Merge pull request #10171 from ElenaGvozdeva:Threshold

modules/imgproc/include/opencv2/imgproc.hpp

@@ -2805,7 +2805,8 @@ The function can process the image in-place.
 @param src Source 8-bit single-channel image.
 @param dst Destination image of the same size and the same type as src.
 @param maxValue Non-zero value assigned to the pixels for which the condition is satisfied
-@param adaptiveMethod Adaptive thresholding algorithm to use, see cv::AdaptiveThresholdTypes
+@param adaptiveMethod Adaptive thresholding algorithm to use, see cv::AdaptiveThresholdTypes.
+The BORDER_REPLICATE | BORDER_ISOLATED is used to process boundaries.
 @param thresholdType Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV,
 see cv::ThresholdTypes.
 @param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the

modules/imgproc/include/opencv2/imgproc/hal/interface.h

@@ -21,6 +21,26 @@
 #define CV_HAL_MORPH_DILATE 1
 //! @}
 
+//! @name Threshold types
+//! @sa cv::ThresholdTypes
+//! @{
+#define CV_HAL_THRESH_BINARY      0
+#define CV_HAL_THRESH_BINARY_INV  1
+#define CV_HAL_THRESH_TRUNC       2
+#define CV_HAL_THRESH_TOZERO      3
+#define CV_HAL_THRESH_TOZERO_INV  4
+#define CV_HAL_THRESH_MASK        7
+#define CV_HAL_THRESH_OTSU        8
+#define CV_HAL_THRESH_TRIANGLE    16
+//! @}
+
+//! @name Adaptive threshold algorithm
+//! @sa cv::AdaptiveThresholdTypes
+//! @{
+#define CV_HAL_ADAPTIVE_THRESH_MEAN_C     0
+#define CV_HAL_ADAPTIVE_THRESH_GAUSSIAN_C 1
+//! @}
+
 //! @}
 
 #endif

modules/imgproc/perf/perf_threshold.cpp

@@ -60,15 +60,16 @@ PERF_TEST_P(Size_Only, threshold_otsu, testing::Values(TYPICAL_MAT_SIZES))
 CV_ENUM(AdaptThreshType, THRESH_BINARY, THRESH_BINARY_INV)
 CV_ENUM(AdaptThreshMethod, ADAPTIVE_THRESH_MEAN_C, ADAPTIVE_THRESH_GAUSSIAN_C)
 
-typedef std::tr1::tuple<Size, AdaptThreshType, AdaptThreshMethod, int> Size_AdaptThreshType_AdaptThreshMethod_BlockSize_t;
-typedef perf::TestBaseWithParam<Size_AdaptThreshType_AdaptThreshMethod_BlockSize_t> Size_AdaptThreshType_AdaptThreshMethod_BlockSize;
+typedef std::tr1::tuple<Size, AdaptThreshType, AdaptThreshMethod, int, double> Size_AdaptThreshType_AdaptThreshMethod_BlockSize_Delta_t;
+typedef perf::TestBaseWithParam<Size_AdaptThreshType_AdaptThreshMethod_BlockSize_Delta_t> Size_AdaptThreshType_AdaptThreshMethod_BlockSize_Delta;
 
-PERF_TEST_P(Size_AdaptThreshType_AdaptThreshMethod_BlockSize, adaptiveThreshold,
+PERF_TEST_P(Size_AdaptThreshType_AdaptThreshMethod_BlockSize_Delta, adaptiveThreshold,
             testing::Combine(
                 testing::Values(TYPICAL_MAT_SIZES),
                 AdaptThreshType::all(),
                 AdaptThreshMethod::all(),
-                testing::Values(3, 5)
+                testing::Values(3, 5),
+                testing::Values(0.0, 10.0)
                 )
             )
 {
@@ -76,12 +77,14 @@ PERF_TEST_P(Size_AdaptThreshType_AdaptThreshMethod_BlockSize, adaptiveThreshold,
     AdaptThreshType adaptThreshType = get<1>(GetParam());
     AdaptThreshMethod adaptThreshMethod = get<2>(GetParam());
     int blockSize = get<3>(GetParam());
+    double C = get<4>(GetParam());
 
     double maxValue = theRNG().uniform(1, 254);
-    double C = 10.0;
 
     int type = CV_8UC1;
-    Mat src(sz, type);
+
+    Mat src_full(cv::Size(sz.width + 2, sz.height + 2), type);
+    Mat src = src_full(cv::Rect(1, 1, sz.width, sz.height));
     Mat dst(sz, type);
 
     declare.in(src, WARMUP_RNG).out(dst);

modules/imgproc/src/hal_replacement.hpp

@@ -630,6 +630,40 @@ inline int hal_ni_medianBlur(const uchar* src_data, size_t src_step, uchar* dst_
 #define cv_hal_medianBlur hal_ni_medianBlur
 //! @endcond
 
