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Merge pull request #19258 from notmatthancock:doc/update-sift-patent-blurb

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* Remove sentences about SIFT patent and code location

* Add note about patent in opencv-specific section
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doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
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@ -20,10 +20,10 @@ scale invariant.
![image](images/sift_scale_invariant.jpg) ![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 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 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)*. this paper)*.
There are mainly four steps involved in SIFT algorithm. We will see them one-by-one. 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 greater than 0.8, they are rejected. It eliminates around 90% of false matches while discards only
5% correct matches, as per the paper. 5% correct matches, as per the paper.
So this is a summary of SIFT algorithm. For more details and understanding, reading the original 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 paper is highly recommended.
included in [the opencv contrib repo](https://github.com/opencv/opencv_contrib)
SIFT in OpenCV SIFT in OpenCV
-------------- --------------
So now let's see SIFT functionalities available in OpenCV. Let's start with keypoint detection and Now let's see SIFT functionalities available in OpenCV. Note that these were previously only
draw them. First we have to construct a SIFT object. We can pass different parameters to it which available in [the opencv contrib repo](https://github.com/opencv/opencv_contrib), but the patent
are optional and they are well explained in docs. 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} @code{.py}
import numpy as np import numpy as np
import cv2 as cv import cv2 as cv