generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

fast nlm (class version)

Browse Source
This commit is contained in:
Anatoly Baksheev
2012-10-04 19:36:48 +04:00
parent 4b5bbb7752
commit 9a4265a8d0
8 changed files with 374 additions and 357 deletions
Download Patch File Download Diff File Expand all files Collapse all files

255
modules/gpu/src/denoising.cpp
View File

@@ -104,7 +104,7 @@ void cv::gpu::bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, f
func(src, dst, kernel_size, sigma_spatial, sigma_color, gpuBorderType, StreamAccessor::getStream(s));
}
void cv::gpu::nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window_size, int block_size, int borderMode, Stream& s)
void cv::gpu::nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window, int block_window, int borderMode, Stream& s)
{
using cv::gpu::device::imgproc::nlm_bruteforce_gpu;
typedef void (*func_t)(const PtrStepSzb& src, PtrStepSzb dst, int search_radius, int block_radius, float h, int borderMode, cudaStream_t stream);
@@ -121,12 +121,9 @@ void cv::gpu::nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_
int gpuBorderType;
CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));
int search_radius = search_window_size/2;
int block_radius = block_size/2;
dst.create(src.size(), src.type());
func(src, dst, search_radius, block_radius, h, gpuBorderType, StreamAccessor::getStream(s));
func(src, dst, search_window/2, block_window/2, h, gpuBorderType, StreamAccessor::getStream(s));
}
@@ -143,220 +140,76 @@ namespace cv { namespace gpu { namespace device
template<typename T>
void nlm_fast_gpu(const PtrStepSzb& src, PtrStepSzb dst, PtrStepi buffer,
int search_window, int block_window, float h, cudaStream_t stream);
}
void fnlm_split_channels(const PtrStepSz<uchar3>& lab, PtrStepb l, PtrStep<uchar2> ab, cudaStream_t stream);
void fnlm_merge_channels(const PtrStepb& l, const PtrStep<uchar2>& ab, PtrStepSz<uchar3> lab, cudaStream_t stream);
}
}}}
//class CV_EXPORTS FastNonLocalMeansDenoising
//{
//public:
// FastNonLocalMeansDenoising(float h, int search_radius, int block_radius, const Size& image_size = Size())
// {
// if (size.area() != 0)
// allocate_buffers(image_size);
// }
// void operator()(const GpuMat& src, GpuMat& dst);
//private:
// void allocate_buffers(const Size& image_size)
// {
// col_dist_sums.create(block_window_, search_window_ * search_window_, CV_32S);
// up_col_dist_sums.create(image_size.width, search_window_ * search_window_, CV_32S);
// }
// int search_radius_;
// int block_radius;
// GpuMat col_dist_sums_;
// GpuMat up_col_dist_sums_;
//};
void cv::gpu::fastNlMeansDenoising( const GpuMat& src, GpuMat& dst, float h, int search_radius, int block_radius, Stream& s)
void cv::gpu::FastNonLocalMeansDenoising::simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window, int block_window, Stream& s)
{
dst.create(src.size(), src.type());
CV_Assert(src.depth() == CV_8U && src.channels() < 4);
int border_size = search_window/2 + block_window/2;
Size esize = src.size() + Size(border_size, border_size) * 2;
cv::gpu::ensureSizeIsEnough(esize, CV_8UC3, extended_src_buffer);
GpuMat extended_src(esize, src.type(), extended_src_buffer.ptr(), extended_src_buffer.step);
GpuMat extended_src, src_hdr;
int border_size = search_radius + block_radius;
cv::gpu::copyMakeBorder(src, extended_src, border_size, border_size, border_size, border_size, cv::BORDER_DEFAULT, Scalar(), s);
src_hdr = extended_src(Rect(Point2i(border_size, border_size), src.size()));
GpuMat src_hdr = extended_src(Rect(Point2i(border_size, border_size), src.size()));
