OpenCV 3.4.3

tested with NDK 17b (17.1.4828580)

CMakeLists.txt

@@ -276,7 +276,7 @@ OCV_OPTION(WITH_VA             "Include VA support"                          OFF
 OCV_OPTION(WITH_VA_INTEL       "Include Intel VA-API/OpenCL support"         OFF  IF (UNIX AND NOT ANDROID) )
 OCV_OPTION(WITH_MFX            "Include Intel Media SDK support"             OFF   IF ((UNIX AND NOT ANDROID) OR (WIN32 AND NOT WINRT AND NOT MINGW)) )
 OCV_OPTION(WITH_GDAL           "Include GDAL Support"                        OFF  IF (NOT ANDROID AND NOT IOS AND NOT WINRT) )
-OCV_OPTION(WITH_GPHOTO2        "Include gPhoto2 library support"             ON   IF (UNIX AND NOT ANDROID AND NOT IOS) )
+OCV_OPTION(WITH_GPHOTO2        "Include gPhoto2 library support"             OFF  IF (UNIX AND NOT ANDROID AND NOT IOS) )
 OCV_OPTION(WITH_LAPACK         "Include Lapack library support"              (NOT CV_DISABLE_OPTIMIZATION)  IF (NOT ANDROID AND NOT IOS) )
 OCV_OPTION(WITH_ITT            "Include Intel ITT support"                   ON   IF (NOT APPLE_FRAMEWORK) )
 OCV_OPTION(WITH_PROTOBUF       "Enable libprotobuf"                          ON )

cmake/OpenCVFindVA.cmake

@@ -12,7 +12,9 @@ endif()
 
 if(VA_INCLUDE_DIR)
     set(HAVE_VA TRUE)
-    set(VA_LIBRARIES "-lva" "-lva-drm")
+    if(NOT DEFINED VA_LIBRARIES)
+      set(VA_LIBRARIES "va" "va-drm")
+    endif()
 else()
     set(HAVE_VA FALSE)
     message(WARNING "libva installation is not found.")

doc/tutorials/core/basic_linear_transform/basic_linear_transform.markdown

@@ -1,6 +1,9 @@
 Changing the contrast and brightness of an image! {#tutorial_basic_linear_transform}
 =================================================
 
+@prev_tutorial{tutorial_adding_images}
+@next_tutorial{tutorial_discrete_fourier_transform}
+
 Goal
 ----
 

doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.markdown

@@ -1,7 +1,7 @@
 Discrete Fourier Transform {#tutorial_discrete_fourier_transform}
 ==========================
 
-@prev_tutorial{tutorial_random_generator_and_text}
+@prev_tutorial{tutorial_basic_linear_transform}
 @next_tutorial{tutorial_file_input_output_with_xml_yml}
 
 Goal

doc/tutorials/core/file_input_output_with_xml_yml/file_input_output_with_xml_yml.markdown

@@ -1,6 +1,9 @@
 File Input and Output using XML and YAML files {#tutorial_file_input_output_with_xml_yml}
 ==============================================
 
+@prev_tutorial{tutorial_discrete_fourier_transform}
+@next_tutorial{tutorial_interoperability_with_OpenCV_1}
+
 Goal
 ----
 

doc/tutorials/core/how_to_scan_images/how_to_scan_images.markdown

@@ -1,6 +1,9 @@
 How to scan images, lookup tables and time measurement with OpenCV {#tutorial_how_to_scan_images}
 ==================================================================
 
+@prev_tutorial{tutorial_mat_the_basic_image_container}
+@next_tutorial{tutorial_mat_mask_operations}
+
 Goal
 ----
 

doc/tutorials/core/how_to_use_OpenCV_parallel_for_/how_to_use_OpenCV_parallel_for_.markdown

@@ -1,6 +1,8 @@
 How to use the OpenCV parallel_for_ to parallelize your code {#tutorial_how_to_use_OpenCV_parallel_for_}
 ==================================================================
 
+@prev_tutorial{tutorial_how_to_use_ippa_conversion}
+
 Goal
 ----
 

doc/tutorials/core/how_to_use_ippa_conversion/how_to_use_ippa_conversion.markdown

@@ -1,6 +1,9 @@
 Intel® IPP Asynchronous C/C++ library in OpenCV {#tutorial_how_to_use_ippa_conversion}
 ===============================================
 
+@prev_tutorial{tutorial_interoperability_with_OpenCV_1}
+@next_tutorial{tutorial_how_to_use_OpenCV_parallel_for_}
+
 Goal
 ----
 

doc/tutorials/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.markdown

@@ -1,6 +1,9 @@
 Interoperability with OpenCV 1 {#tutorial_interoperability_with_OpenCV_1}
 ==============================
 
+@prev_tutorial{tutorial_file_input_output_with_xml_yml}
+@next_tutorial{tutorial_how_to_use_ippa_conversion}
+
 Goal
 ----
 

doc/tutorials/core/mat_operations.markdown

@@ -1,6 +1,9 @@
 Operations with images {#tutorial_mat_operations}
 ======================
 
+@prev_tutorial{tutorial_mat_mask_operations}
+@next_tutorial{tutorial_adding_images}
+
 Input/Output
 ------------
 

doc/tutorials/core/mat_the_basic_image_container/mat_the_basic_image_container.markdown

@@ -1,6 +1,8 @@
 Mat - The Basic Image Container {#tutorial_mat_the_basic_image_container}
 ===============================
 
+@next_tutorial{tutorial_how_to_scan_images}
+
 Goal
 ----
 

doc/tutorials/core/table_of_content_core.markdown

@@ -62,24 +62,6 @@ understanding how to manipulate the images on a pixel level.
 
     We will learn how to change our image appearance!
 
--   @subpage tutorial_basic_geometric_drawing
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    We will learn how to draw simple geometry with OpenCV!
-
--   @subpage tutorial_random_generator_and_text
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    We will draw some *fancy-looking* stuff using OpenCV!
-
 -   @subpage tutorial_discrete_fourier_transform
 
     *Languages:* C++, Java, Python

doc/tutorials/imgproc/basic_geometric_drawing/basic_geometric_drawing.markdown

@@ -1,7 +1,6 @@
 Basic Drawing {#tutorial_basic_geometric_drawing}
 =============
 
-@prev_tutorial{tutorial_basic_linear_transform}
 @next_tutorial{tutorial_random_generator_and_text}
 
 Goals
@@ -82,20 +81,20 @@ Code
 
 @add_toggle_cpp
 -   This code is in your OpenCV sample folder. Otherwise you can grab it from
-    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp)
+    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp)
-    @include samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp
+    @include samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp
 @end_toggle
 
 @add_toggle_java
 -   This code is in your OpenCV sample folder. Otherwise you can grab it from
-    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java)
+    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java)
-    @include samples/java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java
+    @include samples/java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java
 @end_toggle
 
 @add_toggle_python
 -   This code is in your OpenCV sample folder. Otherwise you can grab it from
-    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py)
+    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py)
-    @include samples/python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py
+    @include samples/python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py
 @end_toggle
 
 Explanation
@@ -104,42 +103,42 @@ Explanation
 Since we plan to draw two examples (an atom and a rook), we have to create two images and two
 windows to display them.
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp create_images
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp create_images
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java create_images
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java create_images
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py create_images
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py create_images
 @end_toggle
 
 We created functions to draw different geometric shapes. For instance, to draw the atom we used
 **MyEllipse** and **MyFilledCircle**:
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp draw_atom
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp draw_atom
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java draw_atom
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java draw_atom
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py draw_atom
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py draw_atom
 @end_toggle
 
 And to draw the rook we employed **MyLine**, **rectangle** and a **MyPolygon**:
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp draw_rook
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp draw_rook
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java draw_rook
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java draw_rook
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py draw_rook
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py draw_rook
 @end_toggle
 
 
@@ -149,15 +148,15 @@ Let's check what is inside each of these functions:
 
 <H4>MyLine</H4>
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_line
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_line
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_line
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_line
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_line
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_line
 @end_toggle
 
 -   As we can see, **MyLine** just call the function **line()** , which does the following:
@@ -170,15 +169,15 @@ Let's check what is inside each of these functions:
 
 <H4>MyEllipse</H4>
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_ellipse
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_ellipse
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_ellipse
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_ellipse
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_ellipse
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_ellipse
 @end_toggle
 
 -   From the code above, we can observe that the function **ellipse()** draws an ellipse such
@@ -194,15 +193,15 @@ Let's check what is inside each of these functions:
 
 <H4>MyFilledCircle</H4>
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_filled_circle
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_filled_circle
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_filled_circle
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_filled_circle
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_filled_circle
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_filled_circle
 @end_toggle
 
 -   Similar to the ellipse function, we can observe that *circle* receives as arguments:
@@ -215,15 +214,15 @@ Let's check what is inside each of these functions:
 
 <H4>MyPolygon</H4>
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_polygon
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_polygon
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_polygon
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_polygon
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_polygon
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_polygon
 @end_toggle
 
 -   To draw a filled polygon we use the function **fillPoly()** . We note that:
@@ -235,15 +234,15 @@ Let's check what is inside each of these functions:
 
 <H4>rectangle</H4>
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp rectangle
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp rectangle
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java rectangle
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java rectangle
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py rectangle
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py rectangle
 @end_toggle
 
 -   Finally we have the @ref cv::rectangle function (we did not create a special function for

doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.markdown

@@ -1,6 +1,9 @@
 Eroding and Dilating {#tutorial_erosion_dilatation}
 ====================
 
+@prev_tutorial{tutorial_gausian_median_blur_bilateral_filter}
+@next_tutorial{tutorial_opening_closing_hats}
+
 Goal
 ----
 

doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/gausian_median_blur_bilateral_filter.markdown

@@ -1,6 +1,7 @@
 Smoothing Images {#tutorial_gausian_median_blur_bilateral_filter}
 ================
 
+@prev_tutorial{tutorial_random_generator_and_text}
 @next_tutorial{tutorial_erosion_dilatation}
 
 Goal

doc/tutorials/imgproc/histograms/back_projection/back_projection.markdown

@@ -1,6 +1,9 @@
 Back Projection {#tutorial_back_projection}
 ===============
 
+@prev_tutorial{tutorial_histogram_comparison}
+@next_tutorial{tutorial_template_matching}
+
 Goal
 ----
 

doc/tutorials/imgproc/histograms/histogram_calculation/histogram_calculation.markdown

@@ -1,6 +1,9 @@
 Histogram Calculation {#tutorial_histogram_calculation}
 =====================
 
+@prev_tutorial{tutorial_histogram_equalization}
+@next_tutorial{tutorial_histogram_comparison}
+
 Goal
 ----
 

doc/tutorials/imgproc/histograms/histogram_comparison/histogram_comparison.markdown

@@ -1,6 +1,9 @@
 Histogram Comparison {#tutorial_histogram_comparison}
 ====================
 
+@prev_tutorial{tutorial_histogram_calculation}
+@next_tutorial{tutorial_back_projection}
+
 Goal
 ----
 

doc/tutorials/imgproc/histograms/histogram_equalization/histogram_equalization.markdown

@@ -1,6 +1,9 @@
 Histogram Equalization {#tutorial_histogram_equalization}
 ======================
 
+@prev_tutorial{tutorial_warp_affine}
+@next_tutorial{tutorial_histogram_calculation}
+
 Goal
 ----
 

doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.markdown

@@ -1,6 +1,9 @@
 Canny Edge Detector {#tutorial_canny_detector}
 ===================
 
+@prev_tutorial{tutorial_laplace_operator}
+@next_tutorial{tutorial_hough_lines}
+
 Goal
 ----
 

doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown

@@ -1,6 +1,9 @@
 Image Segmentation with Distance Transform and Watershed Algorithm {#tutorial_distance_transform}
 =============
 
+@prev_tutorial{tutorial_point_polygon_test}
+@next_tutorial{tutorial_out_of_focus_deblur_filter}
+
 Goal
 ----
 

doc/tutorials/imgproc/imgtrans/remap/remap.markdown

@@ -1,6 +1,9 @@
 Remapping {#tutorial_remap}
 =========
 
+@prev_tutorial{tutorial_hough_circle}
+@next_tutorial{tutorial_warp_affine}
+
 Goal
 ----
 

doc/tutorials/imgproc/imgtrans/warp_affine/warp_affine.markdown

@@ -1,6 +1,9 @@
 Affine Transformations {#tutorial_warp_affine}
 ======================
 
+@prev_tutorial{tutorial_remap}
+@next_tutorial{tutorial_histogram_equalization}
+
 Goal
 ----
 

doc/tutorials/imgproc/opening_closing_hats/opening_closing_hats.markdown

@@ -1,6 +1,9 @@
 More Morphology Transformations {#tutorial_opening_closing_hats}
 ===============================
 
+@prev_tutorial{tutorial_erosion_dilatation}
+@next_tutorial{tutorial_hitOrMiss}
+
 Goal
 ----
 

doc/tutorials/imgproc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.markdown

@@ -1,6 +1,8 @@
 Out-of-focus Deblur Filter {#tutorial_out_of_focus_deblur_filter}
 ==========================
 
+@prev_tutorial{tutorial_distance_transform}
+
 Goal
 ----
 

doc/tutorials/imgproc/random_generator_and_text/random_generator_and_text.markdown

@@ -1,6 +1,9 @@
 Random generator and text with OpenCV {#tutorial_random_generator_and_text}
 =====================================
 
+@prev_tutorial{tutorial_basic_geometric_drawing}
+@next_tutorial{tutorial_gausian_median_blur_bilateral_filter}
+
 Goals
 -----
 

doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.markdown

@@ -1,6 +1,9 @@
 Creating Bounding boxes and circles for contours {#tutorial_bounding_rects_circles}
 ================================================
 
+@prev_tutorial{tutorial_hull}
+@next_tutorial{tutorial_bounding_rotated_ellipses}
+
 Goal
 ----
 

doc/tutorials/imgproc/shapedescriptors/bounding_rotated_ellipses/bounding_rotated_ellipses.markdown

@@ -1,6 +1,9 @@
 Creating Bounding rotated boxes and ellipses for contours {#tutorial_bounding_rotated_ellipses}
 =========================================================
 
+@prev_tutorial{tutorial_bounding_rects_circles}
+@next_tutorial{tutorial_moments}
+
 Goal
 ----
 

doc/tutorials/imgproc/shapedescriptors/find_contours/find_contours.markdown

@@ -1,6 +1,9 @@
 Finding contours in your image {#tutorial_find_contours}
 ==============================
 
+@prev_tutorial{tutorial_template_matching}
+@next_tutorial{tutorial_hull}
+
 Goal
 ----
 

doc/tutorials/imgproc/shapedescriptors/hull/hull.markdown

@@ -1,6 +1,9 @@
 Convex Hull {#tutorial_hull}
 ===========
 
+@prev_tutorial{tutorial_find_contours}
+@next_tutorial{tutorial_bounding_rects_circles}
+
 Goal
 ----
 

doc/tutorials/imgproc/shapedescriptors/moments/moments.markdown

@@ -1,6 +1,9 @@
 Image Moments {#tutorial_moments}
 =============
 
+@prev_tutorial{tutorial_bounding_rotated_ellipses}
+@next_tutorial{tutorial_point_polygon_test}
+
 Goal
 ----
 

doc/tutorials/imgproc/shapedescriptors/point_polygon_test/point_polygon_test.markdown

@@ -1,6 +1,9 @@
 Point Polygon Test {#tutorial_point_polygon_test}
 ==================
 
+@prev_tutorial{tutorial_moments}
+@next_tutorial{tutorial_distance_transform}
+
 Goal
 ----
 

doc/tutorials/imgproc/table_of_content_imgproc.markdown

@@ -3,6 +3,24 @@ Image Processing (imgproc module) {#tutorial_table_of_content_imgproc}
 
 In this section you will learn about the image processing (manipulation) functions inside OpenCV.
 
+-   @subpage tutorial_basic_geometric_drawing
+
+    *Languages:* C++, Java, Python
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Ana Huamán
+
+    We will learn how to draw simple geometry with OpenCV!
+
+-   @subpage tutorial_random_generator_and_text
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Ana Huamán
+
+    We will draw some *fancy-looking* stuff using OpenCV!
+
 -   @subpage tutorial_gausian_median_blur_bilateral_filter
 
     *Languages:* C++, Java, Python

doc/tutorials/imgproc/threshold/threshold.markdown

@@ -1,6 +1,9 @@
 Basic Thresholding Operations {#tutorial_threshold}
 =============================
 
+@prev_tutorial{tutorial_pyramids}
+@next_tutorial{tutorial_threshold_inRange}
+
 Goal
 ----
 

doc/tutorials/imgproc/threshold_inRange/threshold_inRange.markdown

@@ -1,6 +1,9 @@
 Thresholding Operations using inRange {#tutorial_threshold_inRange}
 =====================================
 
+@prev_tutorial{tutorial_threshold}
+@next_tutorial{tutorial_filter_2d}
+
 Goal
 ----
 

doc/tutorials/stitching/stitcher/stitcher.markdown

@@ -24,17 +24,7 @@ Explanation
 
 The most important code part is:
 
-@code{.cpp}
+@snippet cpp/stitching.cpp stitching
-Mat pano;
-Ptr<Stitcher> stitcher = Stitcher::create(mode, try_use_gpu);
-Stitcher::Status status = stitcher->stitch(imgs, pano);
-
-if (status != Stitcher::OK)
-{
-    cout << "Can't stitch images, error code = " << int(status) << endl;
-    return -1;
-}
-@endcode
 
 A new instance of stitcher is created and the @ref cv::Stitcher::stitch will
 do all the hard work.

doc/tutorials/tutorials.markdown

@@ -15,7 +15,7 @@ As always, we would be happy to hear your comments and receive your contribution
 -   @subpage tutorial_table_of_content_core
 
     Here you will learn
-    the about the basic building blocks of this library. A must read for understanding how
+    about the basic building blocks of this library. A must read for understanding how
     to manipulate the images on a pixel level.
 
 -   @subpage tutorial_table_of_content_imgproc

modules/calib3d/include/opencv2/calib3d.hpp

@@ -118,7 +118,7 @@ v = f_y*y'' + c_y
 tangential distortion coefficients. \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$, are the thin prism distortion
 coefficients. Higher-order coefficients are not considered in OpenCV.
 
-The next figure shows two common types of radial distortion: barrel distortion (typically \f$ k_1 > 0 \f$ and pincushion distortion (typically \f$ k_1 < 0 \f$).
+The next figure shows two common types of radial distortion: barrel distortion (typically \f$ k_1 > 0 \f$) and pincushion distortion (typically \f$ k_1 < 0 \f$).
 
 ![](pics/distortion_examples.png)
 
@@ -307,11 +307,11 @@ optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP .
  */
 CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() );
 
-/** @example pose_from_homography.cpp
+/** @example samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp
-  An example program about pose estimation from coplanar points
+An example program about pose estimation from coplanar points
 
-  Check @ref tutorial_homography "the corresponding tutorial" for more details
+Check @ref tutorial_homography "the corresponding tutorial" for more details
- */
+*/
 
 /** @brief Finds a perspective transformation between two planes.
 
@@ -526,11 +526,11 @@ CV_EXPORTS_W void projectPoints( InputArray objectPoints,
                                  OutputArray jacobian = noArray(),
                                  double aspectRatio = 0 );
 
-/** @example homography_from_camera_displacement.cpp
+/** @example samples/cpp/tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp
-  An example program about homography from the camera displacement
+An example program about homography from the camera displacement
 
-  Check @ref tutorial_homography "the corresponding tutorial" for more details
+Check @ref tutorial_homography "the corresponding tutorial" for more details
- */
+*/
 
 /** @brief Finds an object pose from 3D-2D point correspondences.
 
@@ -1966,11 +1966,11 @@ CV_EXPORTS_W cv::Mat estimateAffinePartial2D(InputArray from, InputArray to, Out
                                   size_t maxIters = 2000, double confidence = 0.99,
                                   size_t refineIters = 10);
 
-/** @example decompose_homography.cpp
+/** @example samples/cpp/tutorial_code/features2D/Homography/decompose_homography.cpp
-  An example program with homography decomposition.
+An example program with homography decomposition.
 
-  Check @ref tutorial_homography "the corresponding tutorial" for more details.
+Check @ref tutorial_homography "the corresponding tutorial" for more details.
- */
+*/
 
 /** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
 

modules/core/CMakeLists.txt

@@ -33,9 +33,14 @@ if(CV_TRACE AND HAVE_ITT AND BUILD_ITT)
   add_definitions(-DOPENCV_WITH_ITT=1)
 endif()
 
-file(GLOB lib_cuda_hdrs        "include/opencv2/${name}/cuda/*.hpp"        "include/opencv2/${name}/cuda/*.h")
+file(GLOB lib_cuda_hdrs
-file(GLOB lib_cuda_hdrs_detail "include/opencv2/${name}/cuda/detail/*.hpp" "include/opencv2/${name}/cuda/detail/*.h")
+    "${CMAKE_CURRENT_LIST_DIR}/include/opencv2/${name}/cuda/*.hpp"
-file(GLOB_RECURSE module_opencl_hdrs "include/opencv2/${name}/opencl/*")
+    "${CMAKE_CURRENT_LIST_DIR}/include/opencv2/${name}/cuda/*.h")
+file(GLOB lib_cuda_hdrs_detail
+    "${CMAKE_CURRENT_LIST_DIR}/include/opencv2/${name}/cuda/detail/*.hpp"
+    "${CMAKE_CURRENT_LIST_DIR}/include/opencv2/${name}/cuda/detail/*.h")
+file(GLOB_RECURSE module_opencl_hdrs
+    "${CMAKE_CURRENT_LIST_DIR}/include/opencv2/${name}/opencl/*")
 
 source_group("Include\\Cuda Headers"         FILES ${lib_cuda_hdrs})
 source_group("Include\\Cuda Headers\\Detail" FILES ${lib_cuda_hdrs_detail})

modules/core/include/opencv2/core.hpp

@@ -273,9 +273,11 @@ of p and len.
 */
 CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType);
 
-/** @example copyMakeBorder_demo.cpp
+/** @example samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp
-An example using copyMakeBorder function
+An example using copyMakeBorder function.
- */
+Check @ref tutorial_copyMakeBorder "the corresponding tutorial" for more details
+*/
+
 /** @brief Forms a border around an image.
 
 The function copies the source image into the middle of the destination image. The areas to the
@@ -474,9 +476,10 @@ The function can also be emulated with a matrix expression, for example:
 */
 CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst);
 
-/** @example AddingImagesTrackbar.cpp
+/** @example samples/cpp/tutorial_code/HighGUI/AddingImagesTrackbar.cpp
+Check @ref tutorial_trackbar "the corresponding tutorial" for more details
+*/
 
- */
 /** @brief Calculates the weighted sum of two arrays.
 
