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The initial commit for generic optimization

Browse Source 
Generic optimization package for openCV project, will be developed
between the June and September of 2013. This work is funded by Google
Summer of Code 2013 project. This project is about
implementing several algorithms, that will find global maxima/minima of a
given function on a given domain subject to a given constraints.

All comments/suggestions are warmly appreciated and to be sent to
alozz1991@gmail.com (please, mention the word "openCV" in topic of
message, for I'm using the spam-filters)
This commit is contained in:
Alex Leontiev
2013-05-31 07:39:15 +03:00
parent d81d3fc830
commit 47ce461d97
24 changed files with 2847 additions and 0 deletions
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modules/optim/src/arrays.hpp
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/*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_DENOISING_ARRAYS_HPP__
#define __OPENCV_DENOISING_ARRAYS_HPP__
template <class T> struct Array2d {
T* a;
int n1,n2;
bool needToDeallocArray;
Array2d(const Array2d& array2d):
a(array2d.a), n1(array2d.n1), n2(array2d.n2), needToDeallocArray(false)
{
if (array2d.needToDeallocArray) {
// copy constructor for self allocating arrays not supported
throw new std::exception();
}
}
Array2d(T* _a, int _n1, int _n2):
a(_a), n1(_n1), n2(_n2), needToDeallocArray(false) {}
Array2d(int _n1, int _n2):
n1(_n1), n2(_n2), needToDeallocArray(true)
{
a = new T[n1*n2];
}
~Array2d() {
if (needToDeallocArray) {
delete[] a;
}
}
T* operator [] (int i) {
return a + i*n2;
}
inline T* row_ptr(int i) {
return (*this)[i];
}
};
template <class T> struct Array3d {
T* a;
int n1,n2,n3;
bool needToDeallocArray;
Array3d(T* _a, int _n1, int _n2, int _n3):
a(_a), n1(_n1), n2(_n2), n3(_n3), needToDeallocArray(false) {}
Array3d(int _n1, int _n2, int _n3):
n1(_n1), n2(_n2), n3(_n3), needToDeallocArray(true)
{
a = new T[n1*n2*n3];
}
~Array3d() {
if (needToDeallocArray) {
delete[] a;
}
}
Array2d<T> operator [] (int i) {
Array2d<T> array2d(a + i*n2*n3, n2, n3);
return array2d;
}
inline T* row_ptr(int i1, int i2) {
return a + i1*n2*n3 + i2*n3;
}
};
template <class T> struct Array4d {
T* a;
int n1,n2,n3,n4;
bool needToDeallocArray;
int steps[4];
void init_steps() {
steps[0] = n2*n3*n4;
steps[1] = n3*n4;
steps[2] = n4;
steps[3] = 1;
}
Array4d(T* _a, int _n1, int _n2, int _n3, int _n4):
a(_a), n1(_n1), n2(_n2), n3(_n3), n4(_n4), needToDeallocArray(false)
{
init_steps();
}
Array4d(int _n1, int _n2, int _n3, int _n4):
n1(_n1), n2(_n2), n3(_n3), n4(_n4), needToDeallocArray(true)
{
a = new T[n1*n2*n3*n4];
init_steps();
}
~Array4d() {
if (needToDeallocArray) {
delete[] a;
}
}
Array3d<T> operator [] (int i) {
Array3d<T> array3d(a + i*n2*n3*n4, n2, n3, n4);
return array3d;
}
inline T* row_ptr(int i1, int i2, int i3) {
return a + i1*n2*n3*n4 + i2*n3*n4 + i3*n4;
}
inline int step_size(int dimension) {
return steps[dimension];
}
};
#endif
modules/optim/src/denoising.cpp
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/*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*/
#include "precomp.hpp"
#include "opencv2/photo.hpp"
#include "opencv2/imgproc.hpp"
#include "fast_nlmeans_denoising_invoker.hpp"
#include "fast_nlmeans_multi_denoising_invoker.hpp"
void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, float h,
int templateWindowSize, int searchWindowSize)
{
Mat src = _src.getMat();
_dst.create(src.size(), src.type());
Mat dst = _dst.getMat();
#ifdef HAVE_TEGRA_OPTIMIZATION
if(tegra::fastNlMeansDenoising(src, dst, h, templateWindowSize, searchWindowSize))
return;
#endif
switch (src.type()) {
case CV_8U:
parallel_for(cv::BlockedRange(0, src.rows),
FastNlMeansDenoisingInvoker<uchar>(
src, dst, templateWindowSize, searchWindowSize, h));
break;
case CV_8UC2:
parallel_for(cv::BlockedRange(0, src.rows),
FastNlMeansDenoisingInvoker<cv::Vec2b>(
src, dst, templateWindowSize, searchWindowSize, h));
break;
case CV_8UC3:
parallel_for(cv::BlockedRange(0, src.rows),
FastNlMeansDenoisingInvoker<cv::Vec3b>(
src, dst, templateWindowSize, searchWindowSize, h));
break;
default:
CV_Error(Error::StsBadArg,
"Unsupported image format! Only CV_8UC1, CV_8UC2 and CV_8UC3 are supported");
}
}
void cv::fastNlMeansDenoisingColored( InputArray _src, OutputArray _dst,
float h, float hForColorComponents,
int templateWindowSize, int searchWindowSize)
{
Mat src = _src.getMat();
_dst.create(src.size(), src.type());
Mat dst = _dst.getMat();
if (src.type() != CV_8UC3) {
CV_Error(Error::StsBadArg, "Type of input image should be CV_8UC3!");
return;
}
Mat src_lab;
cvtColor(src, src_lab, COLOR_LBGR2Lab);
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);
fastNlMeansDenoising(l, l, h, templateWindowSize, searchWindowSize);
fastNlMeansDenoising(ab, ab, hForColorComponents, templateWindowSize, searchWindowSize);
