Chiebot-Mirror
/
opencv
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Merge branch 'master' of git://code.opencv.org/opencv

Browse Source
pull/32/head
Alexander Kapustin 13 years ago
parent 8f4ddad52d 44e8d76d74
commit 858f437162
4 changed files with 30 additions and 43 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 8
      modules/photo/include/opencv2/photo/photo.hpp
  2. 8
      modules/photo/src/denoising.cpp
  3. 25
      modules/photo/src/fast_nlmeans_denoising_invoker.hpp
  4. 32
      modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp

8
modules/photo/include/opencv2/photo/photo.hpp
Unescape View File

@ -68,20 +68,20 @@ CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
OutputArray dst, double inpaintRadius, int flags );
CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, int h = 3,
CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, float h = 3,
int templateWindowSize = 7, int searchWindowSize = 21);
CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst,
int h = 3, int hColor = 3,
float h = 3, float hColor = 3,
int templateWindowSize = 7, int searchWindowSize = 21);
CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
int imgToDenoiseIndex, int temporalWindowSize,
int h = 3, int templateWindowSize = 7, int searchWindowSize = 21);
float h = 3, int templateWindowSize = 7, int searchWindowSize = 21);
CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst,
int imgToDenoiseIndex, int temporalWindowSize,
int h = 3, int hColor = 3,
float h = 3, float hColor = 3,
int templateWindowSize = 7, int searchWindowSize = 21);
}

8
modules/photo/src/denoising.cpp
Unescape View File

@ -45,7 +45,7 @@
#include "fast_nlmeans_denoising_invoker.hpp"
#include "fast_nlmeans_multi_denoising_invoker.hpp"
void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, int h,
void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, float h,
int templateWindowSize, int searchWindowSize)
{
Mat src = _src.getMat();
@ -75,7 +75,7 @@ void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, int h,
}
void cv::fastNlMeansDenoisingColored( InputArray _src, OutputArray _dst,
int h, int hForColorComponents,
float h, float hForColorComponents,
int templateWindowSize, int searchWindowSize)
{
Mat src = _src.getMat();
@ -140,7 +140,7 @@ static void fastNlMeansDenoisingMultiCheckPreconditions(
void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
int imgToDenoiseIndex, int temporalWindowSize,
int h, int templateWindowSize, int searchWindowSize)
float h, int templateWindowSize, int searchWindowSize)
{
vector<Mat> srcImgs;
_srcImgs.getMatVector(srcImgs);
@ -179,7 +179,7 @@ void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _ds
void cv::fastNlMeansDenoisingColoredMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
int imgToDenoiseIndex, int temporalWindowSize,
int h, int hForColorComponents,
float h, float hForColorComponents,
int templateWindowSize, int searchWindowSize)
{
vector<Mat> srcImgs;

25
modules/photo/src/fast_nlmeans_denoising_invoker.hpp
Unescape View File

@ -58,7 +58,7 @@ template <typename T>
struct FastNlMeansDenoisingInvoker {
public:
FastNlMeansDenoisingInvoker(const Mat& src, Mat& dst,
int template_window_size, int search_window_size, const double h);
int template_window_size, int search_window_size, const float h);
void operator() (const BlockedRange& range) const;
@ -109,7 +109,7 @@ FastNlMeansDenoisingInvoker<T>::FastNlMeansDenoisingInvoker(
cv::Mat& dst,
int template_window_size,
int search_window_size,
const double h) : src_(src), dst_(dst)
const float h) : src_(src), dst_(dst)
{
CV_Assert(src.channels() == sizeof(T)); //T is Vec1b or Vec2b or Vec3b
@ -133,21 +133,21 @@ FastNlMeansDenoisingInvoker<T>::FastNlMeansDenoisingInvoker(
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 = 256 * 256 * src_.channels();
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 * src_.channels())));
int weight = cvRound(fixed_point_mult_ * std::exp(-dist / (h * h * sizeof(T))));
if (weight < WEIGHT_THRESHOLD * fixed_point_mult_) {
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()) {
@ -237,7 +237,7 @@ void FastNlMeansDenoisingInvoker<T>::operator() (const BlockedRange& range) cons
int weights_sum = 0;
int estimation[3];
for (int channel_num = 0; channel_num < src_.channels(); channel_num++) {
for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++) {
estimation[channel_num] = 0;
}
@ -256,15 +256,10 @@ void FastNlMeansDenoisingInvoker<T>::operator() (const BlockedRange& range) cons
}
}
if (weights_sum > 0) {
for (int channel_num = 0; channel_num < src_.channels(); channel_num++)
estimation[channel_num] = (estimation[channel_num] + weights_sum/2) / weights_sum;
dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++)
estimation[channel_num] = (estimation[channel_num] + weights_sum/2) / weights_sum;
} else { // weights_sum == 0
dst_.at<T>(i,j) = src_.at<T>(i,j);
}
dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
}
}
}