int bcols, brows;
device::imgproc::nln_fast_get_buffer_size(src_hdr, search_window, block_window, bcols, brows);
buffer.create(brows, bcols, CV_32S);
using namespace cv::gpu::device::imgproc;
typedef void (*nlm_fast_t)(const PtrStepSzb&, PtrStepSzb, PtrStepi, int, int, float, cudaStream_t);
static const nlm_fast_t funcs[] = { nlm_fast_gpu<uchar>, nlm_fast_gpu<uchar2>, nlm_fast_gpu<uchar3>, 0 };
int search_window = 2 * search_radius + 1;
int block_window = 2 * block_radius + 1;
int bcols, brows;
nln_fast_get_buffer_size(src_hdr, search_window, block_window, bcols, brows);
//GpuMat col_dist_sums(block_window * gx, search_window * search_window * gy, CV_32S);
//GpuMat up_col_dist_sums(src.cols, search_window * search_window * gy, CV_32S);
GpuMat buffer(brows, bcols, CV_32S);
static const nlm_fast_t funcs[] = { nlm_fast_gpu<uchar>, nlm_fast_gpu<uchar2>, nlm_fast_gpu<uchar3>, 0};
dst.create(src.size(), src.type());
funcs[src.channels()-1](src_hdr, dst, buffer, search_window, block_window, h, StreamAccessor::getStream(s));
}
//void cv::gpu::fastNlMeansDenoisingColored( const GpuMat& src, GpuMat& dst, float h, float hForColorComponents, int templateWindowSize, int searchWindowSize)
//{
// Mat src = _src.getMat();
// _dst.create(src.size(), src.type());
// Mat dst = _dst.getMat();
void cv::gpu::FastNonLocalMeansDenoising::labMethod( const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window, int block_window, Stream& s)
{
#if (CUDA_VERSION < 5000)
(void)src;
(void)dst;
(void)h_luminance;
(void)h_color;
(void)search_window;
(void)block_window;
(void)s;
CV_Error( CV_GpuApiCallError, "Lab method required CUDA 5.0 and higher" );
#else
// if (src.type() != CV_8UC3) {
// CV_Error(CV_StsBadArg, "Type of input image should be CV_8UC3!");
// return;
// }
// Mat src_lab;
// cvtColor(src, src_lab, CV_LBGR2Lab);
CV_Assert(src.type() == CV_8UC3);
lab.create(src.size(), src.type());
cv::gpu::cvtColor(src, lab, CV_BGR2Lab, 0, s);
// Mat l(src.size(), CV_8U);
// Mat ab(src.size(), CV_8UC2);
// Mat l_ab[] = { l, ab };
// int from_to[] = { 0,0, 1,1, 2,2 };
// mixChannels(&src_lab, 1, l_ab, 2, from_to, 3);
/*Mat t;
cv::cvtColor(Mat(src), t, CV_BGR2Lab);
lab.upload(t);*/
// fastNlMeansDenoising(l, l, h, templateWindowSize, searchWindowSize);
// fastNlMeansDenoising(ab, ab, hForColorComponents, templateWindowSize, searchWindowSize);
l.create(src.size(), CV_8U);
ab.create(src.size(), CV_8UC2);
device::imgproc::fnlm_split_channels(lab, l, ab, StreamAccessor::getStream(s));
simpleMethod(l, l, h_luminance, search_window, block_window, s);
simpleMethod(ab, ab, h_color, search_window, block_window, s);
// Mat l_ab_denoised[] = { l, ab };
// Mat dst_lab(src.size(), src.type());
// mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
device::imgproc::fnlm_merge_channels(l, ab, lab, StreamAccessor::getStream(s));
cv::gpu::cvtColor(lab, dst, CV_Lab2BGR, 0, s);
// cvtColor(dst_lab, dst, CV_Lab2LBGR);
//}
/*cv::cvtColor(Mat(lab), t, CV_Lab2BGR);
dst.upload(t);*/
//static void fastNlMeansDenoisingMultiCheckPreconditions(
// const std::vector<Mat>& srcImgs,
// int imgToDenoiseIndex, int temporalWindowSize,
// int templateWindowSize, int searchWindowSize)
//{
// int src_imgs_size = (int)srcImgs.size();
// if (src_imgs_size == 0) {
// CV_Error(CV_StsBadArg, "Input images vector should not be empty!");
// }
// if (temporalWindowSize % 2 == 0 ||
// searchWindowSize % 2 == 0 ||
// templateWindowSize % 2 == 0) {
// CV_Error(CV_StsBadArg, "All windows sizes should be odd!");
// }
