Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

fixed and generalized ocl::blendLinear

Browse Source
pull/1544/head
Ilya Lavrenov 11 years ago
parent 529f086b62
commit c49c3e0a91
4 changed files with 203 additions and 182 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 64
      modules/ocl/perf/perf_blend.cpp
  2. 66
      modules/ocl/src/blend.cpp
  3. 116
      modules/ocl/src/opencl/blend_linear.cl
  4. 135
      modules/ocl/test/test_blend.cpp

64
modules/ocl/perf/perf_blend.cpp
Unescape View File

@ -47,48 +47,61 @@
#include "perf_precomp.hpp"
using namespace perf;
using namespace cv;
using std::tr1::get;
///////////// blend ////////////////////////
template <typename T>
static void blendLinearGold(const cv::Mat &img1, const cv::Mat &img2,
const cv::Mat &weights1, const cv::Mat &weights2,
cv::Mat &result_gold)
static void blendLinearGold(const Mat &img1, const Mat &img2,
const Mat &weights1, const Mat &weights2,
Mat &result_gold)
{
CV_Assert(img1.size() == img2.size() && img1.type() == img2.type());
CV_Assert(weights1.size() == weights2.size() && weights1.size() == img1.size() &&
weights1.type() == CV_32FC1 && weights2.type() == CV_32FC1);
result_gold.create(img1.size(), img1.type());
int cn = img1.channels();
int step1 = img1.cols * img1.channels();
for (int y = 0; y < img1.rows; ++y)
{
const float *weights1_row = weights1.ptr<float>(y);
const float *weights2_row = weights2.ptr<float>(y);
const T *img1_row = img1.ptr<T>(y);
const T *img2_row = img2.ptr<T>(y);
T *result_gold_row = result_gold.ptr<T>(y);
const float * const weights1_row = weights1.ptr<float>(y);
const float * const weights2_row = weights2.ptr<float>(y);
const T * const img1_row = img1.ptr<T>(y);
const T * const img2_row = img2.ptr<T>(y);
T * const result_gold_row = result_gold.ptr<T>(y);
for (int x = 0; x < img1.cols * cn; ++x)
for (int x = 0; x < step1; ++x)
{
int x1 = x * cn;
float w1 = weights1_row[x];
float w2 = weights2_row[x];
result_gold_row[x] = static_cast<T>((img1_row[x1] * w1
+ img2_row[x1] * w2) / (w1 + w2 + 1e-5f));
int x1 = x / cn;
float w1 = weights1_row[x1], w2 = weights2_row[x1];
result_gold_row[x] = saturate_cast<T>(((float)img1_row[x] * w1
+ (float)img2_row[x] * w2) / (w1 + w2 + 1e-5f));
}
}
}
typedef TestBaseWithParam<Size> blendLinearFixture;
typedef void (*blendFunction)(const Mat &img1, const Mat &img2,
const Mat &weights1, const Mat &weights2,
Mat &result_gold);
typedef Size_MatType blendLinearFixture;
PERF_TEST_P(blendLinearFixture, blendLinear, OCL_TYPICAL_MAT_SIZES)
PERF_TEST_P(blendLinearFixture, blendLinear, ::testing::Combine(
OCL_TYPICAL_MAT_SIZES, testing::Values(CV_8UC1, CV_8UC3, CV_32FC1)))
{
const Size srcSize = GetParam();
const int type = CV_8UC1;
Size_MatType_t params = GetParam();
const Size srcSize = get<0>(params);
const int srcType = get<1>(params);
const double eps = CV_MAT_DEPTH(srcType) <= CV_32S ? 1.0 : 0.2;
Mat src1(srcSize, type), src2(srcSize, CV_8UC1), dst;
Mat src1(srcSize, srcType), src2(srcSize, srcType), dst(srcSize, srcType);
Mat weights1(srcSize, CV_32FC1), weights2(srcSize, CV_32FC1);
declare.in(src1, src2, WARMUP_RNG);
declare.in(src1, src2, WARMUP_RNG).out(dst);
randu(weights1, 0.0f, 1.0f);
randu(weights2, 0.0f, 1.0f);
@ -97,17 +110,20 @@ PERF_TEST_P(blendLinearFixture, blendLinear, OCL_TYPICAL_MAT_SIZES)
ocl::oclMat oclSrc1(src1), oclSrc2(src2), oclDst;
ocl::oclMat oclWeights1(weights1), oclWeights2(weights2);
OCL_TEST_CYCLE() cv::ocl::blendLinear(oclSrc1, oclSrc2, oclWeights1, oclWeights2, oclDst);
OCL_TEST_CYCLE() ocl::blendLinear(oclSrc1, oclSrc2, oclWeights1, oclWeights2, oclDst);
oclDst.download(dst);
SANITY_CHECK(dst);
SANITY_CHECK(dst, eps);
}
else if (RUN_PLAIN_IMPL)
{
TEST_CYCLE() blendLinearGold<uchar>(src1, src2, weights1, weights2, dst);
blendFunction funcs[] = { (blendFunction)blendLinearGold<uchar>, (blendFunction)blendLinearGold<float> };
int funcIdx = CV_MAT_DEPTH(srcType) == CV_8UC1 ? 0 : 1;
TEST_CYCLE() (funcs[funcIdx])(src1, src2, weights1, weights2, dst);
SANITY_CHECK(dst);
SANITY_CHECK(dst, eps);
}
else
OCL_PERF_ELSE

