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January 22, 2016 05:11
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import numpy as np | |
import cv2 | |
from matplotlib import pyplot as plt | |
#http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_matcher/py_matcher.html | |
def drawMatches(img1, kp1, img2, kp2, matches): | |
""" | |
source: | |
http://stackoverflow.com/questions/20259025/module-object-has-no-attribute-drawmatches-opencv-python | |
My own implementation of cv2.drawMatches as OpenCV 2.4.9 | |
does not have this function available but it's supported in | |
OpenCV 3.0.0 | |
This function takes in two images with their associated | |
keypoints, as well as a list of DMatch data structure (matches) | |
that contains which keypoints matched in which images. | |
An image will be produced where a montage is shown with | |
the first image followed by the second image beside it. | |
Keypoints are delineated with circles, while lines are connected | |
between matching keypoints. | |
img1,img2 - Grayscale images | |
kp1,kp2 - Detected list of keypoints through any of the OpenCV keypoint | |
detection algorithms | |
matches - A list of matches of corresponding keypoints through any | |
OpenCV keypoint matching algorithm | |
""" | |
# Create a new output image that concatenates the two images together | |
# (a.k.a) a montage | |
rows1 = img1.shape[0] | |
cols1 = img1.shape[1] | |
rows2 = img2.shape[0] | |
cols2 = img2.shape[1] | |
out = np.zeros((max([rows1,rows2]),cols1+cols2,3), dtype='uint8') | |
# Place the first image to the left | |
out[:rows1,:cols1] = np.dstack([img1, img1, img1]) | |
# Place the next image to the right of it | |
out[:rows2,cols1:] = np.dstack([img2, img2, img2]) | |
# For each pair of points we have between both images | |
# draw circles, then connect a line between them | |
for mat in matches: | |
# Get the matching keypoints for each of the images | |
img1_idx = mat.queryIdx | |
img2_idx = mat.trainIdx | |
# x - columns | |
# y - rows | |
(x1,y1) = kp1[img1_idx].pt | |
(x2,y2) = kp2[img2_idx].pt | |
# Draw a small circle at both co-ordinates | |
# radius 4 | |
# colour blue | |
# thickness = 1 | |
cv2.circle(out, (int(x1),int(y1)), 4, (255, 0, 0), 1) | |
cv2.circle(out, (int(x2)+cols1,int(y2)), 4, (255, 0, 0), 1) | |
# Draw a line in between the two points | |
# thickness = 1 | |
# colour blue | |
cv2.line(out, (int(x1),int(y1)), (int(x2)+cols1,int(y2)), (255, 0, 0), 1) | |
# Show the image | |
cv2.imshow('Matched Features', out) | |
cv2.waitKey(0) | |
cv2.destroyWindow('Matched Features') | |
# Also return the image if you'd like a copy | |
return out | |
img1 = cv2.imread('blue.png',0) # queryImage | |
img2 = cv2.imread('all.png',0) # trainImage | |
surf = cv2.SURF(400) | |
# find the keypoints and descriptors with SIFT | |
kp1, des1 = surf.detectAndCompute(img1,None) | |
kp2, des2 = surf.detectAndCompute(img2,None) | |
# create BFMatcher object | |
bf = cv2.BFMatcher() | |
# Match descriptors. | |
matches = bf.match(des1,des2) | |
#matches = sorted(matches, key = lambda x:x.distance) | |
drawMatches(img1, kp1, img2, kp2, matches) | |
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