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Merge pull request #13616 from atinfinity:fixed-py_matcher-tutorial

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* fixed tutorial code of py_matcher

* fixed imread mode
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atinfinity 2019-01-13 18:34:05 +09:00 committed by Alexander Alekhin
parent 6e39856623
commit e48682a9f7
1 changed files with 15 additions and 15 deletions
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30
doc/py_tutorials/py_feature2d/py_matcher/py_matcher.markdown
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@ -53,8 +53,8 @@ import numpy as np
import cv2 as cv import cv2 as cv
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
img1 = cv.imread('box.png',0) # queryImage img1 = cv.imread('box.png',cv.IMREAD_GRAYSCALE) # queryImage
img2 = cv.imread('box_in_scene.png',0) # trainImage img2 = cv.imread('box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
# Initiate ORB detector # Initiate ORB detector
orb = cv.ORB_create() orb = cv.ORB_create()
@ -79,7 +79,7 @@ matches = bf.match(des1,des2)
matches = sorted(matches, key = lambda x:x.distance) matches = sorted(matches, key = lambda x:x.distance)
# Draw first 10 matches. # Draw first 10 matches.
img3 = cv.drawMatches(img1,kp1,img2,kp2,matches[:10], flags=2) img3 = cv.drawMatches(img1,kp1,img2,kp2,matches[:10],None,flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
plt.imshow(img3),plt.show() plt.imshow(img3),plt.show()
@endcode @endcode
@ -104,13 +104,13 @@ so that we can apply ratio test explained by D.Lowe in his paper.
@code{.py} @code{.py}
import numpy as np import numpy as np
import cv2 as cv import cv2 as cv
from matplotlib import pyplot as plt import matplotlib.pyplot as plt
img1 = cv.imread('box.png',0) # queryImage img1 = cv.imread('box.png',cv.IMREAD_GRAYSCALE) # queryImage
img2 = cv.imread('box_in_scene.png',0) # trainImage img2 = cv.imread('box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
# Initiate SIFT detector # Initiate SIFT detector
sift = cv.SIFT() sift = cv.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT # find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None) kp1, des1 = sift.detectAndCompute(img1,None)
@ -118,7 +118,7 @@ kp2, des2 = sift.detectAndCompute(img2,None)
# BFMatcher with default params # BFMatcher with default params
bf = cv.BFMatcher() bf = cv.BFMatcher()
matches = bf.knnMatch(des1,des2, k=2) matches = bf.knnMatch(des1,des2,k=2)
# Apply ratio test # Apply ratio test
good = [] good = []
@ -127,7 +127,7 @@ for m,n in matches:
good.append([m]) good.append([m])
# cv.drawMatchesKnn expects list of lists as matches. # cv.drawMatchesKnn expects list of lists as matches.
img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,good,flags=2) img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
plt.imshow(img3),plt.show() plt.imshow(img3),plt.show()
@endcode @endcode
@ -168,13 +168,13 @@ With this information, we are good to go.
@code{.py} @code{.py}
import numpy as np import numpy as np
import cv2 as cv import cv2 as cv
from matplotlib import pyplot as plt import matplotlib.pyplot as plt
img1 = cv.imread('box.png',0) # queryImage img1 = cv.imread('box.png',cv.IMREAD_GRAYSCALE) # queryImage
img2 = cv.imread('box_in_scene.png',0) # trainImage img2 = cv.imread('box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
# Initiate SIFT detector # Initiate SIFT detector
sift = cv.SIFT() sift = cv.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT # find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None) kp1, des1 = sift.detectAndCompute(img1,None)
@ -190,7 +190,7 @@ flann = cv.FlannBasedMatcher(index_params,search_params)
matches = flann.knnMatch(des1,des2,k=2) matches = flann.knnMatch(des1,des2,k=2)
# Need to draw only good matches, so create a mask # Need to draw only good matches, so create a mask
matchesMask = [[0,0] for i in xrange(len(matches))] matchesMask = [[0,0] for i in range(len(matches))]
# ratio test as per Lowe's paper # ratio test as per Lowe's paper
for i,(m,n) in enumerate(matches): for i,(m,n) in enumerate(matches):
@ -200,7 +200,7 @@ for i,(m,n) in enumerate(matches):
draw_params = dict(matchColor = (0,255,0), draw_params = dict(matchColor = (0,255,0),
singlePointColor = (255,0,0), singlePointColor = (255,0,0),
matchesMask = matchesMask, matchesMask = matchesMask,
flags = 0) flags = cv.DrawMatchesFlags_DEFAULT)
img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params) img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)