66
modules/ocl/src/blend.cpp
Unescape View File

@ -49,35 +49,51 @@
using namespace cv;
using namespace cv::ocl;
void cv::ocl::blendLinear(const oclMat &img1, const oclMat &img2, const oclMat &weights1, const oclMat &weights2,
oclMat &result)
void cv::ocl::blendLinear(const oclMat &src1, const oclMat &src2, const oclMat &weights1, const oclMat &weights2,
oclMat &dst)
{
cv::ocl::Context *ctx = img1.clCxt;
assert(ctx == img2.clCxt && ctx == weights1.clCxt && ctx == weights2.clCxt);
int channels = img1.oclchannels();
int depth = img1.depth();
int rows = img1.rows;
int cols = img1.cols;
int istep = img1.step1();
int wstep = weights1.step1();
size_t globalSize[] = {cols * channels / 4, rows, 1};
size_t localSize[] = {256, 1, 1};
CV_Assert(src1.depth() <= CV_32F);
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(weights1.size() == weights2.size() && weights1.size() == src1.size() &&
weights1.type() == CV_32FC1 && weights2.type() == CV_32FC1);
dst.create(src1.size(), src1.type());
size_t globalSize[] = { dst.cols, dst.rows, 1};
size_t localSize[] = { 16, 16, 1 };
int depth = dst.depth(), ocn = dst.oclchannels();
int src1_step = src1.step / src1.elemSize(), src1_offset = src1.offset / src1.elemSize();
int src2_step = src2.step / src2.elemSize(), src2_offset = src2.offset / src2.elemSize();
int weight1_step = weights1.step / weights1.elemSize(), weight1_offset = weights1.offset / weights1.elemSize();
int weight2_step = weights2.step / weights2.elemSize(), weight2_offset = weights2.offset / weights2.elemSize();
int dst_step = dst.step / dst.elemSize(), dst_offset = dst.offset / dst.elemSize();
const char * const channelMap[] = { "", "", "2", "4", "4" };
const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
std::string buildOptions = format("-D T=%s%s -D convertToT=convert_%s%s%s -D FT=float%s -D convertToFT=convert_float%s",
typeMap[depth], channelMap[ocn], typeMap[depth], channelMap[ocn],
depth >= CV_32S ? "" : "_sat_rte", channelMap[ocn], channelMap[ocn]);
vector< pair<size_t, const void *> > args;
result.create(img1.size(), CV_MAKE_TYPE(depth,img1.channels()));
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&img1.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&img2.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&src1.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&src1_offset ));
args.push_back( make_pair( sizeof(cl_int), (void *)&src1_step ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&src2.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&src2_offset ));
args.push_back( make_pair( sizeof(cl_int), (void *)&src2_step ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&weights1.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&weight1_offset ));
args.push_back( make_pair( sizeof(cl_int), (void *)&weight1_step ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&weights2.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&cols ));
args.push_back( make_pair( sizeof(cl_int), (void *)&istep ));
args.push_back( make_pair( sizeof(cl_int), (void *)&wstep ));
std::string kernelName = "BlendLinear";
args.push_back( make_pair( sizeof(cl_int), (void *)&weight2_offset ));
args.push_back( make_pair( sizeof(cl_int), (void *)&weight2_step ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&dst.data ));
args.push_back( make_pair( sizeof(cl_int), (void *)&dst_offset ));
args.push_back( make_pair( sizeof(cl_int), (void *)&dst_step ));
args.push_back( make_pair( sizeof(cl_int), (void *)&dst.rows ));
args.push_back( make_pair( sizeof(cl_int), (void *)&dst.cols ));
openCLExecuteKernel(ctx, &blend_linear, kernelName, globalSize, localSize, args, channels, depth);
}
openCLExecuteKernel(src1.clCxt, &blend_linear, "blendLinear", globalSize, localSize, args,
-1, -1, buildOptions.c_str());
}

