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Merge pull request #8187 from hewj03:improve-MultiBandBlender-cuda

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This commit is contained in:
Vadim Pisarevsky 2017-02-16 15:00:58 +00:00
commit 3240f2a6b7
4 changed files with 383 additions and 28 deletions
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4
modules/stitching/include/opencv2/stitching/detail/blenders.hpp
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@ -142,6 +142,10 @@ private:
Rect dst_roi_final_;
bool can_use_gpu_;
int weight_type_; //CV_32F or CV_16S
#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)
std::vector<cuda::GpuMat> gpu_dst_pyr_laplace_;
std::vector<cuda::GpuMat> gpu_dst_band_weights_;
#endif
};

202
modules/stitching/src/blenders.cpp
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@ -43,6 +43,23 @@
#include "precomp.hpp"
#include "opencl_kernels_stitching.hpp"
#ifdef HAVE_CUDA
namespace cv { namespace cuda { namespace device
{
namespace blend
{
void addSrcWeightGpu16S(const PtrStep<short> src, const PtrStep<short> src_weight,
PtrStep<short> dst, PtrStep<short> dst_weight, cv::Rect &rc);
void addSrcWeightGpu32F(const PtrStep<short> src, const PtrStepf src_weight,
PtrStep<short> dst, PtrStepf dst_weight, cv::Rect &rc);
void normalizeUsingWeightMapGpu16S(const PtrStep<short> weight, PtrStep<short> src,
const int width, const int height);
void normalizeUsingWeightMapGpu32F(const PtrStepf weight, PtrStep<short> src,
const int width, const int height);
}
}}}
#endif
namespace cv {
namespace detail {
@ -228,21 +245,46 @@ void MultiBandBlender::prepare(Rect dst_roi)
Blender::prepare(dst_roi);
dst_pyr_laplace_.resize(num_bands_ + 1);
dst_pyr_laplace_[0] = dst_;
dst_band_weights_.resize(num_bands_ + 1);
dst_band_weights_[0].create(dst_roi.size(), weight_type_);
dst_band_weights_[0].setTo(0);
for (int i = 1; i <= num_bands_; ++i)
#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)
if (can_use_gpu_)
{
dst_pyr_laplace_[i].create((dst_pyr_laplace_[i - 1].rows + 1) / 2,
(dst_pyr_laplace_[i - 1].cols + 1) / 2, CV_16SC3);
dst_band_weights_[i].create((dst_band_weights_[i - 1].rows + 1) / 2,
(dst_band_weights_[i - 1].cols + 1) / 2, weight_type_);
dst_pyr_laplace_[i].setTo(Scalar::all(0));
dst_band_weights_[i].setTo(0);
gpu_dst_pyr_laplace_.resize(num_bands_ + 1);
gpu_dst_pyr_laplace_[0].create(dst_roi.size(), CV_16SC3);
gpu_dst_pyr_laplace_[0].setTo(Scalar::all(0));
gpu_dst_band_weights_.resize(num_bands_ + 1);
gpu_dst_band_weights_[0].create(dst_roi.size(), weight_type_);
gpu_dst_band_weights_[0].setTo(0);
for (int i = 1; i <= num_bands_; ++i)
{
gpu_dst_pyr_laplace_[i].create((gpu_dst_pyr_laplace_[i - 1].rows + 1) / 2,
(gpu_dst_pyr_laplace_[i - 1].cols + 1) / 2, CV_16SC3);
gpu_dst_band_weights_[i].create((gpu_dst_band_weights_[i - 1].rows + 1) / 2,
(gpu_dst_band_weights_[i - 1].cols + 1) / 2, weight_type_);
gpu_dst_pyr_laplace_[i].setTo(Scalar::all(0));
gpu_dst_band_weights_[i].setTo(0);
}
}
else
#endif
{
dst_pyr_laplace_.resize(num_bands_ + 1);
dst_pyr_laplace_[0] = dst_;
dst_band_weights_.resize(num_bands_ + 1);
dst_band_weights_[0].create(dst_roi.size(), weight_type_);
dst_band_weights_[0].setTo(0);
for (int i = 1; i <= num_bands_; ++i)
{
dst_pyr_laplace_[i].create((dst_pyr_laplace_[i - 1].rows + 1) / 2,
(dst_pyr_laplace_[i - 1].cols + 1) / 2, CV_16SC3);
dst_band_weights_[i].create((dst_band_weights_[i - 1].rows + 1) / 2,
(dst_band_weights_[i - 1].cols + 1) / 2, weight_type_);
