feature_matching.py

orb = cv2.ORB_create()

kp1, des1 = orb.detectAndCompute(l, None)
kp2, des2 = orb.detectAndCompute(r, None)

# create BFMatcher object
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
# Match descriptors.
matches = bf.match(des1, des2)

# Sort them in the order of their distance.
matches = sorted(matches, key=lambda x: x.distance)

# Graph the difference in x and y to see if we can identify a trend.
x_coords = []
y_coords = []

for match in matches[:50]:
    x_coords.append(kp2[match.trainIdx].pt[0] - kp1[match.queryIdx].pt[0])
    y_coords.append(kp2[match.trainIdx].pt[1] - kp1[match.queryIdx].pt[1])

plt.scatter(x_coords, y_coords)
plt.show()