How to get pixel coordinates from Feature Matching in OpenCV Python
I need to get the list of the x and y coordinates of the pixels that the feature matcher selects in the code provided. I'm using Python and OpenCV. Can anyone help me?
img1=cv2.imread('DSC_0216.jpg',0)
img2=cv2.imread('DSC_0217.jpg',0)
orb=cv2.ORB(nfeatures=100000)
kp1,des1=orb.detectAndCompute(img1,None)
kp2,des2=orb.detectAndCompute(img2,None)
img1kp=cv2.drawKeypoints(img1,kp1,color=(0,255,0),flags=0)
img2kp=cv2.drawKeypoints(img2,kp2,color=(0,255,0),flags=0)
cv2.imwrite('m_img1.jpg',img1kp)
cv2.imwrite('m_img2.jpg',img2kp)
bf=cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches=bf.match(des1,des2)
matches=sorted(matches, key= lambda x:x.distance)
We know that your keypoints are stored in kp1 and kp2 where they are the features matches for the first and second image respectively. In the cv2.ORB perspective, these are 2D matrices where each row is a keypoint that is detected in first image, kp1 and the second image, kp2 .
In your case because you are using cv2.BFMatch , matches returns a list of cv2.DMatch objects where each object contains several members.... among them are two important members:
queryIdx - The index or row of the kp1 interest point matrix that matches trainIdx - The index or row of the kp2 interest point matrix that matches Therefore, queryIdx and trainIdx tell you which ORB features match between kp1 and kp2 . Therefore, you'd use these to index into kp1 and kp2 and obtain the pt member, which is a tuple of (x,y) coordinates that determine the actual spatial coordinates of the matches.
All you have to do is iterate through each cv2.DMatch object in matches , append to a list of coordinates for both kp1 and kp2 and you're done.
Something like this:
# Initialize lists
list_kp1 = []
list_kp2 = []
# For each match...
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
# Get the coordinates
(x1,y1) = kp1[img1_idx].pt
(x2,y2) = kp2[img2_idx].pt
# Append to each list
list_kp1.append((x1, y1))
list_kp2.append((x2, y2))
Note that I could have just done list_kp1.append(kp1[img1_idx].pt) and the same for list_kp2 , but I wanted to make it very clear on how to interpret the spatial coordinates. You could also go one step further and do a list comprehension:
list_kp1 = [kp1[mat.queryIdx].pt for mat in matches]
list_kp2 = [kp2[mat.trainIdx].pt for mat in matches]
list_kp1 will contain the spatial coordinates of a feature point that matched with the corresponding position in list_kp2 . In other words, element i of list_kp1 contains the spatial coordinates of the feature point from img1 that matched with the corresponding feature point from img2 in list_kp2 whose spatial coordinates are in element i .
As a minor sidenote, I used this concept when I wrote a workaround for drawMatches because for OpenCV 2.4.x, the Python wrapper to the C++ function does not exist, so I made use of the above concept in locating the spatial coordinates of the matching features between the two images to write my own implementation of it.
Check it out if you like!
module' object has no attribute 'drawMatches' opencv python
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