 The function addWeighted calculates the weighted sum of two arrays as follows:
@@ -2527,14 +2530,18 @@ public:
     Mat mean; //!< mean value subtracted before the projection and added after the back projection
 };
 
-/** @example pca.cpp
+/** @example samples/cpp/pca.cpp
-  An example using %PCA for dimensionality reduction while maintaining an amount of variance
+An example using %PCA for dimensionality reduction while maintaining an amount of variance
- */
+*/
+
+/** @example samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
+Check @ref tutorial_introduction_to_pca "the corresponding tutorial" for more details
+*/
 
 /**
-   @brief Linear Discriminant Analysis
+@brief Linear Discriminant Analysis
-   @todo document this class
+@todo document this class
- */
+*/
 class CV_EXPORTS LDA
 {
 public:
@@ -2850,7 +2857,7 @@ public:
     use explicit type cast operators, as in the a1 initialization above.
     @param a lower inclusive boundary of the returned random number.
     @param b upper non-inclusive boundary of the returned random number.
-      */
+    */
     int uniform(int a, int b);
     /** @overload */
     float uniform(float a, float b);
@@ -2912,7 +2919,7 @@ public:
 
 Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c
 @todo document
- */
+*/
 class CV_EXPORTS RNG_MT19937
 {
 public:
@@ -2930,17 +2937,11 @@ public:
     unsigned operator ()(unsigned N);
     unsigned operator ()();
 
-    /** @brief returns uniformly distributed integer random number from [a,b) range
+    /** @brief returns uniformly distributed integer random number from [a,b) range*/
-
-*/
     int uniform(int a, int b);
-    /** @brief returns uniformly distributed floating-point random number from [a,b) range
+    /** @brief returns uniformly distributed floating-point random number from [a,b) range*/
-
-*/
     float uniform(float a, float b);
-    /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range
+    /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range*/
-
-*/
     double uniform(double a, double b);
 
 private:
@@ -2954,8 +2955,8 @@ private:
 //! @addtogroup core_cluster
 //!  @{
 
-/** @example kmeans.cpp
+/** @example samples/cpp/kmeans.cpp
-  An example on K-means clustering
+An example on K-means clustering
 */
 
 /** @brief Finds centers of clusters and groups input samples around the clusters.
@@ -3067,7 +3068,7 @@ etc.).
 
 Here is example of SimpleBlobDetector use in your application via Algorithm interface:
 @snippet snippets/core_various.cpp Algorithm
- */
+*/
 class CV_EXPORTS_W Algorithm
 {
 public:
@@ -3083,8 +3084,8 @@ public:
     virtual void write(FileStorage& fs) const { (void)fs; }
 
     /** @brief simplified API for language bindings
-     * @overload
+    * @overload
-     */
+    */
     CV_WRAP void write(const Ptr<FileStorage>& fs, const String& name = String()) const;
 
     /** @brief Reads algorithm parameters from a file storage
@@ -3092,20 +3093,20 @@ public:
     CV_WRAP virtual void read(const FileNode& fn) { (void)fn; }
 
     /** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
-     */
+    */
     CV_WRAP virtual bool empty() const { return false; }
 
     /** @brief Reads algorithm from the file node
 
-     This is static template method of Algorithm. It's usage is following (in the case of SVM):
+    This is static template method of Algorithm. It's usage is following (in the case of SVM):
-     @code
+    @code
-     cv::FileStorage fsRead("example.xml", FileStorage::READ);
+    cv::FileStorage fsRead("example.xml", FileStorage::READ);
-     Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());
+    Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());
-     @endcode
+    @endcode
-     In order to make this method work, the derived class must overwrite Algorithm::read(const
+    In order to make this method work, the derived class must overwrite Algorithm::read(const
-     FileNode& fn) and also have static create() method without parameters
+    FileNode& fn) and also have static create() method without parameters
-     (or with all the optional parameters)
+    (or with all the optional parameters)
-     */
+    */
     template<typename _Tp> static Ptr<_Tp> read(const FileNode& fn)
     {
         Ptr<_Tp> obj = _Tp::create();
@@ -3115,16 +3116,16 @@ public:
 
     /** @brief Loads algorithm from the file
 
-     @param filename Name of the file to read.
+    @param filename Name of the file to read.
-     @param objname The optional name of the node to read (if empty, the first top-level node will be used)
+    @param objname The optional name of the node to read (if empty, the first top-level node will be used)
 
-     This is static template method of Algorithm. It's usage is following (in the case of SVM):
+    This is static template method of Algorithm. It's usage is following (in the case of SVM):
-     @code
+    @code
-     Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
+    Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
-     @endcode
+    @endcode
-     In order to make this method work, the derived class must overwrite Algorithm::read(const
+    In order to make this method work, the derived class must overwrite Algorithm::read(const
-     FileNode& fn).
+    FileNode& fn).
-     */
+    */
     template<typename _Tp> static Ptr<_Tp> load(const String& filename, const String& objname=String())
     {
         FileStorage fs(filename, FileStorage::READ);
@@ -3138,14 +3139,14 @@ public:
 
     /** @brief Loads algorithm from a String
 
-     @param strModel The string variable containing the model you want to load.
+    @param strModel The string variable containing the model you want to load.
-     @param objname The optional name of the node to read (if empty, the first top-level node will be used)
+    @param objname The optional name of the node to read (if empty, the first top-level node will be used)
 
-     This is static template method of Algorithm. It's usage is following (in the case of SVM):
+    This is static template method of Algorithm. It's usage is following (in the case of SVM):
-     @code
+    @code
-     Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
+    Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
-     @endcode
+    @endcode
-     */
+    */
     template<typename _Tp> static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String())
     {
         FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY);
@@ -3156,11 +3157,11 @@ public:
     }
 
     /** Saves the algorithm to a file.
-     In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
+    In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
     CV_WRAP virtual void save(const String& filename) const;
 
     /** Returns the algorithm string identifier.
-     This string is used as top level xml/yml node tag when the object is saved to a file or string. */
+    This string is used as top level xml/yml node tag when the object is saved to a file or string. */
     CV_WRAP virtual String getDefaultName() const;
 
 protected:

modules/core/include/opencv2/core/base.hpp

@@ -414,7 +414,7 @@ CV_INLINE CV_NORETURN void errorNoReturn(int _code, const String& _err, const ch
 // We need to use simplified definition for them.
 #define CV_Error(...) do { abort(); } while (0)
 #define CV_Error_( code, args ) do { cv::format args; abort(); } while (0)
-#define CV_Assert_1( expr ) do { if (!(expr)) abort(); } while (0)
+#define CV_Assert( expr ) do { if (!(expr)) abort(); } while (0)
 
 #else // CV_STATIC_ANALYSIS
 
@@ -444,7 +444,13 @@ for example:
 */
 #define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ )
 
-#define CV_Assert_1( expr ) if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ )
+/** @brief Checks a condition at runtime and throws exception if it fails
+
+The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros
+raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release
+configurations while CV_DbgAssert is only retained in the Debug configuration.
+*/
+#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0)
 
 //! @cond IGNORED
 #define CV__ErrorNoReturn( code, msg ) cv::errorNoReturn( code, msg, CV_Func, __FILE__, __LINE__ )
@@ -454,8 +460,8 @@ for example:
 #define CV_Error CV__ErrorNoReturn
 #undef CV_Error_
 #define CV_Error_ CV__ErrorNoReturn_
-#undef CV_Assert_1
+#undef CV_Assert
-#define CV_Assert_1( expr ) if(!!(expr)) ; else cv::errorNoReturn( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ )
+#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::errorNoReturn( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0)
 #else
 // backward compatibility
 #define CV_ErrorNoReturn CV__ErrorNoReturn
@@ -465,6 +471,18 @@ for example:
 
 #endif // CV_STATIC_ANALYSIS
 
+//! @cond IGNORED
+
+#if defined OPENCV_FORCE_MULTIARG_ASSERT_CHECK && defined CV_STATIC_ANALYSIS
+#warning "OPENCV_FORCE_MULTIARG_ASSERT_CHECK can't be used with CV_STATIC_ANALYSIS"
+#undef OPENCV_FORCE_MULTIARG_ASSERT_CHECK
+#endif
+
+#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK
+#define CV_Assert_1( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0)
+#else
+#define CV_Assert_1 CV_Assert
+#endif
 #define CV_Assert_2( expr1, expr2 ) CV_Assert_1(expr1); CV_Assert_1(expr2)
 #define CV_Assert_3( expr1, expr2, expr3 ) CV_Assert_2(expr1, expr2); CV_Assert_1(expr3)
 #define CV_Assert_4( expr1, expr2, expr3, expr4 ) CV_Assert_3(expr1, expr2, expr3); CV_Assert_1(expr4)
@@ -475,21 +493,18 @@ for example:
 #define CV_Assert_9( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ) CV_Assert_8(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ); CV_Assert_1(expr9)
 #define CV_Assert_10( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9, expr10 ) CV_Assert_9(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ); CV_Assert_1(expr10)
 
-#define CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N
+#define CV_Assert_N(...) do { __CV_CAT(CV_Assert_, __CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__); } while(0)
-#define CV_VA_NUM_ARGS(...) CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0)
 
-/** @brief Checks a condition at runtime and throws exception if it fails
+#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK
+#undef CV_Assert
+#define CV_Assert CV_Assert_N
+#endif
+//! @endcond
 
-The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros
-raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release
-configurations while CV_DbgAssert is only retained in the Debug configuration.
-*/
-#define CV_Assert(...) do { CVAUX_CONCAT(CV_Assert_, CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__); } while(0)
-
-/** replaced with CV_Assert(expr) in Debug configuration */
 #if defined _DEBUG || defined CV_STATIC_ANALYSIS
 #  define CV_DbgAssert(expr) CV_Assert(expr)
 #else
+/** replaced with CV_Assert(expr) in Debug configuration */
 #  define CV_DbgAssert(expr)
 #endif
 

modules/core/include/opencv2/core/cvdef.h

@@ -79,6 +79,8 @@ namespace cv { namespace debug_build_guard { } using namespace debug_build_guard
 #define __CV_CAT(x, y) __CV_CAT_(x, y)
 #endif
 
+#define __CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N
+#define __CV_VA_NUM_ARGS(...) __CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0)
 
 // undef problematic defines sometimes defined by system headers (windows.h in particular)
 #undef small
@@ -347,7 +349,13 @@ Cv64suf;
 // We need to use simplified definition for them.
 #ifndef CV_STATIC_ANALYSIS
 # if defined(__KLOCWORK__) || defined(__clang_analyzer__) || defined(__COVERITY__)
-#   define CV_STATIC_ANALYSIS
+#   define CV_STATIC_ANALYSIS 1
+# endif
+#else
+# if defined(CV_STATIC_ANALYSIS) && !(__CV_CAT(1, CV_STATIC_ANALYSIS) == 1)  // defined and not empty
+#   if 0 == CV_STATIC_ANALYSIS
+#     undef CV_STATIC_ANALYSIS
+#   endif
 # endif
 #endif
 

modules/core/include/opencv2/core/hal/intrin.hpp

@@ -204,6 +204,18 @@ CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
 #define CV_SIMD512_64F 0
 #endif
 
+#ifndef CV_SIMD128_FP16
+#define CV_SIMD128_FP16 0
+#endif
+
+#ifndef CV_SIMD256_FP16
+#define CV_SIMD256_FP16 0
+#endif
+
+#ifndef CV_SIMD512_FP16
+#define CV_SIMD512_FP16 0
+#endif
+
 //==================================================================================================
 
 #define CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \
@@ -274,8 +286,8 @@ template<typename _Tp> struct V_RegTraits
 #if CV_SIMD128_64F
     CV_DEF_REG_TRAITS(v, v_float64x2, double, f64, v_float64x2, void, void, v_int64x2, v_int32x4);
 #endif
-#if CV_FP16
+#if CV_SIMD128_FP16
-    CV_DEF_REG_TRAITS(v, v_float16x8, short, f16, v_float32x4, void, void, v_int16x8, v_int16x8);
+    CV_DEF_REG_TRAITS(v, v_float16x8, short, f16, v_float16x8, void, void, v_int16x8, v_int16x8);
 #endif
 #endif
 
@@ -290,8 +302,8 @@ template<typename _Tp> struct V_RegTraits
     CV_DEF_REG_TRAITS(v256, v_uint64x4, uint64, u64, v_uint64x4, void, void, v_int64x4, void);
     CV_DEF_REG_TRAITS(v256, v_int64x4, int64, s64, v_uint64x4, void, void, v_int64x4, void);
     CV_DEF_REG_TRAITS(v256, v_float64x4, double, f64, v_float64x4, void, void, v_int64x4, v_int32x8);
-#if CV_FP16
+#if CV_SIMD256_FP16
-    CV_DEF_REG_TRAITS(v256, v_float16x16, short, f16, v_float32x8, void, void, v_int16x16, void);
+    CV_DEF_REG_TRAITS(v256, v_float16x16, short, f16, v_float16x16, void, void, v_int16x16, void);
 #endif
 #endif
 
@@ -309,6 +321,7 @@ using namespace CV__SIMD_NAMESPACE;
 namespace CV__SIMD_NAMESPACE {
     #define CV_SIMD 1
     #define CV_SIMD_64F CV_SIMD256_64F
+    #define CV_SIMD_FP16 CV_SIMD256_FP16
     #define CV_SIMD_WIDTH 32
     typedef v_uint8x32   v_uint8;
     typedef v_int8x32    v_int8;
@@ -323,6 +336,10 @@ namespace CV__SIMD_NAMESPACE {
     typedef v_float64x4  v_float64;
     #endif
     #if CV_FP16
+    #define vx_load_fp16_f32 v256_load_fp16_f32
+    #define vx_store_fp16 v_store_fp16
+    #endif
+    #if CV_SIMD256_FP16
     typedef v_float16x16  v_float16;
     CV_INTRIN_DEFINE_WIDE_INTRIN(short, v_float16, f16, v256, load_f16)
     #endif
@@ -336,6 +353,7 @@ using namespace CV__SIMD_NAMESPACE;
 namespace CV__SIMD_NAMESPACE {
     #define CV_SIMD CV_SIMD128
     #define CV_SIMD_64F CV_SIMD128_64F
+    #define CV_SIMD_FP16 CV_SIMD128_FP16
     #define CV_SIMD_WIDTH 16
     typedef v_uint8x16  v_uint8;
     typedef v_int8x16   v_int8;
@@ -350,6 +368,10 @@ namespace CV__SIMD_NAMESPACE {
     typedef v_float64x2 v_float64;
     #endif
     #if CV_FP16
+    #define vx_load_fp16_f32 v128_load_fp16_f32
+    #define vx_store_fp16 v_store_fp16
+    #endif
+    #if CV_SIMD128_FP16
     typedef v_float16x8  v_float16;
     CV_INTRIN_DEFINE_WIDE_INTRIN(short, v_float16, f16, v, load_f16)
     #endif
@@ -393,6 +415,11 @@ CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
 #define CV_SIMD_64F 0
 #endif
 
+#ifndef CV_SIMD_FP16
+#define CV_SIMD_FP16 0  //!< Defined to 1 on native support of operations with float16x8_t / float16x16_t (SIMD256) types
+#endif
+
+
 #ifndef CV_SIMD
 #define CV_SIMD 0
 #endif

modules/core/include/opencv2/core/hal/intrin_avx.hpp

@@ -7,6 +7,7 @@
 
 #define CV_SIMD256 1
 #define CV_SIMD256_64F 1
+#define CV_SIMD256_FP16 0  // no native operations with FP16 type. Only load/store from float32x8 are available (if CV_FP16 == 1)
 
 namespace cv
 {
@@ -262,26 +263,6 @@ struct v_float64x4
     double get0() const { return _mm_cvtsd_f64(_mm256_castpd256_pd128(val)); }
 };
 
-struct v_float16x16
-{
-    typedef short lane_type;
-    enum { nlanes = 16 };
-    __m256i val;
-
-    explicit v_float16x16(__m256i v) : val(v) {}
-    v_float16x16(short v0, short v1, short v2, short v3,
-                 short v4, short v5, short v6, short v7,
-                 short v8, short v9, short v10, short v11,
-                 short v12, short v13, short v14, short v15)
-    {
-        val = _mm256_setr_epi16(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15);
-    }
-    v_float16x16() : val(_mm256_setzero_si256()) {}
-    short get0() const { return (short)_v_cvtsi256_si32(val); }
-};
-inline v_float16x16 v256_setzero_f16() { return v_float16x16(_mm256_setzero_si256()); }
-inline v_float16x16 v256_setall_f16(short val) { return v_float16x16(_mm256_set1_epi16(val)); }
-
 //////////////// Load and store operations ///////////////
 
 #define OPENCV_HAL_IMPL_AVX_LOADSTORE(_Tpvec, _Tp)                    \
@@ -424,20 +405,18 @@ inline v_float64x4 v_reinterpret_as_f64(const v_float64x4& a)
 inline v_float64x4 v_reinterpret_as_f64(const v_float32x8& a)
 { return v_float64x4(_mm256_castps_pd(a.val)); }
 
-inline v_float16x16 v256_load_f16(const short* ptr)
+#if CV_FP16
-{ return v_float16x16(_mm256_loadu_si256((const __m256i*)ptr)); }
+inline v_float32x8 v256_load_fp16_f32(const short* ptr)
-inline v_float16x16 v256_load_f16_aligned(const short* ptr)
+{
-{ return v_float16x16(_mm256_load_si256((const __m256i*)ptr)); }
+    return v_float32x8(_mm256_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr)));
+}
 
-inline v_float16x16 v256_load_f16_low(const short* ptr)
+inline void v_store_fp16(short* ptr, const v_float32x8& a)
-{ return v_float16x16(v256_load_low(ptr).val); }
+{
-inline v_float16x16 v256_load_f16_halves(const short* ptr0, const short* ptr1)
+    __m128i fp16_value = _mm256_cvtps_ph(a.val, 0);
-{ return v_float16x16(v256_load_halves(ptr0, ptr1).val); }
+    _mm_store_si128((__m128i*)ptr, fp16_value);
-
+}
-inline void v_store(short* ptr, const v_float16x16& a)
+#endif
-{ _mm256_storeu_si256((__m256i*)ptr, a.val); }
-inline void v_store_aligned(short* ptr, const v_float16x16& a)
-{ _mm256_store_si256((__m256i*)ptr, a.val); }
 
 /* Recombine */
 /*#define OPENCV_HAL_IMPL_AVX_COMBINE(_Tpvec, perm)                    \
@@ -1262,20 +1241,6 @@ inline v_float64x4 v_cvt_f64(const v_float32x8& a)
 inline v_float64x4 v_cvt_f64_high(const v_float32x8& a)
 { return v_float64x4(_mm256_cvtps_pd(_v256_extract_high(a.val))); }
 
-#if CV_FP16
-inline v_float32x8 v_cvt_f32(const v_float16x16& a)
-{ return v_float32x8(_mm256_cvtph_ps(_v256_extract_low(a.val))); }
-
-inline v_float32x8 v_cvt_f32_high(const v_float16x16& a)
-{ return v_float32x8(_mm256_cvtph_ps(_v256_extract_high(a.val))); }
-
-inline v_float16x16 v_cvt_f16(const v_float32x8& a, const v_float32x8& b)
-{
-    __m128i ah = _mm256_cvtps_ph(a.val, 0), bh = _mm256_cvtps_ph(b.val, 0);
-    return v_float16x16(_mm256_inserti128_si256(_mm256_castsi128_si256(ah), bh, 1));
-}
-#endif
-
 ////////////// Lookup table access ////////////////////
 
 inline v_int32x8 v_lut(const int* tab, const v_int32x8& idxvec)

modules/core/include/opencv2/core/hal/intrin_neon.hpp

@@ -62,6 +62,15 @@ CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
 #define CV_SIMD128_64F 0
 #endif
 
+#ifndef CV_SIMD128_FP16
+# if CV_FP16 && (defined(__GNUC__) && __GNUC__ >= 5)  // #12027: float16x8_t is missing in GCC 4.8.2
+#   define CV_SIMD128_FP16 1
+# endif
+#endif
+#ifndef CV_SIMD128_FP16
+# define CV_SIMD128_FP16 0
+#endif
+
 #if CV_SIMD128_64F
 #define OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv, suffix) \
 template <typename T> static inline \
@@ -280,28 +289,9 @@ struct v_float64x2
 
 #if CV_FP16
 // Workaround for old compilers
-static inline int16x8_t vreinterpretq_s16_f16(float16x8_t a) { return (int16x8_t)a; }
-static inline float16x8_t vreinterpretq_f16_s16(int16x8_t a) { return (float16x8_t)a; }
 static inline int16x4_t vreinterpret_s16_f16(float16x4_t a) { return (int16x4_t)a; }
 static inline float16x4_t vreinterpret_f16_s16(int16x4_t a) { return (float16x4_t)a; }
 
-static inline float16x8_t cv_vld1q_f16(const void* ptr)
-{
-#ifndef vld1q_f16 // APPLE compiler defines vld1_f16 as macro
-    return vreinterpretq_f16_s16(vld1q_s16((const short*)ptr));
-#else
-    return vld1q_f16((const __fp16*)ptr);
-#endif
-}
-static inline void cv_vst1q_f16(void* ptr, float16x8_t a)
-{
-#ifndef vst1q_f16 // APPLE compiler defines vst1_f16 as macro
-    vst1q_s16((short*)ptr, vreinterpretq_s16_f16(a));
-#else
-    vst1q_f16((__fp16*)ptr, a);
-#endif
-}
-
 static inline float16x4_t cv_vld1_f16(const void* ptr)
 {
 #ifndef vld1_f16 // APPLE compiler defines vld1_f16 as macro
@@ -323,6 +313,45 @@ static inline void cv_vst1_f16(void* ptr, float16x4_t a)
     #define vdup_n_f16(v) (float16x4_t){v, v, v, v}
 #endif
 
+#endif // CV_FP16
+
+#if CV_FP16
+inline v_float32x4 v128_load_fp16_f32(const short* ptr)
+{
+    float16x4_t a = cv_vld1_f16((const __fp16*)ptr);
+    return v_float32x4(vcvt_f32_f16(a));
+}
+
+inline void v_store_fp16(short* ptr, const v_float32x4& a)
+{
+    float16x4_t fp16 = vcvt_f16_f32(a.val);
+    cv_vst1_f16((short*)ptr, fp16);
+}
+#endif
+
+
+#if CV_SIMD128_FP16
+// Workaround for old compilers
+static inline int16x8_t vreinterpretq_s16_f16(float16x8_t a) { return (int16x8_t)a; }
+static inline float16x8_t vreinterpretq_f16_s16(int16x8_t a) { return (float16x8_t)a; }
+
+static inline float16x8_t cv_vld1q_f16(const void* ptr)
+{
+#ifndef vld1q_f16 // APPLE compiler defines vld1_f16 as macro
+    return vreinterpretq_f16_s16(vld1q_s16((const short*)ptr));
+#else
+    return vld1q_f16((const __fp16*)ptr);
+#endif
+}
+static inline void cv_vst1q_f16(void* ptr, float16x8_t a)
+{
+#ifndef vst1q_f16 // APPLE compiler defines vst1_f16 as macro
+    vst1q_s16((short*)ptr, vreinterpretq_s16_f16(a));
+#else
+    vst1q_f16((__fp16*)ptr, a);
+#endif
+}
+
 struct v_float16x8
 {
     typedef short lane_type;
@@ -344,7 +373,8 @@ struct v_float16x8
 
 inline v_float16x8 v_setzero_f16() { return v_float16x8(vreinterpretq_f16_s16(vdupq_n_s16((short)0))); }
 inline v_float16x8 v_setall_f16(short v) { return v_float16x8(vreinterpretq_f16_s16(vdupq_n_s16(v))); }
-#endif
+
+#endif // CV_SIMD128_FP16
 
