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python: 'cv2.' -> 'cv.' via 'import cv2 as cv'

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Alexander Alekhin
2017-12-11 12:55:03 +03:00
parent 9665dde678
commit 5560db73bf
162 changed files with 2083 additions and 2084 deletions
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doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
+11 -11
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@@ -113,30 +113,30 @@ So now let's see SIFT functionalities available in OpenCV. Let's start with keyp
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 cv2
import numpy as np
import cv2 as cv
img = cv2.imread('home.jpg')
gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img = cv.imread('home.jpg')
gray= cv.cvtColor(img,cv.COLOR_BGR2GRAY)
sift = cv2.xfeatures2d.SIFT_create()
sift = cv.xfeatures2d.SIFT_create()
kp = sift.detect(gray,None)
img=cv2.drawKeypoints(gray,kp,img)
img=cv.drawKeypoints(gray,kp,img)
cv2.imwrite('sift_keypoints.jpg',img)
cv.imwrite('sift_keypoints.jpg',img)
@endcode
**sift.detect()** function finds the keypoint in the images. You can pass a mask if you want to
search only a part of image. Each keypoint is a special structure which has many attributes like its
(x,y) coordinates, size of the meaningful neighbourhood, angle which specifies its orientation,
response that specifies strength of keypoints etc.
OpenCV also provides **cv2.drawKeyPoints()** function which draws the small circles on the locations
of keypoints. If you pass a flag, **cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS** to it, it will
OpenCV also provides **cv.drawKeyPoints()** function which draws the small circles on the locations
of keypoints. If you pass a flag, **cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS** to it, it will
draw a circle with size of keypoint and it will even show its orientation. See below example.
@code{.py}
img=cv2.drawKeypoints(gray,kp,img,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imwrite('sift_keypoints.jpg',img)
img=cv.drawKeypoints(gray,kp,img,flags=cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv.imwrite('sift_keypoints.jpg',img)
@endcode
See the two results below:
@@ -151,7 +151,7 @@ Now to calculate the descriptor, OpenCV provides two methods.
We will see the second method:
@code{.py}
sift = cv2.xfeatures2d.SIFT_create()
sift = cv.xfeatures2d.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