-set(the_description "Computational Photography")
-ocv_define_module(photo opencv_imgproc)
+set(the_description "Generic optimization")
+ocv_define_module(optim)
+++ /dev/null
-/*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
-
-
+++ /dev/null
-/*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);
-}
-
-
+++ /dev/null
-/*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
+++ /dev/null
-/*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
+++ /dev/null
-/*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
+++ /dev/null
-/*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 );
-}
--- /dev/null
+#include "opencv2/opencv.hpp"
+
+namespace cv {
+ namespace optim {
+
+class Solver : public Algorithm /* Algorithm is base OpenCV class */
+{
+ class Function
+ {
+ public:
+ virtual ~Function() {}
+ virtual double calc(InputArray args) const = 0;
+ virtual double calc(InputArgs, OutputArray grad) const = 0;
+ };
+
+ // could be reused for all the generic algorithms like downhill simplex.
+ virtual void solve(InputArray x0, OutputArray result) const = 0;
+
+ virtual void setTermCriteria(const TermCriteria& criteria) = 0;
+ virtual TermCriteria getTermCriteria() = 0;
+
+ // more detailed API to be defined later ...
+};
+
+class LPSolver : public Solver
+{
+public:
+ virtual void solve(InputArray coeffs, InputArray constraints, OutputArray result) const = 0;
+ // ...
+};
+
+Ptr<LPSolver> createLPSimplexSolver();
+
+}}
+
+/*===============
+Hill climbing solver is more generic one:*/
+/*
+class DownhillSolver : public Solver
+{
+public:
+ // various setters and getters, if needed
+};
+
+Ptr<DownhillSolver> createDownhillSolver(const Ptr<Solver::Function>& func);*/
+++ /dev/null
-/*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. */
+++ /dev/null
-/*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
projects / profile / ivi / opencv.git / commitdiff