class CLAHE_Impl : public cv::ocl::CLAHE
{
public:
CLAHE_Impl(double clipLimit = 40.0, int tilesX = 8, int tilesY = 8);
cv::AlgorithmInfo* info() const;
void apply(const oclMat &src, oclMat &dst);
void setClipLimit(double clipLimit);
double getClipLimit() const;
void setTilesGridSize(cv::Size tileGridSize);
cv::Size getTilesGridSize() const;
void collectGarbage();
private:
double clipLimit_;
int tilesX_;
int tilesY_;
oclMat srcExt_;
oclMat lut_;
};
CLAHE_Impl::CLAHE_Impl(double clipLimit, int tilesX, int tilesY) :
clipLimit_(clipLimit), tilesX_(tilesX), tilesY_(tilesY)
{
}
void CLAHE_Impl::apply(const oclMat &src, oclMat &dst)
{
CV_Assert( src.type() == CV_8UC1 );
dst.create( src.size(), src.type() );
const int histSize = 256;
ensureSizeIsEnough(tilesX_ * tilesY_, histSize, CV_8UC1, lut_);
cv::Size tileSize;
oclMat srcForLut;
if (src.cols % tilesX_ == 0 && src.rows % tilesY_ == 0)
{
tileSize = cv::Size(src.cols / tilesX_, src.rows / tilesY_);
srcForLut = src;
}
else
{
cv::ocl::copyMakeBorder(src, srcExt_, 0, tilesY_ - (src.rows % tilesY_), 0, tilesX_ - (src.cols % tilesX_), cv::BORDER_REFLECT_101, cv::Scalar());
tileSize = cv::Size(srcExt_.cols / tilesX_, srcExt_.rows / tilesY_);
srcForLut = srcExt_;
}
const int tileSizeTotal = tileSize.area();
const float lutScale = static_cast<float>(histSize - 1) / tileSizeTotal;
int clipLimit = 0;
if (clipLimit_ > 0.0)
{
clipLimit = static_cast<int>(clipLimit_ * tileSizeTotal / histSize);
clipLimit = std::max(clipLimit, 1);
}
clahe::calcLut(srcForLut, lut_, tilesX_, tilesY_, tileSize, clipLimit, lutScale);
//finish();
clahe::transform(src, dst, lut_, tilesX_, tilesY_, tileSize);
}
void CLAHE_Impl::setClipLimit(double clipLimit)
{
clipLimit_ = clipLimit;
}
double CLAHE_Impl::getClipLimit() const
{
return clipLimit_;
}
void CLAHE_Impl::setTilesGridSize(cv::Size tileGridSize)
{
tilesX_ = tileGridSize.width;
tilesY_ = tileGridSize.height;
}
cv::Size CLAHE_Impl::getTilesGridSize() const
{
return cv::Size(tilesX_, tilesY_);
}
void CLAHE_Impl::collectGarbage()
{
srcExt_.release();
lut_.release();
}
}
cv::Ptr<cv::ocl::CLAHE> createCLAHE(double clipLimit, cv::Size tileGridSize)
{
return new CLAHE_Impl(clipLimit, tileGridSize.width, tileGridSize.height);
}
//////////////////////////////////bilateralFilter////////////////////////////////////////////////////
static void
oclbilateralFilter_8u( const oclMat &src, oclMat &dst, int d,
......
/*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
// Sen Liu, swjtuls1987@126.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other oclMaterials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors as is and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef WAVE_SIZE
#define WAVE_SIZE 1
#endif
int calc_lut(__local int* smem, int val, int tid)
{
smem[tid] = val;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid == 0)
{
for (int i = 1; i < 256; ++i)
{
smem[i] += smem[i - 1];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
return smem[tid];
}
#ifdef CPU
void reduce(volatile __local int* smem, int val, int tid)
{
smem[tid] = val;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 128)
{
smem[tid] = val += smem[tid + 128];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 64)
{
smem[tid] = val += smem[tid + 64];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 32)
{
smem[tid] += smem[tid + 32];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16)
{
smem[tid] += smem[tid + 16];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
smem[tid] += smem[tid + 8];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 4)
{
smem[tid] += smem[tid + 4];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 2)
{
smem[tid] += smem[tid + 2];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 1)
{
smem[256] = smem[tid] + smem[tid + 1];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
#else
void reduce(__local volatile int* smem, int val, int tid)
{
smem[tid] = val;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 128)
{
smem[tid] = val += smem[tid + 128];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 64)
{
smem[tid] = val += smem[tid + 64];
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 32)
{
smem[tid] += smem[tid + 32];
#if WAVE_SIZE < 32
} barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16) {
#endif
smem[tid] += smem[tid + 16];
#if WAVE_SIZE < 16
} barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8) {
#endif
smem[tid] += smem[tid + 8];
smem[tid] += smem[tid + 4];
smem[tid] += smem[tid + 2];
