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Merge pull request #17119 from alalek:move_sift

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Alexander Alekhin
2020-04-24 18:15:15 +00:00
parent 3c14a8c507 a327418767
commit 8d05dab32c
17 changed files with 1384 additions and 26 deletions
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doc/opencv.bib
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@@ -620,7 +620,7 @@
volume = {1},
publisher = {IEEE}
}
@article{Lowe:2004:DIF:993451.996342,
@article{Lowe04,
author = {Lowe, David G.},
title = {Distinctive Image Features from Scale-Invariant Keypoints},
journal = {Int. J. Comput. Vision},
doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown
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@@ -44,7 +44,7 @@ img1 = cv.imread('box.png',0) # queryImage
img2 = cv.imread('box_in_scene.png',0) # trainImage
# Initiate SIFT detector
sift = cv.xfeatures2d.SIFT_create()
sift = cv.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
doc/py_tutorials/py_feature2d/py_matcher/py_matcher.markdown
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@@ -110,7 +110,7 @@ img1 = cv.imread('box.png',cv.IMREAD_GRAYSCALE) # queryImage
img2 = cv.imread('box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
# Initiate SIFT detector
sift = cv.xfeatures2d.SIFT_create()
sift = cv.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
@@ -174,7 +174,7 @@ img1 = cv.imread('box.png',cv.IMREAD_GRAYSCALE) # queryImage
img2 = cv.imread('box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
# Initiate SIFT detector
sift = cv.xfeatures2d.SIFT_create()
sift = cv.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
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@@ -119,7 +119,7 @@ import cv2 as cv
img = cv.imread('home.jpg')
gray= cv.cvtColor(img,cv.COLOR_BGR2GRAY)
sift = cv.xfeatures2d.SIFT_create()
sift = cv.SIFT_create()
kp = sift.detect(gray,None)
img=cv.drawKeypoints(gray,kp,img)
@@ -151,7 +151,7 @@ Now to calculate the descriptor, OpenCV provides two methods.
We will see the second method:
@code{.py}
sift = cv.xfeatures2d.SIFT_create()
sift = cv.SIFT_create()
kp, des = sift.detectAndCompute(gray,None)
@endcode
Here kp will be a list of keypoints and des is a numpy array of shape
doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.markdown
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@@ -27,7 +27,7 @@ Binary descriptors (ORB, BRISK, ...) are matched using the <a href="https://en.w
This distance is equivalent to count the number of different elements for binary strings (population count after applying a XOR operation):
\f[ d_{hamming} \left ( a,b \right ) = \sum_{i=0}^{n-1} \left ( a_i \oplus b_i \right ) \f]
To filter the matches, Lowe proposed in @cite Lowe:2004:DIF:993451.996342 to use a distance ratio test to try to eliminate false matches.
To filter the matches, Lowe proposed in @cite Lowe04 to use a distance ratio test to try to eliminate false matches.
The distance ratio between the two nearest matches of a considered keypoint is computed and it is a good match when this value is below
a threshold. Indeed, this ratio allows helping to discriminate between ambiguous matches (distance ratio between the two nearest neighbors
is close to one) and well discriminated matches. The figure below from the SIFT paper illustrates the probability that a match is correct