Merge pull request #9303 from alalek:akaze_update

modules/core/include/opencv2/core/utility.hpp

@@ -444,6 +444,23 @@ static inline size_t alignSize(size_t sz, int n)
     return (sz + n-1) & -n;
 }
 
+/** @brief Integer division with result round up.
+
+Use this function instead of `ceil((float)a / b)` expressions.
+
+@sa alignSize
+*/
+static inline int divUp(int a, unsigned int b)
+{
+    CV_DbgAssert(a >= 0);
+    return (a + b - 1) / b;
+}
+/** @overload */
+static inline size_t divUp(size_t a, unsigned int b)
+{
+    return (a + b - 1) / b;
+}
+
 /** @brief Enables or disables the optimized code.
 
 The function can be used to dynamically turn on and off optimized code (code that uses SSE2, AVX,

modules/core/src/matrix.cpp

@@ -3406,11 +3406,6 @@ static TransposeInplaceFunc transposeInplaceTab[] =
 
 #ifdef HAVE_OPENCL
 
-static inline int divUp(int a, int b)
-{
-    return (a + b - 1) / b;
-}
-
 static bool ocl_transpose( InputArray _src, OutputArray _dst )
 {
     const ocl::Device & dev = ocl::Device::getDefault();

modules/features2d/src/akaze.cpp

@@ -113,12 +113,12 @@ namespace cv
                 if (descriptor_size == 0)
                 {
                     int t = (6 + 36 + 120) * descriptor_channels;
-                    return (int)ceil(t / 8.);
+                    return divUp(t, 8);
                 }
                 else
                 {
                     // We use the random bit selection length binary descriptor
-                    return (int)ceil(descriptor_size / 8.);
+                    return divUp(descriptor_size, 8);
                 }
 
             default:

modules/features2d/src/kaze/AKAZEFeatures.cpp

@@ -74,12 +74,12 @@ void AKAZEFeatures::Allocate_Memory_Evolution(void) {
       Evolution step;
       step.size = Size(level_width, level_height);
       step.esigma = options_.soffset*pow(2.f, (float)(j) / (float)(options_.nsublevels) + i);
-      step.sigma_size = fRound(step.esigma * options_.derivative_factor / power);  // In fact sigma_size only depends on j
+      step.sigma_size = cvRound(step.esigma * options_.derivative_factor / power);  // In fact sigma_size only depends on j
       step.etime = 0.5f * (step.esigma * step.esigma);
       step.octave = i;
       step.sublevel = j;
       step.octave_ratio = (float)power;
-      step.border = fRound(smax * step.sigma_size) + 1;
+      step.border = cvRound(smax * step.sigma_size) + 1;
 
       evolution_.push_back(step);
     }
@@ -106,7 +106,7 @@ void AKAZEFeatures::Allocate_Memory_Evolution(void) {
  */
 static inline int getGaussianKernelSize(float sigma) {
   // Compute an appropriate kernel size according to the specified sigma
-  int ksize = (int)ceil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
+  int ksize = (int)cvCeil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
   ksize |= 1; // kernel should be odd
   return ksize;
 }
@@ -890,11 +890,11 @@ public:
   {
     for (int i = range.start; i < range.end; i++)
     {
-      Get_SURF_Descriptor_Upright_64((*keypoints_)[i], descriptors_->ptr<float>(i));
+      Get_SURF_Descriptor_Upright_64((*keypoints_)[i], descriptors_->ptr<float>(i), descriptors_->cols);
     }
   }
 
-  void Get_SURF_Descriptor_Upright_64(const KeyPoint& kpt, float* desc) const;
+  void Get_SURF_Descriptor_Upright_64(const KeyPoint& kpt, float* desc, int desc_size) const;
 
 private:
   std::vector<KeyPoint>* keypoints_;
@@ -916,11 +916,11 @@ public:
   {
     for (int i = range.start; i < range.end; i++)
     {
-      Get_SURF_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i));
+      Get_SURF_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i), descriptors_->cols);
     }
   }
 
-  void Get_SURF_Descriptor_64(const KeyPoint& kpt, float* desc) const;
+  void Get_SURF_Descriptor_64(const KeyPoint& kpt, float* desc, int desc_size) const;
 
 private:
   std::vector<KeyPoint>* keypoints_;
@@ -942,11 +942,11 @@ public:
   {
     for (int i = range.start; i < range.end; i++)
     {
-      Get_MSURF_Upright_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i));
+      Get_MSURF_Upright_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i), descriptors_->cols);
     }
   }
 
-  void Get_MSURF_Upright_Descriptor_64(const KeyPoint& kpt, float* desc) const;
+  void Get_MSURF_Upright_Descriptor_64(const KeyPoint& kpt, float* desc, int desc_size) const;
 
 private:
   std::vector<KeyPoint>* keypoints_;
@@ -968,11 +968,11 @@ public:
   {
     for (int i = range.start; i < range.end; i++)
     {
-      Get_MSURF_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i));
+      Get_MSURF_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i), descriptors_->cols);
     }
   }
 
-  void Get_MSURF_Descriptor_64(const KeyPoint& kpt, float* desc) const;
+  void Get_MSURF_Descriptor_64(const KeyPoint& kpt, float* desc, int desc_size) const;
 
 private:
   std::vector<KeyPoint>* keypoints_;
@@ -995,11 +995,11 @@ public:
   {
     for (int i = range.start; i < range.end; i++)
     {
-      Get_Upright_MLDB_Full_Descriptor((*keypoints_)[i], descriptors_->ptr<unsigned char>(i));
+      Get_Upright_MLDB_Full_Descriptor((*keypoints_)[i], descriptors_->ptr<unsigned char>(i), descriptors_->cols);
     }
   }
 
-  void Get_Upright_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char* desc) const;
+  void Get_Upright_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char* desc, int desc_size) const;
 
 private:
   std::vector<KeyPoint>* keypoints_;
@@ -1030,11 +1030,11 @@ public:
   {
     for (int i = range.start; i < range.end; i++)
     {
-      Get_Upright_MLDB_Descriptor_Subset((*keypoints_)[i], descriptors_->ptr<unsigned char>(i));
+      Get_Upright_MLDB_Descriptor_Subset((*keypoints_)[i], descriptors_->ptr<unsigned char>(i), descriptors_->cols);
     }
   }
 
-  void Get_Upright_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char* desc) const;
+  void Get_Upright_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char* desc, int desc_size) const;
 
 private:
   std::vector<KeyPoint>* keypoints_;
@@ -1061,11 +1061,11 @@ public:
   {
     for (int i = range.start; i < range.end; i++)
     {
-      Get_MLDB_Full_Descriptor((*keypoints_)[i], descriptors_->ptr<unsigned char>(i));
+      Get_MLDB_Full_Descriptor((*keypoints_)[i], descriptors_->ptr<unsigned char>(i), descriptors_->cols);
     }
   }
 
-  void Get_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char* desc) const;
+  void Get_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char* desc, int desc_size) const;
   void MLDB_Fill_Values(float* values, int sample_step, int level,
                         float xf, float yf, float co, float si, float scale) const;
   void MLDB_Binary_Comparisons(float* values, unsigned char* desc,
@@ -1100,11 +1100,11 @@ public:
   {
     for (int i = range.start; i < range.end; i++)
     {
-      Get_MLDB_Descriptor_Subset((*keypoints_)[i], descriptors_->ptr<unsigned char>(i));
+      Get_MLDB_Descriptor_Subset((*keypoints_)[i], descriptors_->ptr<unsigned char>(i), descriptors_->cols);
     }
   }
 