 #define OPENCV_HAL_IMPL_NEON_INIT(_Tpv, _Tp, suffix) \
 inline v_##_Tpv v_setzero_##suffix() { return v_##_Tpv(vdupq_n_##suffix((_Tp)0)); } \
@@ -889,7 +919,7 @@ OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float32x4, float, f32)
 OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float64x2, double, f64)
 #endif
 
-#if CV_FP16
+#if CV_SIMD128_FP16
 // Workaround for old comiplers
 inline v_float16x8 v_load_f16(const short* ptr)
 { return v_float16x8(cv_vld1q_f16(ptr)); }
@@ -1462,7 +1492,7 @@ inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
 }
 #endif
 
-#if CV_FP16
+#if CV_SIMD128_FP16
 inline v_float32x4 v_cvt_f32(const v_float16x8& a)
 {
     return v_float32x4(vcvt_f32_f16(vget_low_f16(a.val)));

modules/core/include/opencv2/core/hal/intrin_sse.hpp

@@ -50,6 +50,7 @@
 
 #define CV_SIMD128 1
 #define CV_SIMD128_64F 1
+#define CV_SIMD128_FP16 0  // no native operations with FP16 type.
 
 namespace cv
 {
@@ -272,28 +273,6 @@ struct v_float64x2
     __m128d val;
 };
 
-struct v_float16x8
-{
-    typedef short lane_type;
-    typedef __m128i vector_type;
-    enum { nlanes = 8 };
-
-    v_float16x8() : val(_mm_setzero_si128()) {}
-    explicit v_float16x8(__m128i v) : val(v) {}
-    v_float16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
-    {
-        val = _mm_setr_epi16(v0, v1, v2, v3, v4, v5, v6, v7);
-    }
-    short get0() const
-    {
-        return (short)_mm_cvtsi128_si32(val);
-    }
-
-    __m128i val;
-};
-inline v_float16x8 v_setzero_f16() { return v_float16x8(_mm_setzero_si128()); }
-inline v_float16x8 v_setall_f16(short val) { return v_float16x8(_mm_set1_epi16(val)); }
-
 namespace hal_sse_internal
 {
     template <typename to_sse_type, typename from_sse_type>
@@ -1330,21 +1309,6 @@ inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
 OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float32x4, float, ps)
 OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float64x2, double, pd)
 
-inline v_float16x8 v_load_f16(const short* ptr)
-{ return v_float16x8(_mm_loadu_si128((const __m128i*)ptr)); }
-inline v_float16x8 v_load_f16_aligned(const short* ptr)
-{ return v_float16x8(_mm_load_si128((const __m128i*)ptr)); }
-
-inline v_float16x8 v_load_f16_low(const short* ptr)
-{ return v_float16x8(v_load_low(ptr).val); }
-inline v_float16x8 v_load_f16_halves(const short* ptr0, const short* ptr1)
-{ return v_float16x8(v_load_halves(ptr0, ptr1).val); }
-
-inline void v_store(short* ptr, const v_float16x8& a)
-{ _mm_storeu_si128((__m128i*)ptr, a.val); }
-inline void v_store_aligned(short* ptr, const v_float16x8& a)
-{ _mm_store_si128((__m128i*)ptr, a.val); }
-
 #define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(_Tpvec, scalartype, func, suffix, sbit) \
 inline scalartype v_reduce_##func(const v_##_Tpvec& a) \
 { \
@@ -2622,19 +2586,15 @@ inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
 }
 
 #if CV_FP16
-inline v_float32x4 v_cvt_f32(const v_float16x8& a)
+inline v_float32x4 v128_load_fp16_f32(const short* ptr)
 {
-    return v_float32x4(_mm_cvtph_ps(a.val));
+    return v_float32x4(_mm_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr)));
 }
 
-inline v_float32x4 v_cvt_f32_high(const v_float16x8& a)
+inline void v_store_fp16(short* ptr, const v_float32x4& a)
 {
-    return v_float32x4(_mm_cvtph_ps(_mm_unpackhi_epi64(a.val, a.val)));
+    __m128i fp16_value = _mm_cvtps_ph(a.val, 0);
-}
+    _mm_storel_epi64((__m128i*)ptr, fp16_value);
-
-inline v_float16x8 v_cvt_f16(const v_float32x4& a, const v_float32x4& b)
-{
-    return v_float16x8(_mm_unpacklo_epi64(_mm_cvtps_ph(a.val, 0), _mm_cvtps_ph(b.val, 0)));
 }
 #endif
 

modules/core/include/opencv2/core/mat.hpp

@@ -575,7 +575,7 @@ protected:
     MatStep& operator = (const MatStep&);
 };
 
-/** @example cout_mat.cpp
+/** @example samples/cpp/cout_mat.cpp
 An example demonstrating the serial out capabilities of cv::Mat
 */
 

modules/core/include/opencv2/core/persistence.hpp

@@ -287,12 +287,12 @@ element is a structure of 2 integers, followed by a single-precision floating-po
 equivalent notations of the above specification are `iif`, `2i1f` and so forth. Other examples: `u`
 means that the array consists of bytes, and `2d` means the array consists of pairs of doubles.
 
-@see @ref filestorage.cpp
+@see @ref samples/cpp/filestorage.cpp
 */
 
 //! @{
 
-/** @example filestorage.cpp
+/** @example samples/cpp/filestorage.cpp
 A complete example using the FileStorage interface
 */
 

modules/core/include/opencv2/core/version.hpp

@@ -8,7 +8,7 @@
 #define CV_VERSION_MAJOR    3
 #define CV_VERSION_MINOR    4
 #define CV_VERSION_REVISION 3
-#define CV_VERSION_STATUS   "-openvino.2018R3"
+#define CV_VERSION_STATUS   ""
 
 #define CVAUX_STR_EXP(__A)  #__A
 #define CVAUX_STR(__A)      CVAUX_STR_EXP(__A)

modules/core/misc/java/src/java/core+MatOfRotatedRect.java

@@ -0,0 +1,86 @@
+package org.opencv.core;
+
+import java.util.Arrays;
+import java.util.List;
+
+import org.opencv.core.RotatedRect;
+
+
+
+public class MatOfRotatedRect extends Mat {
+    // 32FC5
+    private static final int _depth = CvType.CV_32F;
+    private static final int _channels = 5;
+
+    public MatOfRotatedRect() {
+        super();
+    }
+
+    protected MatOfRotatedRect(long addr) {
+        super(addr);
+        if( !empty() && checkVector(_channels, _depth) < 0 )
+            throw new IllegalArgumentException("Incompatible Mat");
+        //FIXME: do we need release() here?
+    }
+
+    public static MatOfRotatedRect fromNativeAddr(long addr) {
+        return new MatOfRotatedRect(addr);
+    }
+
+    public MatOfRotatedRect(Mat m) {
+        super(m, Range.all());
+        if( !empty() && checkVector(_channels, _depth) < 0 )
+            throw new IllegalArgumentException("Incompatible Mat");
+        //FIXME: do we need release() here?
+    }
+
+    public MatOfRotatedRect(RotatedRect...a) {
+        super();
+        fromArray(a);
+    }
+
+    public void alloc(int elemNumber) {
+        if(elemNumber>0)
+            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
+    }
+
+    public void fromArray(RotatedRect...a) {
+        if(a==null || a.length==0)
+            return;
+        int num = a.length;
+        alloc(num);
+        float buff[] = new float[num * _channels];
+        for(int i=0; i<num; i++) {
+            RotatedRect r = a[i];
+            buff[_channels*i+0] = (float) r.center.x;
+            buff[_channels*i+1] = (float) r.center.y;
+            buff[_channels*i+2] = (float) r.size.width;
+            buff[_channels*i+3] = (float) r.size.height;
+            buff[_channels*i+4] = (float) r.angle;
+        }
+        put(0, 0, buff); //TODO: check ret val!
+    }
+
+    public RotatedRect[] toArray() {
+        int num = (int) total();
+        RotatedRect[] a = new RotatedRect[num];
+        if(num == 0)
+            return a;
+        float buff[] = new float[_channels];
+        for(int i=0; i<num; i++) {
+            get(i, 0, buff); //TODO: check ret val!
+            a[i] = new RotatedRect(new Point(buff[0],buff[1]),new Size(buff[2],buff[3]),buff[4]);
+        }
+        return a;
+    }
+
+    public void fromList(List<RotatedRect> lr) {
+        RotatedRect ap[] = lr.toArray(new RotatedRect[0]);
+        fromArray(ap);
+    }
+
+    public List<RotatedRect> toList() {
+        RotatedRect[] ar = toArray();
+        return Arrays.asList(ar);
+    }
+}

modules/core/misc/java/test/RotatedRectTest.java

@@ -1,11 +1,16 @@
 package org.opencv.test.core;
 
+import org.opencv.core.CvType;
 import org.opencv.core.Point;
 import org.opencv.core.Rect;
 import org.opencv.core.RotatedRect;
+import org.opencv.core.MatOfRotatedRect;
 import org.opencv.core.Size;
 import org.opencv.test.OpenCVTestCase;
 
+import java.util.Arrays;
+import java.util.List;
+
 public class RotatedRectTest extends OpenCVTestCase {
 
     private double angle;
@@ -188,4 +193,21 @@ public class RotatedRectTest extends OpenCVTestCase {
         assertEquals(expected, actual);
     }
 
+    public void testMatOfRotatedRect() {
+        RotatedRect a = new RotatedRect(new Point(1,2),new Size(3,4),5.678);
+        RotatedRect b = new RotatedRect(new Point(9,8),new Size(7,6),5.432);
+        MatOfRotatedRect m = new MatOfRotatedRect(a,b,a,b,a,b,a,b);
+        assertEquals(m.rows(), 8);
+        assertEquals(m.cols(), 1);
+        assertEquals(m.type(), CvType.CV_32FC(5));
+        RotatedRect[] arr = m.toArray();
+        assertEquals(arr[2].angle, a.angle, EPS);
+        assertEquals(arr[3].center.x, b.center.x);
+        assertEquals(arr[3].size.width, b.size.width);
+        List<RotatedRect> li = m.toList();
+        assertEquals(li.size(), 8);
+        RotatedRect rr = li.get(7);
+        assertEquals(rr.angle, b.angle, EPS);
+        assertEquals(rr.center.y, b.center.y);
+    }
 }

modules/core/perf/perf_arithm.cpp

@@ -4,11 +4,9 @@ namespace opencv_test
 {
 using namespace perf;
 
-#define TYPICAL_MAT_SIZES_CORE_ARITHM   szVGA, sz720p, sz1080p
+typedef Size_MatType BinaryOpTest;
-#define TYPICAL_MAT_TYPES_CORE_ARITHM   CV_8UC1, CV_8SC1, CV_16SC1, CV_16SC2, CV_16SC3, CV_16SC4, CV_8UC4, CV_32SC1, CV_32FC1
-#define TYPICAL_MATS_CORE_ARITHM        testing::Combine( testing::Values( TYPICAL_MAT_SIZES_CORE_ARITHM ), testing::Values( TYPICAL_MAT_TYPES_CORE_ARITHM ) )
 
-PERF_TEST_P(Size_MatType, min, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, min)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -20,10 +18,10 @@ PERF_TEST_P(Size_MatType, min, TYPICAL_MATS_CORE_ARITHM)
 
     TEST_CYCLE() cv::min(a, b, c);
 
-    SANITY_CHECK(c);
+    SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, minScalar, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, minScalarDouble)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -35,10 +33,34 @@ PERF_TEST_P(Size_MatType, minScalar, TYPICAL_MATS_CORE_ARITHM)
 
     TEST_CYCLE() cv::min(a, b, c);
 
-    SANITY_CHECK(c);
+    SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, max, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, minScalarSameType)
+{
+    Size sz = get<0>(GetParam());
+    int type = get<1>(GetParam());
+    cv::Mat a = Mat(sz, type);
+    cv::Scalar b;
+    cv::Mat c = Mat(sz, type);
+
+    declare.in(a, b, WARMUP_RNG).out(c);
+
+    if (CV_MAT_DEPTH(type) < CV_32S)
+    {
+        b = Scalar(1, 0, 3, 4); // don't pass non-integer values for 8U/8S/16U/16S processing
+    }
+    else if (CV_MAT_DEPTH(type) == CV_32S)
+    {
+        b = Scalar(1, 0, -3, 4); // don't pass non-integer values for 32S processing
+    }
+
+    TEST_CYCLE() cv::min(a, b, c);
+
+    SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST_P_(BinaryOpTest, max)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -50,10 +72,10 @@ PERF_TEST_P(Size_MatType, max, TYPICAL_MATS_CORE_ARITHM)
 
     TEST_CYCLE() cv::max(a, b, c);
 
-    SANITY_CHECK(c);
+    SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, maxScalar, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, maxScalarDouble)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -65,34 +87,10 @@ PERF_TEST_P(Size_MatType, maxScalar, TYPICAL_MATS_CORE_ARITHM)
 
     TEST_CYCLE() cv::max(a, b, c);
 
-    SANITY_CHECK(c);
+    SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, absdiff, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, maxScalarSameType)
-{
-    Size sz = get<0>(GetParam());
-    int type = get<1>(GetParam());
-    cv::Mat a = Mat(sz, type);
-    cv::Mat b = Mat(sz, type);
-    cv::Mat c = Mat(sz, type);
-
-    declare.in(a, b, WARMUP_RNG).out(c);
-
-    double eps = 1e-8;
-    if (CV_MAT_DEPTH(type) == CV_32S)
-    {
-        //see ticket 1529: absdiff can be without saturation on 32S
-        a /= 2;
-        b /= 2;
-        eps = 1;
-    }
-
-    TEST_CYCLE() cv::absdiff(a, b, c);
-
-    SANITY_CHECK(c, eps);
-}
-
-PERF_TEST_P(Size_MatType, absdiffScalar, TYPICAL_MATS_CORE_ARITHM)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -102,21 +100,91 @@ PERF_TEST_P(Size_MatType, absdiffScalar, TYPICAL_MATS_CORE_ARITHM)
 
     declare.in(a, b, WARMUP_RNG).out(c);
 
-    double eps = 1e-8;
+    if (CV_MAT_DEPTH(type) < CV_32S)
+    {
+        b = Scalar(1, 0, 3, 4); // don't pass non-integer values for 8U/8S/16U/16S processing
+    }
+    else if (CV_MAT_DEPTH(type) == CV_32S)
+    {
+        b = Scalar(1, 0, -3, 4); // don't pass non-integer values for 32S processing
+    }
+
+    TEST_CYCLE() cv::max(a, b, c);
+
+    SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST_P_(BinaryOpTest, absdiff)
+{
+    Size sz = get<0>(GetParam());
+    int type = get<1>(GetParam());
+    cv::Mat a = Mat(sz, type);
+    cv::Mat b = Mat(sz, type);
+    cv::Mat c = Mat(sz, type);
+
+    declare.in(a, b, WARMUP_RNG).out(c);
+
     if (CV_MAT_DEPTH(type) == CV_32S)
     {
         //see ticket 1529: absdiff can be without saturation on 32S
         a /= 2;
         b /= 2;
-        eps = 1;
     }
 
     TEST_CYCLE() cv::absdiff(a, b, c);
 
-    SANITY_CHECK(c, eps);
+    SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, add, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, absdiffScalarDouble)
+{
+    Size sz = get<0>(GetParam());
+    int type = get<1>(GetParam());
+    cv::Mat a = Mat(sz, type);
+    cv::Scalar b;
+    cv::Mat c = Mat(sz, type);
+
+    declare.in(a, b, WARMUP_RNG).out(c);
+
+    if (CV_MAT_DEPTH(type) == CV_32S)
+    {
+        //see ticket 1529: absdiff can be without saturation on 32S
+        a /= 2;
+        b /= 2;
+    }
+
+    TEST_CYCLE() cv::absdiff(a, b, c);
+
+    SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST_P_(BinaryOpTest, absdiffScalarSameType)
+{
+    Size sz = get<0>(GetParam());
+    int type = get<1>(GetParam());
+    cv::Mat a = Mat(sz, type);
+    cv::Scalar b;
+    cv::Mat c = Mat(sz, type);
+
+    declare.in(a, b, WARMUP_RNG).out(c);
+
+    if (CV_MAT_DEPTH(type) < CV_32S)
+    {
+        b = Scalar(1, 0, 3, 4); // don't pass non-integer values for 8U/8S/16U/16S processing
+    }
+    else if (CV_MAT_DEPTH(type) == CV_32S)
+    {
+        //see ticket 1529: absdiff can be without saturation on 32S
+        a /= 2;
+        b = Scalar(1, 0, -3, 4); // don't pass non-integer values for 32S processing
+    }
+
+    TEST_CYCLE() cv::absdiff(a, b, c);
+
+    SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST_P_(BinaryOpTest, add)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -127,21 +195,19 @@ PERF_TEST_P(Size_MatType, add, TYPICAL_MATS_CORE_ARITHM)
     declare.in(a, b, WARMUP_RNG).out(c);
     declare.time(50);
 
-    double eps = 1e-8;
     if (CV_MAT_DEPTH(type) == CV_32S)
     {
         //see ticket 1529: add can be without saturation on 32S
         a /= 2;
         b /= 2;
-        eps = 1;
     }
 
     TEST_CYCLE() cv::add(a, b, c);
 
-    SANITY_CHECK(c, eps);
+    SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, addScalar, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, addScalarDouble)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -151,21 +217,45 @@ PERF_TEST_P(Size_MatType, addScalar, TYPICAL_MATS_CORE_ARITHM)
 
     declare.in(a, b, WARMUP_RNG).out(c);
 
-    double eps = 1e-8;
     if (CV_MAT_DEPTH(type) == CV_32S)
     {
         //see ticket 1529: add can be without saturation on 32S
         a /= 2;
         b /= 2;
-        eps = 1;
     }
 
     TEST_CYCLE() cv::add(a, b, c);
 
-    SANITY_CHECK(c, eps);
+    SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, subtract, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, addScalarSameType)
+{
+    Size sz = get<0>(GetParam());
+    int type = get<1>(GetParam());
+    cv::Mat a = Mat(sz, type);
+    cv::Scalar b;
+    cv::Mat c = Mat(sz, type);
+
+    declare.in(a, b, WARMUP_RNG).out(c);
+
+    if (CV_MAT_DEPTH(type) < CV_32S)
+    {
+        b = Scalar(1, 0, 3, 4); // don't pass non-integer values for 8U/8S/16U/16S processing
+    }
+    else if (CV_MAT_DEPTH(type) == CV_32S)
+    {
+        //see ticket 1529: add can be without saturation on 32S
+        a /= 2;
+        b = Scalar(1, 0, -3, 4); // don't pass non-integer values for 32S processing
+    }
+
+    TEST_CYCLE() cv::add(a, b, c, noArray(), type);
+
+    SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST_P_(BinaryOpTest, subtract)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -175,21 +265,19 @@ PERF_TEST_P(Size_MatType, subtract, TYPICAL_MATS_CORE_ARITHM)
 
     declare.in(a, b, WARMUP_RNG).out(c);
 
-    double eps = 1e-8;
     if (CV_MAT_DEPTH(type) == CV_32S)
     {
         //see ticket 1529: subtract can be without saturation on 32S
         a /= 2;
         b /= 2;
-        eps = 1;
     }
 
     TEST_CYCLE() cv::subtract(a, b, c);
 
-    SANITY_CHECK(c, eps);
+    SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, subtractScalar, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, subtractScalarDouble)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -199,21 +287,45 @@ PERF_TEST_P(Size_MatType, subtractScalar, TYPICAL_MATS_CORE_ARITHM)
 
     declare.in(a, b, WARMUP_RNG).out(c);
 
-    double eps = 1e-8;
     if (CV_MAT_DEPTH(type) == CV_32S)
     {
         //see ticket 1529: subtract can be without saturation on 32S
         a /= 2;
         b /= 2;
-        eps = 1;
     }
 
     TEST_CYCLE() cv::subtract(a, b, c);
 
-    SANITY_CHECK(c, eps);
+    SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, multiply, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, subtractScalarSameType)
+{
+    Size sz = get<0>(GetParam());
+    int type = get<1>(GetParam());
+    cv::Mat a = Mat(sz, type);
+    cv::Scalar b;
+    cv::Mat c = Mat(sz, type);
+
+    declare.in(a, b, WARMUP_RNG).out(c);
+
+    if (CV_MAT_DEPTH(type) < CV_32S)
+    {
+        b = Scalar(1, 0, 3, 4); // don't pass non-integer values for 8U/8S/16U/16S processing
+    }
+    else if (CV_MAT_DEPTH(type) == CV_32S)
+    {
+        //see ticket 1529: subtract can be without saturation on 32S
+        a /= 2;
+        b = Scalar(1, 0, -3, 4); // don't pass non-integer values for 32S processing
+    }
+
+    TEST_CYCLE() cv::subtract(a, b, c, noArray(), type);
+
+    SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST_P_(BinaryOpTest, multiply)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -229,10 +341,10 @@ PERF_TEST_P(Size_MatType, multiply, TYPICAL_MATS_CORE_ARITHM)
 
     TEST_CYCLE() cv::multiply(a, b, c);
 
-    SANITY_CHECK(c, 1e-8);
+    SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, multiplyScale, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, multiplyScale)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -250,10 +362,10 @@ PERF_TEST_P(Size_MatType, multiplyScale, TYPICAL_MATS_CORE_ARITHM)
 
     TEST_CYCLE() cv::multiply(a, b, c, scale);
 
-    SANITY_CHECK(c, 1e-8);
+    SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, divide, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, divide)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -267,7 +379,7 @@ PERF_TEST_P(Size_MatType, divide, TYPICAL_MATS_CORE_ARITHM)
     SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST_P(Size_MatType, reciprocal, TYPICAL_MATS_CORE_ARITHM)
+PERF_TEST_P_(BinaryOpTest, reciprocal)
 {
     Size sz = get<0>(GetParam());
     int type = get<1>(GetParam());
@@ -281,4 +393,11 @@ PERF_TEST_P(Size_MatType, reciprocal, TYPICAL_MATS_CORE_ARITHM)
     SANITY_CHECK_NOTHING();
 }
 
+INSTANTIATE_TEST_CASE_P(/*nothing*/ , BinaryOpTest,
+    testing::Combine(
+        testing::Values(szVGA, sz720p, sz1080p),
+        testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_8SC1, CV_16SC1, CV_16SC2, CV_16SC3, CV_16SC4, CV_32SC1, CV_32FC1)
+    )
+);
+
 } // namespace

modules/core/src/mathfuncs.cpp

@@ -45,6 +45,7 @@
 #include "opencl_kernels_core.hpp"
 #include <limits>
 #include <iostream>
+#include "mathfuncs.hpp"
 
 namespace cv
 {
@@ -2023,4 +2024,382 @@ void cvSolvePoly(const CvMat* a, CvMat *r, int maxiter, int)
 }
 