dst_pyr_laplace_[i].setTo(Scalar::all(0));
dst_band_weights_[i].setTo(0);
}
}
}
@ -312,6 +354,76 @@ void MultiBandBlender::feed(InputArray _img, InputArray mask, Point tl)
int bottom = br_new.y - tl.y - img.rows;
int right = br_new.x - tl.x - img.cols;
#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)
if (can_use_gpu_)
{
// Create the source image Laplacian pyramid
cuda::GpuMat gpu_img;
gpu_img.upload(img);
cuda::GpuMat img_with_border;
cuda::copyMakeBorder(gpu_img, img_with_border, top, bottom, left, right, BORDER_REFLECT);
std::vector<cuda::GpuMat> gpu_src_pyr_laplace(num_bands_ + 1);
img_with_border.convertTo(gpu_src_pyr_laplace[0], CV_16S);
for (int i = 0; i < num_bands_; ++i)
cuda::pyrDown(gpu_src_pyr_laplace[i], gpu_src_pyr_laplace[i + 1]);
for (int i = 0; i < num_bands_; ++i)
{
cuda::GpuMat up;
cuda::pyrUp(gpu_src_pyr_laplace[i + 1], up);
cuda::subtract(gpu_src_pyr_laplace[i], up, gpu_src_pyr_laplace[i]);
}
// Create the weight map Gaussian pyramid
cuda::GpuMat gpu_mask;
gpu_mask.upload(mask);
cuda::GpuMat weight_map;
std::vector<cuda::GpuMat> gpu_weight_pyr_gauss(num_bands_ + 1);
if (weight_type_ == CV_32F)
{
gpu_mask.convertTo(weight_map, CV_32F, 1. / 255.);
}
else // weight_type_ == CV_16S
{
gpu_mask.convertTo(weight_map, CV_16S);
cuda::GpuMat add_mask;
cuda::compare(gpu_mask, 0, add_mask, CMP_NE);
cuda::add(weight_map, Scalar::all(1), weight_map, add_mask);
}
cuda::copyMakeBorder(weight_map, gpu_weight_pyr_gauss[0], top, bottom, left, right, BORDER_CONSTANT);
for (int i = 0; i < num_bands_; ++i)
cuda::pyrDown(gpu_weight_pyr_gauss[i], gpu_weight_pyr_gauss[i + 1]);
int y_tl = tl_new.y - dst_roi_.y;
int y_br = br_new.y - dst_roi_.y;
int x_tl = tl_new.x - dst_roi_.x;
int x_br = br_new.x - dst_roi_.x;
// Add weighted layer of the source image to the final Laplacian pyramid layer
for (int i = 0; i <= num_bands_; ++i)
{
Rect rc(x_tl, y_tl, x_br - x_tl, y_br - y_tl);
cuda::GpuMat &_src_pyr_laplace = gpu_src_pyr_laplace[i];
cuda::GpuMat _dst_pyr_laplace = gpu_dst_pyr_laplace_[i](rc);
cuda::GpuMat &_weight_pyr_gauss = gpu_weight_pyr_gauss[i];
cuda::GpuMat _dst_band_weights = gpu_dst_band_weights_[i](rc);
using namespace cv::cuda::device::blend;
if (weight_type_ == CV_32F)
{
addSrcWeightGpu32F(_src_pyr_laplace, _weight_pyr_gauss, _dst_pyr_laplace, _dst_band_weights, rc);
}
else
{
addSrcWeightGpu16S(_src_pyr_laplace, _weight_pyr_gauss, _dst_pyr_laplace, _dst_band_weights, rc);
}
x_tl /= 2; y_tl /= 2;
x_br /= 2; y_br /= 2;
}
return;
}
#endif
// Create the source image Laplacian pyramid
UMat img_with_border;
copyMakeBorder(_img, img_with_border, top, bottom, left, right,
@ -322,10 +434,7 @@ void MultiBandBlender::feed(InputArray _img, InputArray mask, Point tl)
#endif
std::vector<UMat> src_pyr_laplace;
if (can_use_gpu_ && img_with_border.depth() == CV_16S)
createLaplacePyrGpu(img_with_border, num_bands_, src_pyr_laplace);
else
createLaplacePyr(img_with_border, num_bands_, src_pyr_laplace);
createLaplacePyr(img_with_border, num_bands_, src_pyr_laplace);
LOGLN(" Create the source image Laplacian pyramid, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
#if ENABLE_LOG
@ -431,20 +540,57 @@ void MultiBandBlender::feed(InputArray _img, InputArray mask, Point tl)
void MultiBandBlender::blend(InputOutputArray dst, InputOutputArray dst_mask)
{
for (int i = 0; i <= num_bands_; ++i)
normalizeUsingWeightMap(dst_band_weights_[i], dst_pyr_laplace_[i]);
cv::UMat dst_band_weights_0;
Rect dst_rc(0, 0, dst_roi_final_.width, dst_roi_final_.height);