-  void Get_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char* desc) const;
+  void Get_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char* desc, int desc_size) const;
 
 private:
   std::vector<KeyPoint>* keypoints_;
@@ -1131,20 +1131,17 @@ void AKAZEFeatures::Compute_Descriptors(std::vector<KeyPoint>& kpts, OutputArray
   }
 
   // Allocate memory for the matrix with the descriptors
-  if (options_.descriptor < AKAZE::DESCRIPTOR_MLDB_UPRIGHT) {
-    descriptors.create((int)kpts.size(), 64, CV_32FC1);
-  }
-  else {
-    // We use the full length binary descriptor -> 486 bits
-    if (options_.descriptor_size == 0) {
-      int t = (6 + 36 + 120)*options_.descriptor_channels;
-      descriptors.create((int)kpts.size(), (int)ceil(t / 8.), CV_8UC1);
-    }
-    else {
-      // We use the random bit selection length binary descriptor
-      descriptors.create((int)kpts.size(), (int)ceil(options_.descriptor_size / 8.), CV_8UC1);
-    }
+  int descriptor_size = 64;
+  int descriptor_type = CV_32FC1;
+  if (options_.descriptor >= AKAZE::DESCRIPTOR_MLDB_UPRIGHT)
+  {
+    int descriptor_bits = (options_.descriptor_size == 0)
+          ? (6 + 36 + 120)*options_.descriptor_channels // the full length binary descriptor -> 486 bits
+          : options_.descriptor_size; // the random bit selection length binary descriptor
+    descriptor_size = divUp(descriptor_bits, 8);
+    descriptor_type = CV_8UC1;
   }
+  descriptors.create((int)kpts.size(), descriptor_size, descriptor_type);
 
   Mat desc = descriptors.getMat();
 
@@ -1208,12 +1205,11 @@ void Sample_Derivative_Response_Radius6(const Mat &Lx, const Mat &Ly,
         { 0.00344629f, 0.00318132f, 0.00250252f, 0.00167749f, 0.00095820f, 0.00046640f, 0.00019346f },
         { 0.00142946f, 0.00131956f, 0.00103800f, 0.00069579f, 0.00039744f, 0.00019346f, 0.00008024f }
     };
-    static const int id[] = { 6, 5, 4, 3, 2, 1, 0, 1, 2, 3, 4, 5, 6 };
     static const struct gtable
     {
       float weight[109];
-      int8_t xidx[109];
-      int8_t yidx[109];
+      int xidx[109];
+      int yidx[109];
 
       explicit gtable(void)
       {
@@ -1222,29 +1218,28 @@ void Sample_Derivative_Response_Radius6(const Mat &Lx, const Mat &Ly,
         for (int i = -6; i <= 6; ++i) {
           for (int j = -6; j <= 6; ++j) {
             if (i*i + j*j < 36) {
-              weight[k] = gauss25[id[i + 6]][id[j + 6]];
-              yidx[k] = static_cast<int8_t>(i);
-              xidx[k] = static_cast<int8_t>(j);
+              CV_Assert(k < 109);
+              weight[k] = gauss25[abs(i)][abs(j)];
+              yidx[k] = i;
+              xidx[k] = j;
               ++k;
             }
           }
         }
-        CV_DbgAssert(k == 109);
       }
     } g;
 
-  const float * lx = Lx.ptr<float>(0);
-  const float * ly = Ly.ptr<float>(0);
-  int cols = Lx.cols;
-
-  for (int i = 0; i < 109; i++) {
-    int j = (y0 + g.yidx[i] * scale) * cols + (x0 + g.xidx[i] * scale);
+  CV_Assert(x0 - 6 * scale >= 0 && x0 + 6 * scale < Lx.cols);
+  CV_Assert(y0 - 6 * scale >= 0 && y0 + 6 * scale < Lx.rows);
 
-    resX[i] = g.weight[i] * lx[j];
-    resY[i] = g.weight[i] * ly[j];
+  for (int i = 0; i < 109; i++)
+  {
+    int y = y0 + g.yidx[i] * scale;
+    int x = x0 + g.xidx[i] * scale;
 
-    CV_DbgAssert(isfinite(resX[i]));
-    CV_DbgAssert(isfinite(resY[i]));
+    float w = g.weight[i];
+    resX[i] = w * Lx.at<float>(y, x);
+    resY[i] = w * Ly.at<float>(y, x);
   }
 }
 
@@ -1253,7 +1248,7 @@ void Sample_Derivative_Response_Radius6(const Mat &Lx, const Mat &Ly,
  * @param a[] Input floating point array to sort
  * @param n The length of a[]
  * @param quantum The interval to convert a[i]'s float values to integers
- * @param max The upper bound of a[], meaning a[i] must be in [0, max]
+ * @param nkeys a[i] < nkeys * quantum
  * @param idx[] Output array of the indices: a[idx[i]] forms a sorted array
  * @param cum[] Output array of the starting indices of quantized floats
  * @note The values of a[] in [k*quantum, (k + 1)*quantum) is labeled by
@@ -1263,25 +1258,35 @@ void Sample_Derivative_Response_Radius6(const Mat &Lx, const Mat &Ly,
  */
 static inline
 void quantized_counting_sort(const float a[], const int n,
-                             const float quantum, const float max,
-                             uint8_t idx[], uint8_t cum[])
+                             const float quantum, const int nkeys,
+                             int idx[/*n*/], int cum[/*nkeys + 1*/])
 {
-  const int nkeys = (int)(max / quantum);
-
-  // The size of cum[] must be nkeys + 1
-  memset(cum, 0, nkeys + 1);
+  memset(cum, 0, sizeof(cum[0]) * (nkeys + 1));
 
   // Count up the quantized values
   for (int i = 0; i < n; i++)
-    cum[(int)(a[i] / quantum)]++;
+  {
+    int b = (int)(a[i] / quantum);
+    if (b < 0 || b >= nkeys)
+      b = 0;
+    cum[b]++;
+  }
 
   // Compute the inclusive prefix sum i.e. the end indices; cum[nkeys] is the total
   for (int i = 1; i <= nkeys; i++)
+  {
     cum[i] += cum[i - 1];
+  }
+  CV_Assert(cum[nkeys] == n);
 
   // Generate the sorted indices; cum[] becomes the exclusive prefix sum i.e. the start indices of keys
   for (int i = 0; i < n; i++)
-    idx[--cum[(int)(a[i] / quantum)]] = static_cast<uint8_t>(i);
+  {
+    int b = (int)(a[i] / quantum);
+    if (b < 0 || b >= nkeys)
+      b = 0;
+    idx[--cum[b]] = i;
+  }
 }
 
 /**
@@ -1296,9 +1301,9 @@ void Compute_Main_Orientation(KeyPoint& kpt, const std::vector<Evolution>& evolu
   // get the right evolution level for this keypoint
   const Evolution& e = evolution[kpt.class_id];
   // Get the information from the keypoint
-  int scale = fRound(0.5f * kpt.size / e.octave_ratio);
-  int x0 = fRound(kpt.pt.x / e.octave_ratio);
-  int y0 = fRound(kpt.pt.y / e.octave_ratio);
+  int scale = cvRound(0.5f * kpt.size / e.octave_ratio);
+  int x0 = cvRound(kpt.pt.x / e.octave_ratio);
+  int y0 = cvRound(kpt.pt.y / e.octave_ratio);
 