 
+
+// Common constants for dispatched code
+namespace cv { namespace details {
+
+#define EXPTAB_SCALE 6
+#define EXPTAB_MASK  ((1 << EXPTAB_SCALE) - 1)
+
+#define EXPPOLY_32F_A0 .9670371139572337719125840413672004409288e-2
+
+static const double CV_DECL_ALIGNED(64) expTab[EXPTAB_MASK + 1] = {
+    1.0 * EXPPOLY_32F_A0,
+    1.0108892860517004600204097905619 * EXPPOLY_32F_A0,
+    1.0218971486541166782344801347833 * EXPPOLY_32F_A0,
+    1.0330248790212284225001082839705 * EXPPOLY_32F_A0,
+    1.0442737824274138403219664787399 * EXPPOLY_32F_A0,
+    1.0556451783605571588083413251529 * EXPPOLY_32F_A0,
+    1.0671404006768236181695211209928 * EXPPOLY_32F_A0,
+    1.0787607977571197937406800374385 * EXPPOLY_32F_A0,
+    1.0905077326652576592070106557607 * EXPPOLY_32F_A0,
+    1.1023825833078409435564142094256 * EXPPOLY_32F_A0,
+    1.1143867425958925363088129569196 * EXPPOLY_32F_A0,
+    1.126521618608241899794798643787 * EXPPOLY_32F_A0,
+    1.1387886347566916537038302838415 * EXPPOLY_32F_A0,
+    1.151189229952982705817759635202 * EXPPOLY_32F_A0,
+    1.1637248587775775138135735990922 * EXPPOLY_32F_A0,
+    1.1763969916502812762846457284838 * EXPPOLY_32F_A0,
+    1.1892071150027210667174999705605 * EXPPOLY_32F_A0,
+    1.2021567314527031420963969574978 * EXPPOLY_32F_A0,
+    1.2152473599804688781165202513388 * EXPPOLY_32F_A0,
+    1.2284805361068700056940089577928 * EXPPOLY_32F_A0,
+    1.2418578120734840485936774687266 * EXPPOLY_32F_A0,
+    1.2553807570246910895793906574423 * EXPPOLY_32F_A0,
+    1.2690509571917332225544190810323 * EXPPOLY_32F_A0,
+    1.2828700160787782807266697810215 * EXPPOLY_32F_A0,
+    1.2968395546510096659337541177925 * EXPPOLY_32F_A0,
+    1.3109612115247643419229917863308 * EXPPOLY_32F_A0,
+    1.3252366431597412946295370954987 * EXPPOLY_32F_A0,
+    1.3396675240533030053600306697244 * EXPPOLY_32F_A0,
+    1.3542555469368927282980147401407 * EXPPOLY_32F_A0,
+    1.3690024229745906119296011329822 * EXPPOLY_32F_A0,
+    1.3839098819638319548726595272652 * EXPPOLY_32F_A0,
+    1.3989796725383111402095281367152 * EXPPOLY_32F_A0,
+    1.4142135623730950488016887242097 * EXPPOLY_32F_A0,
+    1.4296133383919700112350657782751 * EXPPOLY_32F_A0,
+    1.4451808069770466200370062414717 * EXPPOLY_32F_A0,
+    1.4609177941806469886513028903106 * EXPPOLY_32F_A0,
+    1.476826145939499311386907480374 * EXPPOLY_32F_A0,
+    1.4929077282912648492006435314867 * EXPPOLY_32F_A0,
+    1.5091644275934227397660195510332 * EXPPOLY_32F_A0,
+    1.5255981507445383068512536895169 * EXPPOLY_32F_A0,
+    1.5422108254079408236122918620907 * EXPPOLY_32F_A0,
+    1.5590044002378369670337280894749 * EXPPOLY_32F_A0,
+    1.5759808451078864864552701601819 * EXPPOLY_32F_A0,
+    1.5931421513422668979372486431191 * EXPPOLY_32F_A0,
+    1.6104903319492543081795206673574 * EXPPOLY_32F_A0,
+    1.628027421857347766848218522014 * EXPPOLY_32F_A0,
+    1.6457554781539648445187567247258 * EXPPOLY_32F_A0,
+    1.6636765803267364350463364569764 * EXPPOLY_32F_A0,
+    1.6817928305074290860622509524664 * EXPPOLY_32F_A0,
+    1.7001063537185234695013625734975 * EXPPOLY_32F_A0,
+    1.7186192981224779156293443764563 * EXPPOLY_32F_A0,
+    1.7373338352737062489942020818722 * EXPPOLY_32F_A0,
+    1.7562521603732994831121606193753 * EXPPOLY_32F_A0,
+    1.7753764925265212525505592001993 * EXPPOLY_32F_A0,
+    1.7947090750031071864277032421278 * EXPPOLY_32F_A0,
+    1.8142521755003987562498346003623 * EXPPOLY_32F_A0,
+    1.8340080864093424634870831895883 * EXPPOLY_32F_A0,
+    1.8539791250833855683924530703377 * EXPPOLY_32F_A0,
+    1.8741676341102999013299989499544 * EXPPOLY_32F_A0,
+    1.8945759815869656413402186534269 * EXPPOLY_32F_A0,
+    1.9152065613971472938726112702958 * EXPPOLY_32F_A0,
+    1.9360617934922944505980559045667 * EXPPOLY_32F_A0,
+    1.9571441241754002690183222516269 * EXPPOLY_32F_A0,
+    1.9784560263879509682582499181312 * EXPPOLY_32F_A0,
+};
+
+const double* getExpTab64f()
+{
+    return expTab;
+}
+
+const float* getExpTab32f()
+{
+    static float CV_DECL_ALIGNED(64) expTab_f[EXPTAB_MASK+1];
+    static volatile bool expTab_f_initialized = false;
+    if (!expTab_f_initialized)
+    {
+        for( int j = 0; j <= EXPTAB_MASK; j++ )
+            expTab_f[j] = (float)expTab[j];
+        expTab_f_initialized = true;
+    }
+    return expTab_f;
+}
+
+
+
+#define LOGTAB_SCALE        8
+#define LOGTAB_MASK         ((1 << LOGTAB_SCALE) - 1)
+
+static const double CV_DECL_ALIGNED(64) logTab[(LOGTAB_MASK+1)*2] = {
+    0.0000000000000000000000000000000000000000,    1.000000000000000000000000000000000000000,
+    .00389864041565732288852075271279318258166,    .9961089494163424124513618677042801556420,
+    .00778214044205494809292034119607706088573,    .9922480620155038759689922480620155038760,
+    .01165061721997527263705585198749759001657,    .9884169884169884169884169884169884169884,
+    .01550418653596525274396267235488267033361,    .9846153846153846153846153846153846153846,
+    .01934296284313093139406447562578250654042,    .9808429118773946360153256704980842911877,
+    .02316705928153437593630670221500622574241,    .9770992366412213740458015267175572519084,
+    .02697658769820207233514075539915211265906,    .9733840304182509505703422053231939163498,
+    .03077165866675368732785500469617545604706,    .9696969696969696969696969696969696969697,
+    .03455238150665972812758397481047722976656,    .9660377358490566037735849056603773584906,
+    .03831886430213659461285757856785494368522,    .9624060150375939849624060150375939849624,
+    .04207121392068705056921373852674150839447,    .9588014981273408239700374531835205992509,
+    .04580953603129420126371940114040626212953,    .9552238805970149253731343283582089552239,
+    .04953393512227662748292900118940451648088,    .9516728624535315985130111524163568773234,
+    .05324451451881227759255210685296333394944,    .9481481481481481481481481481481481481481,
+    .05694137640013842427411105973078520037234,    .9446494464944649446494464944649446494465,
+    .06062462181643483993820353816772694699466,    .9411764705882352941176470588235294117647,
+    .06429435070539725460836422143984236754475,    .9377289377289377289377289377289377289377,
+    .06795066190850773679699159401934593915938,    .9343065693430656934306569343065693430657,
+    .07159365318700880442825962290953611955044,    .9309090909090909090909090909090909090909,
+    .07522342123758751775142172846244648098944,    .9275362318840579710144927536231884057971,
+    .07884006170777602129362549021607264876369,    .9241877256317689530685920577617328519856,
+    .08244366921107458556772229485432035289706,    .9208633093525179856115107913669064748201,
+    .08603433734180314373940490213499288074675,    .9175627240143369175627240143369175627240,
+    .08961215868968712416897659522874164395031,    .9142857142857142857142857142857142857143,
+    .09317722485418328259854092721070628613231,    .9110320284697508896797153024911032028470,
+    .09672962645855109897752299730200320482256,    .9078014184397163120567375886524822695035,
+    .10026945316367513738597949668474029749630,    .9045936395759717314487632508833922261484,
+    .10379679368164355934833764649738441221420,    .9014084507042253521126760563380281690141,
+    .10731173578908805021914218968959175981580,    .8982456140350877192982456140350877192982,
+    .11081436634029011301105782649756292812530,    .8951048951048951048951048951048951048951,
+    .11430477128005862852422325204315711744130,    .8919860627177700348432055749128919860627,
+    .11778303565638344185817487641543266363440,    .8888888888888888888888888888888888888889,
+    .12124924363286967987640707633545389398930,    .8858131487889273356401384083044982698962,
+    .12470347850095722663787967121606925502420,    .8827586206896551724137931034482758620690,
+    .12814582269193003360996385708858724683530,    .8797250859106529209621993127147766323024,
+    .13157635778871926146571524895989568904040,    .8767123287671232876712328767123287671233,
+    .13499516453750481925766280255629681050780,    .8737201365187713310580204778156996587031,
+    .13840232285911913123754857224412262439730,    .8707482993197278911564625850340136054422,
+    .14179791186025733629172407290752744302150,    .8677966101694915254237288135593220338983,
+    .14518200984449788903951628071808954700830,    .8648648648648648648648648648648648648649,
+    .14855469432313711530824207329715136438610,    .8619528619528619528619528619528619528620,
+    .15191604202584196858794030049466527998450,    .8590604026845637583892617449664429530201,
+    .15526612891112392955683674244937719777230,    .8561872909698996655518394648829431438127,
+    .15860503017663857283636730244325008243330,    .8533333333333333333333333333333333333333,
+    .16193282026931324346641360989451641216880,    .8504983388704318936877076411960132890365,
+    .16524957289530714521497145597095368430010,    .8476821192052980132450331125827814569536,
+    .16855536102980664403538924034364754334090,    .8448844884488448844884488448844884488449,
+    .17185025692665920060697715143760433420540,    .8421052631578947368421052631578947368421,
+    .17513433212784912385018287750426679849630,    .8393442622950819672131147540983606557377,
+    .17840765747281828179637841458315961062910,    .8366013071895424836601307189542483660131,
+    .18167030310763465639212199675966985523700,    .8338762214983713355048859934853420195440,
+    .18492233849401198964024217730184318497780,    .8311688311688311688311688311688311688312,
+    .18816383241818296356839823602058459073300,    .8284789644012944983818770226537216828479,
+    .19139485299962943898322009772527962923050,    .8258064516129032258064516129032258064516,
+    .19461546769967164038916962454095482826240,    .8231511254019292604501607717041800643087,
+    .19782574332991986754137769821682013571260,    .8205128205128205128205128205128205128205,
+    .20102574606059073203390141770796617493040,    .8178913738019169329073482428115015974441,
+    .20421554142869088876999228432396193966280,    .8152866242038216560509554140127388535032,
+    .20739519434607056602715147164417430758480,    .8126984126984126984126984126984126984127,
+    .21056476910734961416338251183333341032260,    .8101265822784810126582278481012658227848,
+    .21372432939771812687723695489694364368910,    .8075709779179810725552050473186119873817,
+    .21687393830061435506806333251006435602900,    .8050314465408805031446540880503144654088,
+    .22001365830528207823135744547471404075630,    .8025078369905956112852664576802507836991,
+    .22314355131420973710199007200571941211830,    .8000000000000000000000000000000000000000,
+    .22626367865045338145790765338460914790630,    .7975077881619937694704049844236760124611,
+    .22937410106484582006380890106811420992010,    .7950310559006211180124223602484472049689,
+    .23247487874309405442296849741978803649550,    .7925696594427244582043343653250773993808,
+    .23556607131276688371634975283086532726890,    .7901234567901234567901234567901234567901,
+    .23864773785017498464178231643018079921600,    .7876923076923076923076923076923076923077,
+    .24171993688714515924331749374687206000090,    .7852760736196319018404907975460122699387,
+    .24478272641769091566565919038112042471760,    .7828746177370030581039755351681957186544,
+    .24783616390458124145723672882013488560910,    .7804878048780487804878048780487804878049,
+    .25088030628580937353433455427875742316250,    .7781155015197568389057750759878419452888,
+    .25391520998096339667426946107298135757450,    .7757575757575757575757575757575757575758,
+    .25694093089750041913887912414793390780680,    .7734138972809667673716012084592145015106,
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+};
+
+const double* getLogTab64f()
+{
+    return logTab;
+}
+
+const float* getLogTab32f()
+{
+    static float CV_DECL_ALIGNED(64) logTab_f[(LOGTAB_MASK+1)*2];
+    static volatile bool logTab_f_initialized = false;
+    if (!logTab_f_initialized)
+    {
+        for (int j = 0; j < (LOGTAB_MASK+1)*2; j++)
+            logTab_f[j] = (float)logTab[j];
+        logTab_f_initialized = true;
+    }
+    return logTab_f;
+}
+
+}} // namespace
+
 /* End of file. */

modules/core/src/mathfuncs.hpp

@@ -0,0 +1,15 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_SRC_MATHFUNCS_HPP
+#define OPENCV_CORE_SRC_MATHFUNCS_HPP
+
+namespace cv { namespace details {
+const double* getExpTab64f();
+const float*  getExpTab32f();
+const double* getLogTab64f();
+const float*  getLogTab32f();
+}} // namespace
+
+#endif // OPENCV_CORE_SRC_MATHFUNCS_HPP

modules/core/src/mathfuncs_core.simd.hpp

@@ -2,6 +2,8 @@
 // It is subject to the license terms in the LICENSE file found in the top-level directory
 // of this distribution and at http://opencv.org/license.html.
 
+#include "mathfuncs.hpp"
+
 namespace cv { namespace hal {
 
 CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN
@@ -409,76 +411,6 @@ void log64f(const double *src, double *dst, int n)
 
 #define EXPPOLY_32F_A0 .9670371139572337719125840413672004409288e-2
 
-static const double expTab[] = {
-    1.0 * EXPPOLY_32F_A0,
-    1.0108892860517004600204097905619 * EXPPOLY_32F_A0,
-    1.0218971486541166782344801347833 * EXPPOLY_32F_A0,
-    1.0330248790212284225001082839705 * EXPPOLY_32F_A0,
-    1.0442737824274138403219664787399 * EXPPOLY_32F_A0,
-    1.0556451783605571588083413251529 * EXPPOLY_32F_A0,
-    1.0671404006768236181695211209928 * EXPPOLY_32F_A0,
-    1.0787607977571197937406800374385 * EXPPOLY_32F_A0,
-    1.0905077326652576592070106557607 * EXPPOLY_32F_A0,
-    1.1023825833078409435564142094256 * EXPPOLY_32F_A0,
-    1.1143867425958925363088129569196 * EXPPOLY_32F_A0,
-    1.126521618608241899794798643787 * EXPPOLY_32F_A0,
-    1.1387886347566916537038302838415 * EXPPOLY_32F_A0,
-    1.151189229952982705817759635202 * EXPPOLY_32F_A0,
-    1.1637248587775775138135735990922 * EXPPOLY_32F_A0,
-    1.1763969916502812762846457284838 * EXPPOLY_32F_A0,
-    1.1892071150027210667174999705605 * EXPPOLY_32F_A0,
-    1.2021567314527031420963969574978 * EXPPOLY_32F_A0,
-    1.2152473599804688781165202513388 * EXPPOLY_32F_A0,
-    1.2284805361068700056940089577928 * EXPPOLY_32F_A0,
-    1.2418578120734840485936774687266 * EXPPOLY_32F_A0,
-    1.2553807570246910895793906574423 * EXPPOLY_32F_A0,
-    1.2690509571917332225544190810323 * EXPPOLY_32F_A0,
-    1.2828700160787782807266697810215 * EXPPOLY_32F_A0,
-    1.2968395546510096659337541177925 * EXPPOLY_32F_A0,
-    1.3109612115247643419229917863308 * EXPPOLY_32F_A0,
-    1.3252366431597412946295370954987 * EXPPOLY_32F_A0,
-    1.3396675240533030053600306697244 * EXPPOLY_32F_A0,
-    1.3542555469368927282980147401407 * EXPPOLY_32F_A0,
-    1.3690024229745906119296011329822 * EXPPOLY_32F_A0,
-    1.3839098819638319548726595272652 * EXPPOLY_32F_A0,
-    1.3989796725383111402095281367152 * EXPPOLY_32F_A0,
-    1.4142135623730950488016887242097 * EXPPOLY_32F_A0,
-    1.4296133383919700112350657782751 * EXPPOLY_32F_A0,
-    1.4451808069770466200370062414717 * EXPPOLY_32F_A0,
-    1.4609177941806469886513028903106 * EXPPOLY_32F_A0,
-    1.476826145939499311386907480374 * EXPPOLY_32F_A0,
-    1.4929077282912648492006435314867 * EXPPOLY_32F_A0,
-    1.5091644275934227397660195510332 * EXPPOLY_32F_A0,
-    1.5255981507445383068512536895169 * EXPPOLY_32F_A0,
-    1.5422108254079408236122918620907 * EXPPOLY_32F_A0,
-    1.5590044002378369670337280894749 * EXPPOLY_32F_A0,
-    1.5759808451078864864552701601819 * EXPPOLY_32F_A0,
-    1.5931421513422668979372486431191 * EXPPOLY_32F_A0,
-    1.6104903319492543081795206673574 * EXPPOLY_32F_A0,
-    1.628027421857347766848218522014 * EXPPOLY_32F_A0,
-    1.6457554781539648445187567247258 * EXPPOLY_32F_A0,
-    1.6636765803267364350463364569764 * EXPPOLY_32F_A0,
-    1.6817928305074290860622509524664 * EXPPOLY_32F_A0,
-    1.7001063537185234695013625734975 * EXPPOLY_32F_A0,
-    1.7186192981224779156293443764563 * EXPPOLY_32F_A0,
-    1.7373338352737062489942020818722 * EXPPOLY_32F_A0,
-    1.7562521603732994831121606193753 * EXPPOLY_32F_A0,
-    1.7753764925265212525505592001993 * EXPPOLY_32F_A0,
-    1.7947090750031071864277032421278 * EXPPOLY_32F_A0,
-    1.8142521755003987562498346003623 * EXPPOLY_32F_A0,
-    1.8340080864093424634870831895883 * EXPPOLY_32F_A0,
-    1.8539791250833855683924530703377 * EXPPOLY_32F_A0,
-    1.8741676341102999013299989499544 * EXPPOLY_32F_A0,
-    1.8945759815869656413402186534269 * EXPPOLY_32F_A0,
-    1.9152065613971472938726112702958 * EXPPOLY_32F_A0,
-    1.9360617934922944505980559045667 * EXPPOLY_32F_A0,
-    1.9571441241754002690183222516269 * EXPPOLY_32F_A0,
-    1.9784560263879509682582499181312 * EXPPOLY_32F_A0,
-};
-
-static float expTab_f[EXPTAB_MASK+1];
-static volatile bool extTab_f_initialized = false;
-
 // the code below uses _mm_cast* intrinsics, which are not avialable on VS2005
 #if (defined _MSC_VER && _MSC_VER < 1500) || \
 (!defined __APPLE__ && defined __GNUC__ && __GNUC__*100 + __GNUC_MINOR__ < 402)
@@ -494,14 +426,9 @@ void exp32f( const float *_x, float *y, int n )
 {
     CV_INSTRUMENT_REGION()
 
-    if( !extTab_f_initialized )
+    const float* const expTab_f = cv::details::getExpTab32f();
-    {
-        for( int j = 0; j <= EXPTAB_MASK; j++ )
-            expTab_f[j] = (float)expTab[j];
-        extTab_f_initialized = true;
-    }
 
-    static const float
+    const float
     A4 = (float)(1.000000000000002438532970795181890933776 / EXPPOLY_32F_A0),
     A3 = (float)(.6931471805521448196800669615864773144641 / EXPPOLY_32F_A0),
     A2 = (float)(.2402265109513301490103372422686535526573 / EXPPOLY_32F_A0),
@@ -612,7 +539,9 @@ void exp64f( const double *_x, double *y, int n )
 {
     CV_INSTRUMENT_REGION()
 
-    static const double
+    const double* const expTab = cv::details::getExpTab64f();
+
+    const double
     A5 = .99999999999999999998285227504999 / EXPPOLY_32F_A0,
     A4 = .69314718055994546743029643825322 / EXPPOLY_32F_A0,
     A3 = .24022650695886477918181338054308 / EXPPOLY_32F_A0,
@@ -737,268 +666,6 @@ void exp64f( const double *_x, double *y, int n )
 #define LOGTAB_SCALE        8
 #define LOGTAB_MASK         ((1 << LOGTAB_SCALE) - 1)
 
