I’m trying to use RANSAC to prune false match after SIFT match.

cv.getPerspectiveTransform()

method requires Point2f data ,and in python i got a tuple.

Here is my code

import cv2
import numpy as np
def ransac(pts1, pts2, img_l, img_r, max_iters=500, epsilon=1):
best_matches = []
# Number of samples
N = 4
for i in range(max_iters):
# Get 4 random samples from features
id1 = np.random.randint(0, len(pts1), N)
id2 = np.random.randint(0, len(pts2), N)
src = []
dst = []
for i in range(N):
src.append(pts1[id1[i]].pt)
dst.append(pts2[id2[i]].pt)
src = np.mat(src)
dst = np.mat(dst)
# Calculate the homography matrix H
H = cv2.getPerspectiveTransform(src, dst)
Hp = cv2.perspectiveTransform(pts1[None], H)[0]
# Find the inliers by computing the SSD(p',Hp) and saving inliers (feature pairs) that are SSD(p',Hp) < epsilon
inliers = []
for i in range(len(pts1)):
ssd = np.sum(np.square(pts2[i] - Hp[i]))
if ssd < epsilon:
inliers.append([pts1[i], pts2[i]])
# Keep the largest set of inliers and the corresponding homography matrix
if len(inliers) > len(best_matches):
best_matches = inliers
return best_matches

please highlight, where the current problem is.
(e.g. KeyPoint.ptis already a Point2f)

everything below that looks broken. you probably should convert all keypoints to 2d points before (not only 4), and do calculations on those, not the actual kp.

def ransac(pts1, pts2, img_l, img_r, max_iters=500, epsilon=1):
p1 = cv2.convert(pts1)
p2 = cv2.convert(pts2)
best_matches = []
# Number of samples
N = 4
for i in range(max_iters):
# Get 4 random samples from features
id1 = np.random.randint(0, len(pts1), N)
id2 = np.random.randint(0, len(pts2), N)
src = np.array([p1[i] for i in id1], dtype=np.float32)
dst = np.array([p2[i] for i in id2], dtype=np.float32)
# Calculate the homography matrix H
H = cv2.getPerspectiveTransform(src, dst)
Hp = cv2.perspectiveTransform(p1, H)
# Find the inliers by computing the SSD(p',Hp) and saving inliers (feature pairs) that are SSD(p',Hp) < epsilon
inliers = []
for i in range(len(p1)):
ssd = np.sum(np.square(p2[i] - Hp[i]))
if ssd < epsilon:
inliers.append([p1[i], p2[i]])
# Keep the largest set of inliers and the corresponding homography matrix
if len(inliers) > len(best_matches):
best_matches = inliers
return best_matches

however, i dont think it will work properly !
imo, there are 2 broken assumptions here:

for i in range(len(pts1)):

what if len(p1) > len(p2) ?

ssd = np.sum(np.square(p2[i] - Hp[i]))

you wrongly assume, that p2[i] is the corresponding point to the projected p1[i], however, it’s an arbitrary kp from the p2 set, you’re comparing apples to pears

IF you did some descriptor matching before, you should re-use the matches here

Thanks for replying ! And pointing out the problems ,I’ll fix those problems. I didn’t think much about this approach . I cloned this codes from this repository , code it wrong . I did sift descriptor matching before this , but the result is bad (I only use 2 images for test), only 1 of 6 pairs is the correct match . But I think I got your point. Your proposal and explanation are very helpful ! Also I looked the doc you provide , The convert function is a static function , like this cv2.KeyPoint.convert().