   // Sample derivatives responses for the points within radius of 6*scale
   const int ang_size = 109;
@@ -1312,17 +1317,18 @@ void Compute_Main_Orientation(KeyPoint& kpt, const std::vector<Evolution>& evolu
   // Sort by the angles; angles are labeled by slices of 0.15 radian
   const int slices = 42;
   const float ang_step = (float)(2.0 * CV_PI / slices);
-  uint8_t slice[slices + 1];
-  uint8_t sorted_idx[ang_size];
-  quantized_counting_sort(Ang, ang_size, ang_step, (float)(2.0 * CV_PI), sorted_idx, slice);
+  int slice[slices + 1];
+  int sorted_idx[ang_size];
+  quantized_counting_sort(Ang, ang_size, ang_step, slices, sorted_idx, slice);
 
   // Find the main angle by sliding a window of 7-slice size(=PI/3) around the keypoint
   const int win = 7;
 
   float maxX = 0.0f, maxY = 0.0f;
   for (int i = slice[0]; i < slice[win]; i++) {
-    maxX += resX[sorted_idx[i]];
-    maxY += resY[sorted_idx[i]];
+    const int idx = sorted_idx[i];
+    maxX += resX[idx];
+    maxY += resY[idx];
   }
   float maxNorm = maxX * maxX + maxY * maxY;
 
@@ -1333,8 +1339,9 @@ void Compute_Main_Orientation(KeyPoint& kpt, const std::vector<Evolution>& evolu
 
     float sumX = 0.0f, sumY = 0.0f;
     for (int i = slice[sn]; i < slice[sn + win]; i++) {
-      sumX += resX[sorted_idx[i]];
-      sumY += resY[sorted_idx[i]];
+      const int idx = sorted_idx[i];
+      sumX += resX[idx];
+      sumY += resY[idx];
     }
 
     float norm = sumX * sumX + sumY * sumY;
@@ -1350,12 +1357,14 @@ void Compute_Main_Orientation(KeyPoint& kpt, const std::vector<Evolution>& evolu
 
     float sumX = 0.0f, sumY = 0.0f;
     for (int i = slice[sn]; i < slice[slices]; i++) {
-      sumX += resX[sorted_idx[i]];
-      sumY += resY[sorted_idx[i]];
+      const int idx = sorted_idx[i];
+      sumX += resX[idx];
+      sumY += resY[idx];
     }
     for (int i = slice[0]; i < slice[remain]; i++) {
-      sumX += resX[sorted_idx[i]];
-      sumY += resY[sorted_idx[i]];
+      const int idx = sorted_idx[i];
+      sumX += resX[idx];
+      sumY += resY[idx];
     }
 
     float norm = sumX * sumX + sumY * sumY;
@@ -1410,7 +1419,10 @@ void AKAZEFeatures::Compute_Keypoints_Orientation(std::vector<KeyPoint>& kpts) c
  * from Agrawal et al., CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,
  * ECCV 2008
  */
-void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const KeyPoint& kpt, float *desc) const {
+void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const KeyPoint& kpt, float *desc, int desc_size) const {
+
+  const int dsize = 64;
+  CV_Assert(desc_size == dsize);
 
   float dx = 0.0, dy = 0.0, mdx = 0.0, mdy = 0.0, gauss_s1 = 0.0, gauss_s2 = 0.0;
   float rx = 0.0, ry = 0.0, len = 0.0, xf = 0.0, yf = 0.0, ys = 0.0, xs = 0.0;
@@ -1418,7 +1430,7 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
   int x1 = 0, y1 = 0, sample_step = 0, pattern_size = 0;
   int x2 = 0, y2 = 0, kx = 0, ky = 0, i = 0, j = 0, dcount = 0;
   float fx = 0.0, fy = 0.0, ratio = 0.0, res1 = 0.0, res2 = 0.0, res3 = 0.0, res4 = 0.0;
-  int scale = 0, dsize = 0;
+  int scale = 0;
 
   // Subregion centers for the 4x4 gaussian weighting
   float cx = -0.5f, cy = 0.5f;
@@ -1426,13 +1438,12 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
   const std::vector<Evolution>& evolution = *evolution_;
 
   // Set the descriptor size and the sample and pattern sizes
-  dsize = 64;
   sample_step = 5;
   pattern_size = 12;
 
   // Get the information from the keypoint
   ratio = (float)(1 << kpt.octave);
-  scale = fRound(0.5f*kpt.size / ratio);
+  scale = cvRound(0.5f*kpt.size / ratio);
   const int level = kpt.class_id;
   Mat Lx = evolution[level].Mx;
   Mat Ly = evolution[level].My;
@@ -1469,11 +1480,11 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
           //Get the gaussian weighted x and y responses
           gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.50f*scale);
 
-          y1 = (int)(sample_y - .5);
-          x1 = (int)(sample_x - .5);
+          y1 = (int)(sample_y - .5f);
+          x1 = (int)(sample_x - .5f);
 
-          y2 = (int)(sample_y + .5);
-          x2 = (int)(sample_x + .5);
+          y2 = (int)(sample_y + .5f);
+          x2 = (int)(sample_x + .5f);
 
           fx = sample_x - x1;
           fy = sample_y - y1;
@@ -1517,6 +1528,8 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
     i += 9;
   }
 
+  CV_Assert(dcount == desc_size);
+
   // convert to unit vector
   len = sqrt(len);
 
@@ -1535,7 +1548,10 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
  * from Agrawal et al., CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,
  * ECCV 2008
  */
-void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, float *desc) const {
+void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, float *desc, int desc_size) const {
+
+  const int dsize = 64;
+  CV_Assert(desc_size == dsize);
 
   float dx = 0.0, dy = 0.0, mdx = 0.0, mdy = 0.0, gauss_s1 = 0.0, gauss_s2 = 0.0;
   float rx = 0.0, ry = 0.0, rrx = 0.0, rry = 0.0, len = 0.0, xf = 0.0, yf = 0.0, ys = 0.0, xs = 0.0;
@@ -1543,7 +1559,7 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
   float fx = 0.0, fy = 0.0, ratio = 0.0, res1 = 0.0, res2 = 0.0, res3 = 0.0, res4 = 0.0;
   int x1 = 0, y1 = 0, x2 = 0, y2 = 0, sample_step = 0, pattern_size = 0;
   int kx = 0, ky = 0, i = 0, j = 0, dcount = 0;
-  int scale = 0, dsize = 0;
+  int scale = 0;
 
   // Subregion centers for the 4x4 gaussian weighting
   float cx = -0.5f, cy = 0.5f;
@@ -1551,14 +1567,13 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
   const std::vector<Evolution>& evolution = *evolution_;
 
   // Set the descriptor size and the sample and pattern sizes
-  dsize = 64;
   sample_step = 5;
   pattern_size = 12;
 
   // Get the information from the keypoint
   ratio = (float)(1 << kpt.octave);
-  scale = fRound(0.5f*kpt.size / ratio);
-  angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
+  scale = cvRound(0.5f*kpt.size / ratio);
+  angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
   const int level = kpt.class_id;
   Mat Lx = evolution[level].Mx;
   Mat Ly = evolution[level].My;
@@ -1598,11 +1613,11 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
           // Get the gaussian weighted x and y responses
           gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f*scale);
 