116
modules/ocl/src/opencl/blend_linear.cl
Unescape View File

@ -42,99 +42,37 @@
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
__kernel void BlendLinear_C1_D0(
__global uchar4 *dst,
__global uchar4 *img1,
__global uchar4 *img2,
__global float4 *weight1,
__global float4 *weight2,
int rows,
int cols,
int istep,
int wstep
)
{
int idx = get_global_id(0);
int idy = get_global_id(1);
if (idx << 2 < cols && idy < rows)
{
int pos = mad24(idy,istep >> 2,idx);
int wpos = mad24(idy,wstep >> 2,idx);
float4 w1 = weight1[wpos], w2 = weight2[wpos];
dst[pos] = convert_uchar4((convert_float4(img1[pos]) * w1 +
convert_float4(img2[pos]) * w2) / (w1 + w2 + 1e-5f));
}
}
__kernel void BlendLinear_C4_D0(
__global uchar4 *dst,
__global uchar4 *img1,
__global uchar4 *img2,
__global float *weight1,
__global float *weight2,
int rows,
int cols,
int istep,
int wstep
)
{
int idx = get_global_id(0);
int idy = get_global_id(1);
if (idx < cols && idy < rows)
{
int pos = mad24(idy,istep >> 2,idx);
int wpos = mad24(idy,wstep, idx);
float w1 = weight1[wpos];
float w2 = weight2[wpos];
dst[pos] = convert_uchar4((convert_float4(img1[pos]) * w1 +
convert_float4(img2[pos]) * w2) / (w1 + w2 + 1e-5f));
}
}
#if defined (DOUBLE_SUPPORT)
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void BlendLinear_C1_D5(
__global float4 *dst,
__global float4 *img1,
__global float4 *img2,
__global float4 *weight1,
__global float4 *weight2,
int rows,
int cols,
int istep,
int wstep
)
{
int idx = get_global_id(0);
int idy = get_global_id(1);
if (idx << 2 < cols && idy < rows)
__kernel void blendLinear(__global const T * src1, int src1_offset, int src1_step,
__global const T * src2, int src2_offset, int src2_step,
__global const float * weight1, int weight1_offset, int weight1_step,
__global const float * weight2, int weight2_offset, int weight2_step,
__global T * dst, int dst_offset, int dst_step,
int rows, int cols)
{
int pos = mad24(idy,istep >> 2,idx);
int wpos = mad24(idy,wstep >> 2,idx);
float4 w1 = weight1[wpos], w2 = weight2[wpos];
dst[pos] = (img1[pos] * w1 + img2[pos] * w2) / (w1 + w2 + 1e-5f);
}
}
int x = get_global_id(0);
int y = get_global_id(1);
__kernel void BlendLinear_C4_D5(
__global float4 *dst,
__global float4 *img1,
__global float4 *img2,
__global float *weight1,
__global float *weight2,
int rows,
int cols,
int istep,
int wstep
)
if (x < cols && y < rows)
{
int idx = get_global_id(0);
int idy = get_global_id(1);
if (idx < cols && idy < rows)
{
int pos = mad24(idy,istep >> 2,idx);
int wpos = mad24(idy,wstep, idx);
float w1 = weight1[wpos];
float w2 = weight2[wpos];
dst[pos] = (img1[pos] * w1 + img2[pos] * w2) / (w1 + w2 + 1e-5f);
int src1_index = mad24(y, src1_step, src1_offset + x);
int src2_index = mad24(y, src2_step, src2_offset + x);
int weight1_index = mad24(y, weight1_step, weight1_offset + x);
int weight2_index = mad24(y, weight2_step, weight2_offset + x);
int dst_index = mad24(y, dst_step, dst_offset + x);
FT w1 = (FT)(weight1[weight1_index]), w2 = (FT)(weight2[weight2_index]);
FT den = w1 + w2 + (FT)(1e-5f);
FT num = w1 * convertToFT(src1[src1_index]) + w2 * convertToFT(src2[src2_index]);
dst[dst_index] = convertToT(num / den);
}
}