#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)
if (can_use_gpu_)
restoreImageFromLaplacePyrGpu(dst_pyr_laplace_);
{
for (int i = 0; i <= num_bands_; ++i)
{
cuda::GpuMat dst_i = gpu_dst_pyr_laplace_[i];
cuda::GpuMat weight_i = gpu_dst_band_weights_[i];
using namespace ::cv::cuda::device::blend;
if (weight_type_ == CV_32F)
{
normalizeUsingWeightMapGpu32F(weight_i, dst_i, weight_i.cols, weight_i.rows);
}
else
{
normalizeUsingWeightMapGpu16S(weight_i, dst_i, weight_i.cols, weight_i.rows);
}
}
// Restore image from Laplacian pyramid
for (size_t i = num_bands_; i > 0; --i)
{
cuda::GpuMat up;
cuda::pyrUp(gpu_dst_pyr_laplace_[i], up);
cuda::add(up, gpu_dst_pyr_laplace_[i - 1], gpu_dst_pyr_laplace_[i - 1]);
}
gpu_dst_pyr_laplace_[0](dst_rc).download(dst_);
gpu_dst_band_weights_[0].download(dst_band_weights_0);
gpu_dst_pyr_laplace_.clear();
gpu_dst_band_weights_.clear();
}
else
#endif
{
for (int i = 0; i <= num_bands_; ++i)
normalizeUsingWeightMap(dst_band_weights_[i], dst_pyr_laplace_[i]);
restoreImageFromLaplacePyr(dst_pyr_laplace_);
Rect dst_rc(0, 0, dst_roi_final_.width, dst_roi_final_.height);
dst_ = dst_pyr_laplace_[0](dst_rc);
UMat _dst_mask;
compare(dst_band_weights_[0](dst_rc), WEIGHT_EPS, dst_mask_, CMP_GT);
dst_pyr_laplace_.clear();
dst_band_weights_.clear();
dst_ = dst_pyr_laplace_[0](dst_rc);
dst_band_weights_0 = dst_band_weights_[0];
dst_pyr_laplace_.clear();
dst_band_weights_.clear();
}
compare(dst_band_weights_0(dst_rc), WEIGHT_EPS, dst_mask_, CMP_GT);
Blender::blend(dst, dst_mask);
}

112
modules/stitching/src/cuda/multiband_blend.cu Normal file
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@ -0,0 +1,112 @@
#if !defined CUDA_DISABLER
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/types.hpp"
namespace cv { namespace cuda { namespace device
{
namespace blend
{
__global__ void addSrcWeightKernel16S(const PtrStep<short> src, const PtrStep<short> src_weight,
PtrStep<short> dst, PtrStep<short> dst_weight, int rows, int cols)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y < rows && x < cols)
{
const short3 v = ((const short3*)src.ptr(y))[x];
short w = src_weight.ptr(y)[x];
((short3*)dst.ptr(y))[x].x += short((v.x * w) >> 8);
((short3*)dst.ptr(y))[x].y += short((v.y * w) >> 8);
((short3*)dst.ptr(y))[x].z += short((v.z * w) >> 8);
dst_weight.ptr(y)[x] += w;
}
}
void addSrcWeightGpu16S(const PtrStep<short> src, const PtrStep<short> src_weight,
PtrStep<short> dst, PtrStep<short> dst_weight, cv::Rect &rc)
{
dim3 threads(16, 16);
dim3 grid(divUp(rc.width, threads.x), divUp(rc.height, threads.y));
addSrcWeightKernel16S<<<grid, threads>>>(src, src_weight, dst, dst_weight, rc.height, rc.width);
cudaSafeCall(cudaGetLastError());
}
__global__ void addSrcWeightKernel32F(const PtrStep<short> src, const PtrStepf src_weight,
PtrStep<short> dst, PtrStepf dst_weight, int rows, int cols)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y < rows && x < cols)
{
const short3 v = ((const short3*)src.ptr(y))[x];
float w = src_weight.ptr(y)[x];
((short3*)dst.ptr(y))[x].x += static_cast<short>(v.x * w);
((short3*)dst.ptr(y))[x].y += static_cast<short>(v.y * w);
((short3*)dst.ptr(y))[x].z += static_cast<short>(v.z * w);
dst_weight.ptr(y)[x] += w;
}
}
void addSrcWeightGpu32F(const PtrStep<short> src, const PtrStepf src_weight,
PtrStep<short> dst, PtrStepf dst_weight, cv::Rect &rc)
{
dim3 threads(16, 16);
dim3 grid(divUp(rc.width, threads.x), divUp(rc.height, threads.y));
addSrcWeightKernel32F<<<grid, threads>>>(src, src_weight, dst, dst_weight, rc.height, rc.width);