-          y1 = fRound(sample_y - 0.5f);
-          x1 = fRound(sample_x - 0.5f);
+          y1 = cvRound(sample_y - 0.5f);
+          x1 = cvRound(sample_x - 0.5f);
 
-          y2 = fRound(sample_y + 0.5f);
-          x2 = fRound(sample_x + 0.5f);
+          y2 = cvRound(sample_y + 0.5f);
+          x2 = cvRound(sample_x + 0.5f);
 
           // fix crash: indexing with out-of-bounds index, this might happen near the edges of image
           // clip values so they fit into the image
@@ -1655,6 +1670,8 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
     i += 9;
   }
 
+  CV_Assert(dcount == desc_size);
+
   // convert to unit vector
   len = sqrt(len);
 
@@ -1670,7 +1687,7 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
  * @param kpt Input keypoint
  * @param desc Descriptor vector
  */
-void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char *desc) const {
+void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char *desc, int desc_size) const {
 
   float di = 0.0, dx = 0.0, dy = 0.0;
   float ri = 0.0, rx = 0.0, ry = 0.0, xf = 0.0, yf = 0.0;
@@ -1682,16 +1699,14 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
   const AKAZEOptions & options = *options_;
   const std::vector<Evolution>& evolution = *evolution_;
 
-  // Matrices for the M-LDB descriptor
-  Mat values[3] = {
-    Mat(4, options.descriptor_channels, CV_32FC1),
-    Mat(9, options.descriptor_channels, CV_32FC1),
-    Mat(16, options.descriptor_channels, CV_32FC1)
-  };
+  // Buffer for the M-LDB descriptor
+  const int max_channels = 3;
+  CV_Assert(options.descriptor_channels <= max_channels);
+  float values[16*max_channels];
 
   // Get the information from the keypoint
   ratio = (float)(1 << kpt.octave);
-  scale = fRound(0.5f*kpt.size / ratio);
+  scale = cvRound(0.5f*kpt.size / ratio);
   const int level = kpt.class_id;
   Mat Lx = evolution[level].Mx;
   Mat Ly = evolution[level].My;
@@ -1701,12 +1716,15 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
 
   // For 2x2 grid, 3x3 grid and 4x4 grid
   const int pattern_size = options_->descriptor_pattern_size;
-  int sample_step[3] = {
+  CV_Assert((pattern_size & 1) == 0);
+  const int sample_step[3] = {
     pattern_size,
-    static_cast<int>(ceil(pattern_size*2./3.)),
-    pattern_size / 2
+    divUp(pattern_size * 2, 3),
+    divUp(pattern_size, 2)
   };
 
+  memset(desc, 0, desc_size);
+
   // For the three grids
   for (int z = 0; z < 3; z++) {
     dcount2 = 0;
@@ -1723,8 +1741,8 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
             sample_y = yf + l*scale;
             sample_x = xf + k*scale;
 
-            y1 = fRound(sample_y);
-            x1 = fRound(sample_x);
+            y1 = cvRound(sample_y);
+            x1 = cvRound(sample_x);
 
             ri = *(Lt.ptr<float>(y1)+x1);
             rx = *(Lx.ptr<float>(y1)+x1);
@@ -1741,7 +1759,7 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
         dx /= nsamples;
         dy /= nsamples;
 
-        float *val = values[z].ptr<float>(dcount2);
+        float *val = &values[dcount2*max_channels];
         *(val) = di;
         *(val+1) = dx;
         *(val+2) = dy;
@@ -1753,13 +1771,11 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
     const int num = (z + 2) * (z + 2);
     for (int i = 0; i < num; i++) {
       for (int j = i + 1; j < num; j++) {
-        const float * valI = values[z].ptr<float>(i);
-        const float * valJ = values[z].ptr<float>(j);
+        const float * valI = &values[i*max_channels];
+        const float * valJ = &values[j*max_channels];
         for (int k = 0; k < 3; ++k) {
           if (*(valI + k) > *(valJ + k)) {
             desc[dcount1 / 8] |= (1 << (dcount1 % 8));
-          } else {
-            desc[dcount1 / 8] &= ~(1 << (dcount1 % 8));
           }
           dcount1++;
         }
@@ -1767,6 +1783,9 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
     }
 
   } // for (int z = 0; z < 3; z++)
+
+  CV_Assert(dcount1 <= desc_size*8);
+  CV_Assert(divUp(dcount1, 8) == desc_size);
 }
 
 void MLDB_Full_Descriptor_Invoker::MLDB_Fill_Values(float* values, int sample_step, const int level,
@@ -1791,8 +1810,8 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Fill_Values(float* values, int sample_st
                 float sample_y = yf + (l*co * scale + k*si*scale);
                 float sample_x = xf + (-l*si * scale + k*co*scale);
 
-                int y1 = fRound(sample_y);
-                int x1 = fRound(sample_x);
+                int y1 = cvRound(sample_y);
+                int x1 = cvRound(sample_x);
 
                 // fix crash: indexing with out-of-bounds index, this might happen near the edges of image
                 // clip values so they fit into the image
@@ -1852,10 +1871,6 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Binary_Comparisons(float* values, unsign
                 if (ival > ivalues[chan * j + pos]) {
                   desc[dpos >> 3] |= (1 << (dpos & 7));
                 }
-                else {
-                  desc[dpos >> 3] &= ~(1 << (dpos & 7));
-                }
-
                 dpos++;
             }
         }
@@ -1869,30 +1884,41 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Binary_Comparisons(float* values, unsign
  * @param kpt Input keypoint
  * @param desc Descriptor vector
  */
-void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char *desc) const {
+void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char *desc, int desc_size) const {
 
   const int max_channels = 3;
   CV_Assert(options_->descriptor_channels <= max_channels);
+  const int pattern_size = options_->descriptor_pattern_size;
+
   float values[16*max_channels];
-  const double size_mult[3] = {1, 2.0/3.0, 1.0/2.0};
+  CV_Assert((pattern_size & 1) == 0);
+  //const double size_mult[3] = {1, 2.0/3.0, 1.0/2.0};
+  const int sample_step[3] = { // static_cast<int>(ceil(pattern_size * size_mult[lvl]))
+    pattern_size,
+    divUp(pattern_size * 2, 3),
+    divUp(pattern_size, 2)
+  };
 
   float ratio = (float)(1 << kpt.octave);
-  float scale = (float)fRound(0.5f*kpt.size / ratio);
+  float scale = (float)cvRound(0.5f*kpt.size / ratio);
   float xf = kpt.pt.x / ratio;
   float yf = kpt.pt.y / ratio;
-  float angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
+  float angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
   float co = cos(angle);
   float si = sin(angle);
-  int pattern_size = options_->descriptor_pattern_size;
 
-  int dpos = 0;
-  for(int lvl = 0; lvl < 3; lvl++) {
+  memset(desc, 0, desc_size);
 
+  int dpos = 0;
+  for(int lvl = 0; lvl < 3; lvl++)
+  {
       int val_count = (lvl + 2) * (lvl + 2);
-      int sample_step = static_cast<int>(ceil(pattern_size * size_mult[lvl]));
-      MLDB_Fill_Values(values, sample_step, kpt.class_id, xf, yf, co, si, scale);
+      MLDB_Fill_Values(values, sample_step[lvl], kpt.class_id, xf, yf, co, si, scale);
       MLDB_Binary_Comparisons(values, desc, val_count, dpos);
   }
+
+  CV_Assert(dpos == 486);
+  CV_Assert(divUp(dpos, 8) == desc_size);
 }
 