Mat l_ab_denoised[] = { l, ab };
Mat dst_lab(src.size(), src.type());
mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
cvtColor(dst_lab, dst, COLOR_Lab2LBGR);
}
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(Error::StsBadArg, "Input images vector should not be empty!");
}
if (temporalWindowSize % 2 == 0 ||
searchWindowSize % 2 == 0 ||
templateWindowSize % 2 == 0) {
CV_Error(Error::StsBadArg, "All windows sizes should be odd!");
}
int temporalWindowHalfSize = temporalWindowSize / 2;
if (imgToDenoiseIndex - temporalWindowHalfSize < 0 ||
imgToDenoiseIndex + temporalWindowHalfSize >= src_imgs_size)
{
CV_Error(Error::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(Error::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)
{
std::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(Error::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)
{
std::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(Error::StsBadArg, "Type of input images should be CV_8UC3!");
return;
}
int from_to[] = { 0,0, 1,1, 2,2 };
// TODO convert only required images
std::vector<Mat> src_lab(src_imgs_size);
std::vector<Mat> l(src_imgs_size);
std::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], COLOR_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, COLOR_Lab2LBGR);
}
modules/optim/src/fast_nlmeans_denoising_invoker.hpp
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/*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_DENOISING_INVOKER_HPP__
#define __OPENCV_FAST_NLMEANS_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 FastNlMeansDenoisingInvoker {
public:
FastNlMeansDenoisingInvoker(const Mat& src, Mat& dst,
int template_window_size, int search_window_size, const float h);
void operator() (const BlockedRange& range) const;
private:
void operator= (const FastNlMeansDenoisingInvoker&);
const Mat& src_;
Mat& dst_;
Mat extended_src_;
int border_size_;
int template_window_size_;
int search_window_size_;
int template_window_half_size_;
int search_window_half_size_;
int fixed_point_mult_;
int almost_template_window_size_sq_bin_shift_;
std::vector<int> almost_dist2weight_;
void calcDistSumsForFirstElementInRow(
int i,
Array2d<int>& dist_sums,
Array3d<int>& col_dist_sums,
Array3d<int>& up_col_dist_sums) const;
void calcDistSumsForElementInFirstRow(
int i,
int j,
int first_col_num,
Array2d<int>& dist_sums,
Array3d<int>& col_dist_sums,
Array3d<int>& up_col_dist_sums) const;
};
inline int getNearestPowerOf2(int value)
{
int p = 0;
while( 1 << p < value) ++p;
return p;
}
template <class T>
FastNlMeansDenoisingInvoker<T>::FastNlMeansDenoisingInvoker(
const cv::Mat& src,
cv::Mat& dst,
int template_window_size,
int search_window_size,
const float h) : src_(src), dst_(dst)
{
CV_Assert(src.channels() == sizeof(T)); //T is Vec1b or Vec2b or Vec3b
template_window_half_size_ = template_window_size / 2;
search_window_half_size_ = search_window_size / 2;
template_window_size_ = template_window_half_size_ * 2 + 1;
search_window_size_ = search_window_half_size_ * 2 + 1;
border_size_ = search_window_half_size_ + template_window_half_size_;
copyMakeBorder(src_, extended_src_,
border_size_, border_size_, border_size_, border_size_, cv::BORDER_DEFAULT);
const int max_estimate_sum_value = 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
CV_Assert(template_window_size_ <= 46340 ); // sqrt(INT_MAX)
int template_window_size_sq = template_window_size_ * template_window_size_;
almost_template_window_size_sq_bin_shift_ = getNearestPowerOf2(template_window_size_sq);
double almost_dist2actual_dist_multiplier = ((double)(1 << almost_template_window_size_sq_bin_shift_)) / 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(src_.size(), src_.type());
}
}
template <class T>
void FastNlMeansDenoisingInvoker<T>::operator() (const BlockedRange& range) const {
int row_from = range.begin();
int row_to = range.end() - 1;
Array2d<int> dist_sums(search_window_size_, search_window_size_);
// for lazy calc optimization
Array3d<int> col_dist_sums(template_window_size_, search_window_size_, search_window_size_);
int first_col_num = -1;
Array3d<int> up_col_dist_sums(src_.cols, search_window_size_, search_window_size_);
for (int i = row_from; i <= row_to; i++) {
for (int j = 0; j < src_.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 = extended_src_.at<T>(ay - template_window_half_size_ - 1, ax);
T a_down = 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 y = 0; y < search_window_size; y++) {
int* dist_sums_row = dist_sums.row_ptr(y);
int* col_dist_sums_row = col_dist_sums.row_ptr(first_col_num,y);
int* up_col_dist_sums_row = up_col_dist_sums.row_ptr(j, y);
const T* b_up_ptr =
extended_src_.ptr<T>(start_by - template_window_half_size_ - 1 + y);
const T* b_down_ptr =
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 y = 0; y < search_window_size_; y++) {
const T* cur_row_ptr = extended_src_.ptr<T>(border_size_ + search_window_y + y);
