some refactoring

modules/photo/src/fast_nlmeans_denoising_invoker.hpp

@@ -51,14 +51,16 @@
 using namespace cv;
 
 template <typename T>
-struct FastNlMeansDenoisingInvoker : ParallelLoopBody {
-    public:
+struct FastNlMeansDenoisingInvoker :
+        public ParallelLoopBody
+{
+public:
     FastNlMeansDenoisingInvoker(const Mat& src, Mat& dst,
         int template_window_size, int search_window_size, const float h);
 
     void operator() (const Range& range) const;
 
-    private:
+private:
     void operator= (const FastNlMeansDenoisingInvoker&);
 
     const Mat& src_;
@@ -78,15 +80,12 @@ struct FastNlMeansDenoisingInvoker : ParallelLoopBody {
     std::vector<int> almost_dist2weight_;
 
     void calcDistSumsForFirstElementInRow(
-            int i,
-            Array2d<int>& dist_sums,
+        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,
+        int i, int j, int first_col_num,
         Array2d<int>& dist_sums,
         Array3d<int>& col_dist_sums,
         Array3d<int>& up_col_dist_sums) const;
@@ -95,17 +94,18 @@ struct FastNlMeansDenoisingInvoker : ParallelLoopBody {
 inline int getNearestPowerOf2(int value)
 {
     int p = 0;
-    while( 1 << p < value) ++p;
+    while( 1 << p < value)
+        ++p;
     return p;
 }
 
 template <class T>
 FastNlMeansDenoisingInvoker<T>::FastNlMeansDenoisingInvoker(
-    const cv::Mat& src,
-    cv::Mat& dst,
+    const cv::Mat& src, cv::Mat& dst,
     int template_window_size,
     int search_window_size,
-    const float 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
 
@@ -134,7 +134,8 @@ FastNlMeansDenoisingInvoker<T>::FastNlMeansDenoisingInvoker(
     almost_dist2weight_.resize(almost_max_dist);
 
     const double WEIGHT_THRESHOLD = 0.001;
-    for (int almost_dist = 0; almost_dist < almost_max_dist; almost_dist++) {
+    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))));
 
@@ -144,15 +145,15 @@ FastNlMeansDenoisingInvoker<T>::FastNlMeansDenoisingInvoker(
         almost_dist2weight_[almost_dist] = weight;
     }
     CV_Assert(almost_dist2weight_[0] == fixed_point_mult_);
-    // additional optimization init end
 
-    if (dst_.empty()) {
+    // additional optimization init end
+    if (dst_.empty())
         dst_ = Mat::zeros(src_.size(), src_.type());
-    }
 }
 
 template <class T>
-void FastNlMeansDenoisingInvoker<T>::operator() (const Range& range) const {
+void FastNlMeansDenoisingInvoker<T>::operator() (const Range& range) const
+{
     int row_from = range.start;
     int row_to = range.end - 1;
 
@@ -164,30 +165,36 @@ void FastNlMeansDenoisingInvoker<T>::operator() (const Range& range) const {
     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++) {
+    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) {
+            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) {
+            }
+            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 {
+                }
+                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_;
+                    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);
@@ -195,20 +202,18 @@ void FastNlMeansDenoisingInvoker<T>::operator() (const Range& range) const {
                     // copy class member to local variable for optimization
                     int search_window_size = search_window_size_;
 
-                    for (int y = 0; y < search_window_size; y++) {
+                    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_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);
 
-                        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++) {
+                        for (int x = 0; x < search_window_size; x++)
+                        {
                             dist_sums_row[x] -= col_dist_sums_row[x];
 
                             col_dist_sums_row[x] =
@@ -233,14 +238,15 @@ void FastNlMeansDenoisingInvoker<T>::operator() (const Range& range) const {
             int weights_sum = 0;
 
             int estimation[3];
-            for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++) {
+            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++) {
+            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++) {
+                for (int x = 0; x < search_window_size_; x++)
+                {
                     int almostAvgDist =
                         dist_sums_row[x] >> almost_template_window_size_sq_bin_shift_;
 
@@ -269,18 +275,19 @@ inline void FastNlMeansDenoisingInvoker<T>::calcDistSumsForFirstElementInRow(
 {
     int j = 0;
 
-    for (int y = 0; y < search_window_size_; y++) {
-        for (int x = 0; x < search_window_size_; x++) {
+    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++) {
+            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++) {
+            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);
@@ -288,11 +295,9 @@ inline void FastNlMeansDenoisingInvoker<T>::calcDistSumsForFirstElementInRow(
                     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>
@@ -312,23 +317,21 @@ inline void FastNlMeansDenoisingInvoker<T>::calcDistSumsForElementInFirstRow(
 
     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++) {
+    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++) {
+            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/photo/src/fast_nlmeans_denoising_invoker_commons.hpp

@@ -46,29 +46,35 @@ 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) {
+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) {
+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) {
+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) {
+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) {
+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) {
+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);
@@ -76,16 +82,20 @@ template <> inline int calcUpDownDist(uchar a_up, uchar a_down, uchar  b_up, uch
 
 template <typename T> static inline void incWithWeight(int* estimation, int weight, T p);
 
-template <> inline void incWithWeight(int* estimation, int weight, uchar 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) {
+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) {
+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];
@@ -93,18 +103,21 @@ template <> inline void incWithWeight(int* estimation, int weight, Vec3b p) {
 
 template <typename T> static inline T saturateCastFromArray(int* estimation);
 
-template <> inline uchar saturateCastFromArray(int* estimation) {
+template <> inline uchar saturateCastFromArray(int* estimation)
+{
     return saturate_cast<uchar>(estimation[0]);
 }
 
-template <> inline Vec2b saturateCastFromArray(int* estimation) {
+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) {
+template <> inline Vec3b saturateCastFromArray(int* estimation)
+{
     Vec3b res;
     res[0] = saturate_cast<uchar>(estimation[0]);
     res[1] = saturate_cast<uchar>(estimation[1]);