Panorama Stitching with OpenCV: SIFT Feature Detection and RANSAC

Image panorama stitching combines two overlapping images into a single wide view using computer‑vision techniques such as SIFT keypoint detection, feature matching, RANSAC homography estimation and perspective warping.

Steps

(1) Detect SIFT keypoints and extract local invariant descriptors for both left and right images. (2) Match the descriptors with a k‑nearest‑neighbors matcher and filter matches using the ratio test. (3) Compute the homography matrix H from the matched points with RANSAC. (4) Warp the right image with cv.warpPerspective using H and blend the left image onto the warped result to obtain the final panorama.

Code

<code>import cv2 as cv        # import OpenCV
import numpy as np      # import NumPy for matrix operations

# Detect SIFT keypoints
def sift_keypoints_detect(image):
    gray_image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    sift = cv.xfeatures2d.SIFT_create()
    keypoints, features = sift.detectAndCompute(image, None)
    keypoints_image = cv.drawKeypoints(
        gray_image, keypoints, None, flags=cv.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
    return keypoints_image, keypoints, features

# Match features with KNN
def get_feature_point_ensemble(features_right, features_left):
    bf = cv.BFMatcher()
    matches = bf.knnMatch(features_right, features_left, k=2)
    matches = sorted(matches, key=lambda x: x[0].distance / x[1].distance)
    good = []
    for m, n in matches:
        ratio = 0.6
        if m.distance < ratio * n.distance:
            good.append(m)
    return good

# Panorama stitching
def Panorama_stitching(image_right, image_left):
    _, kp_r, f_r = sift_keypoints_detect(image_right)
    _, kp_l, f_l = sift_keypoints_detect(image_left)
    goodMatch = get_feature_point_ensemble(f_r, f_l)
    if len(goodMatch) > 4:
        ptsR = np.float32([kp_r[m.queryIdx].pt for m in goodMatch]).reshape(-1,1,2)
        ptsL = np.float32([kp_l[m.trainIdx].pt for m in goodMatch]).reshape(-1,1,2)
        ransacReprojThreshold = 4
        Homography, status = cv.findHomography(ptsR, ptsL, cv.RANSAC, ransacReprojThreshold)
        Panorama = cv.warpPerspective(image_right, Homography,
                                      (image_right.shape[1] + image_left.shape[1],
                                       image_right.shape[0]))
        Panorama[0:image_left.shape[0], 0:image_left.shape[1]] = image_left
        return Panorama

if __name__ == '__main__':
    image_left = cv.imread("./Left.jpg")
    image_right = cv.imread("./Right.jpg")
    image_right = cv.resize(image_right, None, fx=0.4, fy=0.24)
    image_left = cv.resize(image_left, (image_right.shape[1], image_right.shape[0]))
    # Additional display and saving steps can be added here
</code>