int* dist_sums_row = dist_sums.row_ptr(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 FastNlMeansDenoisingInvoker<T>::calcDistSumsForFirstElementInRow(
int i,
Array2d<int>& dist_sums,
Array3d<int>& col_dist_sums,
Array3d<int>& up_col_dist_sums) const
{
int j = 0;
for (int y = 0; y < search_window_size_; y++) {
for (int x = 0; x < search_window_size_; x++) {
dist_sums[y][x] = 0;
for (int tx = 0; tx < template_window_size_; tx++) {
col_dist_sums[tx][y][x] = 0;
}
int start_y = i + y - search_window_half_size_;
int start_x = j + x - search_window_half_size_;
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
for (int tx = -template_window_half_size_; tx <= template_window_half_size_; tx++) {
int dist = calcDist<T>(extended_src_,
border_size_ + i + ty, border_size_ + j + tx,
border_size_ + start_y + ty, border_size_ + start_x + tx);
dist_sums[y][x] += dist;
col_dist_sums[tx + template_window_half_size_][y][x] += dist;
}
}
up_col_dist_sums[j][y][x] = col_dist_sums[template_window_size_ - 1][y][x];
}
}
}
template <class T>
inline void FastNlMeansDenoisingInvoker<T>::calcDistSumsForElementInFirstRow(
int i,
int j,
int first_col_num,
Array2d<int>& dist_sums,
Array3d<int>& col_dist_sums,
Array3d<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 y = 0; y < search_window_size_; y++) {
for (int x = 0; x < search_window_size_; x++) {
dist_sums[y][x] -= col_dist_sums[first_col_num][y][x];
col_dist_sums[new_last_col_num][y][x] = 0;
int by = start_by + y;
int bx = start_bx + x;
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
col_dist_sums[new_last_col_num][y][x] +=
calcDist<T>(extended_src_, ay + ty, ax, by + ty, bx);
}
dist_sums[y][x] += col_dist_sums[new_last_col_num][y][x];
up_col_dist_sums[j][y][x] = col_dist_sums[new_last_col_num][y][x];
}
}
}
#endif
modules/optim/src/fast_nlmeans_denoising_invoker_commons.hpp
+115
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/*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_DENOISING_INVOKER_COMMONS_HPP__
#define __OPENCV_FAST_NLMEANS_DENOISING_INVOKER_COMMONS_HPP__
using namespace cv;
template <typename T> static inline int calcDist(const T a, const T b);
template <> inline int calcDist(const uchar a, const uchar b) {
return (a-b) * (a-b);
}
template <> inline int calcDist(const Vec2b a, const Vec2b b) {
return (a[0]-b[0])*(a[0]-b[0]) + (a[1]-b[1])*(a[1]-b[1]);
}
template <> inline int calcDist(const Vec3b a, const Vec3b b) {
return (a[0]-b[0])*(a[0]-b[0]) + (a[1]-b[1])*(a[1]-b[1]) + (a[2]-b[2])*(a[2]-b[2]);
}
template <typename T> static inline int calcDist(const Mat& m, int i1, int j1, int i2, int j2) {
const T a = m.at<T>(i1, j1);
const T b = m.at<T>(i2, j2);
return calcDist<T>(a,b);
}
template <typename T> static inline int calcUpDownDist(T a_up, T a_down, T b_up, T b_down) {
return calcDist(a_down,b_down) - calcDist(a_up, b_up);
}
template <> inline int calcUpDownDist(uchar a_up, uchar a_down, uchar b_up, uchar b_down) {
int A = a_down - b_down;
int B = a_up - b_up;
return (A-B)*(A+B);
}
template <typename T> static inline void incWithWeight(int* estimation, int weight, T p);
template <> inline void incWithWeight(int* estimation, int weight, uchar p) {
estimation[0] += weight * p;
}
template <> inline void incWithWeight(int* estimation, int weight, Vec2b p) {
estimation[0] += weight * p[0];
estimation[1] += weight * p[1];
}
template <> inline void incWithWeight(int* estimation, int weight, Vec3b p) {
estimation[0] += weight * p[0];
estimation[1] += weight * p[1];
estimation[2] += weight * p[2];
}
template <typename T> static inline T saturateCastFromArray(int* estimation);
template <> inline uchar saturateCastFromArray(int* estimation) {
return saturate_cast<uchar>(estimation[0]);
}
template <> inline Vec2b saturateCastFromArray(int* estimation) {
Vec2b res;
res[0] = saturate_cast<uchar>(estimation[0]);
res[1] = saturate_cast<uchar>(estimation[1]);
return res;
}
template <> inline Vec3b saturateCastFromArray(int* estimation) {
Vec3b res;
res[0] = saturate_cast<uchar>(estimation[0]);
res[1] = saturate_cast<uchar>(estimation[1]);
res[2] = saturate_cast<uchar>(estimation[2]);
return res;
}
#endif
modules/optim/src/fast_nlmeans_multi_denoising_invoker.hpp
+383
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/*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 {
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 BlockedRange& 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 BlockedRange& range) const {
int row_from = range.begin();
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
modules/optim/src/inpaint.cpp
+817
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/*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*/
/* ////////////////////////////////////////////////////////////////////
//
// Geometrical transforms on images and matrices: rotation, zoom etc.
