mirror of
https://github.com/opencv/opencv.git
synced 2024-12-12 15:19:11 +08:00
bae85660da
Pull requests: #943 from jet47:cuda-5.5-support #944 from jet47:cmake-2.8.11-cuda-fix #912 from SpecLad:contributing #934 from SpecLad:parallel-for #931 from jet47:gpu-test-fixes #932 from bitwangyaoyao:2.4_fixBFM #918 from bitwangyaoyao:2.4_samples #924 from pengx17:2.4_arithm_fix #925 from pengx17:2.4_canny_tmp_fix #927 from bitwangyaoyao:2.4_perf #930 from pengx17:2.4_haar_ext #928 from apavlenko:bugfix_3027 #920 from asmorkalov:android_move #910 from pengx17:2.4_oclgfft #913 from janm399:2.4 #916 from bitwangyaoyao:2.4_fixPyrLK #919 from abidrahmank:2.4 #923 from pengx17:2.4_macfix Conflicts: modules/calib3d/src/stereobm.cpp modules/features2d/src/detectors.cpp modules/gpu/src/error.cpp modules/gpu/src/precomp.hpp modules/imgproc/src/distransform.cpp modules/imgproc/src/morph.cpp modules/ocl/include/opencv2/ocl/ocl.hpp modules/ocl/perf/perf_color.cpp modules/ocl/perf/perf_imgproc.cpp modules/ocl/perf/perf_match_template.cpp modules/ocl/perf/precomp.cpp modules/ocl/perf/precomp.hpp modules/ocl/src/arithm.cpp modules/ocl/src/canny.cpp modules/ocl/src/filtering.cpp modules/ocl/src/haar.cpp modules/ocl/src/hog.cpp modules/ocl/src/imgproc.cpp modules/ocl/src/opencl/haarobjectdetect.cl modules/ocl/src/pyrlk.cpp modules/video/src/bgfg_gaussmix2.cpp modules/video/src/lkpyramid.cpp platforms/linux/scripts/cmake_arm_gnueabi_hardfp.sh platforms/linux/scripts/cmake_arm_gnueabi_softfp.sh platforms/scripts/ABI_compat_generator.py samples/ocl/facedetect.cpp
384 lines
15 KiB
C++
384 lines
15 KiB
C++
/*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 icvers.
|
|
//
|
|
// 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*/
|
|
|
|
#ifndef __OPENCV_FAST_NLMEANS_MULTI_DENOISING_INVOKER_HPP__
|
|
#define __OPENCV_FAST_NLMEANS_MULTI_DENOISING_INVOKER_HPP__
|
|
|
|
#include "precomp.hpp"
|
|
#include <limits>
|
|
|
|
#include "fast_nlmeans_denoising_invoker_commons.hpp"
|
|
#include "arrays.hpp"
|
|
|
|
using namespace cv;
|
|
|
|
template <typename T>
|
|
struct FastNlMeansMultiDenoisingInvoker : ParallelLoopBody {
|
|
public:
|
|
FastNlMeansMultiDenoisingInvoker(
|
|
const std::vector<Mat>& srcImgs, int imgToDenoiseIndex, int temporalWindowSize,
|
|
Mat& dst, int template_window_size, int search_window_size, const float h);
|
|
|
|
void operator() (const Range& range) const;
|
|
|
|
private:
|
|
void operator= (const FastNlMeansMultiDenoisingInvoker&);
|
|
|
|
int rows_;
|
|
int cols_;
|
|
|
|
Mat& dst_;
|
|
|
|
std::vector<Mat> extended_srcs_;
|
|
Mat main_extended_src_;
|
|
int border_size_;
|
|
|
|
int template_window_size_;
|
|
int search_window_size_;
|
|
int temporal_window_size_;
|
|
|
|
int template_window_half_size_;
|
|
int search_window_half_size_;
|
|
int temporal_window_half_size_;
|
|
|
|
int fixed_point_mult_;
|
|
int almost_template_window_size_sq_bin_shift;
|
|
std::vector<int> almost_dist2weight;
|
|
|
|
void calcDistSumsForFirstElementInRow(
|
|
int i,
|
|
Array3d<int>& dist_sums,
|
|
Array4d<int>& col_dist_sums,
|
|
Array4d<int>& up_col_dist_sums) const;
|
|
|
|
void calcDistSumsForElementInFirstRow(
|
|
int i,
|
|
int j,
|
|
int first_col_num,
|
|
Array3d<int>& dist_sums,
|
|
Array4d<int>& col_dist_sums,
