Merge pull request #3360 from mirab:threshold_triangle

modules/cudaarithm/doc/element_operations.rst

@@ -430,7 +430,7 @@ Applies a fixed-level threshold to each array element.
 
     :param maxval: Maximum value to use with  ``THRESH_BINARY`` and  ``THRESH_BINARY_INV`` threshold types.
 
-    :param type: Threshold type. For details, see  :ocv:func:`threshold` . The ``THRESH_OTSU`` threshold type is not supported.
+    :param type: Threshold type. For details, see  :ocv:func:`threshold` . The ``THRESH_OTSU`` and ``THRESH_TRIANGLE`` threshold types are not supported.
 
     :param stream: Stream for the asynchronous version.
 

modules/imgproc/doc/miscellaneous_transformations.rst

@@ -712,11 +712,11 @@ types of thresholding supported by the function. They are determined by ``type``
 
               \texttt{dst} (x,y) =  \fork{0}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{src}(x,y)}{otherwise}
 
-Also, the special value ``THRESH_OTSU`` may be combined with
-one of the above values. In this case, the function determines the optimal threshold
-value using the Otsu's algorithm and uses it instead of the specified ``thresh`` .
+Also, the special values ``THRESH_OTSU`` or ``THRESH_TRIANGLE`` may be combined with
+one of the above values. In these cases, the function determines the optimal threshold
+value using the Otsu's or Triangle algorithm and uses it instead of the specified ``thresh`` .
 The function returns the computed threshold value.
-Currently, the Otsu's method is implemented only for 8-bit images.
+Currently, the Otsu's and Triangle methods are implemented only for 8-bit images.
 
 
 .. image:: pics/threshold.png

modules/imgproc/include/opencv2/imgproc.hpp

@@ -109,7 +109,8 @@ enum { THRESH_BINARY     = 0, // value = value > threshold ? max_value : 0
        THRESH_TOZERO     = 3, // value = value > threshold ? value : 0
        THRESH_TOZERO_INV = 4, // value = value > threshold ? 0 : value
        THRESH_MASK       = 7,
-       THRESH_OTSU       = 8  // use Otsu algorithm to choose the optimal threshold value
+       THRESH_OTSU       = 8, // use Otsu algorithm to choose the optimal threshold value
+       THRESH_TRIANGLE   = 16  // use Triangle algorithm to choose the optimal threshold value
      };
 
 //! adaptive threshold algorithm

modules/imgproc/include/opencv2/imgproc/types_c.h

@@ -551,8 +551,11 @@ enum
     CV_THRESH_TOZERO      =3,  /* value = value > threshold ? value : 0           */
     CV_THRESH_TOZERO_INV  =4,  /* value = value > threshold ? 0 : value           */
     CV_THRESH_MASK        =7,
-    CV_THRESH_OTSU        =8  /* use Otsu algorithm to choose the optimal threshold value;
+    CV_THRESH_OTSU        =8, /* use Otsu algorithm to choose the optimal threshold value;
                                  combine the flag with one of the above CV_THRESH_* values */
+    CV_THRESH_TRIANGLE    =16  /* use Triangle algorithm to choose the optimal threshold value;
+                                 combine the flag with one of the above CV_THRESH_* values, but not
+                                 with CV_THRESH_OTSU */
 };
 
 /* Adaptive threshold methods */

modules/imgproc/src/thresh.cpp

@@ -986,6 +986,110 @@ getThreshVal_Otsu_8u( const Mat& _src )
     return max_val;
 }
 
+static double
+getThreshVal_Triangle_8u( const Mat& _src )
+{
+    Size size = _src.size();
+    int step = (int) _src.step;
+    if( _src.isContinuous() )
+    {
+        size.width *= size.height;
+        size.height = 1;
+        step = size.width;
+    }
+
+    const int N = 256;
+    int i, j, h[N] = {0};
+    for( i = 0; i < size.height; i++ )
+    {
+        const uchar* src = _src.ptr() + step*i;
+        j = 0;
+        #if CV_ENABLE_UNROLLED
+        for( ; j <= size.width - 4; j += 4 )
+        {
+            int v0 = src[j], v1 = src[j+1];
+            h[v0]++; h[v1]++;
+            v0 = src[j+2]; v1 = src[j+3];
+            h[v0]++; h[v1]++;
+        }
+        #endif
+        for( ; j < size.width; j++ )
+            h[src[j]]++;
+    }
+
+    int left_bound = 0, right_bound = 0, max_ind = 0, max = 0;
+    int temp;
+    bool isflipped = false;
+
+    for( i = 0; i < N; i++ )
+    {
+        if( h[i] > 0 )
+        {
+            left_bound = i;
+            break;
+        }
+    }
+    if( left_bound > 0 )
+        left_bound--;
+
+    for( i = N-1; i > 0; i-- )
+    {
+        if( h[i] > 0 )
+        {
+            right_bound = i;
+            break;
+        }
+    }
+    if( right_bound < N-1 )
+        right_bound++;
+
+    for( i = 0; i < N; i++ )
+    {
+        if( h[i] > max)
+        {
+            max = h[i];
+            max_ind = i;
+        }
+    }
+
+    if( max_ind-left_bound < right_bound-max_ind)
+    {
+        isflipped = true;
+        i = 0, j = N-1;
+        while( i < j )
+        {
+            temp = h[i]; h[i] = h[j]; h[j] = temp;
+            i++; j--;
+        }
+        left_bound = N-1-right_bound;
+        max_ind = N-1-max_ind;
+    }
+
+    double thresh = left_bound;
+    double a, b, dist = 0, tempdist;
+
+    /*
+     * We do not need to compute precise distance here. Distance is maximized, so some constants can
+     * be omitted. This speeds up a computation a bit.
+     */
+    a = max; b = left_bound-max_ind;
+    for( i = left_bound+1; i <= max_ind; i++ )
+    {
+        tempdist = a*i + b*h[i];
+        if( tempdist > dist)
+        {
+            dist = tempdist;
+            thresh = i;
+        }
+    }
+    thresh--;
+
+    if( isflipped )
+        thresh = N-1-thresh;
+
+    return thresh;
+}
+
 class ThresholdRunner : public ParallelLoopBody
 {
 public:
@@ -1085,13 +1189,19 @@ double cv::threshold( InputArray _src, OutputArray _dst, double thresh, double m
                 ocl_threshold(_src, _dst, thresh, maxval, type), thresh)
 
     Mat src = _src.getMat();
-    bool use_otsu = (type & THRESH_OTSU) != 0;
+    int automatic_thresh = (type & ~CV_THRESH_MASK);
     type &= THRESH_MASK;
 
-    if( use_otsu )
+    CV_Assert( automatic_thresh != (CV_THRESH_OTSU | CV_THRESH_TRIANGLE) );
+    if( automatic_thresh == CV_THRESH_OTSU )
+    {
+        CV_Assert( src.type() == CV_8UC1 );
+        thresh = getThreshVal_Otsu_8u( src );
+    }
+    else if( automatic_thresh == CV_THRESH_TRIANGLE )
     {
         CV_Assert( src.type() == CV_8UC1 );
-        thresh = getThreshVal_Otsu_8u(src);
+        thresh = getThreshVal_Triangle_8u( src );
     }
 
     _dst.create( src.size(), src.type() );