135
modules/ocl/test/test_blend.cpp
Unescape View File

@ -47,73 +47,124 @@
using namespace cv;
using namespace cv::ocl;
using namespace cvtest;
using namespace testing;
using namespace std;
#ifdef HAVE_OPENCL
template <typename T>
void blendLinearGold(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &weights1, const cv::Mat &weights2, cv::Mat &result_gold)
static void blendLinearGold(const Mat &img1, const Mat &img2,
const Mat &weights1, const Mat &weights2,
Mat &result_gold)
{
CV_Assert(img1.size() == img2.size() && img1.type() == img2.type());
CV_Assert(weights1.size() == weights2.size() && weights1.size() == img1.size() &&
weights1.type() == CV_32FC1 && weights2.type() == CV_32FC1);
result_gold.create(img1.size(), img1.type());
int cn = img1.channels();
int step1 = img1.cols * img1.channels();
for (int y = 0; y < img1.rows; ++y)
{
const float *weights1_row = weights1.ptr<float>(y);
const float *weights2_row = weights2.ptr<float>(y);
const T *img1_row = img1.ptr<T>(y);
const T *img2_row = img2.ptr<T>(y);
T *result_gold_row = result_gold.ptr<T>(y);
const float * const weights1_row = weights1.ptr<float>(y);
const float * const weights2_row = weights2.ptr<float>(y);
const T * const img1_row = img1.ptr<T>(y);
const T * const img2_row = img2.ptr<T>(y);
T * const result_gold_row = result_gold.ptr<T>(y);
for (int x = 0; x < img1.cols * cn; ++x)
for (int x = 0; x < step1; ++x)
{
float w1 = weights1_row[x / cn];
float w2 = weights2_row[x / cn];
result_gold_row[x] = static_cast<T>((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f));
int x1 = x / cn;
float w1 = weights1_row[x1], w2 = weights2_row[x1];
result_gold_row[x] = saturate_cast<T>(((float)img1_row[x] * w1
+ (float)img2_row[x] * w2) / (w1 + w2 + 1e-5f));
}
}
}
PARAM_TEST_CASE(Blend, cv::Size, MatType/*, UseRoi*/)
PARAM_TEST_CASE(Blend, MatDepth, int, bool)
{
cv::Size size;
int type;
int depth, channels;
bool useRoi;
Mat src1, src2, weights1, weights2, dst;
Mat src1_roi, src2_roi, weights1_roi, weights2_roi, dst_roi;
oclMat gsrc1, gsrc2, gweights1, gweights2, gdst, gst;
oclMat gsrc1_roi, gsrc2_roi, gweights1_roi, gweights2_roi, gdst_roi;
virtual void SetUp()
{
size = GET_PARAM(0);
type = GET_PARAM(1);
depth = GET_PARAM(0);
channels = GET_PARAM(1);
useRoi = GET_PARAM(2);
}
void random_roi()
{
const int type = CV_MAKE_TYPE(depth, channels);
const double upValue = 1200;
Size roiSize = randomSize(1, 20);
Border src1Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
randomSubMat(src1, src1_roi, roiSize, src1Border, type, -upValue, upValue);
Border src2Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
randomSubMat(src2, src2_roi, roiSize, src2Border, type, -upValue, upValue);