|
|
Array4d<int>& up_col_dist_sums) const;
|
|
};
|
|
|
|
template <class T>
|
|
FastNlMeansMultiDenoisingInvoker<T>::FastNlMeansMultiDenoisingInvoker(
|
|
const std::vector<Mat>& srcImgs,
|
|
int imgToDenoiseIndex,
|
|
int temporalWindowSize,
|
|
cv::Mat& dst,
|
|
int template_window_size,
|
|
int search_window_size,
|
|
const float h) : dst_(dst), extended_srcs_(srcImgs.size())
|
|
{
|
|
CV_Assert(srcImgs.size() > 0);
|
|
CV_Assert(srcImgs[0].channels() == sizeof(T));
|
|
|
|
rows_ = srcImgs[0].rows;
|
|
cols_ = srcImgs[0].cols;
|
|
|
|
template_window_half_size_ = template_window_size / 2;
|
|
search_window_half_size_ = search_window_size / 2;
|
|
temporal_window_half_size_ = temporalWindowSize / 2;
|
|
|
|
template_window_size_ = template_window_half_size_ * 2 + 1;
|
|
search_window_size_ = search_window_half_size_ * 2 + 1;
|
|
temporal_window_size_ = temporal_window_half_size_ * 2 + 1;
|
|
|
|
border_size_ = search_window_half_size_ + template_window_half_size_;
|
|
for (int i = 0; i < temporal_window_size_; i++) {
|
|
copyMakeBorder(
|
|
srcImgs[imgToDenoiseIndex - temporal_window_half_size_ + i], extended_srcs_[i],
|
|
border_size_, border_size_, border_size_, border_size_, cv::BORDER_DEFAULT);
|
|
}
|
|
main_extended_src_ = extended_srcs_[temporal_window_half_size_];
|
|
|
|
const int max_estimate_sum_value =
|
|
temporal_window_size_ * search_window_size_ * search_window_size_ * 255;
|
|
|
|
fixed_point_mult_ = std::numeric_limits<int>::max() / max_estimate_sum_value;
|
|
|
|
// precalc weight for every possible l2 dist between blocks
|
|
// additional optimization of precalced weights to replace division(averaging) by binary shift
|
|
int template_window_size_sq = template_window_size_ * template_window_size_;
|
|
almost_template_window_size_sq_bin_shift = 0;
|
|
while (1 << almost_template_window_size_sq_bin_shift < template_window_size_sq) {
|
|
almost_template_window_size_sq_bin_shift++;
|
|
}
|
|
|
|
int almost_template_window_size_sq = 1 << almost_template_window_size_sq_bin_shift;
|
|
double almost_dist2actual_dist_multiplier =
|
|
((double) almost_template_window_size_sq) / template_window_size_sq;
|
|
|
|
int max_dist = 255 * 255 * sizeof(T);
|
|
int almost_max_dist = (int) (max_dist / almost_dist2actual_dist_multiplier + 1);
|
|
almost_dist2weight.resize(almost_max_dist);
|
|
|
|
const double WEIGHT_THRESHOLD = 0.001;
|
|
for (int almost_dist = 0; almost_dist < almost_max_dist; almost_dist++) {
|
|
double dist = almost_dist * almost_dist2actual_dist_multiplier;
|
|
int weight = cvRound(fixed_point_mult_ * std::exp(-dist / (h * h * sizeof(T))));
|
|
|
|
if (weight < WEIGHT_THRESHOLD * fixed_point_mult_) {
|
|
weight = 0;
|
|
}
|
|
|
|
almost_dist2weight[almost_dist] = weight;
|
|
}
|
|
CV_Assert(almost_dist2weight[0] == fixed_point_mult_);
|
|
// additional optimization init end
|
|
|
|
if (dst_.empty()) {
|
|
dst_ = Mat::zeros(srcImgs[0].size(), srcImgs[0].type());
|
|
}
|
|
}
|
|
|
|
template <class T>
|
|
void FastNlMeansMultiDenoisingInvoker<T>::operator() (const Range& range) const {
|
|
int row_from = range.start;
|
|
int row_to = range.end - 1;
|
|
|
|
Array3d<int> dist_sums(temporal_window_size_, search_window_size_, search_window_size_);
|
|
|
|
// for lazy calc optimization