-static const double CV_DECL_ALIGNED(16) logTab[] = {
-    0.0000000000000000000000000000000000000000,    1.000000000000000000000000000000000000000,
-    .00389864041565732288852075271279318258166,    .9961089494163424124513618677042801556420,
-    .00778214044205494809292034119607706088573,    .9922480620155038759689922480620155038760,
-    .01165061721997527263705585198749759001657,    .9884169884169884169884169884169884169884,
-    .01550418653596525274396267235488267033361,    .9846153846153846153846153846153846153846,
-    .01934296284313093139406447562578250654042,    .9808429118773946360153256704980842911877,
-    .02316705928153437593630670221500622574241,    .9770992366412213740458015267175572519084,
-    .02697658769820207233514075539915211265906,    .9733840304182509505703422053231939163498,
-    .03077165866675368732785500469617545604706,    .9696969696969696969696969696969696969697,
-    .03455238150665972812758397481047722976656,    .9660377358490566037735849056603773584906,
-    .03831886430213659461285757856785494368522,    .9624060150375939849624060150375939849624,
-    .04207121392068705056921373852674150839447,    .9588014981273408239700374531835205992509,
-    .04580953603129420126371940114040626212953,    .9552238805970149253731343283582089552239,
-    .04953393512227662748292900118940451648088,    .9516728624535315985130111524163568773234,
-    .05324451451881227759255210685296333394944,    .9481481481481481481481481481481481481481,
-    .05694137640013842427411105973078520037234,    .9446494464944649446494464944649446494465,
-    .06062462181643483993820353816772694699466,    .9411764705882352941176470588235294117647,
-    .06429435070539725460836422143984236754475,    .9377289377289377289377289377289377289377,
-    .06795066190850773679699159401934593915938,    .9343065693430656934306569343065693430657,
-    .07159365318700880442825962290953611955044,    .9309090909090909090909090909090909090909,
-    .07522342123758751775142172846244648098944,    .9275362318840579710144927536231884057971,
-    .07884006170777602129362549021607264876369,    .9241877256317689530685920577617328519856,
-    .08244366921107458556772229485432035289706,    .9208633093525179856115107913669064748201,
-    .08603433734180314373940490213499288074675,    .9175627240143369175627240143369175627240,
-    .08961215868968712416897659522874164395031,    .9142857142857142857142857142857142857143,
-    .09317722485418328259854092721070628613231,    .9110320284697508896797153024911032028470,
-    .09672962645855109897752299730200320482256,    .9078014184397163120567375886524822695035,
-    .10026945316367513738597949668474029749630,    .9045936395759717314487632508833922261484,
-    .10379679368164355934833764649738441221420,    .9014084507042253521126760563380281690141,
-    .10731173578908805021914218968959175981580,    .8982456140350877192982456140350877192982,
-    .11081436634029011301105782649756292812530,    .8951048951048951048951048951048951048951,
-    .11430477128005862852422325204315711744130,    .8919860627177700348432055749128919860627,
-    .11778303565638344185817487641543266363440,    .8888888888888888888888888888888888888889,
-    .12124924363286967987640707633545389398930,    .8858131487889273356401384083044982698962,
-    .12470347850095722663787967121606925502420,    .8827586206896551724137931034482758620690,
-    .12814582269193003360996385708858724683530,    .8797250859106529209621993127147766323024,
-    .13157635778871926146571524895989568904040,    .8767123287671232876712328767123287671233,
-    .13499516453750481925766280255629681050780,    .8737201365187713310580204778156996587031,
-    .13840232285911913123754857224412262439730,    .8707482993197278911564625850340136054422,
-    .14179791186025733629172407290752744302150,    .8677966101694915254237288135593220338983,
-    .14518200984449788903951628071808954700830,    .8648648648648648648648648648648648648649,
-    .14855469432313711530824207329715136438610,    .8619528619528619528619528619528619528620,
-    .15191604202584196858794030049466527998450,    .8590604026845637583892617449664429530201,
-    .15526612891112392955683674244937719777230,    .8561872909698996655518394648829431438127,
-    .15860503017663857283636730244325008243330,    .8533333333333333333333333333333333333333,
-    .16193282026931324346641360989451641216880,    .8504983388704318936877076411960132890365,
-    .16524957289530714521497145597095368430010,    .8476821192052980132450331125827814569536,
-    .16855536102980664403538924034364754334090,    .8448844884488448844884488448844884488449,
-    .17185025692665920060697715143760433420540,    .8421052631578947368421052631578947368421,
-    .17513433212784912385018287750426679849630,    .8393442622950819672131147540983606557377,
-    .17840765747281828179637841458315961062910,    .8366013071895424836601307189542483660131,
-    .18167030310763465639212199675966985523700,    .8338762214983713355048859934853420195440,
-    .18492233849401198964024217730184318497780,    .8311688311688311688311688311688311688312,
-    .18816383241818296356839823602058459073300,    .8284789644012944983818770226537216828479,
-    .19139485299962943898322009772527962923050,    .8258064516129032258064516129032258064516,
-    .19461546769967164038916962454095482826240,    .8231511254019292604501607717041800643087,
-    .19782574332991986754137769821682013571260,    .8205128205128205128205128205128205128205,
-    .20102574606059073203390141770796617493040,    .8178913738019169329073482428115015974441,
-    .20421554142869088876999228432396193966280,    .8152866242038216560509554140127388535032,
-    .20739519434607056602715147164417430758480,    .8126984126984126984126984126984126984127,
-    .21056476910734961416338251183333341032260,    .8101265822784810126582278481012658227848,
-    .21372432939771812687723695489694364368910,    .8075709779179810725552050473186119873817,
-    .21687393830061435506806333251006435602900,    .8050314465408805031446540880503144654088,
-    .22001365830528207823135744547471404075630,    .8025078369905956112852664576802507836991,
-    .22314355131420973710199007200571941211830,    .8000000000000000000000000000000000000000,
-    .22626367865045338145790765338460914790630,    .7975077881619937694704049844236760124611,
-    .22937410106484582006380890106811420992010,    .7950310559006211180124223602484472049689,
-    .23247487874309405442296849741978803649550,    .7925696594427244582043343653250773993808,
-    .23556607131276688371634975283086532726890,    .7901234567901234567901234567901234567901,
-    .23864773785017498464178231643018079921600,    .7876923076923076923076923076923076923077,
-    .24171993688714515924331749374687206000090,    .7852760736196319018404907975460122699387,
-    .24478272641769091566565919038112042471760,    .7828746177370030581039755351681957186544,
-    .24783616390458124145723672882013488560910,    .7804878048780487804878048780487804878049,
-    .25088030628580937353433455427875742316250,    .7781155015197568389057750759878419452888,
-    .25391520998096339667426946107298135757450,    .7757575757575757575757575757575757575758,
-    .25694093089750041913887912414793390780680,    .7734138972809667673716012084592145015106,
-    .25995752443692604627401010475296061486000,    .7710843373493975903614457831325301204819,
-    .26296504550088134477547896494797896593800,    .7687687687687687687687687687687687687688,
-    .26596354849713793599974565040611196309330,    .7664670658682634730538922155688622754491,
-    .26895308734550393836570947314612567424780,    .7641791044776119402985074626865671641791,
-    .27193371548364175804834985683555714786050,    .7619047619047619047619047619047619047619,
-    .27490548587279922676529508862586226314300,    .7596439169139465875370919881305637982196,
-    .27786845100345625159121709657483734190480,    .7573964497041420118343195266272189349112,
-    .28082266290088775395616949026589281857030,    .7551622418879056047197640117994100294985,
-    .28376817313064456316240580235898960381750,    .7529411764705882352941176470588235294118,
-    .28670503280395426282112225635501090437180,    .7507331378299120234604105571847507331378,
-    .28963329258304265634293983566749375313530,    .7485380116959064327485380116959064327485,
-    .29255300268637740579436012922087684273730,    .7463556851311953352769679300291545189504,
-    .29546421289383584252163927885703742504130,    .7441860465116279069767441860465116279070,
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-    .30702503529491181888388950937951449304830,    .7356321839080459770114942528735632183908,
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-    .31845373111853458869546784626436419785030,    .7272727272727272727272727272727272727273,
-    .32129061245373424782201254856772720813750,    .7252124645892351274787535410764872521246,
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-    .34092658697059319283795275623560883104800,    .7111111111111111111111111111111111111111,
-    .34370051385331840121395430287520866841080,    .7091412742382271468144044321329639889197,
-    .34646676734620857063262633346312213689100,    .7071823204419889502762430939226519337017,
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-    .36018440357500774995358483465679455548530,    .6975476839237057220708446866485013623978,
-    .36290549368936841911903457003063522279280,    .6956521739130434782608695652173913043478,
-    .36561919956096466943762379742111079394830,    .6937669376693766937669376693766937669377,
-    .36832556115870762614150635272380895912650,    .6918918918918918918918918918918918918919,
-    .37102461812787262962487488948681857436900,    .6900269541778975741239892183288409703504,
-    .37371640979358405898480555151763837784530,    .6881720430107526881720430107526881720430,
-    .37640097516425302659470730759494472295050,    .6863270777479892761394101876675603217158,
-    .37907835293496944251145919224654790014030,    .6844919786096256684491978609625668449198,
-    .38174858149084833769393299007788300514230,    .6826666666666666666666666666666666666667,
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-};
-
-static float logTab_f[(LOGTAB_MASK+1)*2];
-static volatile bool logTab_f_initialized = false;
-
 #define LOGTAB_TRANSLATE(tab, x, h) (((x) - 1.f)*tab[(h)+1])
 static const double ln_2 = 0.69314718055994530941723212145818;
 
@@ -1006,15 +673,10 @@ void log32f( const float *_x, float *y, int n )
 {
     CV_INSTRUMENT_REGION()
 
-    if( !logTab_f_initialized )
+    const float* const logTab_f = cv::details::getLogTab32f();
-    {
-        for( int j = 0; j < (LOGTAB_MASK+1)*2; j++ )
-            logTab_f[j] = (float)logTab[j];
-        logTab_f_initialized = true;
-    }
 
-    static const int LOGTAB_MASK2_32F = (1 << (23 - LOGTAB_SCALE)) - 1;
+    const int LOGTAB_MASK2_32F = (1 << (23 - LOGTAB_SCALE)) - 1;
-    static const float
+    const float
     A0 = 0.3333333333333333333333333f,
     A1 = -0.5f,
     A2 = 1.f;
@@ -1082,8 +744,10 @@ void log64f( const double *x, double *y, int n )
 {
     CV_INSTRUMENT_REGION()
 
-    static const int64 LOGTAB_MASK2_64F = ((int64)1 << (52 - LOGTAB_SCALE)) - 1;
+    const double* const logTab = cv::details::getLogTab64f();
-    static const double
+
+    const int64 LOGTAB_MASK2_64F = ((int64)1 << (52 - LOGTAB_SCALE)) - 1;
+    const double
     A7 = 1.0,
     A6 = -0.5,
     A5 = 0.333333333333333314829616256247390992939472198486328125,

modules/core/src/matmul.cpp

@@ -796,7 +796,7 @@ static bool ocl_gemm( InputArray matA, InputArray matB, double alpha,
     int depth = matA.depth(), cn = matA.channels();
     int type = CV_MAKETYPE(depth, cn);
 
-    CV_Assert( type == matB.type(), (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2) );
+    CV_Assert_N( type == matB.type(), (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2) );
 
     const ocl::Device & dev = ocl::Device::getDefault();
     bool doubleSupport = dev.doubleFPConfig() > 0;
@@ -1555,7 +1555,7 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
     Size a_size = A.size(), d_size;
     int len = 0, type = A.type();
 
-    CV_Assert( type == B.type(), (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2) );
+    CV_Assert_N( type == B.type(), (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2) );
 
     switch( flags & (GEMM_1_T|GEMM_2_T) )
     {
@@ -1583,7 +1583,7 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
 
     if( !C.empty() )
     {
-        CV_Assert( C.type() == type,
+        CV_Assert_N( C.type() == type,
             (((flags&GEMM_3_T) == 0 && C.rows == d_size.height && C.cols == d_size.width) ||
              ((flags&GEMM_3_T) != 0 && C.rows == d_size.width && C.cols == d_size.height)));
     }
@@ -2457,7 +2457,7 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean,
 {
     CV_INSTRUMENT_REGION()
 
-    CV_Assert( data, nsamples > 0 );
+    CV_Assert_N( data, nsamples > 0 );
     Size size = data[0].size();
     int sz = size.width * size.height, esz = (int)data[0].elemSize();
     int type = data[0].type();
@@ -2480,7 +2480,7 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean,
 
     for( int i = 0; i < nsamples; i++ )
     {
-        CV_Assert( data[i].size() == size, data[i].type() == type );
+        CV_Assert_N( data[i].size() == size, data[i].type() == type );
         if( data[i].isContinuous() )
             memcpy( _data.ptr(i), data[i].ptr(), sz*esz );
         else
@@ -2516,7 +2516,7 @@ void cv::calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray
         int i = 0;
         for(std::vector<cv::Mat>::iterator each = src.begin(); each != src.end(); ++each, ++i )
         {
-            CV_Assert( (*each).size() == size, (*each).type() == type );
+            CV_Assert_N( (*each).size() == size, (*each).type() == type );
             Mat dataRow(size.height, size.width, type, _data.ptr(i));
             (*each).copyTo(dataRow);
         }
@@ -2595,7 +2595,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
     AutoBuffer<double> buf(len);
     double result = 0;
 
-    CV_Assert( type == v2.type(), type == icovar.type(),
+    CV_Assert_N( type == v2.type(), type == icovar.type(),
         sz == v2.size(), len == icovar.rows && len == icovar.cols );
 
     sz.width *= v1.channels();
@@ -2888,7 +2888,7 @@ void cv::mulTransposed( InputArray _src, OutputArray _dst, bool ata,
 
     if( !delta.empty() )
     {
-        CV_Assert( delta.channels() == 1,
+        CV_Assert_N( delta.channels() == 1,
             (delta.rows == src.rows || delta.rows == 1),
             (delta.cols == src.cols || delta.cols == 1));
         if( delta.type() != dtype )
@@ -3291,7 +3291,7 @@ double Mat::dot(InputArray _mat) const
     Mat mat = _mat.getMat();
     int cn = channels();
     DotProdFunc func = getDotProdFunc(depth());
-    CV_Assert( mat.type() == type(), mat.size == size, func != 0 );
+    CV_Assert_N( mat.type() == type(), mat.size == size, func != 0 );
 
     if( isContinuous() && mat.isContinuous() )
     {
@@ -3327,7 +3327,7 @@ CV_IMPL void cvGEMM( const CvArr* Aarr, const CvArr* Barr, double alpha,
     if( Carr )
         C = cv::cvarrToMat(Carr);
 
-    CV_Assert( (D.rows == ((flags & CV_GEMM_A_T) == 0 ? A.rows : A.cols)),
+    CV_Assert_N( (D.rows == ((flags & CV_GEMM_A_T) == 0 ? A.rows : A.cols)),
                (D.cols == ((flags & CV_GEMM_B_T) == 0 ? B.cols : B.rows)),
                D.type() == A.type() );
 
@@ -3350,7 +3350,7 @@ cvTransform( const CvArr* srcarr, CvArr* dstarr,
         m = _m;
     }
 
-    CV_Assert( dst.depth() == src.depth(), dst.channels() == m.rows );
+    CV_Assert_N( dst.depth() == src.depth(), dst.channels() == m.rows );
     cv::transform( src, dst, m );
 }
 
@@ -3360,7 +3360,7 @@ cvPerspectiveTransform( const CvArr* srcarr, CvArr* dstarr, const CvMat* mat )
 {
     cv::Mat m = cv::cvarrToMat(mat), src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
 
-    CV_Assert( dst.type() == src.type(), dst.channels() == m.rows-1 );
+    CV_Assert_N( dst.type() == src.type(), dst.channels() == m.rows-1 );
     cv::perspectiveTransform( src, dst, m );
 }
 
@@ -3370,7 +3370,7 @@ CV_IMPL void cvScaleAdd( const CvArr* srcarr1, CvScalar scale,
 {
     cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
 
-    CV_Assert( src1.size == dst.size, src1.type() == dst.type() );
+    CV_Assert_N( src1.size == dst.size, src1.type() == dst.type() );
     cv::scaleAdd( src1, scale.val[0], cv::cvarrToMat(srcarr2), dst );
 }
 
@@ -3380,7 +3380,7 @@ cvCalcCovarMatrix( const CvArr** vecarr, int count,
                    CvArr* covarr, CvArr* avgarr, int flags )
 {
     cv::Mat cov0 = cv::cvarrToMat(covarr), cov = cov0, mean0, mean;
-    CV_Assert( vecarr != 0, count >= 1 );
+    CV_Assert_N( vecarr != 0, count >= 1 );
 
     if( avgarr )
         mean = mean0 = cv::cvarrToMat(avgarr);
@@ -3460,7 +3460,7 @@ cvCalcPCA( const CvArr* data_arr, CvArr* avg_arr, CvArr* eigenvals, CvArr* eigen
     int ecount0 = evals0.cols + evals0.rows - 1;
     int ecount = evals.cols + evals.rows - 1;
 
-    CV_Assert( (evals0.cols == 1 || evals0.rows == 1),
+    CV_Assert_N( (evals0.cols == 1 || evals0.rows == 1),
                 ecount0 <= ecount,
                 evects0.cols == evects.cols,
                 evects0.rows == ecount0 );
@@ -3491,12 +3491,12 @@ cvProjectPCA( const CvArr* data_arr, const CvArr* avg_arr,
     int n;
     if( mean.rows == 1 )
     {
-        CV_Assert(dst.cols <= evects.rows, dst.rows == data.rows);
+        CV_Assert_N(dst.cols <= evects.rows, dst.rows == data.rows);
         n = dst.cols;
     }
     else
     {
-        CV_Assert(dst.rows <= evects.rows, dst.cols == data.cols);
+        CV_Assert_N(dst.rows <= evects.rows, dst.cols == data.cols);
         n = dst.rows;
     }
     pca.eigenvectors = evects.rowRange(0, n);
@@ -3522,12 +3522,12 @@ cvBackProjectPCA( const CvArr* proj_arr, const CvArr* avg_arr,
     int n;
     if( mean.rows == 1 )
     {
-        CV_Assert(data.cols <= evects.rows, dst.rows == data.rows);
+        CV_Assert_N(data.cols <= evects.rows, dst.rows == data.rows);
         n = data.cols;
     }
     else
     {
-        CV_Assert(data.rows <= evects.rows, dst.cols == data.cols);
+        CV_Assert_N(data.rows <= evects.rows, dst.cols == data.cols);
         n = data.rows;
     }
     pca.eigenvectors = evects.rowRange(0, n);

modules/core/src/trace.cpp

@@ -801,10 +801,12 @@ TraceStorage* TraceManagerThreadLocal::getStorage() const
             const char* pos = strrchr(filepath.c_str(), '/'); // extract filename
 #ifdef _WIN32
             if (!pos)
-                strrchr(filepath.c_str(), '\\');
+                pos = strrchr(filepath.c_str(), '\\');
 #endif
             if (!pos)
                 pos = filepath.c_str();
+            else
+                pos += 1; // fix to skip extra slash in filename beginning
             msg.printf("#thread file: %s\n", pos);
             global->put(msg);
             storage.reset(new AsyncTraceStorage(filepath));

modules/core/src/va_intel.cpp

@@ -324,6 +324,163 @@ static void copy_convert_bgr_to_nv12(const VAImage& image, const Mat& bgr, unsig
         dstUV += dstStepUV;
     }
 }
+
+
+static void copy_convert_yv12_to_bgr(const VAImage& image, const unsigned char* buffer, Mat& bgr)
+{
+    const float d1 = 16.0f;
+    const float d2 = 128.0f;
+
+    static const float coeffs[5] =
+        {
+            1.163999557f,
+            2.017999649f,
+            -0.390999794f,
+            -0.812999725f,
+            1.5959997177f
+        };
+
+    CV_CheckEQ(image.format.fourcc, VA_FOURCC_YV12, "Unexpected image format");
+    CV_CheckEQ(image.num_planes, 3, "");
+
+    const size_t srcOffsetY = image.offsets[0];
+    const size_t srcOffsetV = image.offsets[1];
+    const size_t srcOffsetU = image.offsets[2];
+
+    const size_t srcStepY = image.pitches[0];
+    const size_t srcStepU = image.pitches[1];
+    const size_t srcStepV = image.pitches[2];
+
+    const size_t dstStep = bgr.step;
+
+    const unsigned char* srcY_ = buffer + srcOffsetY;
+    const unsigned char* srcV_ = buffer + srcOffsetV;
+    const unsigned char* srcU_ = buffer + srcOffsetU;
+
+    for (int y = 0; y < bgr.rows; y += 2)
+    {
+        const unsigned char* srcY0 = srcY_ + (srcStepY) * y;
+        const unsigned char* srcY1 = srcY0 + srcStepY;
+
+        const unsigned char* srcV = srcV_ + (srcStepV) * y / 2;
+        const unsigned char* srcU = srcU_ + (srcStepU) * y / 2;
+
+        unsigned char* dst0 = bgr.data + (dstStep) * y;
+        unsigned char* dst1 = dst0 + dstStep;
+
+        for (int x = 0; x < bgr.cols; x += 2)
+        {
+            float Y0 = float(srcY0[x+0]);
+            float Y1 = float(srcY0[x+1]);
+            float Y2 = float(srcY1[x+0]);
+            float Y3 = float(srcY1[x+1]);
+
+            float U = float(srcU[x/2]) - d2;
+            float V = float(srcV[x/2]) - d2;
+
+            Y0 = std::max(0.0f, Y0 - d1) * coeffs[0];
+            Y1 = std::max(0.0f, Y1 - d1) * coeffs[0];
+            Y2 = std::max(0.0f, Y2 - d1) * coeffs[0];
+            Y3 = std::max(0.0f, Y3 - d1) * coeffs[0];
+
+            float ruv = coeffs[4]*V;
+            float guv = coeffs[3]*V + coeffs[2]*U;
+            float buv = coeffs[1]*U;
+
+            dst0[(x+0)*NCHANNELS+0] = saturate_cast<unsigned char>(Y0 + buv);
+            dst0[(x+0)*NCHANNELS+1] = saturate_cast<unsigned char>(Y0 + guv);
+            dst0[(x+0)*NCHANNELS+2] = saturate_cast<unsigned char>(Y0 + ruv);
+
+            dst0[(x+1)*NCHANNELS+0] = saturate_cast<unsigned char>(Y1 + buv);
+            dst0[(x+1)*NCHANNELS+1] = saturate_cast<unsigned char>(Y1 + guv);
+            dst0[(x+1)*NCHANNELS+2] = saturate_cast<unsigned char>(Y1 + ruv);
+
+            dst1[(x+0)*NCHANNELS+0] = saturate_cast<unsigned char>(Y2 + buv);
+            dst1[(x+0)*NCHANNELS+1] = saturate_cast<unsigned char>(Y2 + guv);
+            dst1[(x+0)*NCHANNELS+2] = saturate_cast<unsigned char>(Y2 + ruv);
+
+            dst1[(x+1)*NCHANNELS+0] = saturate_cast<unsigned char>(Y3 + buv);
+            dst1[(x+1)*NCHANNELS+1] = saturate_cast<unsigned char>(Y3 + guv);
+            dst1[(x+1)*NCHANNELS+2] = saturate_cast<unsigned char>(Y3 + ruv);
+        }
+    }
+}
+
+static void copy_convert_bgr_to_yv12(const VAImage& image, const Mat& bgr, unsigned char* buffer)
+{
+    const float d1 = 16.0f;
+    const float d2 = 128.0f;
+
+    static const float coeffs[8] =
+        {
+            0.256999969f,  0.50399971f,   0.09799957f,   -0.1479988098f,
+            -0.2909994125f, 0.438999176f, -0.3679990768f, -0.0709991455f
+        };
+
+    CV_CheckEQ(image.format.fourcc, VA_FOURCC_YV12, "Unexpected image format");
+    CV_CheckEQ(image.num_planes, 3, "");
+
+    const size_t dstOffsetY = image.offsets[0];
+    const size_t dstOffsetV = image.offsets[1];
+    const size_t dstOffsetU = image.offsets[2];
+
+    const size_t dstStepY = image.pitches[0];
+    const size_t dstStepU = image.pitches[1];
+    const size_t dstStepV = image.pitches[2];
+
+    unsigned char* dstY_ = buffer + dstOffsetY;
+    unsigned char* dstV_ = buffer + dstOffsetV;
+    unsigned char* dstU_ = buffer + dstOffsetU;
+
+    const size_t srcStep = bgr.step;
+
+    for (int y = 0; y < bgr.rows; y += 2)
+    {
+        unsigned char* dstY0 = dstY_ + (dstStepY) * y;
+        unsigned char* dstY1 = dstY0 + dstStepY;
+
+        unsigned char* dstV = dstV_ + (dstStepV) * y / 2;
+        unsigned char* dstU = dstU_ + (dstStepU) * y / 2;
+
+        const unsigned char* src0 = bgr.data + (srcStep) * y;
+        const unsigned char* src1 = src0 + srcStep;
+
+        for (int x = 0; x < bgr.cols; x += 2)
+        {
+            float B0 = float(src0[(x+0)*NCHANNELS+0]);
+            float G0 = float(src0[(x+0)*NCHANNELS+1]);
+            float R0 = float(src0[(x+0)*NCHANNELS+2]);
+
+            float B1 = float(src0[(x+1)*NCHANNELS+0]);
+            float G1 = float(src0[(x+1)*NCHANNELS+1]);
+            float R1 = float(src0[(x+1)*NCHANNELS+2]);
+
+            float B2 = float(src1[(x+0)*NCHANNELS+0]);
+            float G2 = float(src1[(x+0)*NCHANNELS+1]);
+            float R2 = float(src1[(x+0)*NCHANNELS+2]);
+
+            float B3 = float(src1[(x+1)*NCHANNELS+0]);
+            float G3 = float(src1[(x+1)*NCHANNELS+1]);
+            float R3 = float(src1[(x+1)*NCHANNELS+2]);
+
+            float Y0 = coeffs[0]*R0 + coeffs[1]*G0 + coeffs[2]*B0 + d1;
+            float Y1 = coeffs[0]*R1 + coeffs[1]*G1 + coeffs[2]*B1 + d1;
+            float Y2 = coeffs[0]*R2 + coeffs[1]*G2 + coeffs[2]*B2 + d1;
+            float Y3 = coeffs[0]*R3 + coeffs[1]*G3 + coeffs[2]*B3 + d1;
+
+            float U = coeffs[3]*R0 + coeffs[4]*G0 + coeffs[5]*B0 + d2;
+            float V = coeffs[5]*R0 + coeffs[6]*G0 + coeffs[7]*B0 + d2;
+
+            dstY0[x+0] = saturate_cast<unsigned char>(Y0);
+            dstY0[x+1] = saturate_cast<unsigned char>(Y1);
+            dstY1[x+0] = saturate_cast<unsigned char>(Y2);
+            dstY1[x+1] = saturate_cast<unsigned char>(Y3);
+
+            dstU[x/2] = saturate_cast<unsigned char>(U);
+            dstV[x/2] = saturate_cast<unsigned char>(V);
+        }
+    }
+}
 #endif // HAVE_VA
 
 void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface, Size size)
@@ -412,9 +569,12 @@ void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface,
         if (status != VA_STATUS_SUCCESS)
             CV_Error(cv::Error::StsError, "VA-API: vaMapBuffer failed");
 