+/**
+   @brief Calculates adaptive threshold
+   @param src_data,src_step Source image
+   @param dst_data,dst_step Destination image
+   @param width,height Source image dimensions
+   @param maxValue Value assigned to the pixels for which the condition is satisfied
+   @param adaptiveMethod Adaptive thresholding algorithm
+   @param thresholdType Thresholding type
+   @param blockSize Size of a pixel neighborhood that is used to calculate a threshold value
+   @param C Constant subtracted from the mean or weighted mean
+*/
+inline int hal_ni_adaptiveThreshold(const uchar* src_data, size_t src_step, uchar* dst_data, size_t dst_step, int width, int height, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C) { return CV_HAL_ERROR_NOT_IMPLEMENTED; }
+
+//! @cond IGNORED
+#define cv_hal_adaptiveThreshold  hal_ni_adaptiveThreshold
+//! @endcond
+
+/**
+   @brief Calculates fixed-level threshold to each array element
+   @param src_data,src_step Source image
+   @param dst_data,dst_step Destination image
+   @param width,height Source image dimensions
+   @param depth Depths of source and destination image
+   @param cn Number of channels
+   @param thresh Threshold value
+   @param maxValue Value assigned to the pixels for which the condition is satisfied
+   @param thresholdType Thresholding type
+*/
+inline int hal_ni_threshold(const uchar* src_data, size_t src_step, uchar* dst_data, size_t dst_step, int width, int height, int depth, int cn, double thresh, double maxValue, int thresholdType) { return CV_HAL_ERROR_NOT_IMPLEMENTED; }
+
+//! @cond IGNORED
+#define cv_hal_threshold hal_ni_threshold
+//! @endcond
+
 //! @}
 
 #if defined __GNUC__

modules/imgproc/src/thresh.cpp

@@ -1214,6 +1214,10 @@ public:
         Mat srcStripe = src.rowRange(row0, row1);
         Mat dstStripe = dst.rowRange(row0, row1);
 
+        CALL_HAL(threshold, cv_hal_threshold, srcStripe.data, srcStripe.step, dstStripe.data, dstStripe.step,
+                 srcStripe.cols, srcStripe.rows, srcStripe.depth(), srcStripe.channels(),
+                 thresh, maxval, thresholdType);
+
         if (srcStripe.depth() == CV_8U)
         {
             thresh_8u( srcStripe, dstStripe, (uchar)thresh, (uchar)maxval, thresholdType );
@@ -1530,6 +1534,9 @@ void cv::adaptiveThreshold( InputArray _src, OutputArray _dst, double maxValue,
         return;
     }
 
+    CALL_HAL(adaptiveThreshold, cv_hal_adaptiveThreshold, src.data, src.step, dst.data, dst.step, src.cols, src.rows,
+             maxValue, method, type, blockSize, delta);
+
     Mat mean;
 
     if( src.data != dst.data )
@@ -1537,13 +1544,13 @@ void cv::adaptiveThreshold( InputArray _src, OutputArray _dst, double maxValue,
 
     if (method == ADAPTIVE_THRESH_MEAN_C)
         boxFilter( src, mean, src.type(), Size(blockSize, blockSize),
-                   Point(-1,-1), true, BORDER_REPLICATE );
+                   Point(-1,-1), true, BORDER_REPLICATE|BORDER_ISOLATED );
     else if (method == ADAPTIVE_THRESH_GAUSSIAN_C)
     {
         Mat srcfloat,meanfloat;
         src.convertTo(srcfloat,CV_32F);
         meanfloat=srcfloat;
-        GaussianBlur(srcfloat, meanfloat, Size(blockSize, blockSize), 0, 0, BORDER_REPLICATE);
+        GaussianBlur(srcfloat, meanfloat, Size(blockSize, blockSize), 0, 0, BORDER_REPLICATE|BORDER_ISOLATED);
         meanfloat.convertTo(mean, src.type());
     }
     else