 /* ************************************************************************* */
@@ -1903,7 +1929,7 @@ void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const KeyPoint& kpt,
  * @param kpt Input keypoint
  * @param desc Descriptor vector
  */
-void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char *desc) const {
+void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char *desc, int desc_size) const {
 
   float di = 0.f, dx = 0.f, dy = 0.f;
   float rx = 0.f, ry = 0.f;
@@ -1915,8 +1941,8 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
 
   // Get the information from the keypoint
   float ratio = (float)(1 << kpt.octave);
-  int scale = fRound(0.5f*kpt.size / ratio);
-  float angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
+  int scale = cvRound(0.5f*kpt.size / ratio);
+  float angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
   const int level = kpt.class_id;
   Mat Lx = evolution[level].Mx;
   Mat Ly = evolution[level].My;
@@ -1927,17 +1953,25 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
   float si = sin(angle);
 
   // Allocate memory for the matrix of values
-  Mat values((4 + 9 + 16)*options.descriptor_channels, 1, CV_32FC1);
+  // Buffer for the M-LDB descriptor
+  const int max_channels = 3;
+  const int channels = options.descriptor_channels;
+  CV_Assert(channels <= max_channels);
+  float values[(4 + 9 + 16)*max_channels];
 
   // Sample everything, but only do the comparisons
-  vector<int> steps(3);
-  steps.at(0) = options.descriptor_pattern_size;
-  steps.at(1) = (int)ceil(2.f*options.descriptor_pattern_size / 3.f);
-  steps.at(2) = options.descriptor_pattern_size / 2;
+  const int pattern_size = options.descriptor_pattern_size;
+  CV_Assert((pattern_size & 1) == 0);
+  const int sample_steps[3] = {
+    pattern_size,
+    divUp(pattern_size * 2, 3),
+    divUp(pattern_size, 2)
+  };
 
   for (int i = 0; i < descriptorSamples_.rows; i++) {
     const int *coords = descriptorSamples_.ptr<int>(i);
-    int sample_step = steps.at(coords[0]);
+    CV_Assert(coords[0] >= 0 && coords[0] < 3);
+    const int sample_step = sample_steps[coords[0]];
     di = 0.0f;
     dx = 0.0f;
     dy = 0.0f;
@@ -1949,8 +1983,8 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
         sample_y = yf + (l*scale*co + k*scale*si);
         sample_x = xf + (-l*scale*si + k*scale*co);
 
-        y1 = fRound(sample_y);
-        x1 = fRound(sample_x);
+        y1 = cvRound(sample_y);
+        x1 = cvRound(sample_x);
 
         di += *(Lt.ptr<float>(y1)+x1);
 
@@ -1970,26 +2004,27 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
       }
     }
 
-    *(values.ptr<float>(options.descriptor_channels*i)) = di;
+    float* pValues = &values[channels * i];
+    pValues[0] = di;
 
-    if (options.descriptor_channels == 2) {
-      *(values.ptr<float>(options.descriptor_channels*i + 1)) = dx;
+    if (channels == 2) {
+      pValues[1] = dx;
     }
-    else if (options.descriptor_channels == 3) {
-      *(values.ptr<float>(options.descriptor_channels*i + 1)) = dx;
-      *(values.ptr<float>(options.descriptor_channels*i + 2)) = dy;
+    else if (channels == 3) {
+      pValues[1] = dx;
+      pValues[2] = dy;
     }
   }
 
   // Do the comparisons
-  const float *vals = values.ptr<float>(0);
   const int *comps = descriptorBits_.ptr<int>(0);
 
+  CV_Assert(divUp(descriptorBits_.rows, 8) == desc_size);
+  memset(desc, 0, desc_size);
+
   for (int i = 0; i<descriptorBits_.rows; i++) {
-    if (vals[comps[2 * i]] > vals[comps[2 * i + 1]]) {
+    if (values[comps[2 * i]] > values[comps[2 * i + 1]]) {
       desc[i / 8] |= (1 << (i % 8));
-    } else {
-      desc[i / 8] &= ~(1 << (i % 8));
     }
   }
 }
@@ -2002,7 +2037,7 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
  * @param kpt Input keypoint
  * @param desc Descriptor vector
  */
-void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char *desc) const {
+void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char *desc, int desc_size) const {
 
   float di = 0.0f, dx = 0.0f, dy = 0.0f;
   float rx = 0.0f, ry = 0.0f;
@@ -2014,7 +2049,7 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
 
   // Get the information from the keypoint
   float ratio = (float)(1 << kpt.octave);
-  int scale = fRound(0.5f*kpt.size / ratio);
+  int scale = cvRound(0.5f*kpt.size / ratio);
   const int level = kpt.class_id;
   Mat Lx = evolution[level].Mx;
   Mat Ly = evolution[level].My;
@@ -2025,14 +2060,18 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
   // Allocate memory for the matrix of values
   Mat values ((4 + 9 + 16)*options.descriptor_channels, 1, CV_32FC1);
 
-  vector<int> steps(3);
-  steps.at(0) = options.descriptor_pattern_size;
-  steps.at(1) = static_cast<int>(ceil(2.f*options.descriptor_pattern_size / 3.f));
-  steps.at(2) = options.descriptor_pattern_size / 2;
+  const int pattern_size = options.descriptor_pattern_size;
+  CV_Assert((pattern_size & 1) == 0);
+  const int sample_steps[3] = {
+    pattern_size,
+    divUp(pattern_size * 2, 3),
+    divUp(pattern_size, 2)
+  };
 
   for (int i = 0; i < descriptorSamples_.rows; i++) {
     const int *coords = descriptorSamples_.ptr<int>(i);
-    int sample_step = steps.at(coords[0]);
+    CV_Assert(coords[0] >= 0 && coords[0] < 3);
+    int sample_step = sample_steps[coords[0]];
     di = 0.0f, dx = 0.0f, dy = 0.0f;
 
     for (int k = coords[1]; k < coords[1] + sample_step; k++) {
@@ -2042,8 +2081,8 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
         sample_y = yf + l*scale;
         sample_x = xf + k*scale;
 
-        y1 = fRound(sample_y);
-        x1 = fRound(sample_x);
+        y1 = cvRound(sample_y);
+        x1 = cvRound(sample_x);
         di += *(Lt.ptr<float>(y1)+x1);
 
         if (options.descriptor_channels > 1) {
@@ -2076,11 +2115,12 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
   const float *vals = values.ptr<float>(0);
   const int *comps = descriptorBits_.ptr<int>(0);
 
+  CV_Assert(divUp(descriptorBits_.rows, 8) == desc_size);
+  memset(desc, 0, desc_size);
+
   for (int i = 0; i<descriptorBits_.rows; i++) {
     if (vals[comps[2 * i]] > vals[comps[2 * i + 1]]) {
       desc[i / 8] |= (1 << (i % 8));
-    } else {
-      desc[i / 8] &= ~(1 << (i % 8));
     }
   }
 }
@@ -2120,7 +2160,7 @@ void generateDescriptorSubsample(Mat& sampleList, Mat& comparisons, int nbits,
   for (int i = 0, c = 0; i < 3; i++) {
     int gdiv = i + 2; //grid divisions, per row
     int gsz = gdiv*gdiv;
-    int psz = (int)ceil(2.f*pattern_size / (float)gdiv);
+    int psz = divUp(2*pattern_size, gdiv);
 
     for (int j = 0; j < gsz; j++) {
       for (int k = j + 1; k < gsz; k++, c++) {
@@ -2134,12 +2174,12 @@ void generateDescriptorSubsample(Mat& sampleList, Mat& comparisons, int nbits,
   }
 