smem[tid] += smem[tid + 1];
}
}
#endif
__kernel void calcLut(__global __const uchar * src, __global uchar * lut,
const int srcStep, const int dstStep,
const int2 tileSize, const int tilesX,
const int clipLimit, const float lutScale)
{
__local int smem[512];
const int tx = get_group_id(0);
const int ty = get_group_id(1);
const unsigned int tid = get_local_id(1) * get_local_size(0)
+ get_local_id(0);
smem[tid] = 0;
barrier(CLK_LOCAL_MEM_FENCE);
for (int i = get_local_id(1); i < tileSize.y; i += get_local_size(1))
{
__global const uchar* srcPtr = src + mad24( ty * tileSize.y + i,
srcStep, tx * tileSize.x );
for (int j = get_local_id(0); j < tileSize.x; j += get_local_size(0))
{
const int data = srcPtr[j];
atomic_inc(&smem[data]);
}
}
barrier(CLK_LOCAL_MEM_FENCE);
int tHistVal = smem[tid];
barrier(CLK_LOCAL_MEM_FENCE);
if (clipLimit > 0)
{
// clip histogram bar
int clipped = 0;
if (tHistVal > clipLimit)
{
clipped = tHistVal - clipLimit;
tHistVal = clipLimit;
}
// find number of overall clipped samples
reduce(smem, clipped, tid);
barrier(CLK_LOCAL_MEM_FENCE);
#ifdef CPU
clipped = smem[256];
#else
clipped = smem[0];
#endif
// broadcast evaluated value
__local int totalClipped;
if (tid == 0)
totalClipped = clipped;
barrier(CLK_LOCAL_MEM_FENCE);
// redistribute clipped samples evenly
int redistBatch = totalClipped / 256;
tHistVal += redistBatch;
int residual = totalClipped - redistBatch * 256;
if (tid < residual)
++tHistVal;
}
const int lutVal = calc_lut(smem, tHistVal, tid);
uint ires = (uint)convert_int_rte(lutScale * lutVal);
lut[(ty * tilesX + tx) * dstStep + tid] =
convert_uchar(clamp(ires, (uint)0, (uint)255));
}
__kernel void transform(__global __const uchar * src,
__global uchar * dst,
__global uchar * lut,
const int srcStep, const int dstStep, const int lutStep,
const int cols, const int rows,
const int2 tileSize,
const int tilesX, const int tilesY)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
if (x >= cols || y >= rows)
return;
const float tyf = (convert_float(y) / tileSize.y) - 0.5f;
int ty1 = convert_int_rtn(tyf);
int ty2 = ty1 + 1;
const float ya = tyf - ty1;
ty1 = max(ty1, 0);
ty2 = min(ty2, tilesY - 1);
const float txf = (convert_float(x) / tileSize.x) - 0.5f;
int tx1 = convert_int_rtn(txf);
int tx2 = tx1 + 1;
const float xa = txf - tx1;
tx1 = max(tx1, 0);
tx2 = min(tx2, tilesX - 1);
const int srcVal = src[mad24(y, srcStep, x)];
float res = 0;
res += lut[mad24(ty1 * tilesX + tx1, lutStep, srcVal)] * ((1.0f - xa) * (1.0f - ya));
res += lut[mad24(ty1 * tilesX + tx2, lutStep, srcVal)] * ((xa) * (1.0f - ya));
res += lut[mad24(ty2 * tilesX + tx1, lutStep, srcVal)] * ((1.0f - xa) * (ya));
res += lut[mad24(ty2 * tilesX + tx2, lutStep, srcVal)] * ((xa) * (ya));
uint ires = (uint)convert_int_rte(res);
dst[mad24(y, dstStep, x)] = convert_uchar(clamp(ires, (uint)0, (uint)255));
}
......@@ -23,6 +23,7 @@
// Rock Li, Rock.Li@amd.com
// Wu Zailong, bullet@yeah.net
// Xu Pang, pangxu010@163.com
// Sen Liu, swjtuls1987@126.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
......@@ -1393,6 +1394,46 @@ TEST_P(calcHist, Mat)
EXPECT_MAT_NEAR(dst_hist, cpu_hist, 0.0);
}
}
///////////////////////////////////////////////////////////////////////////////////////////////////////
// CLAHE
namespace
{
IMPLEMENT_PARAM_CLASS(ClipLimit, double)
}
PARAM_TEST_CASE(CLAHE, cv::Size, ClipLimit)
{
cv::Size size;
double clipLimit;
cv::Mat src;
cv::Mat dst_gold;
cv::ocl::oclMat g_src;
cv::ocl::oclMat g_dst;
virtual void SetUp()
{
size = GET_PARAM(0);
clipLimit = GET_PARAM(1);
cv::RNG &rng = TS::ptr()->get_rng();
src = randomMat(rng, size, CV_8UC1, 0, 256, false);
g_src.upload(src);
}
};
TEST_P(CLAHE, Accuracy)
{
cv::Ptr<cv::ocl::CLAHE> clahe = cv::ocl::createCLAHE(clipLimit);
clahe->apply(g_src, g_dst);
cv::Mat dst(g_dst);
cv::Ptr<cv::CLAHE> clahe_gold = cv::createCLAHE(clipLimit);
clahe_gold->apply(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 1.0);
}
///////////////////////////Convolve//////////////////////////////////
PARAM_TEST_CASE(ConvolveTestBase, MatType, bool)
......@@ -1643,6 +1684,10 @@ INSTANTIATE_TEST_CASE_P(histTestBase, calcHist, Combine(
ONE_TYPE(CV_32SC1) //no use
));
INSTANTIATE_TEST_CASE_P(ImgProc, CLAHE, Combine(
Values(cv::Size(128, 128), cv::Size(113, 113), cv::Size(1300, 1300)),
Values(0.0, 40.0)));
//INSTANTIATE_TEST_CASE_P(ConvolveTestBase, Convolve, Combine(
// Values(CV_32FC1, CV_32FC1),
// Values(false))); // Values(false) is the reserved parameter
......
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