// int temporalWindowHalfSize = temporalWindowSize / 2;
// if (imgToDenoiseIndex - temporalWindowHalfSize < 0 ||
// imgToDenoiseIndex + temporalWindowHalfSize >= src_imgs_size)
// {
// CV_Error(CV_StsBadArg,
// "imgToDenoiseIndex and temporalWindowSize "
// "should be choosen corresponding srcImgs size!");
// }
// for (int i = 1; i < src_imgs_size; i++) {
// if (srcImgs[0].size() != srcImgs[i].size() || srcImgs[0].type() != srcImgs[i].type()) {
// CV_Error(CV_StsBadArg, "Input images should have the same size and type!");
// }
// }
//}
//void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
// int imgToDenoiseIndex, int temporalWindowSize,
// float h, int templateWindowSize, int searchWindowSize)
//{
// vector<Mat> srcImgs;
// _srcImgs.getMatVector(srcImgs);
// fastNlMeansDenoisingMultiCheckPreconditions(
// srcImgs, imgToDenoiseIndex,
// temporalWindowSize, templateWindowSize, searchWindowSize
// );
// _dst.create(srcImgs[0].size(), srcImgs[0].type());
// Mat dst = _dst.getMat();
// switch (srcImgs[0].type()) {
// case CV_8U:
// parallel_for(cv::BlockedRange(0, srcImgs[0].rows),
// FastNlMeansMultiDenoisingInvoker<uchar>(
// srcImgs, imgToDenoiseIndex, temporalWindowSize,
// dst, templateWindowSize, searchWindowSize, h));
// break;
// case CV_8UC2:
// parallel_for(cv::BlockedRange(0, srcImgs[0].rows),
// FastNlMeansMultiDenoisingInvoker<cv::Vec2b>(
// srcImgs, imgToDenoiseIndex, temporalWindowSize,
// dst, templateWindowSize, searchWindowSize, h));
// break;
// case CV_8UC3:
// parallel_for(cv::BlockedRange(0, srcImgs[0].rows),
// FastNlMeansMultiDenoisingInvoker<cv::Vec3b>(
// srcImgs, imgToDenoiseIndex, temporalWindowSize,
// dst, templateWindowSize, searchWindowSize, h));
// break;
// default:
// CV_Error(CV_StsBadArg,
// "Unsupported matrix format! Only uchar, Vec2b, Vec3b are supported");
// }
//}
//void cv::fastNlMeansDenoisingColoredMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
// int imgToDenoiseIndex, int temporalWindowSize,
// float h, float hForColorComponents,
// int templateWindowSize, int searchWindowSize)
//{
// vector<Mat> srcImgs;
// _srcImgs.getMatVector(srcImgs);
// fastNlMeansDenoisingMultiCheckPreconditions(
// srcImgs, imgToDenoiseIndex,
// temporalWindowSize, templateWindowSize, searchWindowSize
// );
// _dst.create(srcImgs[0].size(), srcImgs[0].type());
// Mat dst = _dst.getMat();
// int src_imgs_size = (int)srcImgs.size();
// if (srcImgs[0].type() != CV_8UC3) {
// CV_Error(CV_StsBadArg, "Type of input images should be CV_8UC3!");
// return;
// }
// int from_to[] = { 0,0, 1,1, 2,2 };
// // TODO convert only required images
// vector<Mat> src_lab(src_imgs_size);
// vector<Mat> l(src_imgs_size);
// vector<Mat> ab(src_imgs_size);
// for (int i = 0; i < src_imgs_size; i++) {
// src_lab[i] = Mat::zeros(srcImgs[0].size(), CV_8UC3);
// l[i] = Mat::zeros(srcImgs[0].size(), CV_8UC1);
// ab[i] = Mat::zeros(srcImgs[0].size(), CV_8UC2);
// cvtColor(srcImgs[i], src_lab[i], CV_LBGR2Lab);
// Mat l_ab[] = { l[i], ab[i] };
// mixChannels(&src_lab[i], 1, l_ab, 2, from_to, 3);
// }
// Mat dst_l;
// Mat dst_ab;
// fastNlMeansDenoisingMulti(
// l, dst_l, imgToDenoiseIndex, temporalWindowSize,
// h, templateWindowSize, searchWindowSize);
// fastNlMeansDenoisingMulti(
// ab, dst_ab, imgToDenoiseIndex, temporalWindowSize,
// hForColorComponents, templateWindowSize, searchWindowSize);
// Mat l_ab_denoised[] = { dst_l, dst_ab };
// Mat dst_lab(srcImgs[0].size(), srcImgs[0].type());
// mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
// cvtColor(dst_lab, dst, CV_Lab2LBGR);
//}
#endif
}
#endif