Border weights1Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
randomSubMat(weights1, weights1_roi, roiSize, weights1Border, CV_32FC1, -upValue, upValue);
Border weights2Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
randomSubMat(weights2, weights2_roi, roiSize, weights2Border, CV_32FC1, -upValue, upValue);
Border dstBorder = randomBorder(0, useRoi ? MAX_VALUE : 0);
randomSubMat(dst, dst_roi, roiSize, dstBorder, type, 5, 16);
generateOclMat(gsrc1, gsrc1_roi, src1, roiSize, src1Border);
generateOclMat(gsrc2, gsrc2_roi, src2, roiSize, src2Border);
generateOclMat(gweights1, gweights1_roi, weights1, roiSize, weights1Border);
generateOclMat(gweights2, gweights2_roi, weights2, roiSize, weights2Border);
generateOclMat(gdst, gdst_roi, dst, roiSize, dstBorder);
}
void Near(double eps = 0.0)
{
Mat whole, roi;
gdst.download(whole);
gdst_roi.download(roi);
EXPECT_MAT_NEAR(dst, whole, eps);
EXPECT_MAT_NEAR(dst_roi, roi, eps);
}
};
typedef void (*blendLinearFunc)(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &weights1, const cv::Mat &weights2, cv::Mat &result_gold);
OCL_TEST_P(Blend, Accuracy)
{
int depth = CV_MAT_DEPTH(type);
cv::Mat img1 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0);
cv::Mat img2 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0);
cv::Mat weights1 = randomMat(size, CV_32F, 0, 1);
cv::Mat weights2 = randomMat(size, CV_32F, 0, 1);
cv::ocl::oclMat gimg1(img1), gimg2(img2), gweights1(weights1), gweights2(weights2);
cv::ocl::oclMat dst;
cv::ocl::blendLinear(gimg1, gimg2, gweights1, gweights2, dst);
cv::Mat result;
cv::Mat result_gold;
dst.download(result);
if (depth == CV_8U)
blendLinearGold<uchar>(img1, img2, weights1, weights2, result_gold);
else
blendLinearGold<float>(img1, img2, weights1, weights2, result_gold);
EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.f : 1e-5f);
for (int i = 0; i < LOOP_TIMES; ++i)
{
random_roi();
cv::ocl::blendLinear(gsrc1_roi, gsrc2_roi, gweights1_roi, gweights2_roi, gdst_roi);
static blendLinearFunc funcs[] = {
blendLinearGold<uchar>,
blendLinearGold<schar>,
blendLinearGold<ushort>,
blendLinearGold<short>,
blendLinearGold<int>,
blendLinearGold<float>,
};
blendLinearFunc func = funcs[depth];
func(src1_roi, src2_roi, weights1_roi, weights2_roi, dst_roi);
Near(depth <= CV_32S ? 1.0 : 0.2);
}
}
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, Blend, Combine(
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC4))
));
#endif
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, Blend,
Combine(testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F),
testing::Range(1, 5), Bool()));

Write Preview
Loading…
Cancel
Save