-        CV_Assert(image.format.fourcc == VA_FOURCC_NV12);
+        if (image.format.fourcc == VA_FOURCC_NV12)
-
+            copy_convert_bgr_to_nv12(image, m, buffer);
-        copy_convert_bgr_to_nv12(image, m, buffer);
+        if (image.format.fourcc == VA_FOURCC_YV12)
+            copy_convert_bgr_to_yv12(image, m, buffer);
+        else
+            CV_Check((int)image.format.fourcc, image.format.fourcc == VA_FOURCC_NV12 || image.format.fourcc == VA_FOURCC_YV12, "Unexpected image format");
 
         status = vaUnmapBuffer(display, image.buf);
         if (status != VA_STATUS_SUCCESS)
@@ -510,9 +670,12 @@ void convertFromVASurface(VADisplay display, VASurfaceID surface, Size size, Out
         if (status != VA_STATUS_SUCCESS)
             CV_Error(cv::Error::StsError, "VA-API: vaMapBuffer failed");
 
-        CV_Assert(image.format.fourcc == VA_FOURCC_NV12);
+        if (image.format.fourcc == VA_FOURCC_NV12)
-
+            copy_convert_nv12_to_bgr(image, buffer, m);
-        copy_convert_nv12_to_bgr(image, buffer, m);
+        if (image.format.fourcc == VA_FOURCC_YV12)
+            copy_convert_yv12_to_bgr(image, buffer, m);
+        else
+            CV_Check((int)image.format.fourcc, image.format.fourcc == VA_FOURCC_NV12 || image.format.fourcc == VA_FOURCC_YV12, "Unexpected image format");
 
         status = vaUnmapBuffer(display, image.buf);
         if (status != VA_STATUS_SUCCESS)

modules/core/test/test_arithm.cpp

@@ -2117,4 +2117,71 @@ TEST(Core_Norm, IPP_regression_NORM_L1_16UC3_small)
     EXPECT_EQ((double)20*cn, cv::norm(a, b, NORM_L1, mask));
 }
 
+
+TEST(Core_ConvertTo, regression_12121)
+{
+    {
+        Mat src(4, 64, CV_32SC1, Scalar(-1));
+        Mat dst;
+        src.convertTo(dst, CV_8U);
+        EXPECT_EQ(0, dst.at<uchar>(0, 0)) << "src=" << src.at<int>(0, 0);
+    }
+
+    {
+        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN));
+        Mat dst;
+        src.convertTo(dst, CV_8U);
+        EXPECT_EQ(0, dst.at<uchar>(0, 0)) << "src=" << src.at<int>(0, 0);
+    }
+
+    {
+        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN + 32767));
+        Mat dst;
+        src.convertTo(dst, CV_8U);
+        EXPECT_EQ(0, dst.at<uchar>(0, 0)) << "src=" << src.at<int>(0, 0);
+    }
+
+    {
+        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN + 32768));
+        Mat dst;
+        src.convertTo(dst, CV_8U);
+        EXPECT_EQ(0, dst.at<uchar>(0, 0)) << "src=" << src.at<int>(0, 0);
+    }
+
+    {
+        Mat src(4, 64, CV_32SC1, Scalar(32768));
+        Mat dst;
+        src.convertTo(dst, CV_8U);
+        EXPECT_EQ(255, dst.at<uchar>(0, 0)) << "src=" << src.at<int>(0, 0);
+    }
+
+    {
+        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN));
+        Mat dst;
+        src.convertTo(dst, CV_16U);
+        EXPECT_EQ(0, dst.at<ushort>(0, 0)) << "src=" << src.at<int>(0, 0);
+    }
+
+    {
+        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN + 32767));
+        Mat dst;
+        src.convertTo(dst, CV_16U);
+        EXPECT_EQ(0, dst.at<ushort>(0, 0)) << "src=" << src.at<int>(0, 0);
+    }
+
+    {
+        Mat src(4, 64, CV_32SC1, Scalar(INT_MIN + 32768));
+        Mat dst;
+        src.convertTo(dst, CV_16U);
+        EXPECT_EQ(0, dst.at<ushort>(0, 0)) << "src=" << src.at<int>(0, 0);
+    }
+
+    {
+        Mat src(4, 64, CV_32SC1, Scalar(65536));
+        Mat dst;
+        src.convertTo(dst, CV_16U);
+        EXPECT_EQ(65535, dst.at<ushort>(0, 0)) << "src=" << src.at<int>(0, 0);
+    }
+}
+
 }} // namespace

modules/core/test/test_intrin_utils.hpp

@@ -1123,9 +1123,37 @@ template<typename R> struct TheTest
         return *this;
     }
 
+#if CV_FP16
+    TheTest & test_loadstore_fp16_f32()
+    {
+        printf("test_loadstore_fp16_f32 ...\n");
+        AlignedData<v_uint16> data; data.a.clear();
+        data.a.d[0] = 0x3c00; // 1.0
+        data.a.d[R::nlanes - 1] = (unsigned short)0xc000; // -2.0
+        AlignedData<v_float32> data_f32; data_f32.a.clear();
+        AlignedData<v_uint16> out;
+
+        R r1 = vx_load_fp16_f32((short*)data.a.d);
+        R r2(r1);
+        EXPECT_EQ(1.0f, r1.get0());
+        vx_store(data_f32.a.d, r2);
+        EXPECT_EQ(-2.0f, data_f32.a.d[R::nlanes - 1]);
+
+        out.a.clear();
+        vx_store_fp16((short*)out.a.d, r2);
+        for (int i = 0; i < R::nlanes; ++i)
+        {
+            EXPECT_EQ(data.a[i], out.a[i]) << "i=" << i;
+        }
+
+        return *this;
+    }
+#endif
+
+#if CV_SIMD_FP16
     TheTest & test_loadstore_fp16()
     {
-#if CV_FP16 && CV_SIMD
+        printf("test_loadstore_fp16 ...\n");
         AlignedData<R> data;
         AlignedData<R> out;
 
@@ -1149,12 +1177,10 @@ template<typename R> struct TheTest
         EXPECT_EQ(data.a, out.a);
 
         return *this;
-#endif
     }
-
     TheTest & test_float_cvt_fp16()
     {
-#if CV_FP16 && CV_SIMD
+        printf("test_float_cvt_fp16 ...\n");
         AlignedData<v_float32> data;
 
         // check conversion
@@ -1165,9 +1191,8 @@ template<typename R> struct TheTest
         EXPECT_EQ(r3.get0(), r1.get0());
 
         return *this;
-#endif
     }
-
+#endif
 };
 
 
@@ -1448,11 +1473,14 @@ void test_hal_intrin_float64()
 void test_hal_intrin_float16()
 {
     DUMP_ENTRY(v_float16);
-#if CV_SIMD_WIDTH > 16
+#if CV_FP16
+    TheTest<v_float32>().test_loadstore_fp16_f32();
+#endif
+#if CV_SIMD_FP16
     TheTest<v_float16>()
         .test_loadstore_fp16()
         .test_float_cvt_fp16()
-        ;
+    ;
 #endif
 }
 #endif

modules/cudaarithm/src/cuda/math.cu

@@ -278,20 +278,12 @@ namespace
 {
     template<typename T, bool Signed = numeric_limits<T>::is_signed> struct PowOp : unary_function<T, T>
     {
-        float power;
+        typedef typename LargerType<T, float>::type LargerType;
+        LargerType power;
 
         __device__ __forceinline__ T operator()(T e) const
         {
-            return cudev::saturate_cast<T>(__powf((float)e, power));
+            T res = cudev::saturate_cast<T>(__powf(e < 0 ? -e : e, power));
-        }
-    };
-    template<typename T> struct PowOp<T, true> : unary_function<T, T>
-    {
-        float power;
-
-        __device__ __forceinline__ T operator()(T e) const
-        {
-            T res = cudev::saturate_cast<T>(__powf((float)e, power));
 
             if ((e < 0) && (1 & static_cast<int>(power)))
                 res *= -1;
@@ -299,22 +291,15 @@ namespace
             return res;
         }
     };
-    template<> struct PowOp<float> : unary_function<float, float>
-    {
-        float power;
 
-        __device__ __forceinline__ float operator()(float e) const
+    template<typename T> struct PowOp<T, false> : unary_function<T, T>
-        {
-            return __powf(::fabs(e), power);
-        }
-    };
-    template<> struct PowOp<double> : unary_function<double, double>
     {
-        double power;
+        typedef typename LargerType<T, float>::type LargerType;
+        LargerType power;
 
-        __device__ __forceinline__ double operator()(double e) const
+        __device__ __forceinline__ T operator()(T e) const
         {
-            return ::pow(::fabs(e), power);
+            return cudev::saturate_cast<T>(__powf(e, power));
         }
     };
 

modules/dnn/include/opencv2/dnn.hpp

@@ -59,6 +59,20 @@
     A network training is in principle not supported.
   @}
 */
+/** @example samples/dnn/classification.cpp
+Check @ref tutorial_dnn_googlenet "the corresponding tutorial" for more details
+*/
+/** @example samples/dnn/colorization.cpp
+*/
+/** @example samples/dnn/object_detection.cpp
+Check @ref tutorial_dnn_yolo "the corresponding tutorial" for more details
+*/
+/** @example samples/dnn/openpose.cpp
+*/
+/** @example samples/dnn/segmentation.cpp
+*/
+/** @example samples/dnn/text_detection.cpp
+*/
 #include <opencv2/dnn/dnn.hpp>
 
 #endif /* OPENCV_DNN_HPP */

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -900,7 +900,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
                                CV_OUT std::vector<int>& indices,
                                const float eta = 1.f, const int top_k = 0);
 
-    CV_EXPORTS void NMSBoxes(const std::vector<RotatedRect>& bboxes, const std::vector<float>& scores,
+    CV_EXPORTS_AS(NMSBoxesRotated) void NMSBoxes(const std::vector<RotatedRect>& bboxes, const std::vector<float>& scores,
                              const float score_threshold, const float nms_threshold,
                              CV_OUT std::vector<int>& indices,
                              const float eta = 1.f, const int top_k = 0);

modules/dnn/include/opencv2/dnn/shape_utils.hpp

@@ -209,7 +209,7 @@ inline Range clamp(const Range& r, int axisSize)
 {
     Range clamped(std::max(r.start, 0),
                   r.end > 0 ? std::min(r.end, axisSize) : axisSize + r.end + 1);
-    CV_Assert(clamped.start < clamped.end, clamped.end <= axisSize);
+    CV_Assert_N(clamped.start < clamped.end, clamped.end <= axisSize);
     return clamped;
 }
 

modules/dnn/src/caffe/caffe_importer.cpp

@@ -359,7 +359,7 @@ public:
             {
                 if (!layerParams.get<bool>("use_global_stats", true))
                 {
-                    CV_Assert(layer.bottom_size() == 1, layer.top_size() == 1);
+                    CV_Assert_N(layer.bottom_size() == 1, layer.top_size() == 1);
 
                     LayerParams mvnParams;
                     mvnParams.set("eps", layerParams.get<float>("eps", 1e-5));

modules/dnn/src/dnn.cpp

@@ -134,7 +134,7 @@ void blobFromImages(InputArrayOfArrays images_, OutputArray blob_, double scalef
     if (ddepth == CV_8U)
     {
         CV_CheckEQ(scalefactor, 1.0, "Scaling is not supported for CV_8U blob depth");
-        CV_Assert(mean_ == Scalar(), "Mean subtraction is not supported for CV_8U blob depth");
+        CV_Assert(mean_ == Scalar() && "Mean subtraction is not supported for CV_8U blob depth");
     }
 
     std::vector<Mat> images;
@@ -451,8 +451,8 @@ struct DataLayer : public Layer
         {
             double scale = scaleFactors[i];
             Scalar& mean = means[i];
-            CV_Assert(mean == Scalar() || inputsData[i].size[1] <= 4,
+            CV_Assert(mean == Scalar() || inputsData[i].size[1] <= 4);
-                      outputs[i].type() == CV_32F);
+            CV_CheckTypeEQ(outputs[i].type(), CV_32FC1, "");
 
             bool singleMean = true;
             for (int j = 1; j < std::min(4, inputsData[i].size[1]) && singleMean; ++j)
@@ -569,7 +569,7 @@ struct DataLayer : public Layer
 
     void finalize(const std::vector<Mat*>&, std::vector<Mat>& outputs) CV_OVERRIDE
     {
-        CV_Assert(outputs.size() == scaleFactors.size(), outputs.size() == means.size(),
+        CV_Assert_N(outputs.size() == scaleFactors.size(), outputs.size() == means.size(),
                   inputsData.size() == outputs.size());
         skip = true;
         for (int i = 0; skip && i < inputsData.size(); ++i)
@@ -588,7 +588,8 @@ struct DataLayer : public Layer
         lp.precision = InferenceEngine::Precision::FP32;
         std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
 
-        CV_Assert(inputsData.size() == 1, inputsData[0].dims == 4);
+        CV_CheckEQ(inputsData.size(), (size_t)1, "");
+        CV_CheckEQ(inputsData[0].dims, 4, "");
         const size_t numChannels = inputsData[0].size[1];
         CV_Assert(numChannels <= 4);
 
@@ -698,9 +699,9 @@ public:
         }
     }
 
-    void reuseOrCreate(const MatShape& shape, const LayerPin& lp, Mat& dst, bool forceCreate, bool use_half)
+    void reuseOrCreate(const MatShape& shape, const LayerPin& lp, Mat& dst, bool use_half)
     {
-        if (!DNN_DISABLE_MEMORY_OPTIMIZATIONS && !forceCreate)
+        if (!DNN_DISABLE_MEMORY_OPTIMIZATIONS)
         {
             Mat bestBlob;
             LayerPin bestBlobPin;
@@ -746,7 +747,7 @@ public:
 
     void allocateBlobsForLayer(LayerData &ld, const LayerShapes& layerShapes,
                                std::vector<LayerPin>& pinsForInternalBlobs,
-                               bool forceCreate = false, bool use_half = false)
+                               bool use_half = false)
     {
         CV_TRACE_FUNCTION();
 
@@ -817,7 +818,7 @@ public:
                         reuse(ld.inputBlobsId[0], blobPin);
                     }
                     else
-                        reuseOrCreate(shapes[index], blobPin, *blobs[index], forceCreate, use_half);
+                        reuseOrCreate(shapes[index], blobPin, *blobs[index], use_half);
                 }
             }
         }
@@ -1237,7 +1238,7 @@ struct Net::Impl
     void initHalideBackend()
     {
         CV_TRACE_FUNCTION();
-        CV_Assert(preferableBackend == DNN_BACKEND_HALIDE, haveHalide());
+        CV_Assert_N(preferableBackend == DNN_BACKEND_HALIDE, haveHalide());
 
         // Iterator to current layer.
         MapIdToLayerData::iterator it = layers.begin();
@@ -1302,7 +1303,7 @@ struct Net::Impl
             if (!node.empty())
             {
                 Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
-                CV_Assert(!ieNode.empty(), !ieNode->net.empty());
+                CV_Assert(!ieNode.empty()); CV_Assert(!ieNode->net.empty());
                 layerNet = ieNode->net;
             }
         }
@@ -1316,7 +1317,7 @@ struct Net::Impl
             if (!inpNode.empty())
             {
                 Ptr<InfEngineBackendNode> ieInpNode = inpNode.dynamicCast<InfEngineBackendNode>();
-                CV_Assert(!ieInpNode.empty(), !ieInpNode->net.empty());
+                CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty());
                 if (layerNet != ieInpNode->net)
                 {
                     // layerNet is empty or nodes are from different graphs.
@@ -1330,7 +1331,7 @@ struct Net::Impl
     void initInfEngineBackend()
     {
         CV_TRACE_FUNCTION();
-        CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE, haveInfEngine());
+        CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE, haveInfEngine());
 #ifdef HAVE_INF_ENGINE
         MapIdToLayerData::iterator it;
         Ptr<InfEngineBackendNet> net;
@@ -1425,7 +1426,7 @@ struct Net::Impl
                 if (!inpNode.empty())
                 {
                     Ptr<InfEngineBackendNode> ieInpNode = inpNode.dynamicCast<InfEngineBackendNode>();
-                    CV_Assert(!ieInpNode.empty(), !ieInpNode->net.empty());
+                    CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty());
                     if (ieInpNode->net != net)
                     {
                         net = Ptr<InfEngineBackendNet>();
@@ -1606,7 +1607,6 @@ struct Net::Impl
 
         std::vector<LayerPin> pinsForInternalBlobs;
         blobManager.allocateBlobsForLayer(ld, layerShapesIt->second, pinsForInternalBlobs,
-                                          preferableBackend == DNN_BACKEND_INFERENCE_ENGINE,
                                           preferableBackend == DNN_BACKEND_OPENCV &&
                                           preferableTarget == DNN_TARGET_OPENCL_FP16);
         ld.outputBlobsWrappers.resize(ld.outputBlobs.size());
@@ -1827,7 +1827,7 @@ struct Net::Impl
                                         // To prevent memory collisions (i.e. when input of
                                         // [conv] and output of [eltwise] is the same blob)
                                         // we allocate a new blob.
-                                        CV_Assert(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1);
+                                        CV_Assert_N(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1);
                                         ld.outputBlobs[0] = ld.outputBlobs[0].clone();
                                         ld.outputBlobsWrappers[0] = wrap(ld.outputBlobs[0]);
 
@@ -1984,7 +1984,7 @@ struct Net::Impl
                             }
                             // Layers that refer old input Mat will refer to the
                             // new data but the same Mat object.
-                            CV_Assert(curr_output.data == output_slice.data, oldPtr == &curr_output);
+                            CV_Assert_N(curr_output.data == output_slice.data, oldPtr == &curr_output);
                         }
                         ld.skip = true;
                         printf_(("\toptimized out Concat layer %s\n", concatLayer->name.c_str()));

modules/dnn/src/layers/batch_norm_layer.cpp

@@ -48,7 +48,7 @@ public:
 
         float varMeanScale = 1.f;
         if (!hasWeights && !hasBias && blobs.size() > 2 && useGlobalStats) {
-            CV_Assert(blobs.size() == 3, blobs[2].type() == CV_32F);
+            CV_Assert(blobs.size() == 3); CV_CheckTypeEQ(blobs[2].type(), CV_32FC1, "");
             varMeanScale = blobs[2].at<float>(0);
             if (varMeanScale != 0)
                 varMeanScale = 1/varMeanScale;

modules/dnn/src/layers/convolution_layer.cpp

@@ -81,6 +81,7 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
         {
             if (type == "Convolution")
@@ -91,13 +92,19 @@ public:
                 const int outGroupCn = blobs[0].size[1];  // Weights are in IOHW layout
                 const int group = numOutput / outGroupCn;
                 if (group != 1)
+                {
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R3)
+                    return preferableTarget == DNN_TARGET_CPU;
+#endif
                     return false;
+                }
                 if (preferableTarget == DNN_TARGET_OPENCL || preferableTarget == DNN_TARGET_OPENCL_FP16)
                     return dilation.width == 1 && dilation.height == 1;
                 return true;
             }
         }
         else
+#endif  // HAVE_INF_ENGINE
             return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
     }
 
@@ -349,8 +356,8 @@ public:
         // (conv(I) + b1 ) * w + b2
         // means to replace convolution's weights to [w*conv(I)] and bias to [b1 * w + b2]
         const int outCn = weightsMat.size[0];
-        CV_Assert(!weightsMat.empty(), biasvec.size() == outCn + 2,
+        CV_Assert_N(!weightsMat.empty(), biasvec.size() == outCn + 2,
-                  w.empty() || outCn == w.total(), b.empty() || outCn == b.total());
+                    w.empty() || outCn == w.total(), b.empty() || outCn == b.total());
 
         if (!w.empty())
         {
@@ -512,13 +519,14 @@ public:
                          Size kernel, Size pad, Size stride, Size dilation,
                          const ActivationLayer* activ, int ngroups, int nstripes )
         {
-            CV_Assert( input.dims == 4 && output.dims == 4,
+            CV_Assert_N(
+                       input.dims == 4 && output.dims == 4,
                        input.size[0] == output.size[0],
                        weights.rows == output.size[1],
                        weights.cols == (input.size[1]/ngroups)*kernel.width*kernel.height,
                        input.type() == output.type(),
                        input.type() == weights.type(),
-                       input.type() == CV_32F,
+                       input.type() == CV_32FC1,
                        input.isContinuous(),
                        output.isContinuous(),
                        biasvec.size() == (size_t)output.size[1]+2);
@@ -1009,8 +1017,8 @@ public:
                name.c_str(), inputs[0]->size[0], inputs[0]->size[1], inputs[0]->size[2], inputs[0]->size[3],
                kernel.width, kernel.height, pad.width, pad.height,
                stride.width, stride.height, dilation.width, dilation.height);*/
-        CV_Assert(inputs.size() == (size_t)1, inputs[0]->size[1] % blobs[0].size[1] == 0,
+        CV_Assert_N(inputs.size() == (size_t)1, inputs[0]->size[1] % blobs[0].size[1] == 0,
-                  outputs.size() == 1, inputs[0]->data != outputs[0].data);
+                    outputs.size() == 1, inputs[0]->data != outputs[0].data);
 
         int ngroups = inputs[0]->size[1]/blobs[0].size[1];
         CV_Assert(outputs[0].size[1] % ngroups == 0);

modules/dnn/src/layers/crop_and_resize_layer.cpp

@@ -14,7 +14,7 @@ class CropAndResizeLayerImpl CV_FINAL : public CropAndResizeLayer
 public:
     CropAndResizeLayerImpl(const LayerParams& params)
     {
-        CV_Assert(params.has("width"), params.has("height"));
+        CV_Assert_N(params.has("width"), params.has("height"));
         outWidth = params.get<float>("width");
         outHeight = params.get<float>("height");
     }
@@ -24,7 +24,7 @@ public:
                          std::vector<MatShape> &outputs,
                          std::vector<MatShape> &internals) const CV_OVERRIDE
     {
-        CV_Assert(inputs.size() == 2, inputs[0].size() == 4);
+        CV_Assert_N(inputs.size() == 2, inputs[0].size() == 4);
         if (inputs[0][0] != 1)
             CV_Error(Error::StsNotImplemented, "");
         outputs.resize(1, MatShape(4));
@@ -56,7 +56,7 @@ public:
         const int inpWidth = inp.size[3];
         const int inpSpatialSize = inpHeight * inpWidth;
         const int outSpatialSize = outHeight * outWidth;
-        CV_Assert(inp.isContinuous(), out.isContinuous());
+        CV_Assert_N(inp.isContinuous(), out.isContinuous());
 
         for (int b = 0; b < boxes.rows; ++b)
         {
@@ -99,6 +99,13 @@ public:
                 }
             }
         }
+        if (boxes.rows < out.size[0])
+        {
+            // left = top = right = bottom = 0
+            std::vector<cv::Range> dstRanges(4, Range::all());
+            dstRanges[0] = Range(boxes.rows, out.size[0]);
+            out(dstRanges).setTo(inp.ptr<float>(0, 0, 0)[0]);
+        }
     }
 
 private:

modules/dnn/src/layers/detection_output_layer.cpp

@@ -115,6 +115,7 @@ public:
     // It's true whenever predicted bounding boxes and proposals are normalized to [0, 1].
     bool _bboxesNormalized;
     bool _clip;
+    bool _groupByClasses;
 
     enum { _numAxes = 4 };
     static const std::string _layerName;
@@ -183,6 +184,7 @@ public:
         _locPredTransposed = getParameter<bool>(params, "loc_pred_transposed", 0, false, false);
         _bboxesNormalized = getParameter<bool>(params, "normalized_bbox", 0, false, true);
         _clip = getParameter<bool>(params, "clip", 0, false, false);
+        _groupByClasses = getParameter<bool>(params, "group_by_classes", 0, false, true);
 
         getCodeType(params);
 