|
|
Array4d<int> col_dist_sums(
|
|
template_window_size_, temporal_window_size_, search_window_size_, search_window_size_);
|
|
|
|
int first_col_num = -1;
|
|
|
|
Array4d<int> up_col_dist_sums(
|
|
cols_, temporal_window_size_, search_window_size_, search_window_size_);
|
|
|
|
for (int i = row_from; i <= row_to; i++) {
|
|
for (int j = 0; j < cols_; j++) {
|
|
int search_window_y = i - search_window_half_size_;
|
|
int search_window_x = j - search_window_half_size_;
|
|
|
|
// calc dist_sums
|
|
if (j == 0) {
|
|
calcDistSumsForFirstElementInRow(i, dist_sums, col_dist_sums, up_col_dist_sums);
|
|
first_col_num = 0;
|
|
|
|
} else { // calc cur dist_sums using previous dist_sums
|
|
if (i == row_from) {
|
|
calcDistSumsForElementInFirstRow(i, j, first_col_num,
|
|
dist_sums, col_dist_sums, up_col_dist_sums);
|
|
|
|
} else {
|
|
int ay = border_size_ + i;
|
|
int ax = border_size_ + j + template_window_half_size_;
|
|
|
|
int start_by =
|
|
border_size_ + i - search_window_half_size_;
|
|
|
|
int start_bx =
|
|
border_size_ + j - search_window_half_size_ + template_window_half_size_;
|
|
|
|
T a_up = main_extended_src_.at<T>(ay - template_window_half_size_ - 1, ax);
|
|
T a_down = main_extended_src_.at<T>(ay + template_window_half_size_, ax);
|
|
|
|
// copy class member to local variable for optimization
|
|
int search_window_size = search_window_size_;
|
|
|
|
for (int d = 0; d < temporal_window_size_; d++) {
|
|
Mat cur_extended_src = extended_srcs_[d];
|
|
Array2d<int> cur_dist_sums = dist_sums[d];
|
|
Array2d<int> cur_col_dist_sums = col_dist_sums[first_col_num][d];
|
|
Array2d<int> cur_up_col_dist_sums = up_col_dist_sums[j][d];
|
|
for (int y = 0; y < search_window_size; y++) {
|
|
int* dist_sums_row = cur_dist_sums.row_ptr(y);
|
|
|
|
int* col_dist_sums_row = cur_col_dist_sums.row_ptr(y);
|
|
|
|
int* up_col_dist_sums_row = cur_up_col_dist_sums.row_ptr(y);
|
|
|
|
const T* b_up_ptr =
|
|
cur_extended_src.ptr<T>(start_by - template_window_half_size_ - 1 + y);
|
|
const T* b_down_ptr =
|
|
cur_extended_src.ptr<T>(start_by + template_window_half_size_ + y);
|
|
|
|
for (int x = 0; x < search_window_size; x++) {
|
|
dist_sums_row[x] -= col_dist_sums_row[x];
|
|
|
|
col_dist_sums_row[x] = up_col_dist_sums_row[x] +
|
|
calcUpDownDist(
|
|
a_up, a_down,
|
|
b_up_ptr[start_bx + x], b_down_ptr[start_bx + x]
|
|
);
|
|
|
|
dist_sums_row[x] += col_dist_sums_row[x];
|
|
|
|
up_col_dist_sums_row[x] = col_dist_sums_row[x];
|
|
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
first_col_num = (first_col_num + 1) % template_window_size_;
|
|
}
|
|
|
|
// calc weights
|
|
int weights_sum = 0;
|
|
|
|
int estimation[3];
|
|
for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++) {
|
|
estimation[channel_num] = 0;
|
|
}
|
|
for (int d = 0; d < temporal_window_size_; d++) {
|
|
const Mat& esrc_d = extended_srcs_[d];
|
|
for (int y = 0; y < search_window_size_; y++) {
|
|
const T* cur_row_ptr = esrc_d.ptr<T>(border_size_ + search_window_y + y);
|
|
|
|
int* dist_sums_row = dist_sums.row_ptr(d, y);
|
|
|
|
for (int x = 0; x < search_window_size_; x++) {
|
|
int almostAvgDist =
|
|
dist_sums_row[x] >> almost_template_window_size_sq_bin_shift;
|
|
|
|
int weight = almost_dist2weight[almostAvgDist];
|
|