//
// */
#include "precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/photo/photo_c.h"
#undef CV_MAT_ELEM_PTR_FAST
#define CV_MAT_ELEM_PTR_FAST( mat, row, col, pix_size ) \
((mat).data.ptr + (size_t)(mat).step*(row) + (pix_size)*(col))
inline float
min4( float a, float b, float c, float d )
{
a = MIN(a,b);
c = MIN(c,d);
return MIN(a,c);
}
#define CV_MAT_3COLOR_ELEM(img,type,y,x,c) CV_MAT_ELEM(img,type,y,(x)*3+(c))
#define KNOWN 0 //known outside narrow band
#define BAND 1 //narrow band (known)
#define INSIDE 2 //unknown
#define CHANGE 3 //servise
typedef struct CvHeapElem
{
float T;
int i,j;
struct CvHeapElem* prev;
struct CvHeapElem* next;
}
CvHeapElem;
class CvPriorityQueueFloat
{
protected:
CvHeapElem *mem,*empty,*head,*tail;
int num,in;
public:
bool Init( const CvMat* f )
{
int i,j;
for( i = num = 0; i < f->rows; i++ )
{
for( j = 0; j < f->cols; j++ )
num += CV_MAT_ELEM(*f,uchar,i,j)!=0;
}
if (num<=0) return false;
mem = (CvHeapElem*)cvAlloc((num+2)*sizeof(CvHeapElem));
if (mem==NULL) return false;
head = mem;
head->i = head->j = -1;
head->prev = NULL;
head->next = mem+1;
head->T = -FLT_MAX;
empty = mem+1;
for (i=1; i<=num; i++) {
mem[i].prev = mem+i-1;
mem[i].next = mem+i+1;
mem[i].i = -1;
mem[i].T = FLT_MAX;
}
tail = mem+i;
tail->i = tail->j = -1;
tail->prev = mem+i-1;
tail->next = NULL;
tail->T = FLT_MAX;
return true;
}
bool Add(const CvMat* f) {
int i,j;
for (i=0; i<f->rows; i++) {
for (j=0; j<f->cols; j++) {
if (CV_MAT_ELEM(*f,uchar,i,j)!=0) {
if (!Push(i,j,0)) return false;
}
}
}
return true;
}
bool Push(int i, int j, float T) {
CvHeapElem *tmp=empty,*add=empty;
if (empty==tail) return false;
while (tmp->prev->T>T) tmp = tmp->prev;
if (tmp!=empty) {
add->prev->next = add->next;
add->next->prev = add->prev;
empty = add->next;
add->prev = tmp->prev;
add->next = tmp;
add->prev->next = add;
add->next->prev = add;
} else {
empty = empty->next;
}
add->i = i;
add->j = j;
add->T = T;
in++;
// printf("push i %3d j %3d T %12.4e in %4d\n",i,j,T,in);
return true;
}
bool Pop(int *i, int *j) {
CvHeapElem *tmp=head->next;
if (empty==tmp) return false;
*i = tmp->i;
*j = tmp->j;
tmp->prev->next = tmp->next;
tmp->next->prev = tmp->prev;
tmp->prev = empty->prev;
tmp->next = empty;
tmp->prev->next = tmp;
tmp->next->prev = tmp;
empty = tmp;
in--;
// printf("pop i %3d j %3d T %12.4e in %4d\n",tmp->i,tmp->j,tmp->T,in);
return true;
}
bool Pop(int *i, int *j, float *T) {
CvHeapElem *tmp=head->next;
if (empty==tmp) return false;
*i = tmp->i;
*j = tmp->j;
*T = tmp->T;
tmp->prev->next = tmp->next;
tmp->next->prev = tmp->prev;
tmp->prev = empty->prev;
tmp->next = empty;
tmp->prev->next = tmp;
tmp->next->prev = tmp;
empty = tmp;
in--;
// printf("pop i %3d j %3d T %12.4e in %4d\n",tmp->i,tmp->j,tmp->T,in);
return true;
}
CvPriorityQueueFloat(void) {
num=in=0;
mem=empty=head=tail=NULL;
}
~CvPriorityQueueFloat(void)
{
cvFree( &mem );
}
};
inline float VectorScalMult(CvPoint2D32f v1,CvPoint2D32f v2) {
return v1.x*v2.x+v1.y*v2.y;
}
inline float VectorLength(CvPoint2D32f v1) {
return v1.x*v1.x+v1.y*v1.y;
}
///////////////////////////////////////////////////////////////////////////////////////////
//HEAP::iterator Heap_Iterator;
//HEAP Heap;
static float FastMarching_solve(int i1,int j1,int i2,int j2, const CvMat* f, const CvMat* t)
{
double sol, a11, a22, m12;
a11=CV_MAT_ELEM(*t,float,i1,j1);
a22=CV_MAT_ELEM(*t,float,i2,j2);
m12=MIN(a11,a22);
if( CV_MAT_ELEM(*f,uchar,i1,j1) != INSIDE )
if( CV_MAT_ELEM(*f,uchar,i2,j2) != INSIDE )
if( fabs(a11-a22) >= 1.0 )
sol = 1+m12;
else
sol = (a11+a22+sqrt((double)(2-(a11-a22)*(a11-a22))))*0.5;
else
sol = 1+a11;
else if( CV_MAT_ELEM(*f,uchar,i2,j2) != INSIDE )
sol = 1+a22;
else
sol = 1+m12;
return (float)sol;
}
/////////////////////////////////////////////////////////////////////////////////////
static void
icvCalcFMM(const CvMat *f, CvMat *t, CvPriorityQueueFloat *Heap, bool negate) {
int i, j, ii = 0, jj = 0, q;
float dist;
while (Heap->Pop(&ii,&jj)) {
unsigned known=(negate)?CHANGE:KNOWN;
CV_MAT_ELEM(*f,uchar,ii,jj) = (uchar)known;
for (q=0; q<4; q++) {
i=0; j=0;
if (q==0) {i=ii-1; j=jj;}
else if(q==1) {i=ii; j=jj-1;}
else if(q==2) {i=ii+1; j=jj;}
else {i=ii; j=jj+1;}
if ((i<=0)||(j<=0)||(i>f->rows)||(j>f->cols)) continue;
if (CV_MAT_ELEM(*f,uchar,i,j)==INSIDE) {