   RNG rng(1024);
-  Mat_<int> comps = Mat_<int>(nchannels * (int)ceil(nbits / (float)nchannels), 2);
+  const int npicks = divUp(nbits, nchannels);
+  Mat_<int> comps = Mat_<int>(nchannels * npicks, 2);
   comps = 1000;
 
   // Select some samples. A sample includes all channels
   int count = 0;
-  int npicks = (int)ceil(nbits / (float)nchannels);
   Mat_<int> samples(29, 3);
   Mat_<int> fullcopy = fullM.clone();
   samples = -1;

modules/features2d/src/kaze/AKAZEFeatures.h

@@ -25,6 +25,8 @@ struct Evolution
     octave = 0;
     sublevel = 0;
     sigma_size = 0;
+    octave_ratio = 0.0f;
+    border = 0;
   }
 
   UMat Lx, Ly;           ///< First order spatial derivatives

modules/features2d/src/kaze/KAZEFeatures.cpp

@@ -68,7 +68,7 @@ void KAZEFeatures::Allocate_Memory_Evolution(void) {
             aux.Ldet = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
             aux.esigma = options_.soffset*pow((float)2.0f, (float)(j) / (float)(options_.nsublevels)+i);
             aux.etime = 0.5f*(aux.esigma*aux.esigma);
-            aux.sigma_size = fRound(aux.esigma);
+            aux.sigma_size = cvRound(aux.esigma);
             aux.octave = i;
             aux.sublevel = j;
             evolution_.push_back(aux);
@@ -363,10 +363,10 @@ void KAZEFeatures::Determinant_Hessian(std::vector<KeyPoint>& kpts)
 
             if (is_extremum == true) {
                 // Check that the point is under the image limits for the descriptor computation
-                left_x = fRound(kpts_par_[i][j].pt.x - smax*kpts_par_[i][j].size);
-                right_x = fRound(kpts_par_[i][j].pt.x + smax*kpts_par_[i][j].size);
-                up_y = fRound(kpts_par_[i][j].pt.y - smax*kpts_par_[i][j].size);
-                down_y = fRound(kpts_par_[i][j].pt.y + smax*kpts_par_[i][j].size);
+                left_x = cvRound(kpts_par_[i][j].pt.x - smax*kpts_par_[i][j].size);
+                right_x = cvRound(kpts_par_[i][j].pt.x + smax*kpts_par_[i][j].size);
+                up_y = cvRound(kpts_par_[i][j].pt.y - smax*kpts_par_[i][j].size);
+                down_y = cvRound(kpts_par_[i][j].pt.y + smax*kpts_par_[i][j].size);
 
                 if (left_x < 0 || right_x >= evolution_[level].Ldet.cols ||
                     up_y < 0 || down_y >= evolution_[level].Ldet.rows) {
@@ -587,14 +587,14 @@ void KAZEFeatures::Compute_Main_Orientation(KeyPoint &kpt, const std::vector<TEv
     xf = kpt.pt.x;
     yf = kpt.pt.y;
     level = kpt.class_id;
-    s = fRound(kpt.size / 2.0f);
+    s = cvRound(kpt.size / 2.0f);
 
     // Calculate derivatives responses for points within radius of 6*scale
     for (int i = -6; i <= 6; ++i) {
         for (int j = -6; j <= 6; ++j) {
             if (i*i + j*j < 36) {
-                iy = fRound(yf + j*s);
-                ix = fRound(xf + i*s);
+                iy = cvRound(yf + j*s);
+                ix = cvRound(xf + i*s);
 
                 if (iy >= 0 && iy < options.img_height && ix >= 0 && ix < options.img_width) {
                     gweight = gaussian(iy - yf, ix - xf, 2.5f*s);
@@ -606,7 +606,7 @@ void KAZEFeatures::Compute_Main_Orientation(KeyPoint &kpt, const std::vector<TEv
                     resY[idx] = 0.0;
                 }
 
-                Ang[idx] = getAngle(resX[idx], resY[idx]);
+                Ang[idx] = fastAtan2(resX[idx], resY[idx]) * (float)(CV_PI / 180.0f);
                 ++idx;
             }
         }
@@ -638,7 +638,7 @@ void KAZEFeatures::Compute_Main_Orientation(KeyPoint &kpt, const std::vector<TEv
         if (sumX*sumX + sumY*sumY > max) {
             // store largest orientation
             max = sumX*sumX + sumY*sumY;
-            kpt.angle = getAngle(sumX, sumY) * 180.f / static_cast<float>(CV_PI);
+            kpt.angle = fastAtan2(sumX, sumY);
         }
     }
 }
@@ -676,7 +676,7 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_64(const KeyPoint &kpt
     // Get the information from the keypoint
     yf = kpt.pt.y;
     xf = kpt.pt.x;
-    scale = fRound(kpt.size / 2.0f);
+    scale = cvRound(kpt.size / 2.0f);
     level = kpt.class_id;
 
     i = -8;
@@ -804,8 +804,8 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_64(const KeyPoint &kpt, float
     // Get the information from the keypoint
     yf = kpt.pt.y;
     xf = kpt.pt.x;
-    scale = fRound(kpt.size / 2.0f);
-    angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
+    scale = cvRound(kpt.size / 2.0f);
+    angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
     level = kpt.class_id;
     co = cos(angle);
     si = sin(angle);
@@ -843,13 +843,13 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_64(const KeyPoint &kpt, float
 
                     // Get the gaussian weighted x and y responses
                     gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f*scale);
-                    y1 = fRound(sample_y - 0.5f);
-                    x1 = fRound(sample_x - 0.5f);
+                    y1 = cvFloor(sample_y);
+                    x1 = cvFloor(sample_x);
 
                                         checkDescriptorLimits(x1, y1, options_.img_width, options_.img_height);
 
-                    y2 = (int)(sample_y + 0.5f);
-                    x2 = (int)(sample_x + 0.5f);
+                    y2 = y1 + 1;
+                    x2 = x1 + 1;
 
                                         checkDescriptorLimits(x2, y2, options_.img_width, options_.img_height);
 
@@ -935,7 +935,7 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_128(const KeyPoint &kp
     // Get the information from the keypoint
     yf = kpt.pt.y;
     xf = kpt.pt.x;
-    scale = fRound(kpt.size / 2.0f);
+    scale = cvRound(kpt.size / 2.0f);
     level = kpt.class_id;
 
     i = -8;
@@ -1087,8 +1087,8 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_128(const KeyPoint &kpt, float
     // Get the information from the keypoint
     yf = kpt.pt.y;
     xf = kpt.pt.x;
-    scale = fRound(kpt.size / 2.0f);
-    angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
+    scale = cvRound(kpt.size / 2.0f);
+    angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
     level = kpt.class_id;
     co = cos(angle);
     si = sin(angle);
@@ -1129,13 +1129,13 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_128(const KeyPoint &kpt, float
                     // Get the gaussian weighted x and y responses
                     gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f*scale);
 