@@ -381,7 +383,7 @@ public:
             {
                 count += outputDetections_(i, &outputsData[count * 7],
                                            allDecodedBBoxes[i], allConfidenceScores[i],
-                                           allIndices[i]);
+                                           allIndices[i], _groupByClasses);
             }
             CV_Assert(count == numKept);
         }
@@ -497,7 +499,7 @@ public:
         {
             count += outputDetections_(i, &outputsData[count * 7],
                                        allDecodedBBoxes[i], allConfidenceScores[i],
-                                       allIndices[i]);
+                                       allIndices[i], _groupByClasses);
         }
         CV_Assert(count == numKept);
     }
@@ -505,9 +507,36 @@ public:
     size_t outputDetections_(
             const int i, float* outputsData,
             const LabelBBox& decodeBBoxes, Mat& confidenceScores,
-            const std::map<int, std::vector<int> >& indicesMap
+            const std::map<int, std::vector<int> >& indicesMap,
+            bool groupByClasses
     )
     {
+        std::vector<int> dstIndices;
+        std::vector<std::pair<float, int> > allScores;
+        for (std::map<int, std::vector<int> >::const_iterator it = indicesMap.begin(); it != indicesMap.end(); ++it)
+        {
+            int label = it->first;
+            if (confidenceScores.rows <= label)
+                CV_Error_(cv::Error::StsError, ("Could not find confidence predictions for label %d", label));
+            const std::vector<float>& scores = confidenceScores.row(label);
+            const std::vector<int>& indices = it->second;
+
+            const int numAllScores = allScores.size();
+            allScores.reserve(numAllScores + indices.size());
+            for (size_t j = 0; j < indices.size(); ++j)
+            {
+                allScores.push_back(std::make_pair(scores[indices[j]], numAllScores + j));
+            }
+        }
+        if (!groupByClasses)
+            std::sort(allScores.begin(), allScores.end(), util::SortScorePairDescend<int>);
+
+        dstIndices.resize(allScores.size());
+        for (size_t j = 0; j < dstIndices.size(); ++j)
+        {
+            dstIndices[allScores[j].second] = j;
+        }
+
         size_t count = 0;
         for (std::map<int, std::vector<int> >::const_iterator it = indicesMap.begin(); it != indicesMap.end(); ++it)
         {
@@ -524,14 +553,15 @@ public:
             for (size_t j = 0; j < indices.size(); ++j, ++count)
             {
                 int idx = indices[j];
+                int dstIdx = dstIndices[count];
                 const util::NormalizedBBox& decode_bbox = label_bboxes->second[idx];
-                outputsData[count * 7] = i;
+                outputsData[dstIdx * 7] = i;
-                outputsData[count * 7 + 1] = label;
+                outputsData[dstIdx * 7 + 1] = label;
-                outputsData[count * 7 + 2] = scores[idx];
+                outputsData[dstIdx * 7 + 2] = scores[idx];
-                outputsData[count * 7 + 3] = decode_bbox.xmin;
+                outputsData[dstIdx * 7 + 3] = decode_bbox.xmin;
-                outputsData[count * 7 + 4] = decode_bbox.ymin;
+                outputsData[dstIdx * 7 + 4] = decode_bbox.ymin;
-                outputsData[count * 7 + 5] = decode_bbox.xmax;
+                outputsData[dstIdx * 7 + 5] = decode_bbox.xmax;
-                outputsData[count * 7 + 6] = decode_bbox.ymax;
+                outputsData[dstIdx * 7 + 6] = decode_bbox.ymax;
             }
         }
         return count;

modules/dnn/src/layers/elementwise_layers.cpp

@@ -599,7 +599,8 @@ struct ELUFunctor
 
     bool supportBackend(int backendId, int)
     {
-        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
+               backendId == DNN_BACKEND_INFERENCE_ENGINE;
     }
 
     void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@@ -653,8 +654,8 @@ struct ELUFunctor
 #ifdef HAVE_INF_ENGINE
     InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
     {
-        CV_Error(Error::StsNotImplemented, "ELU");
+        lp.type = "ELU";
-        return InferenceEngine::CNNLayerPtr();
+        return InferenceEngine::CNNLayerPtr(new InferenceEngine::CNNLayer(lp));
     }
 #endif  // HAVE_INF_ENGINE
 

modules/dnn/src/layers/eltwise_layer.cpp

@@ -139,7 +139,7 @@ public:
                         const std::vector<float>& coeffs, EltwiseOp op,
                         const ActivationLayer* activ, int nstripes)
         {
-            CV_Assert(1 < dst.dims && dst.dims <= 4, dst.type() == CV_32F, dst.isContinuous());
+            CV_Check(dst.dims, 1 < dst.dims && dst.dims <= 4, ""); CV_CheckTypeEQ(dst.type(), CV_32FC1, ""); CV_Assert(dst.isContinuous());
             CV_Assert(coeffs.empty() || coeffs.size() == (size_t)nsrcs);
 
             for( int i = 0; i > nsrcs; i++ )

modules/dnn/src/layers/lrn_layer.cpp

@@ -91,8 +91,8 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_HALIDE && haveHalide() ||
+               backendId == DNN_BACKEND_HALIDE ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
+               backendId == DNN_BACKEND_INFERENCE_ENGINE && (preferableTarget != DNN_TARGET_MYRIAD || type == CHANNEL_NRM);
     }
 
 #ifdef HAVE_OPENCL

modules/dnn/src/layers/padding_layer.cpp

@@ -38,7 +38,7 @@ public:
         {
             paddings[i].first = paddingsParam.get<int>(i * 2);  // Pad before.
             paddings[i].second = paddingsParam.get<int>(i * 2 + 1);  // Pad after.
-            CV_Assert(paddings[i].first >= 0, paddings[i].second >= 0);
+            CV_Assert_N(paddings[i].first >= 0, paddings[i].second >= 0);
         }
     }
 
@@ -127,8 +127,8 @@ public:
             const int padBottom = outHeight - dstRanges[2].end;
             const int padLeft = dstRanges[3].start;
             const int padRight = outWidth - dstRanges[3].end;
-            CV_Assert(padTop < inpHeight, padBottom < inpHeight,
+            CV_CheckLT(padTop, inpHeight, ""); CV_CheckLT(padBottom, inpHeight, "");
-                      padLeft < inpWidth, padRight < inpWidth);
+            CV_CheckLT(padLeft, inpWidth, ""); CV_CheckLT(padRight, inpWidth, "");
 
             for (size_t n = 0; n < inputs[0]->size[0]; ++n)
             {

modules/dnn/src/layers/pooling_layer.cpp

@@ -216,15 +216,15 @@ public:
         switch (type)
         {
             case MAX:
-                CV_Assert(inputs.size() == 1, outputs.size() == 2);
+                CV_Assert_N(inputs.size() == 1, outputs.size() == 2);
                 maxPooling(*inputs[0], outputs[0], outputs[1]);
                 break;
             case AVE:
-                CV_Assert(inputs.size() == 1, outputs.size() == 1);
+                CV_Assert_N(inputs.size() == 1, outputs.size() == 1);
                 avePooling(*inputs[0], outputs[0]);
                 break;
             case ROI: case PSROI:
-                CV_Assert(inputs.size() == 2, outputs.size() == 1);
+                CV_Assert_N(inputs.size() == 2, outputs.size() == 1);
                 roiPooling(*inputs[0], *inputs[1], outputs[0]);
                 break;
             default:
@@ -311,7 +311,8 @@ public:
                         Size stride, Size pad, bool avePoolPaddedArea, int poolingType, float spatialScale,
                         bool computeMaxIdx, int nstripes)
         {
-            CV_Assert(src.isContinuous(), dst.isContinuous(),
+            CV_Assert_N(
+                      src.isContinuous(), dst.isContinuous(),
                       src.type() == CV_32F, src.type() == dst.type(),
                       src.dims == 4, dst.dims == 4,
                       ((poolingType == ROI || poolingType == PSROI) && dst.size[0] ==rois.size[0] || src.size[0] == dst.size[0]),

modules/dnn/src/layers/prior_box_layer.cpp

@@ -254,7 +254,7 @@ public:
         }
         if (params.has("offset_h") || params.has("offset_w"))
         {
-            CV_Assert(!params.has("offset"), params.has("offset_h"), params.has("offset_w"));
+            CV_Assert_N(!params.has("offset"), params.has("offset_h"), params.has("offset_w"));
             getParams("offset_h", params, &_offsetsY);
             getParams("offset_w", params, &_offsetsX);
             CV_Assert(_offsetsX.size() == _offsetsY.size());
@@ -299,7 +299,8 @@ public:
 
     void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
     {
-        CV_Assert(inputs.size() > 1, inputs[0]->dims == 4, inputs[1]->dims == 4);
+        CV_CheckGT(inputs.size(), (size_t)1, "");
+        CV_CheckEQ(inputs[0]->dims, 4, ""); CV_CheckEQ(inputs[1]->dims, 4, "");
         int layerWidth = inputs[0]->size[3];
         int layerHeight = inputs[0]->size[2];
 
@@ -486,8 +487,8 @@ public:
 
         if (_explicitSizes)
         {
-            CV_Assert(!_boxWidths.empty(), !_boxHeights.empty(),
+            CV_Assert(!_boxWidths.empty()); CV_Assert(!_boxHeights.empty());
-                      _boxWidths.size() == _boxHeights.size());
+            CV_Assert(_boxWidths.size() == _boxHeights.size());
             ieLayer->params["width"] = format("%f", _boxWidths[0]);
             ieLayer->params["height"] = format("%f", _boxHeights[0]);
             for (int i = 1; i < _boxWidths.size(); ++i)
@@ -529,7 +530,7 @@ public:
             ieLayer->params["step_h"] = format("%f", _stepY);
             ieLayer->params["step_w"] = format("%f", _stepX);
         }
-        CV_Assert(_offsetsX.size() == 1, _offsetsY.size() == 1, _offsetsX[0] == _offsetsY[0]);
+        CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
         ieLayer->params["offset"] = format("%f", _offsetsX[0]);
 
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));

modules/dnn/src/layers/reshape_layer.cpp

@@ -197,7 +197,7 @@ public:
         }
         else
         {
-            CV_Assert(inputs.size() == 2, total(inputs[0]) == total(inputs[1]));
+            CV_Assert_N(inputs.size() == 2, total(inputs[0]) == total(inputs[1]));
             outputs.assign(1, inputs[1]);
         }
         return true;

modules/dnn/src/layers/resize_layer.cpp

@@ -33,9 +33,7 @@ public:
         interpolation = params.get<String>("interpolation");
         CV_Assert(interpolation == "nearest" || interpolation == "bilinear");
 
-        bool alignCorners = params.get<bool>("align_corners", false);
+        alignCorners = params.get<bool>("align_corners", false);
-        if (alignCorners)
-            CV_Error(Error::StsNotImplemented, "Resize with align_corners=true is not implemented");
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -43,7 +41,7 @@ public:
                          std::vector<MatShape> &outputs,
                          std::vector<MatShape> &internals) const CV_OVERRIDE
     {
-        CV_Assert(inputs.size() == 1, inputs[0].size() == 4);
+        CV_Assert_N(inputs.size() == 1, inputs[0].size() == 4);
         outputs.resize(1, inputs[0]);
         outputs[0][2] = outHeight > 0 ? outHeight : (outputs[0][2] * zoomFactorHeight);
         outputs[0][3] = outWidth > 0 ? outWidth : (outputs[0][3] * zoomFactorWidth);
@@ -66,8 +64,15 @@ public:
             outHeight = outputs[0].size[2];
             outWidth = outputs[0].size[3];
         }
-        scaleHeight = static_cast<float>(inputs[0]->size[2]) / outHeight;
+        if (alignCorners && outHeight > 1)
-        scaleWidth = static_cast<float>(inputs[0]->size[3]) / outWidth;
+            scaleHeight = static_cast<float>(inputs[0]->size[2] - 1) / (outHeight - 1);
+        else
+            scaleHeight = static_cast<float>(inputs[0]->size[2]) / outHeight;
+
+        if (alignCorners && outWidth > 1)
+            scaleWidth = static_cast<float>(inputs[0]->size[3] - 1) / (outWidth - 1);
+        else
+            scaleWidth = static_cast<float>(inputs[0]->size[3]) / outWidth;
     }
 
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
@@ -106,7 +111,7 @@ public:
             const int inpSpatialSize = inpHeight * inpWidth;
             const int outSpatialSize = outHeight * outWidth;
             const int numPlanes = inp.size[0] * inp.size[1];
-            CV_Assert(inp.isContinuous(), out.isContinuous());
+            CV_Assert_N(inp.isContinuous(), out.isContinuous());
 
             Mat inpPlanes = inp.reshape(1, numPlanes * inpHeight);
             Mat outPlanes = out.reshape(1, numPlanes * outHeight);
@@ -166,6 +171,7 @@ protected:
     int outWidth, outHeight, zoomFactorWidth, zoomFactorHeight;
     String interpolation;
     float scaleWidth, scaleHeight;
+    bool alignCorners;
 };
 
 
@@ -184,7 +190,7 @@ public:
                          std::vector<MatShape> &outputs,
                          std::vector<MatShape> &internals) const CV_OVERRIDE
     {
-        CV_Assert(inputs.size() == 1, inputs[0].size() == 4);
+        CV_Assert_N(inputs.size() == 1, inputs[0].size() == 4);
         outputs.resize(1, inputs[0]);
         outputs[0][2] = outHeight > 0 ? outHeight : (1 + zoomFactorHeight * (outputs[0][2] - 1));
         outputs[0][3] = outWidth > 0 ? outWidth : (1 + zoomFactorWidth * (outputs[0][3] - 1));

modules/dnn/src/layers/scale_layer.cpp

@@ -64,7 +64,7 @@ public:
     {
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
-        CV_Assert(outputs.size() == 1, !blobs.empty() || inputs.size() == 2);
+        CV_Assert_N(outputs.size() == 1, !blobs.empty() || inputs.size() == 2);
 
         Mat &inpBlob = *inputs[0];
         Mat &outBlob = outputs[0];
@@ -76,7 +76,9 @@ public:
             weights = weights.reshape(1, 1);
         MatShape inpShape = shape(inpBlob);
         const int numWeights = !weights.empty() ? weights.total() : bias.total();
-        CV_Assert(numWeights != 0, !hasWeights || !hasBias || weights.total() == bias.total());
+        CV_Assert(numWeights != 0);
+        if (hasWeights && hasBias)
+            CV_CheckEQ(weights.total(), bias.total(), "Incompatible weights/bias blobs");
 
         int endAxis;
         for (endAxis = axis + 1; endAxis <= inpBlob.dims; ++endAxis)
@@ -84,9 +86,9 @@ public:
             if (total(inpShape, axis, endAxis) == numWeights)
                 break;
         }
-        CV_Assert(total(inpShape, axis, endAxis) == numWeights,
+        CV_Assert(total(inpShape, axis, endAxis) == numWeights);
-                  !hasBias || numWeights == bias.total(),
+        CV_Assert(!hasBias || numWeights == bias.total());
-                  inpBlob.type() == CV_32F && outBlob.type() == CV_32F);
+        CV_CheckTypeEQ(inpBlob.type(), CV_32FC1, ""); CV_CheckTypeEQ(outBlob.type(), CV_32FC1, "");
 
         int numSlices = total(inpShape, 0, axis);
         float* inpData = (float*)inpBlob.data;

modules/dnn/src/nms.cpp

@@ -25,7 +25,7 @@ void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
                           const float score_threshold, const float nms_threshold,
                           std::vector<int>& indices, const float eta, const int top_k)
 {
-    CV_Assert(bboxes.size() == scores.size(), score_threshold >= 0,
+    CV_Assert_N(bboxes.size() == scores.size(), score_threshold >= 0,
         nms_threshold >= 0, eta > 0);
     NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, rectOverlap);
 }
@@ -46,7 +46,7 @@ void NMSBoxes(const std::vector<RotatedRect>& bboxes, const std::vector<float>&
               const float score_threshold, const float nms_threshold,
               std::vector<int>& indices, const float eta, const int top_k)
 {
-    CV_Assert(bboxes.size() == scores.size(), score_threshold >= 0,
+    CV_Assert_N(bboxes.size() == scores.size(), score_threshold >= 0,
         nms_threshold >= 0, eta > 0);
     NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, rotatedRectIOU);
 }

modules/dnn/src/op_inf_engine.hpp

@@ -32,6 +32,7 @@
 #endif
 
 #define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))
+#define INF_ENGINE_VER_MAJOR_GE(ver) (((INF_ENGINE_RELEASE) / 10000) >= ((ver) / 10000))
 
 #endif  // HAVE_INF_ENGINE
 

modules/dnn/src/tensorflow/tf_graph_simplifier.cpp

@@ -221,7 +221,7 @@ public:
                           std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
     {
         Mat epsMat = getTensorContent(inputNodes.back()->attr().at("value").tensor());
-        CV_Assert(epsMat.total() == 1, epsMat.type() == CV_32FC1);
+        CV_CheckEQ(epsMat.total(), (size_t)1, ""); CV_CheckTypeEQ(epsMat.type(), CV_32FC1, "");
 
         fusedNode->mutable_input()->RemoveLast();
         fusedNode->clear_attr();
@@ -256,7 +256,7 @@ public:
                           std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
     {
         Mat epsMat = getTensorContent(inputNodes.back()->attr().at("value").tensor());
-        CV_Assert(epsMat.total() == 1, epsMat.type() == CV_32FC1);
+        CV_CheckEQ(epsMat.total(), (size_t)1, ""); CV_CheckTypeEQ(epsMat.type(), CV_32FC1, "");
 
         fusedNode->mutable_input()->RemoveLast();
         fusedNode->clear_attr();
@@ -593,7 +593,7 @@ public:
                           std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
     {
         Mat factorsMat = getTensorContent(inputNodes[1]->attr().at("value").tensor());
-        CV_Assert(factorsMat.total() == 2, factorsMat.type() == CV_32SC1);
+        CV_CheckEQ(factorsMat.total(), (size_t)2, ""); CV_CheckTypeEQ(factorsMat.type(), CV_32SC1, "");
 
         // Height scale factor
         tensorflow::TensorProto* factorY = inputNodes[1]->mutable_attr()->at("value").mutable_tensor();

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -545,8 +545,8 @@ const tensorflow::TensorProto& TFImporter::getConstBlob(const tensorflow::NodeDe
     }
     else
     {
-        CV_Assert(nodeIdx < netTxt.node_size(),
+        CV_Assert_N(nodeIdx < netTxt.node_size(),
-                  netTxt.node(nodeIdx).name() == kernel_inp.name);
+                    netTxt.node(nodeIdx).name() == kernel_inp.name);
         return netTxt.node(nodeIdx).attr().at("value").tensor();
     }
 }
@@ -587,8 +587,8 @@ static void addConstNodes(tensorflow::GraphDef& net, std::map<String, int>& cons
 
             Mat qMin = getTensorContent(net.node(minId).attr().at("value").tensor());
             Mat qMax = getTensorContent(net.node(maxId).attr().at("value").tensor());
-            CV_Assert(qMin.total() == 1, qMin.type() == CV_32FC1,
+            CV_Assert_N(qMin.total() == 1, qMin.type() == CV_32FC1,
-                      qMax.total() == 1, qMax.type() == CV_32FC1);
+                        qMax.total() == 1, qMax.type() == CV_32FC1);
 
             Mat content = getTensorContent(*tensor);
 
@@ -737,11 +737,18 @@ void TFImporter::populateNet(Net dstNet)
         int predictedLayout = predictOutputDataLayout(net, layer, data_layouts);
         data_layouts[name] = predictedLayout;
 
-        if (type == "Conv2D" || type == "SpaceToBatchND" || type == "DepthwiseConv2dNative")
+        if (type == "Conv2D" || type == "SpaceToBatchND" || type == "DepthwiseConv2dNative" || type == "Pad")
         {
             // The first node of dilated convolution subgraph.
             // Extract input node, dilation rate and paddings.
             std::string input = layer.input(0);
+            StrIntVector next_layers;
+            if (type == "SpaceToBatchND" || type == "Pad")
+            {
+                next_layers = getNextLayers(net, name, "Conv2D");
+                if (next_layers.empty())
+                    next_layers = getNextLayers(net, name, "DepthwiseConv2dNative");
+            }
             if (type == "SpaceToBatchND")
             {
                 // op: "SpaceToBatchND"
@@ -762,17 +769,57 @@ void TFImporter::populateNet(Net dstNet)
                 layerParams.set("pad_h", paddings.at<float>(0));
                 layerParams.set("pad_w", paddings.at<float>(2));
 