dist = min4(FastMarching_solve(i-1,j,i,j-1,f,t),
FastMarching_solve(i+1,j,i,j-1,f,t),
FastMarching_solve(i-1,j,i,j+1,f,t),
FastMarching_solve(i+1,j,i,j+1,f,t));
CV_MAT_ELEM(*t,float,i,j) = dist;
CV_MAT_ELEM(*f,uchar,i,j) = BAND;
Heap->Push(i,j,dist);
}
}
}
if (negate) {
for (i=0; i<f->rows; i++) {
for(j=0; j<f->cols; j++) {
if (CV_MAT_ELEM(*f,uchar,i,j) == CHANGE) {
CV_MAT_ELEM(*f,uchar,i,j) = KNOWN;
CV_MAT_ELEM(*t,float,i,j) = -CV_MAT_ELEM(*t,float,i,j);
}
}
}
}
}
static void
icvTeleaInpaintFMM(const CvMat *f, CvMat *t, CvMat *out, int range, CvPriorityQueueFloat *Heap ) {
int i = 0, j = 0, ii = 0, jj = 0, k, l, q, color = 0;
float dist;
if (CV_MAT_CN(out->type)==3) {
while (Heap->Pop(&ii,&jj)) {
CV_MAT_ELEM(*f,uchar,ii,jj) = KNOWN;
for(q=0; q<4; q++) {
if (q==0) {i=ii-1; j=jj;}
else if(q==1) {i=ii; j=jj-1;}
else if(q==2) {i=ii+1; j=jj;}
else if(q==3) {i=ii; j=jj+1;}
if ((i<=1)||(j<=1)||(i>t->rows-1)||(j>t->cols-1)) continue;
if (CV_MAT_ELEM(*f,uchar,i,j)==INSIDE) {
dist = min4(FastMarching_solve(i-1,j,i,j-1,f,t),
FastMarching_solve(i+1,j,i,j-1,f,t),
FastMarching_solve(i-1,j,i,j+1,f,t),
FastMarching_solve(i+1,j,i,j+1,f,t));
CV_MAT_ELEM(*t,float,i,j) = dist;
for (color=0; color<=2; color++) {
CvPoint2D32f gradI,gradT,r;
float Ia=0,Jx=0,Jy=0,s=1.0e-20f,w,dst,lev,dir,sat;
if (CV_MAT_ELEM(*f,uchar,i,j+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,i,j-1)!=INSIDE) {
gradT.x=(float)((CV_MAT_ELEM(*t,float,i,j+1)-CV_MAT_ELEM(*t,float,i,j-1)))*0.5f;
} else {
gradT.x=(float)((CV_MAT_ELEM(*t,float,i,j+1)-CV_MAT_ELEM(*t,float,i,j)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,i,j-1)!=INSIDE) {
gradT.x=(float)((CV_MAT_ELEM(*t,float,i,j)-CV_MAT_ELEM(*t,float,i,j-1)));
} else {
gradT.x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,i+1,j)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,i-1,j)!=INSIDE) {
gradT.y=(float)((CV_MAT_ELEM(*t,float,i+1,j)-CV_MAT_ELEM(*t,float,i-1,j)))*0.5f;
} else {
gradT.y=(float)((CV_MAT_ELEM(*t,float,i+1,j)-CV_MAT_ELEM(*t,float,i,j)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,i-1,j)!=INSIDE) {
gradT.y=(float)((CV_MAT_ELEM(*t,float,i,j)-CV_MAT_ELEM(*t,float,i-1,j)));
} else {
gradT.y=0;
}
}
for (k=i-range; k<=i+range; k++) {
int km=k-1+(k==1),kp=k-1-(k==t->rows-2);
for (l=j-range; l<=j+range; l++) {
int lm=l-1+(l==1),lp=l-1-(l==t->cols-2);
if (k>0&&l>0&&k<t->rows-1&&l<t->cols-1) {
if ((CV_MAT_ELEM(*f,uchar,k,l)!=INSIDE)&&
((l-j)*(l-j)+(k-i)*(k-i)<=range*range)) {
r.y = (float)(i-k);
r.x = (float)(j-l);
dst = (float)(1./(VectorLength(r)*sqrt((double)VectorLength(r))));
lev = (float)(1./(1+fabs(CV_MAT_ELEM(*t,float,k,l)-CV_MAT_ELEM(*t,float,i,j))));
dir=VectorScalMult(r,gradT);
if (fabs(dir)<=0.01) dir=0.000001f;
w = (float)fabs(dst*lev*dir);
if (CV_MAT_ELEM(*f,uchar,k,l+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.x=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,km,lp+1,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm-1,color)))*2.0f;
} else {
gradI.x=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,km,lp+1,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.x=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,km,lp,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm-1,color)));
} else {
gradI.x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,k+1,l)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.y=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,kp+1,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km-1,lm,color)))*2.0f;
} else {
gradI.y=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,kp+1,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.y=(float)((CV_MAT_3COLOR_ELEM(*out,uchar,kp,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km-1,lm,color)));
} else {
gradI.y=0;
}
}
Ia += (float)w * (float)(CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color));
Jx -= (float)w * (float)(gradI.x*r.x);
Jy -= (float)w * (float)(gradI.y*r.y);
s += w;
}
}
}
}
sat = (float)((Ia/s+(Jx+Jy)/(sqrt(Jx*Jx+Jy*Jy)+1.0e-20f)+0.5f));
{
CV_MAT_3COLOR_ELEM(*out,uchar,i-1,j-1,color) = cv::saturate_cast<uchar>(sat);
}
}
CV_MAT_ELEM(*f,uchar,i,j) = BAND;
Heap->Push(i,j,dist);
}
}
}
} else if (CV_MAT_CN(out->type)==1) {
while (Heap->Pop(&ii,&jj)) {