-                    y1 = fRound(sample_y - 0.5f);
-                    x1 = fRound(sample_x - 0.5f);
+                    y1 = cvFloor(sample_y);
+                    x1 = cvFloor(sample_x);
 
                                         checkDescriptorLimits(x1, y1, options_.img_width, options_.img_height);
 
-                    y2 = (int)(sample_y + 0.5f);
-                    x2 = (int)(sample_x + 0.5f);
+                    y2 = y1 + 1;
+                    x2 = x1 + 1;
 
                                         checkDescriptorLimits(x2, y2, options_.img_width, options_.img_height);
 

modules/features2d/src/kaze/fed.cpp

@@ -72,7 +72,7 @@ int fed_tau_by_cycle_time(const float& t, const float& tau_max,
   float scale = 0.0;  // Ratio of t we search to maximal t
 
   // Compute necessary number of time steps
-  n = (int)(ceilf(sqrtf(3.0f*t/tau_max+0.25f)-0.5f-1.0e-8f)+ 0.5f);
+  n = cvCeil(sqrtf(3.0f*t/tau_max+0.25f)-0.5f-1.0e-8f);
   scale = 3.0f*t/(tau_max*(float)(n*(n+1)));
 
   // Call internal FED time step creation routine

modules/features2d/src/kaze/nldiffusion_functions.cpp

@@ -49,7 +49,7 @@ void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int
 
     // Compute an appropriate kernel size according to the specified sigma
     if (sigma > ksize_x || sigma > ksize_y || ksize_x == 0 || ksize_y == 0) {
-        ksize_x_ = (int)ceil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
+        ksize_x_ = cvCeil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
         ksize_y_ = ksize_x_;
     }
 

modules/features2d/src/kaze/utils.h

@@ -1,31 +1,6 @@
 #ifndef __OPENCV_FEATURES_2D_KAZE_UTILS_H__
 #define __OPENCV_FEATURES_2D_KAZE_UTILS_H__
 
-/* ************************************************************************* */
-/**
- * @brief This function computes the angle from the vector given by (X Y). From 0 to 2*Pi
- */
-inline float getAngle(float x, float y) {
-
-  if (x >= 0 && y >= 0) {
-    return atanf(y / x);
-  }
-
-  if (x < 0 && y >= 0) {
-    return static_cast<float>(CV_PI)-atanf(-y / x);
-  }
-
-  if (x < 0 && y < 0) {
-    return static_cast<float>(CV_PI)+atanf(y / x);
-  }
-
-  if (x >= 0 && y < 0) {
-    return static_cast<float>(2.0 * CV_PI) - atanf(-y / x);
-  }
-
-  return 0;
-}
-
 /* ************************************************************************* */
 /**
  * @brief This function computes the value of a 2D Gaussian function
@@ -64,34 +39,4 @@ inline void checkDescriptorLimits(int &x, int &y, int width, int height) {
   }
 }
 
-/* ************************************************************************* */
-/**
- * @brief This funtion rounds float to nearest integer
- * @param flt Input float
- * @return dst Nearest integer
- */
-inline int fRound(float flt) {
-  return (int)(flt + 0.5f);
-}
-
-/* ************************************************************************* */
-/**
- * @brief Exponentiation by squaring
- * @param flt Exponentiation base
- * @return dst Exponentiation value
- */
-inline int fastpow(int base, int exp) {
-    int res = 1;
-    while(exp > 0) {
-        if(exp & 1) {
-            exp--;
-            res *= base;
-        } else {
-            exp /= 2;
-            base *= base;
-        }
-    }
-    return res;
-}
-
 #endif

modules/features2d/test/test_descriptors_invariance.cpp

@@ -11,7 +11,7 @@ using std::tr1::make_tuple;
 using std::tr1::get;
 using namespace testing;
 
-#define SHOW_DEBUG_LOG 0
+#define SHOW_DEBUG_LOG 1
 
 typedef std::tr1::tuple<std::string, Ptr<FeatureDetector>, Ptr<DescriptorExtractor>, float>
     String_FeatureDetector_DescriptorExtractor_Float_t;
@@ -72,7 +72,7 @@ TEST_P(DescriptorRotationInvariance, rotation)
     vector<KeyPoint> keypoints0;
     Mat descriptors0;
     featureDetector->detect(image0, keypoints0, mask0);
-    std::cout << "Intial keypoints: " << keypoints0.size() << std::endl;
+    std::cout << "Keypoints: " << keypoints0.size() << std::endl;
     EXPECT_GE(keypoints0.size(), 15u);
     descriptorExtractor->compute(image0, keypoints0, descriptors0);
 
@@ -109,7 +109,7 @@ TEST_P(DescriptorRotationInvariance, rotation)
 #if SHOW_DEBUG_LOG
         std::cout
             << "angle = " << angle
-            << ", keypoints = " << keypoints1.size()
+            << ", inliers = " << descInliersCount
             << ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
             << std::endl;
 #endif
@@ -121,6 +121,7 @@ TEST_P(DescriptorScaleInvariance, scale)
 {
     vector<KeyPoint> keypoints0;
     featureDetector->detect(image0, keypoints0);
+    std::cout << "Keypoints: " << keypoints0.size() << std::endl;
     EXPECT_GE(keypoints0.size(), 15u);
     Mat descriptors0;
     descriptorExtractor->compute(image0, keypoints0, descriptors0);
@@ -159,6 +160,7 @@ TEST_P(DescriptorScaleInvariance, scale)
 #if SHOW_DEBUG_LOG
         std::cout
             << "scale = " << scale
+            << ", inliers = " << descInliersCount
             << ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
             << std::endl;
 #endif

modules/features2d/test/test_descriptors_regression.cpp

@@ -56,6 +56,7 @@ static void writeMatInBin( const Mat& mat, const string& filename )
     FILE* f = fopen( filename.c_str(), "wb");
     if( f )
     {
+        CV_Assert(4 == sizeof(int));
         int type = mat.type();
         fwrite( (void*)&mat.rows, sizeof(int), 1, f );
         fwrite( (void*)&mat.cols, sizeof(int), 1, f );
@@ -72,6 +73,7 @@ static Mat readMatFromBin( const string& filename )
     FILE* f = fopen( filename.c_str(), "rb" );
     if( f )
     {
+        CV_Assert(4 == sizeof(int));
         int rows, cols, type, dataSize;
         size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
         size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
@@ -123,24 +125,37 @@ protected:
         CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
 
         int dimension = validDescriptors.cols;
-        DistanceType curMaxDist = std::numeric_limits<DistanceType>::min();
+        DistanceType curMaxDist = 0;
+        size_t exact_count = 0, failed_count = 0;
         for( int y = 0; y < validDescriptors.rows; y++ )
         {
             DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
+            if (dist == 0)
+                exact_count++;
             if( dist > curMaxDist )
+            {
+                if (dist > maxDist)
+                    failed_count++;
                 curMaxDist = dist;
+            }
+#if 0
+            if (dist > 0)
+            {
+                std::cout << "i=" << y << " fail_count=" << failed_count << " dist=" << dist << std::endl;
+                std::cout << "valid: " << validDescriptors.row(y) << std::endl;
+                std::cout << " calc: " << calcDescriptors.row(y) << std::endl;
+            }
+#endif
         }
 
