OpenCV 3 Computer Vision with Python Cookbook

How to do it...

You need to complete the following steps:

1. Import the necessary modules:

import cv2import numpy as npimport matplotlib.pyplot as plt

2. Load the test images:

img0 = cv2.imread('../data/Lena.png', cv2.IMREAD_GRAYSCALE)img1 = cv2.imread('../data/Lena_rotated.png', cv2.IMREAD_GRAYSCALE)

3. Detect the keypoints and computer descriptors:

detector = cv2.ORB_create(100)kps0, fea0 = detector.detectAndCompute(img0, None)kps1, fea1 = detector.detectAndCompute(img1, None)matcher = cv2.BFMatcher_create(cv2.NORM_HAMMING, False)matches = matcher.match(fea0, fea1)

4. Fit the homography model into the found keypoint correspondences robustly and get a mask of inlier matches:

pts0 = np.float32([kps0[m.queryIdx].pt for m in matches]).reshape(-1,2) ...