-                StrIntVector next_layers = getNextLayers(net, name, "Conv2D");
-                if (next_layers.empty())
-                {
-                    next_layers = getNextLayers(net, name, "DepthwiseConv2dNative");
-                }
                 CV_Assert(next_layers.size() == 1);
                 layer = net.node(next_layers[0].second);
                 layers_to_ignore.insert(next_layers[0].first);
                 name = layer.name();
                 type = layer.op();
             }
+            else if (type == "Pad")
+            {
+                Mat paddings = getTensorContent(getConstBlob(layer, value_id, 1));
+                CV_Assert(paddings.type() == CV_32SC1);
+                if (paddings.total() == 8)
+                {
+                    // Perhabs, we have NHWC padding dimensions order.
+                    //  N    H    W    C
+                    // 0 1  2 3  4 5  6 7
+                    std::swap(paddings.at<int32_t>(2), paddings.at<int32_t>(6));
+                    std::swap(paddings.at<int32_t>(3), paddings.at<int32_t>(7));
+                    //  N    C    W    H
+                    // 0 1  2 3  4 5  6 7
+                    std::swap(paddings.at<int32_t>(4), paddings.at<int32_t>(6));
+                    std::swap(paddings.at<int32_t>(5), paddings.at<int32_t>(7));
+                    //  N    C    H    W
+                    // 0 1  2 3  4 5  6 7
+                }
+                if (next_layers.empty() || paddings.total() != 8 ||
+                    paddings.at<int32_t>(4) != paddings.at<int32_t>(5) ||
+                    paddings.at<int32_t>(6) != paddings.at<int32_t>(7))
+                {
+                    // Just a single padding layer.
+                    layerParams.set("paddings", DictValue::arrayInt<int*>((int*)paddings.data, paddings.total()));
+
+                    int id = dstNet.addLayer(name, "Padding", layerParams);
+                    layer_id[name] = id;
+
+                    connect(layer_id, dstNet, parsePin(input), id, 0);
+                    continue;
+                }
+                else
+                {
+                    // Merge with subsequent convolutional layer.
+                    CV_Assert(next_layers.size() == 1);
+
+                    layerParams.set("pad_h", paddings.at<int32_t>(4));
+                    layerParams.set("pad_w", paddings.at<int32_t>(6));
+
+                    layer = net.node(next_layers[0].second);
+                    layers_to_ignore.insert(next_layers[0].first);
+                    name = layer.name();
+                    type = layer.op();
+                }
+            }
 
             // For the object detection networks, TensorFlow Object Detection API
             // predicts deltas for bounding boxes in yxYX (ymin, xmin, ymax, xmax)
@@ -784,7 +831,7 @@ void TFImporter::populateNet(Net dstNet)
             layerParams.set("bias_term", false);
             layerParams.blobs.resize(1);
 
-            StrIntVector next_layers = getNextLayers(net, name, "BiasAdd");
+            next_layers = getNextLayers(net, name, "BiasAdd");
             if (next_layers.size() == 1) {
                 layerParams.set("bias_term", true);
                 layerParams.blobs.resize(2);
@@ -1295,8 +1342,9 @@ void TFImporter::populateNet(Net dstNet)
             CV_Assert(layer.input_size() == 3);
             Mat begins = getTensorContent(getConstBlob(layer, value_id, 1));
             Mat sizes = getTensorContent(getConstBlob(layer, value_id, 2));
-            CV_Assert(!begins.empty(), !sizes.empty(), begins.type() == CV_32SC1,
+            CV_Assert_N(!begins.empty(), !sizes.empty());
-                      sizes.type() == CV_32SC1);
+            CV_CheckTypeEQ(begins.type(), CV_32SC1, "");
+            CV_CheckTypeEQ(sizes.type(), CV_32SC1, "");
 
             if (begins.total() == 4 && getDataLayout(name, data_layouts) == DATA_LAYOUT_NHWC)
             {
@@ -1415,31 +1463,6 @@ void TFImporter::populateNet(Net dstNet)
                 }
             }
         }
-        else if (type == "Pad")
-        {
-            Mat paddings = getTensorContent(getConstBlob(layer, value_id, 1));
-            CV_Assert(paddings.type() == CV_32SC1);
-            if (paddings.total() == 8)
-            {
-                // Perhabs, we have NHWC padding dimensions order.
-                //  N    H    W    C
-                // 0 1  2 3  4 5  6 7
-                std::swap(*paddings.ptr<int32_t>(0, 2), *paddings.ptr<int32_t>(0, 6));
-                std::swap(*paddings.ptr<int32_t>(0, 3), *paddings.ptr<int32_t>(0, 7));
-                //  N    C    W    H
-                // 0 1  2 3  4 5  6 7
-                std::swap(*paddings.ptr<int32_t>(0, 4), *paddings.ptr<int32_t>(0, 6));
-                std::swap(*paddings.ptr<int32_t>(0, 5), *paddings.ptr<int32_t>(0, 7));
-                //  N    C    H    W
-                // 0 1  2 3  4 5  6 7
-            }
-            layerParams.set("paddings", DictValue::arrayInt<int*>((int*)paddings.data, paddings.total()));
-
-            int id = dstNet.addLayer(name, "Padding", layerParams);
-            layer_id[name] = id;
-
-            connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
-        }
         else if (type == "FusedBatchNorm")
         {
             // op: "FusedBatchNorm"
@@ -1665,7 +1688,7 @@ void TFImporter::populateNet(Net dstNet)
             if (layer.input_size() == 2)
             {
                 Mat outSize = getTensorContent(getConstBlob(layer, value_id, 1));
-                CV_Assert(outSize.type() == CV_32SC1, outSize.total() == 2);
+                CV_CheckTypeEQ(outSize.type(), CV_32SC1, ""); CV_CheckEQ(outSize.total(), (size_t)2, "");
                 layerParams.set("height", outSize.at<int>(0, 0));
                 layerParams.set("width", outSize.at<int>(0, 1));
             }
@@ -1673,8 +1696,8 @@ void TFImporter::populateNet(Net dstNet)
             {
                 Mat factorHeight = getTensorContent(getConstBlob(layer, value_id, 1));
                 Mat factorWidth = getTensorContent(getConstBlob(layer, value_id, 2));
-                CV_Assert(factorHeight.type() == CV_32SC1, factorHeight.total() == 1,
+                CV_CheckTypeEQ(factorHeight.type(), CV_32SC1, ""); CV_CheckEQ(factorHeight.total(), (size_t)1, "");
-                          factorWidth.type() == CV_32SC1, factorWidth.total() == 1);
+                CV_CheckTypeEQ(factorWidth.type(), CV_32SC1, ""); CV_CheckEQ(factorWidth.total(), (size_t)1, "");
                 layerParams.set("zoom_factor_x", factorWidth.at<int>(0));
                 layerParams.set("zoom_factor_y", factorHeight.at<int>(0));
             }
@@ -1772,7 +1795,7 @@ void TFImporter::populateNet(Net dstNet)
             CV_Assert(layer.input_size() == 3);
 
             Mat cropSize = getTensorContent(getConstBlob(layer, value_id, 2));
-            CV_Assert(cropSize.type() == CV_32SC1, cropSize.total() == 2);
+            CV_CheckTypeEQ(cropSize.type(), CV_32SC1, ""); CV_CheckEQ(cropSize.total(), (size_t)2, "");
 
             layerParams.set("height", cropSize.at<int>(0));
             layerParams.set("width", cropSize.at<int>(1));
@@ -1826,8 +1849,8 @@ void TFImporter::populateNet(Net dstNet)
 
             Mat minValue = getTensorContent(getConstBlob(layer, value_id, 1));
             Mat maxValue = getTensorContent(getConstBlob(layer, value_id, 2));
-            CV_Assert(minValue.total() == 1, minValue.type() == CV_32F,
+            CV_CheckEQ(minValue.total(), (size_t)1, ""); CV_CheckTypeEQ(minValue.type(), CV_32FC1, "");
-                      maxValue.total() == 1, maxValue.type() == CV_32F);
+            CV_CheckEQ(maxValue.total(), (size_t)1, ""); CV_CheckTypeEQ(maxValue.type(), CV_32FC1, "");
 
             layerParams.set("min_value", minValue.at<float>(0));
             layerParams.set("max_value", maxValue.at<float>(0));

modules/dnn/src/torch/torch_importer.cpp

@@ -896,8 +896,8 @@ struct TorchImporter
             else if (nnName == "SpatialZeroPadding" || nnName == "SpatialReflectionPadding")
             {
                 readTorchTable(scalarParams, tensorParams);
-                CV_Assert(scalarParams.has("pad_l"), scalarParams.has("pad_r"),
+                CV_Assert_N(scalarParams.has("pad_l"), scalarParams.has("pad_r"),
-                          scalarParams.has("pad_t"), scalarParams.has("pad_b"));
+                            scalarParams.has("pad_t"), scalarParams.has("pad_b"));
                 int padTop = scalarParams.get<int>("pad_t");
                 int padLeft = scalarParams.get<int>("pad_l");
                 int padRight = scalarParams.get<int>("pad_r");

modules/dnn/test/test_backends.cpp

@@ -222,9 +222,12 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
 
 TEST_P(DNNTestNetwork, OpenFace)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+#endif
     if (backend == DNN_BACKEND_HALIDE ||
-        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) ||
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16))
-        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
         throw SkipTestException("");
     processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "");
 }
@@ -253,12 +256,19 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
 
 TEST_P(DNNTestNetwork, DenseNet_121)
 {
-    if ((backend == DNN_BACKEND_HALIDE) ||
+    if (backend == DNN_BACKEND_HALIDE)
-        (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ||
-        (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL_FP16 ||
-                                                     target == DNN_TARGET_MYRIAD)))
         throw SkipTestException("");
-    processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "caffe");
+
+    float l1 = 0.0, lInf = 0.0;
+    if (target == DNN_TARGET_OPENCL_FP16)
+    {
+        l1 = 9e-3; lInf = 5e-2;
+    }
+    else if (target == DNN_TARGET_MYRIAD)
+    {
+        l1 = 6e-2; lInf = 0.27;
+    }
+    processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "", l1, lInf);
 }
 
 TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)

modules/dnn/test/test_caffe_importer.cpp

@@ -374,14 +374,6 @@ TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
 TEST_P(Test_Caffe_nets, Colorization)
 {
     checkBackend();
-    if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) ||
-        (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) ||
-        (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
-        throw SkipTestException("");
-
-    const float l1 = 4e-4;
-    const float lInf = 3e-3;
-
     Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
     Mat ref = blobFromNPY(_tf("colorization_out.npy"));
     Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));
@@ -398,11 +390,15 @@ TEST_P(Test_Caffe_nets, Colorization)
     net.setInput(inp);
     Mat out = net.forward();
 
+    // Reference output values are in range [-29.1, 69.5]
+    const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.21 : 4e-4;
+    const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 5.3 : 3e-3;
     normAssert(out, ref, "", l1, lInf);
 }
 
-TEST(Reproducibility_DenseNet_121, Accuracy)
+TEST_P(Test_Caffe_nets, DenseNet_121)
 {
+    checkBackend();
     const string proto = findDataFile("dnn/DenseNet_121.prototxt", false);
     const string model = findDataFile("dnn/DenseNet_121.caffemodel", false);
 
@@ -411,12 +407,23 @@ TEST(Reproducibility_DenseNet_121, Accuracy)
     Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));
 
     Net net = readNetFromCaffe(proto, model);
-    net.setPreferableBackend(DNN_BACKEND_OPENCV);
+    net.setPreferableBackend(backend);
+    net.setPreferableTarget(target);
 
     net.setInput(inp);
     Mat out = net.forward();
 
-    normAssert(out, ref);
+    // Reference is an array of 1000 values from a range [-6.16, 7.9]
+    float l1 = default_l1, lInf = default_lInf;
+    if (target == DNN_TARGET_OPENCL_FP16)
+    {
+        l1 = 0.017; lInf = 0.067;
+    }
+    else if (target == DNN_TARGET_MYRIAD)
+    {
+        l1 = 0.097; lInf = 0.52;
+    }
+    normAssert(out, ref, "", l1, lInf);
 }
 
 TEST(Test_Caffe, multiple_inputs)

modules/dnn/test/test_halide_layers.cpp

@@ -113,7 +113,11 @@ TEST_P(Convolution, Accuracy)
     bool skipCheck = false;
     if (cvtest::skipUnstableTests && backendId == DNN_BACKEND_OPENCV &&
         (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) &&
-        kernel == Size(3, 1) && stride == Size(1, 1) && pad == Size(0, 1))
+        (
+            (kernel == Size(3, 1) && stride == Size(1, 1) && pad == Size(0, 1)) ||
+            (stride.area() > 1 && !(pad.width == 0 && pad.height == 0))
+        )
+    )
         skipCheck = true;
 
     int sz[] = {outChannels, inChannels / group, kernel.height, kernel.width};

modules/dnn/test/test_ie_models.cpp

@@ -177,7 +177,8 @@ TEST_P(DNNTestOpenVINO, models)
     Target target = (dnn::Target)(int)get<0>(GetParam());
     std::string modelName = get<1>(GetParam());
 
-    if (modelName == "semantic-segmentation-adas-0001" && target == DNN_TARGET_OPENCL_FP16)
+    if ((modelName == "semantic-segmentation-adas-0001" && target == DNN_TARGET_OPENCL_FP16) ||
+        (modelName == "vehicle-license-plate-detection-barrier-0106"))
         throw SkipTestException("");
 
     std::string precision = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "FP16" : "FP32";

modules/dnn/test/test_layers.cpp

@@ -127,15 +127,9 @@ TEST_P(Test_Caffe_layers, Softmax)
     testLayerUsingCaffeModels("layer_softmax");
 }
 
-TEST_P(Test_Caffe_layers, LRN_spatial)
+TEST_P(Test_Caffe_layers, LRN)
 {
-    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-        throw SkipTestException("");
     testLayerUsingCaffeModels("layer_lrn_spatial");
-}
-
-TEST_P(Test_Caffe_layers, LRN_channels)
-{
     testLayerUsingCaffeModels("layer_lrn_channels");
 }
 
@@ -814,7 +808,7 @@ TEST_P(Layer_Test_DWconv_Prelu, Accuracy)
     const int group = 3;                        //outChannels=group when group>1
     const int num_output = get<1>(GetParam());
     const int kernel_depth = num_input/group;
-    CV_Assert(num_output >= group, num_output % group == 0, num_input % group == 0);
+    CV_Assert_N(num_output >= group, num_output % group == 0, num_input % group == 0);
 
     Net net;
     //layer 1: dwconv

modules/dnn/test/test_tf_importer.cpp

@@ -399,8 +399,10 @@ TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
 TEST_P(Test_TensorFlow_nets, EAST_text_detection)
 {
     checkBackend();
-    if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
-        throw SkipTestException("");
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+#endif
 
     std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
     std::string imgPath = findDataFile("cv/ximgproc/sources/08.png", false);
@@ -425,8 +427,25 @@ TEST_P(Test_TensorFlow_nets, EAST_text_detection)
     Mat scores = outs[0];
     Mat geometry = outs[1];
 
-    normAssert(scores, blobFromNPY(refScoresPath), "scores");
+    // Scores are in range [0, 1]. Geometry values are in range [-0.23, 290]
-    normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", 1e-4, 3e-3);
+    double l1_scores = default_l1, lInf_scores = default_lInf;
+    double l1_geometry = default_l1, lInf_geometry = default_lInf;
+    if (target == DNN_TARGET_OPENCL_FP16)
+    {
+        lInf_scores = 0.11;
+        l1_geometry = 0.28; lInf_geometry = 5.94;
+    }
+    else if (target == DNN_TARGET_MYRIAD)
+    {
+        lInf_scores = 0.214;
+        l1_geometry = 0.47; lInf_geometry = 15.34;
+    }
+    else
+    {
+        l1_geometry = 1e-4, lInf_geometry = 3e-3;
+    }
+    normAssert(scores, blobFromNPY(refScoresPath), "scores", l1_scores, lInf_scores);
+    normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", l1_geometry, lInf_geometry);
 }
 
 INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, dnnBackendsAndTargets());
@@ -537,4 +556,56 @@ TEST(Test_TensorFlow, two_inputs)
     normAssert(out, firstInput + secondInput);
 }
 
+TEST(Test_TensorFlow, Mask_RCNN)
+{
+    std::string proto = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt", false);
+    std::string model = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pb", false);
+
+    Net net = readNetFromTensorflow(model, proto);
+    Mat img = imread(findDataFile("dnn/street.png", false));
+    Mat refDetections = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_out.npy"));
+    Mat refMasks = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_masks.npy"));
+    Mat blob = blobFromImage(img, 1.0f, Size(800, 800), Scalar(), true, false);
+
+    net.setPreferableBackend(DNN_BACKEND_OPENCV);
+
+    net.setInput(blob);
+
+    // Mask-RCNN predicts bounding boxes and segmentation masks.
+    std::vector<String> outNames(2);
+    outNames[0] = "detection_out_final";
+    outNames[1] = "detection_masks";
+
+    std::vector<Mat> outs;
+    net.forward(outs, outNames);
+
+    Mat outDetections = outs[0];
+    Mat outMasks = outs[1];
+    normAssertDetections(refDetections, outDetections, "", /*threshold for zero confidence*/1e-5);
+
+    // Output size of masks is NxCxHxW where
+    // N - number of detected boxes
+    // C - number of classes (excluding background)
+    // HxW - segmentation shape
+    const int numDetections = outDetections.size[2];
+
+    int masksSize[] = {1, numDetections, outMasks.size[2], outMasks.size[3]};
+    Mat masks(4, &masksSize[0], CV_32F);
+
+    std::vector<cv::Range> srcRanges(4, cv::Range::all());
+    std::vector<cv::Range> dstRanges(4, cv::Range::all());
+
+    outDetections = outDetections.reshape(1, outDetections.total() / 7);
+    for (int i = 0; i < numDetections; ++i)
+    {
+        // Get a class id for this bounding box and copy mask only for that class.
+        int classId = static_cast<int>(outDetections.at<float>(i, 1));
+        srcRanges[0] = dstRanges[1] = cv::Range(i, i + 1);
+        srcRanges[1] = cv::Range(classId, classId + 1);
+        outMasks(srcRanges).copyTo(masks(dstRanges));
+    }
+    cv::Range topRefMasks[] = {Range::all(), Range(0, numDetections), Range::all(), Range::all()};
+    normAssert(masks, refMasks(&topRefMasks[0]));
+}
+
 }

modules/dnn/test/test_torch_importer.cpp

@@ -242,15 +242,23 @@ TEST_P(Test_Torch_layers, net_residual)
     runTorchNet("net_residual", "", false, true);
 }
 
-typedef testing::TestWithParam<Target> Test_Torch_nets;
+class Test_Torch_nets : public DNNTestLayer {};
 
 TEST_P(Test_Torch_nets, OpenFace_accuracy)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+#endif
+    checkBackend();
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
+        throw SkipTestException("");
+
     const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
     Net net = readNetFromTorch(model);
 
-    net.setPreferableBackend(DNN_BACKEND_OPENCV);
+    net.setPreferableBackend(backend);
-    net.setPreferableTarget(GetParam());
+    net.setPreferableTarget(target);
 
     Mat sample = imread(findDataFile("cv/shared/lena.png", false));
     Mat sampleF32(sample.size(), CV_32FC3);
@@ -264,11 +272,16 @@ TEST_P(Test_Torch_nets, OpenFace_accuracy)
     Mat out = net.forward();
 
     Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
-    normAssert(out, outRef);
+    normAssert(out, outRef, "", default_l1, default_lInf);
 }
 
 TEST_P(Test_Torch_nets, ENet_accuracy)
 {
+    checkBackend();
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
+        (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
+        throw SkipTestException("");
+
     Net net;
     {
         const string model = findDataFile("dnn/Enet-model-best.net", false);
@@ -276,8 +289,8 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
         ASSERT_TRUE(!net.empty());
     }
 
-    net.setPreferableBackend(DNN_BACKEND_OPENCV);
+    net.setPreferableBackend(backend);
-    net.setPreferableTarget(GetParam());
+    net.setPreferableTarget(target);
 
     Mat sample = imread(_tf("street.png", false));
     Mat inputBlob = blobFromImage(sample, 1./255);
@@ -314,6 +327,7 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
 //   -model models/instance_norm/feathers.t7
 TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
 {
+    checkBackend();
     std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
                             "dnn/fast_neural_style_instance_norm_feathers.t7"};
     std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};
@@ -323,8 +337,8 @@ TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
         const string model = findDataFile(models[i], false);
         Net net = readNetFromTorch(model);
 
-        net.setPreferableBackend(DNN_BACKEND_OPENCV);
+        net.setPreferableBackend(backend);
-        net.setPreferableTarget(GetParam());
+        net.setPreferableTarget(target);
 
         Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
         Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
@@ -341,12 +355,20 @@ TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
         Mat ref = imread(findDataFile(targets[i]));
         Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);
 
-        normAssert(out, refBlob, "", 0.5, 1.1);
+        if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
+        {
+            double normL1 = cvtest::norm(refBlob, out, cv::NORM_L1) / refBlob.total();
+            if (target == DNN_TARGET_MYRIAD)
+                EXPECT_LE(normL1, 4.0f);
+            else
+                EXPECT_LE(normL1, 0.6f);
+        }
+        else
+            normAssert(out, refBlob, "", 0.5, 1.1);
     }
 }
 
-INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_nets, availableDnnTargets());
+INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_nets, dnnBackendsAndTargets());
-
 
 // Test a custom layer
 // https://github.com/torch/nn/blob/master/doc/convolution.md#nn.SpatialUpSamplingNearest

modules/highgui/include/opencv2/highgui.hpp

@@ -452,12 +452,13 @@ The function getWindowImageRect returns the client screen coordinates, width and
  */
 CV_EXPORTS_W Rect getWindowImageRect(const String& winname);
 
+/** @example samples/cpp/create_mask.cpp
+This program demonstrates using mouse events and how to make and use a mask image (black and white) .
+*/
 /** @brief Sets mouse handler for the specified window
 
 @param winname Name of the window.
-@param onMouse Mouse callback. See OpenCV samples, such as
+@param onMouse Callback function for mouse events. See OpenCV samples on how to specify and use the callback.
-<https://github.com/opencv/opencv/tree/3.4/samples/cpp/ffilldemo.cpp>, on how to specify and
-use the callback.
 @param userdata The optional parameter passed to the callback.
  */
 CV_EXPORTS void setMouseCallback(const String& winname, MouseCallback onMouse, void* userdata = 0);

modules/highgui/src/window_w32.cpp

@@ -1500,7 +1500,7 @@ MainWindowProc( HWND hwnd, UINT uMsg, WPARAM wParam, LPARAM lParam )
             rgn = CreateRectRgn(0, 0, wrc.right, wrc.bottom);
             rgn1 = CreateRectRgn(cr.left, cr.top, cr.right, cr.bottom);
             rgn2 = CreateRectRgn(tr.left, tr.top, tr.right, tr.bottom);
-            CV_Assert(rgn != 0, rgn1 != 0, rgn2 != 0);
+            CV_Assert_N(rgn != 0, rgn1 != 0, rgn2 != 0);
 
             ret = CombineRgn(rgn, rgn, rgn1, RGN_DIFF);
             ret = CombineRgn(rgn, rgn, rgn2, RGN_DIFF);

modules/imgcodecs/include/opencv2/imgcodecs/ios.h

@@ -50,8 +50,8 @@
 //! @addtogroup imgcodecs_ios
 //! @{
 
-UIImage* MatToUIImage(const cv::Mat& image);
+CV_EXPORTS UIImage* MatToUIImage(const cv::Mat& image);
-void UIImageToMat(const UIImage* image,
+CV_EXPORTS void UIImageToMat(const UIImage* image,
-                         cv::Mat& m, bool alphaExist = false);
+                             cv::Mat& m, bool alphaExist = false);
 
 //! @}