+        float exact_percents = (100 * (float)exact_count / validDescriptors.rows);
+        float failed_percents = (100 * (float)failed_count / validDescriptors.rows);
         stringstream ss;
-        ss << "Max distance between valid and computed descriptors " << curMaxDist;
-        if( curMaxDist <= maxDist )
-            ss << "." << endl;
-        else
-        {
-            ss << ">" << maxDist  << " - bad accuracy!"<< endl;
-            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
-        }
-        ts->printf(cvtest::TS::LOG,  ss.str().c_str() );
+        ss << "Exact count (dist == 0): " << exact_count << " (" << (int)exact_percents << "%)" << std::endl
+                << "Failed count (dist > " << maxDist << "): " << failed_count << " (" << (int)failed_percents << "%)" << std::endl
+                << "Max distance between valid and computed descriptors (" << validDescriptors.size() << "): " << curMaxDist;
+        EXPECT_LE(failed_percents, 20.0f);
+        std::cout << ss.str() << std::endl;
     }
 
     void emptyDataTest()
@@ -202,22 +217,57 @@ protected:
             ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
             return;
         }
+        const std::string keypoints_filename = string(ts->get_data_path()) +
+                (detector.empty()
+                        ? (FEATURES2D_DIR + "/" + std::string("keypoints.xml.gz"))
+                        : (DESCRIPTOR_DIR + "/" + name + "_keypoints.xml.gz"));
+        FileStorage fs(keypoints_filename, FileStorage::READ);
+
         vector<KeyPoint> keypoints;
-        FileStorage fs( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::READ );
-        if(!detector.empty()) {
-            detector->detect(img, keypoints);
-        } else {
-            read( fs.getFirstTopLevelNode(), keypoints );
+        EXPECT_TRUE(fs.isOpened()) << "Keypoint testdata is missing. Re-computing and re-writing keypoints testdata...";
+        if (!fs.isOpened())
+        {
+            fs.open(keypoints_filename, FileStorage::WRITE);
+            ASSERT_TRUE(fs.isOpened()) << "File for writting keypoints can not be opened.";
+            if (detector.empty())
+            {
+                Ptr<ORB> fd = ORB::create();
+                fd->detect(img, keypoints);
+            }
+            else
+            {
+                detector->detect(img, keypoints);
+            }
+            write(fs, "keypoints", keypoints);
+            fs.release();
         }
-        if(!keypoints.empty())
+        else
+        {
+            read(fs.getFirstTopLevelNode(), keypoints);
+            fs.release();
+        }
+
+        if(!detector.empty())
+        {
+            vector<KeyPoint> calcKeypoints;
+            detector->detect(img, calcKeypoints);
+            // TODO validate received keypoints
+            int diff = abs((int)calcKeypoints.size() - (int)keypoints.size());
+            if (diff > 0)
+            {
+                std::cout << "Keypoints difference: " << diff << std::endl;
+                EXPECT_LE(diff, (int)(keypoints.size() * 0.03f));
+            }
+        }
+        ASSERT_FALSE(keypoints.empty());
         {
             Mat calcDescriptors;
             double t = (double)getTickCount();
-            dextractor->compute( img, keypoints, calcDescriptors );
+            dextractor->compute(img, keypoints, calcDescriptors);
             t = getTickCount() - t;
             ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)getTickFrequency()*1000.)/calcDescriptors.rows);
 
-            if( calcDescriptors.rows != (int)keypoints.size() )
+            if (calcDescriptors.rows != (int)keypoints.size())
             {
                 ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
                 ts->printf( cvtest::TS::LOG, "Count of keypoints is            %d.\n", (int)keypoints.size() );
@@ -226,7 +276,7 @@ protected:
                 return;
             }
 
-            if( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() )
+            if (calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType())
             {
                 ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
                 ts->printf( cvtest::TS::LOG, "Expected size is   %d.\n", dextractor->descriptorSize() );
@@ -239,33 +289,14 @@ protected:
 
             // TODO read and write descriptor extractor parameters and check them
             Mat validDescriptors = readDescriptors();
-            if( !validDescriptors.empty() )
-                compareDescriptors( validDescriptors, calcDescriptors );
-            else
+            EXPECT_FALSE(validDescriptors.empty()) << "Descriptors testdata is missing. Re-writing descriptors testdata...";
+            if (!validDescriptors.empty())
             {
-                if( !writeDescriptors( calcDescriptors ) )
-                {
-                    ts->printf( cvtest::TS::LOG, "Descriptors can not be written.\n" );
-                    ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
-                    return;
-                }
-            }
-        }
-        if(!fs.isOpened())
-        {
-            ts->printf( cvtest::TS::LOG, "Compute and write keypoints.\n" );
-            fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE );
-            if( fs.isOpened() )
-            {
-                Ptr<ORB> fd = ORB::create();
-                fd->detect(img, keypoints);
-                write( fs, "keypoints", keypoints );
+                compareDescriptors(validDescriptors, calcDescriptors);
             }
             else
             {
-                ts->printf(cvtest::TS::LOG, "File for writting keypoints can not be opened.\n");
-                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
-                return;
+                ASSERT_TRUE(writeDescriptors(calcDescriptors)) << "Descriptors can not be written.";
             }
         }
     }
@@ -344,7 +375,7 @@ TEST( Features2d_DescriptorExtractor_KAZE, regression )
 TEST( Features2d_DescriptorExtractor_AKAZE, regression )
 {
     CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",
-                                              (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
+                                              (CV_DescriptorExtractorTest<Hamming>::DistanceType)(486*0.05f),
                                               AKAZE::create(),
                                               Hamming(), AKAZE::create());
     test.safe_run();

modules/features2d/test/test_detectors_invariance.cpp

@@ -11,7 +11,7 @@ using std::tr1::make_tuple;
 using std::tr1::get;
 using namespace testing;
 
-#define SHOW_DEBUG_LOG 0
+#define SHOW_DEBUG_LOG 1
 
 typedef std::tr1::tuple<std::string, Ptr<FeatureDetector>, float, float> String_FeatureDetector_Float_Float_t;
 const static std::string IMAGE_TSUKUBA = "features2d/tsukuba.png";

modules/photo/src/fast_nlmeans_denoising_opencl.hpp

@@ -23,11 +23,6 @@ enum
     CTA_SIZE_DEFAULT = 256
 };
 
-static int divUp(int a, int b)
-{
-    return (a + b - 1) / b;
-}
-
 template <typename FT, typename ST, typename WT>
 static bool ocl_calcAlmostDist2Weight(UMat & almostDist2Weight,
                                       int searchWindowSize, int templateWindowSize,

modules/stitching/perf/perf_stich.cpp

@@ -102,7 +102,7 @@ PERF_TEST_P(stitchDatasets, affine, testing::Combine(AFFINE_DATASETS, TEST_DETEC
 
     Mat pano;
     vector<Mat> imgs;
-    int width, height, allowed_diff = 10;
+    int width, height, allowed_diff = 20;
     Ptr<detail::FeaturesFinder> featuresFinder = getFeatureFinder(detector);
 
     if(dataset == "budapest")
@@ -117,7 +117,7 @@ PERF_TEST_P(stitchDatasets, affine, testing::Combine(AFFINE_DATASETS, TEST_DETEC
         height = 1158;
         // this dataset is big, the results between surf and orb differ slightly,
         // but both are still good
-        allowed_diff = 27;
+        allowed_diff = 50;
     }
     else if (dataset == "newspaper")
     {