CV_MAT_ELEM(*f,uchar,ii,jj) = KNOWN;
for(q=0; q<4; q++) {
if (q==0) {i=ii-1; j=jj;}
else if(q==1) {i=ii; j=jj-1;}
else if(q==2) {i=ii+1; j=jj;}
else if(q==3) {i=ii; j=jj+1;}
if ((i<=1)||(j<=1)||(i>t->rows-1)||(j>t->cols-1)) continue;
if (CV_MAT_ELEM(*f,uchar,i,j)==INSIDE) {
dist = min4(FastMarching_solve(i-1,j,i,j-1,f,t),
FastMarching_solve(i+1,j,i,j-1,f,t),
FastMarching_solve(i-1,j,i,j+1,f,t),
FastMarching_solve(i+1,j,i,j+1,f,t));
CV_MAT_ELEM(*t,float,i,j) = dist;
for (color=0; color<=0; color++) {
CvPoint2D32f gradI,gradT,r;
float Ia=0,Jx=0,Jy=0,s=1.0e-20f,w,dst,lev,dir,sat;
if (CV_MAT_ELEM(*f,uchar,i,j+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,i,j-1)!=INSIDE) {
gradT.x=(float)((CV_MAT_ELEM(*t,float,i,j+1)-CV_MAT_ELEM(*t,float,i,j-1)))*0.5f;
} else {
gradT.x=(float)((CV_MAT_ELEM(*t,float,i,j+1)-CV_MAT_ELEM(*t,float,i,j)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,i,j-1)!=INSIDE) {
gradT.x=(float)((CV_MAT_ELEM(*t,float,i,j)-CV_MAT_ELEM(*t,float,i,j-1)));
} else {
gradT.x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,i+1,j)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,i-1,j)!=INSIDE) {
gradT.y=(float)((CV_MAT_ELEM(*t,float,i+1,j)-CV_MAT_ELEM(*t,float,i-1,j)))*0.5f;
} else {
gradT.y=(float)((CV_MAT_ELEM(*t,float,i+1,j)-CV_MAT_ELEM(*t,float,i,j)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,i-1,j)!=INSIDE) {
gradT.y=(float)((CV_MAT_ELEM(*t,float,i,j)-CV_MAT_ELEM(*t,float,i-1,j)));
} else {
gradT.y=0;
}
}
for (k=i-range; k<=i+range; k++) {
int km=k-1+(k==1),kp=k-1-(k==t->rows-2);
for (l=j-range; l<=j+range; l++) {
int lm=l-1+(l==1),lp=l-1-(l==t->cols-2);
if (k>0&&l>0&&k<t->rows-1&&l<t->cols-1) {
if ((CV_MAT_ELEM(*f,uchar,k,l)!=INSIDE)&&
((l-j)*(l-j)+(k-i)*(k-i)<=range*range)) {
r.y = (float)(i-k);
r.x = (float)(j-l);
dst = (float)(1./(VectorLength(r)*sqrt(VectorLength(r))));
lev = (float)(1./(1+fabs(CV_MAT_ELEM(*t,float,k,l)-CV_MAT_ELEM(*t,float,i,j))));
dir=VectorScalMult(r,gradT);
if (fabs(dir)<=0.01) dir=0.000001f;
w = (float)fabs(dst*lev*dir);
if (CV_MAT_ELEM(*f,uchar,k,l+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.x=(float)((CV_MAT_ELEM(*out,uchar,km,lp+1)-CV_MAT_ELEM(*out,uchar,km,lm-1)))*2.0f;
} else {
gradI.x=(float)((CV_MAT_ELEM(*out,uchar,km,lp+1)-CV_MAT_ELEM(*out,uchar,km,lm)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.x=(float)((CV_MAT_ELEM(*out,uchar,km,lp)-CV_MAT_ELEM(*out,uchar,km,lm-1)));
} else {
gradI.x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,k+1,l)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.y=(float)((CV_MAT_ELEM(*out,uchar,kp+1,lm)-CV_MAT_ELEM(*out,uchar,km-1,lm)))*2.0f;
} else {
gradI.y=(float)((CV_MAT_ELEM(*out,uchar,kp+1,lm)-CV_MAT_ELEM(*out,uchar,km,lm)));
}
} else {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.y=(float)((CV_MAT_ELEM(*out,uchar,kp,lm)-CV_MAT_ELEM(*out,uchar,km-1,lm)));
} else {
gradI.y=0;
}
}
Ia += (float)w * (float)(CV_MAT_ELEM(*out,uchar,km,lm));
Jx -= (float)w * (float)(gradI.x*r.x);
Jy -= (float)w * (float)(gradI.y*r.y);
s += w;
}
}
}
}
sat = (float)((Ia/s+(Jx+Jy)/(sqrt(Jx*Jx+Jy*Jy)+1.0e-20f)+0.5f));
{
CV_MAT_ELEM(*out,uchar,i-1,j-1) = cv::saturate_cast<uchar>(sat);
}
}
CV_MAT_ELEM(*f,uchar,i,j) = BAND;
Heap->Push(i,j,dist);
}
}
}
}
}
static void
icvNSInpaintFMM(const CvMat *f, CvMat *t, CvMat *out, int range, CvPriorityQueueFloat *Heap) {
int i = 0, j = 0, ii = 0, jj = 0, k, l, q, color = 0;
float dist;
if (CV_MAT_CN(out->type)==3) {
while (Heap->Pop(&ii,&jj)) {
CV_MAT_ELEM(*f,uchar,ii,jj) = KNOWN;
for(q=0; q<4; q++) {
if (q==0) {i=ii-1; j=jj;}
else if(q==1) {i=ii; j=jj-1;}
else if(q==2) {i=ii+1; j=jj;}
else if(q==3) {i=ii; j=jj+1;}
if ((i<=1)||(j<=1)||(i>t->rows-1)||(j>t->cols-1)) continue;
if (CV_MAT_ELEM(*f,uchar,i,j)==INSIDE) {
dist = min4(FastMarching_solve(i-1,j,i,j-1,f,t),
FastMarching_solve(i+1,j,i,j-1,f,t),
FastMarching_solve(i-1,j,i,j+1,f,t),
FastMarching_solve(i+1,j,i,j+1,f,t));
CV_MAT_ELEM(*t,float,i,j) = dist;
for (color=0; color<=2; color++) {
CvPoint2D32f gradI,r;
float Ia=0,s=1.0e-20f,w,dst,dir;
for (k=i-range; k<=i+range; k++) {
int km=k-1+(k==1),kp=k-1-(k==f->rows-2);
for (l=j-range; l<=j+range; l++) {
int lm=l-1+(l==1),lp=l-1-(l==f->cols-2);
if (k>0&&l>0&&k<f->rows-1&&l<f->cols-1) {
if ((CV_MAT_ELEM(*f,uchar,k,l)!=INSIDE)&&
