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Merge branch 'master' of git://github.com/vpas/opencv into nonlocal_means

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This commit is contained in:
Leonid Beynenson 2012-08-21 16:07:18 +04:00
commit 9f016da484
4 changed files with 25 additions and 21 deletions
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6
modules/photo/src/arrays.hpp
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@ -67,7 +67,7 @@ template <class T> struct Array2d {
~Array2d() { ~Array2d() {
if (needToDeallocArray) { if (needToDeallocArray) {
delete a; delete[] a;
} }
} }
@ -96,7 +96,7 @@ template <class T> struct Array3d {
~Array3d() { ~Array3d() {
if (needToDeallocArray) { if (needToDeallocArray) {
delete a; delete[] a;
} }
} }
@ -138,7 +138,7 @@ template <class T> struct Array4d {
~Array4d() { ~Array4d() {
if (needToDeallocArray) { if (needToDeallocArray) {
delete a; delete[] a;
} }
} }

4
modules/photo/src/denoising.cpp
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@ -103,7 +103,7 @@ static void fastNlMeansDenoisingMultiCheckPreconditions(
int imgToDenoiseIndex, int temporalWindowSize, int imgToDenoiseIndex, int temporalWindowSize,
int templateWindowSize, int searchWindowSize) int templateWindowSize, int searchWindowSize)
{ {
int src_imgs_size = srcImgs.size(); int src_imgs_size = (int)srcImgs.size();
if (src_imgs_size == 0) { if (src_imgs_size == 0) {
CV_Error(CV_StsBadArg, "Input images vector should not be empty!"); CV_Error(CV_StsBadArg, "Input images vector should not be empty!");
} }
@ -176,7 +176,7 @@ void cv::fastNlMeansDenoisingColoredMulti( const std::vector<Mat>& srcImgs,
temporalWindowSize, templateWindowSize, searchWindowSize temporalWindowSize, templateWindowSize, searchWindowSize
); );
int src_imgs_size = srcImgs.size(); int src_imgs_size = (int)srcImgs.size();
if (srcImgs[0].type() != CV_8UC3) { if (srcImgs[0].type() != CV_8UC3) {
CV_Error(CV_StsBadArg, "Type of input images should be CV_8UC3!"); CV_Error(CV_StsBadArg, "Type of input images should be CV_8UC3!");

16
modules/photo/src/fast_nlmeans_denoising_invoker.hpp
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@ -62,6 +62,10 @@ struct FastNlMeansDenoisingInvoker {
void operator() (const BlockedRange& range) const; void operator() (const BlockedRange& range) const;
void operator= (const FastNlMeansDenoisingInvoker& invoker) {
CV_Error(CV_StsNotImplemented, "Assigment operator is not implemented");
}
private: private:
const Mat& src_; const Mat& src_;
Mat& dst_; Mat& dst_;
@ -102,6 +106,8 @@ FastNlMeansDenoisingInvoker<T>::FastNlMeansDenoisingInvoker(
int search_window_size, int search_window_size,
const double h) : src_(src), dst_(dst) const double h) : src_(src), dst_(dst)
{ {
CV_Assert(src.channels() <= 3);
template_window_half_size_ = template_window_size / 2; template_window_half_size_ = template_window_size / 2;
search_window_half_size_ = search_window_size / 2; search_window_half_size_ = search_window_size / 2;
template_window_size_ = template_window_half_size_ * 2 + 1; template_window_size_ = template_window_half_size_ * 2 + 1;
@ -153,16 +159,12 @@ void FastNlMeansDenoisingInvoker<T>::operator() (const BlockedRange& range) cons
int row_from = range.begin(); int row_from = range.begin();
int row_to = range.end() - 1; int row_to = range.end() - 1;
int dist_sums_array[search_window_size_ * search_window_size_]; Array2d<int> dist_sums(search_window_size_, search_window_size_);
Array2d<int> dist_sums(dist_sums_array, search_window_size_, search_window_size_);
// for lazy calc optimization // for lazy calc optimization
int col_dist_sums_array[template_window_size_ * search_window_size_ * search_window_size_]; Array3d<int> col_dist_sums(template_window_size_, search_window_size_, search_window_size_);
Array3d<int> col_dist_sums(&col_dist_sums_array[0],
template_window_size_, search_window_size_, search_window_size_);
int first_col_num = -1; int first_col_num = -1;
Array3d<int> up_col_dist_sums(src_.cols, search_window_size_, search_window_size_); 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 i = row_from; i <= row_to; i++) {
@ -233,7 +235,7 @@ void FastNlMeansDenoisingInvoker<T>::operator() (const BlockedRange& range) cons
// calc weights // calc weights
int weights_sum = 0; int weights_sum = 0;
int estimation[src_.channels()]; int estimation[3];
for (int channel_num = 0; channel_num < src_.channels(); channel_num++) { for (int channel_num = 0; channel_num < src_.channels(); channel_num++) {
estimation[channel_num] = 0; estimation[channel_num] = 0;
} }

20
modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
View File

@ -63,6 +63,10 @@ struct FastNlMeansMultiDenoisingInvoker {
void operator() (const BlockedRange& range) const; void operator() (const BlockedRange& range) const;
void operator= (const FastNlMeansMultiDenoisingInvoker& invoker) {
CV_Error(CV_StsNotImplemented, "Assigment operator is not implemented");
}
private: private:
int rows_; int rows_;
int cols_; int cols_;
@ -111,6 +115,9 @@ FastNlMeansMultiDenoisingInvoker<T>::FastNlMeansMultiDenoisingInvoker(
int search_window_size, int search_window_size,
const double h) : dst_(dst), extended_srcs_(srcImgs.size()) const double h) : dst_(dst), extended_srcs_(srcImgs.size())
{ {
CV_Assert(srcImgs.size() > 0);
CV_Assert(srcImgs[0].channels() <= 3);
rows_ = srcImgs[0].rows; rows_ = srcImgs[0].rows;
cols_ = srcImgs[0].cols; cols_ = srcImgs[0].cols;
channels_count_ = srcImgs[0].channels(); channels_count_ = srcImgs[0].channels();
@ -175,16 +182,11 @@ void FastNlMeansMultiDenoisingInvoker<T>::operator() (const BlockedRange& range)
int row_from = range.begin(); int row_from = range.begin();
int row_to = range.end() - 1; int row_to = range.end() - 1;
int dist_sums_array[temporal_window_size_ * search_window_size_ * search_window_size_]; Array3d<int> dist_sums(temporal_window_size_, search_window_size_, search_window_size_);
Array3d<int> dist_sums(dist_sums_array,
temporal_window_size_, search_window_size_, search_window_size_);
// for lazy calc optimization // for lazy calc optimization
int col_dist_sums_array[ Array4d<int> col_dist_sums(
template_window_size_ * temporal_window_size_ * search_window_size_ * search_window_size_]; template_window_size_, temporal_window_size_, search_window_size_, search_window_size_);
Array4d<int> col_dist_sums(col_dist_sums_array,
template_window_size_, temporal_window_size_, search_window_size_, search_window_size_);
int first_col_num = -1; int first_col_num = -1;
@ -263,7 +265,7 @@ void FastNlMeansMultiDenoisingInvoker<T>::operator() (const BlockedRange& range)
// calc weights // calc weights
int weights_sum = 0; int weights_sum = 0;
int estimation[channels_count_]; int estimation[3];
for (int channel_num = 0; channel_num < channels_count_; channel_num++) { for (int channel_num = 0; channel_num < channels_count_; channel_num++) {
estimation[channel_num] = 0; estimation[channel_num] = 0;
} }