Merge remote-tracking branch 'upstream/3.4' into merge-3.4

CMakeLists.txt

@@ -1047,7 +1047,9 @@ endif()
 if(CMAKE_GENERATOR MATCHES Xcode)
   status("    Xcode:"          ${XCODE_VERSION})
 endif()
-if(NOT CMAKE_GENERATOR MATCHES "Xcode|Visual Studio")
+if(CMAKE_GENERATOR MATCHES "Xcode|Visual Studio|Multi-Config")
+  status("    Configuration:"  ${CMAKE_CONFIGURATION_TYPES})
+else()
   status("    Configuration:"  ${CMAKE_BUILD_TYPE})
 endif()
 

README.md

@@ -5,7 +5,8 @@
 * Homepage: <https://opencv.org>
   * Courses: <https://opencv.org/courses>
 * Docs: <https://docs.opencv.org/master/>
-* Q&A forum: <http://answers.opencv.org>
+* Q&A forum: <https://forum.opencv.org>
+  * previous forum (read only): <http://answers.opencv.org>
 * Issue tracking: <https://github.com/opencv/opencv/issues>
 * Additional OpenCV functionality: <https://github.com/opencv/opencv_contrib> 
 

doc/js_tutorials/js_assets/js_template_matching_matchTemplate.html

@@ -74,7 +74,8 @@ <h2>Template Match Example</h2>
 utils.loadCode('codeSnippet', 'codeEditor');
 utils.loadImageToCanvas('lena.jpg', 'imageCanvasInput');
 utils.loadImageToCanvas('lenaFace.png', 'templateCanvasInput');
-utils.addFileInputHandler('fileInput', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'imageCanvasInput');
+utils.addFileInputHandler('templateFileInput', 'templateCanvasInput');
 
 let tryIt = document.getElementById('tryIt');
 tryIt.addEventListener('click', () => {

doc/js_tutorials/js_setup/js_usage/js_usage.markdown

@@ -82,7 +82,7 @@ In this tutorial, we just show a cv.Mat on screen. To show a cv.Mat, you need a
 
 You can use cv.imshow to show cv.Mat on the canvas.
 @code{.js}
-cv.imshow(mat, "outputCanvas");
+cv.imshow("outputCanvas", mat);
 @endcode
 
 Putting all of the steps together, the final index.html is shown below.

doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown

@@ -20,10 +20,10 @@ scale invariant.
 
 ![image](images/sift_scale_invariant.jpg)
 
-So, in 2004, **D.Lowe**, University of British Columbia, came up with a new algorithm, Scale
+In 2004, **D.Lowe**, University of British Columbia, came up with a new algorithm, Scale
 Invariant Feature Transform (SIFT) in his paper, **Distinctive Image Features from Scale-Invariant
 Keypoints**, which extract keypoints and compute its descriptors. *(This paper is easy to understand
-and considered to be best material available on SIFT. So this explanation is just a short summary of
+and considered to be best material available on SIFT. This explanation is just a short summary of
 this paper)*.
 
 There are mainly four steps involved in SIFT algorithm. We will see them one-by-one.
@@ -102,16 +102,17 @@ reasons. In that case, ratio of closest-distance to second-closest distance is t
 greater than 0.8, they are rejected. It eliminates around 90% of false matches while discards only
 5% correct matches, as per the paper.
 
-So this is a summary of SIFT algorithm. For more details and understanding, reading the original
-paper is highly recommended. Remember one thing, this algorithm is patented. So this algorithm is
-included in [the opencv contrib repo](https://github.com/opencv/opencv_contrib)
+This is a summary of SIFT algorithm. For more details and understanding, reading the original
+paper is highly recommended.
 
 SIFT in OpenCV
 --------------
 
-So now let's see SIFT functionalities available in OpenCV. Let's start with keypoint detection and
-draw them. First we have to construct a SIFT object. We can pass different parameters to it which
-are optional and they are well explained in docs.
+Now let's see SIFT functionalities available in OpenCV. Note that these were previously only
+available in [the opencv contrib repo](https://github.com/opencv/opencv_contrib), but the patent
+expired in the year 2020. So they are now included in the main repo. Let's start with keypoint
+detection and draw them. First we have to construct a SIFT object. We can pass different
+parameters to it which are optional and they are well explained in docs.
 @code{.py}
 import numpy as np
 import cv2 as cv

doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.markdown

@@ -224,7 +224,7 @@ First you apply the transform:
         -   *theta*: The resolution of the parameter \f$\theta\f$ in radians. We use **1 degree**
             (CV_PI/180)
         -   *threshold*: The minimum number of intersections to "*detect*" a line
-        -   *minLinLength*: The minimum number of points that can form a line. Lines with less than
+        -   *minLineLength*: The minimum number of points that can form a line. Lines with less than
             this number of points are disregarded.
         -   *maxLineGap*: The maximum gap between two points to be considered in the same line.