((l-j)*(l-j)+(k-i)*(k-i)<=range*range)) {
r.y=(float)(k-i);
r.x=(float)(l-j);
dst = 1/(VectorLength(r)*VectorLength(r)+1);
if (CV_MAT_ELEM(*f,uchar,k+1,l)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.x=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,kp+1,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,kp,lm,color))+
abs(CV_MAT_3COLOR_ELEM(*out,uchar,kp,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km-1,lm,color)));
} else {
gradI.x=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,kp+1,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,kp,lm,color)))*2.0f;
}
} else {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.x=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,kp,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km-1,lm,color)))*2.0f;
} else {
gradI.x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,k,l+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.y=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,km,lp+1,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color))+
abs(CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm-1,color)));
} else {
gradI.y=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,km,lp+1,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color)))*2.0f;
}
} else {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.y=(float)(abs(CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color)-CV_MAT_3COLOR_ELEM(*out,uchar,km,lm-1,color)))*2.0f;
} else {
gradI.y=0;
}
}
gradI.x=-gradI.x;
dir=VectorScalMult(r,gradI);
if (fabs(dir)<=0.01) {
dir=0.000001f;
} else {
dir = (float)fabs(VectorScalMult(r,gradI)/sqrt(VectorLength(r)*VectorLength(gradI)));
}
w = dst*dir;
Ia += (float)w * (float)(CV_MAT_3COLOR_ELEM(*out,uchar,km,lm,color));
s += w;
}
}
}
}
CV_MAT_3COLOR_ELEM(*out,uchar,i-1,j-1,color) = cv::saturate_cast<uchar>((double)Ia/s);
}
CV_MAT_ELEM(*f,uchar,i,j) = BAND;
Heap->Push(i,j,dist);
}
}
}
} else if (CV_MAT_CN(out->type)==1) {
while (Heap->Pop(&ii,&jj)) {
CV_MAT_ELEM(*f,uchar,ii,jj) = KNOWN;
for(q=0; q<4; q++) {
if (q==0) {i=ii-1; j=jj;}
else if(q==1) {i=ii; j=jj-1;}
else if(q==2) {i=ii+1; j=jj;}
else if(q==3) {i=ii; j=jj+1;}
if ((i<=1)||(j<=1)||(i>t->rows-1)||(j>t->cols-1)) continue;
if (CV_MAT_ELEM(*f,uchar,i,j)==INSIDE) {
dist = min4(FastMarching_solve(i-1,j,i,j-1,f,t),
FastMarching_solve(i+1,j,i,j-1,f,t),
FastMarching_solve(i-1,j,i,j+1,f,t),
FastMarching_solve(i+1,j,i,j+1,f,t));
CV_MAT_ELEM(*t,float,i,j) = dist;
{
CvPoint2D32f gradI,r;
float Ia=0,s=1.0e-20f,w,dst,dir;
for (k=i-range; k<=i+range; k++) {
int km=k-1+(k==1),kp=k-1-(k==t->rows-2);
for (l=j-range; l<=j+range; l++) {
int lm=l-1+(l==1),lp=l-1-(l==t->cols-2);
if (k>0&&l>0&&k<t->rows-1&&l<t->cols-1) {
if ((CV_MAT_ELEM(*f,uchar,k,l)!=INSIDE)&&
((l-j)*(l-j)+(k-i)*(k-i)<=range*range)) {
r.y=(float)(i-k);
r.x=(float)(j-l);
dst = 1/(VectorLength(r)*VectorLength(r)+1);
if (CV_MAT_ELEM(*f,uchar,k+1,l)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.x=(float)(abs(CV_MAT_ELEM(*out,uchar,kp+1,lm)-CV_MAT_ELEM(*out,uchar,kp,lm))+
abs(CV_MAT_ELEM(*out,uchar,kp,lm)-CV_MAT_ELEM(*out,uchar,km-1,lm)));
} else {
gradI.x=(float)(abs(CV_MAT_ELEM(*out,uchar,kp+1,lm)-CV_MAT_ELEM(*out,uchar,kp,lm)))*2.0f;
}
} else {
if (CV_MAT_ELEM(*f,uchar,k-1,l)!=INSIDE) {
gradI.x=(float)(abs(CV_MAT_ELEM(*out,uchar,kp,lm)-CV_MAT_ELEM(*out,uchar,km-1,lm)))*2.0f;
} else {
gradI.x=0;
}
}
if (CV_MAT_ELEM(*f,uchar,k,l+1)!=INSIDE) {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.y=(float)(abs(CV_MAT_ELEM(*out,uchar,km,lp+1)-CV_MAT_ELEM(*out,uchar,km,lm))+
abs(CV_MAT_ELEM(*out,uchar,km,lm)-CV_MAT_ELEM(*out,uchar,km,lm-1)));
} else {
gradI.y=(float)(abs(CV_MAT_ELEM(*out,uchar,km,lp+1)-CV_MAT_ELEM(*out,uchar,km,lm)))*2.0f;
}
} else {
if (CV_MAT_ELEM(*f,uchar,k,l-1)!=INSIDE) {
gradI.y=(float)(abs(CV_MAT_ELEM(*out,uchar,km,lm)-CV_MAT_ELEM(*out,uchar,km,lm-1)))*2.0f;
} else {
gradI.y=0;
}
}
gradI.x=-gradI.x;
dir=VectorScalMult(r,gradI);
if (fabs(dir)<=0.01) {
dir=0.000001f;
} else {
dir = (float)fabs(VectorScalMult(r,gradI)/sqrt(VectorLength(r)*VectorLength(gradI)));
}
w = dst*dir;
Ia += (float)w * (float)(CV_MAT_ELEM(*out,uchar,km,lm));
s += w;
}
}
}
}
CV_MAT_ELEM(*out,uchar,i-1,j-1) = cv::saturate_cast<uchar>((double)Ia/s);
}
CV_MAT_ELEM(*f,uchar,i,j) = BAND;