cudaSafeCall(cudaGetLastError());
}
__global__ void normalizeUsingWeightKernel16S(const PtrStep<short> weight, PtrStep<short> src,
const int width, const int height)
{
int x = (blockIdx.x * blockDim.x) + threadIdx.x;
int y = (blockIdx.y * blockDim.y) + threadIdx.y;
if (x < width && y < height)
{
const short3 v = ((short3*)src.ptr(y))[x];
short w = weight.ptr(y)[x];
((short3*)src.ptr(y))[x] = make_short3(short((v.x << 8) / w),
short((v.y << 8) / w), short((v.z << 8) / w));
}
}
void normalizeUsingWeightMapGpu16S(const PtrStep<short> weight, PtrStep<short> src,
const int width, const int height)
{
dim3 threads(16, 16);
dim3 grid(divUp(width, threads.x), divUp(height, threads.y));
normalizeUsingWeightKernel16S<<<grid, threads>>> (weight, src, width, height);
}
__global__ void normalizeUsingWeightKernel32F(const PtrStepf weight, PtrStep<short> src,
const int width, const int height)
{
int x = (blockIdx.x * blockDim.x) + threadIdx.x;
int y = (blockIdx.y * blockDim.y) + threadIdx.y;
if (x < width && y < height)
{
static const float WEIGHT_EPS = 1e-5f;
const short3 v = ((short3*)src.ptr(y))[x];
float w = weight.ptr(y)[x];
((short3*)src.ptr(y))[x] = make_short3(static_cast<short>(v.x / (w + WEIGHT_EPS)),
static_cast<short>(v.y / (w + WEIGHT_EPS)),
static_cast<short>(v.z / (w + WEIGHT_EPS)));
}
}
void normalizeUsingWeightMapGpu32F(const PtrStepf weight, PtrStep<short> src,
const int width, const int height)
{
dim3 threads(16, 16);
dim3 grid(divUp(width, threads.x), divUp(height, threads.y));
normalizeUsingWeightKernel32F<<<grid, threads>>> (weight, src, width, height);
}
}
}}}
#endif

93
modules/stitching/test/test_blenders.cuda.cpp Normal file
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@ -0,0 +1,93 @@
/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// 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 Intel Corporation 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 "test_precomp.hpp"
#include "opencv2/ts/cuda_test.hpp"
#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)
using namespace cv;
using namespace std;
namespace
{
void multiBandBlend(const cv::Mat& im1, const cv::Mat& im2, const cv::Mat& mask1, const cv::Mat& mask2, cv::Mat& result, bool try_cuda)
{
detail::MultiBandBlender blender(try_cuda, 5);
blender.prepare(Rect(0, 0, max(im1.cols, im2.cols), max(im1.rows, im2.rows)));
blender.feed(im1, mask1, Point(0,0));
blender.feed(im2, mask2, Point(0,0));
Mat result_s, result_mask;
blender.blend(result_s, result_mask);
result_s.convertTo(result, CV_8U);
}
}
TEST(CUDA_MultiBandBlender, Accuracy)
{
Mat image1 = imread(string(cvtest::TS::ptr()->get_data_path()) + "cv/shared/baboon.png");
Mat image2 = imread(string(cvtest::TS::ptr()->get_data_path()) + "cv/shared/lena.png");
ASSERT_EQ(image1.rows, image2.rows); ASSERT_EQ(image1.cols, image2.cols);
Mat image1s, image2s;
image1.convertTo(image1s, CV_16S);
image2.convertTo(image2s, CV_16S);
Mat mask1(image1s.size(), CV_8U);
mask1(Rect(0, 0, mask1.cols/2, mask1.rows)).setTo(255);
mask1(Rect(mask1.cols/2, 0, mask1.cols - mask1.cols/2, mask1.rows)).setTo(0);
Mat mask2(image2s.size(), CV_8U);
mask2(Rect(0, 0, mask2.cols/2, mask2.rows)).setTo(0);
mask2(Rect(mask2.cols/2, 0, mask2.cols - mask2.cols/2, mask2.rows)).setTo(255);
cv::Mat result;
multiBandBlend(image1s, image2s, mask1, mask2, result, false);
cv::Mat result_cuda;
multiBandBlend(image1s, image2s, mask1, mask2, result_cuda, true);
EXPECT_MAT_NEAR(result, result_cuda, 3);
}
#endif