32
modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
Unescape View File

@ -59,18 +59,15 @@ struct FastNlMeansMultiDenoisingInvoker {
public:
FastNlMeansMultiDenoisingInvoker(
const std::vector<Mat>& srcImgs, int imgToDenoiseIndex, int temporalWindowSize,
Mat& dst, int template_window_size, int search_window_size, const double h);
Mat& dst, int template_window_size, int search_window_size, const float h);
void operator() (const BlockedRange& range) const;
void operator= (const FastNlMeansMultiDenoisingInvoker&) {
CV_Error(CV_StsNotImplemented, "Assigment operator is not implemented");
}
private:
void operator= (const FastNlMeansMultiDenoisingInvoker&);
int rows_;
int cols_;
int channels_count_;
Mat& dst_;
@ -113,14 +110,13 @@ FastNlMeansMultiDenoisingInvoker<T>::FastNlMeansMultiDenoisingInvoker(
cv::Mat& dst,
int template_window_size,
int search_window_size,
const double h) : dst_(dst), extended_srcs_(srcImgs.size())
const float h) : dst_(dst), extended_srcs_(srcImgs.size())
{
CV_Assert(srcImgs.size() > 0);
CV_Assert(srcImgs[0].channels() <= 3);
CV_Assert(srcImgs[0].channels() == sizeof(T));
rows_ = srcImgs[0].rows;
cols_ = srcImgs[0].cols;
channels_count_ = srcImgs[0].channels();
template_window_half_size_ = template_window_size / 2;
search_window_half_size_ = search_window_size / 2;
@ -155,14 +151,14 @@ FastNlMeansMultiDenoisingInvoker<T>::FastNlMeansMultiDenoisingInvoker(
double almost_dist2actual_dist_multiplier =
((double) almost_template_window_size_sq) / template_window_size_sq;
int max_dist = 256 * 256 * channels_count_;
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 * channels_count_)));
int weight = cvRound(fixed_point_mult_ * std::exp(-dist / (h * h * sizeof(T))));
if (weight < WEIGHT_THRESHOLD * fixed_point_mult_) {
weight = 0;
@ -170,6 +166,7 @@ FastNlMeansMultiDenoisingInvoker<T>::FastNlMeansMultiDenoisingInvoker(
almost_dist2weight[almost_dist] = weight;
}
CV_Assert(almost_dist2weight[0] == fixed_point_mult_);
// additional optimization init end
if (dst_.empty()) {
@ -266,7 +263,7 @@ void FastNlMeansMultiDenoisingInvoker<T>::operator() (const BlockedRange& range)
int weights_sum = 0;
int estimation[3];
for (int channel_num = 0; channel_num < channels_count_; channel_num++) {
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++) {
@ -289,16 +286,11 @@ void FastNlMeansMultiDenoisingInvoker<T>::operator() (const BlockedRange& range)
}
}
if (weights_sum > 0) {
for (int channel_num = 0; channel_num < channels_count_; channel_num++)
estimation[channel_num] = (estimation[channel_num] + weights_sum / 2) / weights_sum;
for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++)
estimation[channel_num] = (estimation[channel_num] + weights_sum / 2) / weights_sum;
dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
} else { // weights_sum == 0
const Mat& esrc = extended_srcs_[temporal_window_half_size_];
dst_.at<T>(i,j) = esrc.at<T>(i,j);
}
}
}
}

Write Preview
Loading…
Cancel
Save