Heap->Push(i,j,dist);
}
}
}
}
}
#define SET_BORDER1_C1(image,type,value) {\
int i,j;\
for(j=0; j<image->cols; j++) {\
CV_MAT_ELEM(*image,type,0,j) = value;\
}\
for (i=1; i<image->rows-1; i++) {\
CV_MAT_ELEM(*image,type,i,0) = CV_MAT_ELEM(*image,type,i,image->cols-1) = value;\
}\
for(j=0; j<image->cols; j++) {\
CV_MAT_ELEM(*image,type,erows-1,j) = value;\
}\
}
#define COPY_MASK_BORDER1_C1(src,dst,type) {\
int i,j;\
for (i=0; i<src->rows; i++) {\
for(j=0; j<src->cols; j++) {\
if (CV_MAT_ELEM(*src,type,i,j)!=0)\
CV_MAT_ELEM(*dst,type,i+1,j+1) = INSIDE;\
}\
}\
}
namespace cv {
template<> void cv::Ptr<IplConvKernel>::delete_obj()
{
cvReleaseStructuringElement(&obj);
}
}
void
cvInpaint( const CvArr* _input_img, const CvArr* _inpaint_mask, CvArr* _output_img,
double inpaintRange, int flags )
{
cv::Ptr<CvMat> mask, band, f, t, out;
cv::Ptr<CvPriorityQueueFloat> Heap, Out;
cv::Ptr<IplConvKernel> el_cross, el_range;
CvMat input_hdr, mask_hdr, output_hdr;
CvMat* input_img, *inpaint_mask, *output_img;
int range=cvRound(inpaintRange);
int erows, ecols;
input_img = cvGetMat( _input_img, &input_hdr );
inpaint_mask = cvGetMat( _inpaint_mask, &mask_hdr );
output_img = cvGetMat( _output_img, &output_hdr );
if( !CV_ARE_SIZES_EQ(input_img,output_img) || !CV_ARE_SIZES_EQ(input_img,inpaint_mask))
CV_Error( CV_StsUnmatchedSizes, "All the input and output images must have the same size" );
if( (CV_MAT_TYPE(input_img->type) != CV_8UC1 &&
CV_MAT_TYPE(input_img->type) != CV_8UC3) ||
!CV_ARE_TYPES_EQ(input_img,output_img) )
CV_Error( CV_StsUnsupportedFormat,
"Only 8-bit 1-channel and 3-channel input/output images are supported" );
if( CV_MAT_TYPE(inpaint_mask->type) != CV_8UC1 )
CV_Error( CV_StsUnsupportedFormat, "The mask must be 8-bit 1-channel image" );
range = MAX(range,1);
range = MIN(range,100);
ecols = input_img->cols + 2;
erows = input_img->rows + 2;
f = cvCreateMat(erows, ecols, CV_8UC1);
t = cvCreateMat(erows, ecols, CV_32FC1);
band = cvCreateMat(erows, ecols, CV_8UC1);
mask = cvCreateMat(erows, ecols, CV_8UC1);
el_cross = cvCreateStructuringElementEx(3,3,1,1,CV_SHAPE_CROSS,NULL);
cvCopy( input_img, output_img );
cvSet(mask,cvScalar(KNOWN,0,0,0));
COPY_MASK_BORDER1_C1(inpaint_mask,mask,uchar);
SET_BORDER1_C1(mask,uchar,0);
cvSet(f,cvScalar(KNOWN,0,0,0));
cvSet(t,cvScalar(1.0e6f,0,0,0));
cvDilate(mask,band,el_cross,1); // image with narrow band
Heap=new CvPriorityQueueFloat;
if (!Heap->Init(band))
return;
cvSub(band,mask,band,NULL);
SET_BORDER1_C1(band,uchar,0);
if (!Heap->Add(band))
return;
cvSet(f,cvScalar(BAND,0,0,0),band);
cvSet(f,cvScalar(INSIDE,0,0,0),mask);
cvSet(t,cvScalar(0,0,0,0),band);
if( flags == cv::INPAINT_TELEA )
{
out = cvCreateMat(erows, ecols, CV_8UC1);
el_range = cvCreateStructuringElementEx(2*range+1,2*range+1,
range,range,CV_SHAPE_RECT,NULL);
cvDilate(mask,out,el_range,1);
cvSub(out,mask,out,NULL);
Out=new CvPriorityQueueFloat;
if (!Out->Init(out))
return;
if (!Out->Add(band))
return;
cvSub(out,band,out,NULL);
SET_BORDER1_C1(out,uchar,0);
icvCalcFMM(out,t,Out,true);
icvTeleaInpaintFMM(mask,t,output_img,range,Heap);
}
else if (flags == cv::INPAINT_NS) {
icvNSInpaintFMM(mask,t,output_img,range,Heap);
} else {
CV_Error( cv::Error::StsBadArg, "The flags argument must be one of CV_INPAINT_TELEA or CV_INPAINT_NS" );
}
}
void cv::inpaint( InputArray _src, InputArray _mask, OutputArray _dst,
double inpaintRange, int flags )
{
Mat src = _src.getMat(), mask = _mask.getMat();
_dst.create( src.size(), src.type() );
CvMat c_src = src, c_mask = mask, c_dst = _dst.getMat();
cvInpaint( &c_src, &c_mask, &c_dst, inpaintRange, flags );
}
modules/optim/src/precomp.cpp
+44
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/*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 "precomp.hpp"
/* End of file. */
modules/optim/src/precomp.hpp
+53
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@@ -0,0 +1,53 @@
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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 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 __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#include "opencv2/photo.hpp"
#include "opencv2/core/private.hpp"
#ifdef HAVE_TEGRA_OPTIMIZATION
#include "opencv2/photo/photo_tegra.hpp"
#endif
#endif