weights_sum += weight;
|
|
|
|
T p = cur_row_ptr[border_size_ + search_window_x + x];
|
|
incWithWeight(estimation, weight, p);
|
|
}
|
|
}
|
|
}
|
|
|
|
for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++)
|
|
estimation[channel_num] = ((unsigned)estimation[channel_num] + weights_sum / 2) / weights_sum;
|
|
|
|
dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
template <class T>
|
|
inline void FastNlMeansMultiDenoisingInvoker<T>::calcDistSumsForFirstElementInRow(
|
|
int i,
|
|
Array3d<int>& dist_sums,
|
|
Array4d<int>& col_dist_sums,
|
|
Array4d<int>& up_col_dist_sums) const
|
|
{
|
|
int j = 0;
|
|
|
|
for (int d = 0; d < temporal_window_size_; d++) {
|
|
Mat cur_extended_src = extended_srcs_[d];
|
|
for (int y = 0; y < search_window_size_; y++) {
|
|
for (int x = 0; x < search_window_size_; x++) {
|
|
dist_sums[d][y][x] = 0;
|
|
for (int tx = 0; tx < template_window_size_; tx++) {
|
|
col_dist_sums[tx][d][y][x] = 0;
|
|
}
|
|
|
|
int start_y = i + y - search_window_half_size_;
|
|
int start_x = j + x - search_window_half_size_;
|
|
|
|
int* dist_sums_ptr = &dist_sums[d][y][x];
|
|
int* col_dist_sums_ptr = &col_dist_sums[0][d][y][x];
|
|
int col_dist_sums_step = col_dist_sums.step_size(0);
|
|
for (int tx = -template_window_half_size_; tx <= template_window_half_size_; tx++) {
|
|
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
|
|
int dist = calcDist<T>(
|
|
main_extended_src_.at<T>(
|
|
border_size_ + i + ty, border_size_ + j + tx),
|
|
cur_extended_src.at<T>(
|
|
border_size_ + start_y + ty, border_size_ + start_x + tx)
|
|
);
|
|
|
|
*dist_sums_ptr += dist;
|
|
*col_dist_sums_ptr += dist;
|
|
}
|
|
col_dist_sums_ptr += col_dist_sums_step;
|
|
}
|
|
|
|
up_col_dist_sums[j][d][y][x] = col_dist_sums[template_window_size_ - 1][d][y][x];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template <class T>
|
|
inline void FastNlMeansMultiDenoisingInvoker<T>::calcDistSumsForElementInFirstRow(
|
|
int i,
|
|
int j,
|
|
int first_col_num,
|
|
Array3d<int>& dist_sums,
|
|
Array4d<int>& col_dist_sums,
|
|
Array4d<int>& up_col_dist_sums) const
|
|
{
|
|
int ay = border_size_ + i;
|
|
int ax = border_size_ + j + template_window_half_size_;
|
|
|
|
int start_by = border_size_ + i - search_window_half_size_;
|
|
int start_bx = border_size_ + j - search_window_half_size_ + template_window_half_size_;
|
|
|
|
int new_last_col_num = first_col_num;
|
|
|
|
for (int d = 0; d < temporal_window_size_; d++) {
|
|
Mat cur_extended_src = extended_srcs_[d];
|
|
for (int y = 0; y < search_window_size_; y++) {
|
|
for (int x = 0; x < search_window_size_; x++) {
|
|
dist_sums[d][y][x] -= col_dist_sums[first_col_num][d][y][x];
|
|
|
|
col_dist_sums[new_last_col_num][d][y][x] = 0;
|
|
int by = start_by + y;
|
|
int bx = start_bx + x;
|
|
|
|
int* col_dist_sums_ptr = &col_dist_sums[new_last_col_num][d][y][x];
|
|
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
|
|
*col_dist_sums_ptr +=
|
|
calcDist<T>(
|
|
main_extended_src_.at<T>(ay + ty, ax),
|
|
cur_extended_src.at<T>(by + ty, bx)
|
|
);
|
|
}
|
|
|
|
dist_sums[d][y][x] += col_dist_sums[new_last_col_num][d][y][x];
|
|
|
|
up_col_dist_sums[j][d][y][x] = col_dist_sums[new_last_col_num][d][y][x];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
#endif
|