使用opencv(cv2)来计算两张有位移的图片间的区别_opencv两张图变化检测

背景：两张图片间有微小移位需要先对齐再计算，若无偏移可直接使用 cv2.subtract() 计算差异。

main.py目录创建一个img文件夹存放图片。

 直接上脚本：

import cv2
import numpy as np
 
 
def align_images(img1, img2, max_features=5000, good_match_percent=0.15):
    # Convert images to grayscale
    img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
    img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
 
    # Detect ORB features and compute descriptors
    orb = cv2.ORB_create(max_features)
    keypoints1, descriptors1 = orb.detectAndCompute(img1_gray, None)
    keypoints2, descriptors2 = orb.detectAndCompute(img2_gray, None)
 
    # Match features
    matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
    matches = matcher.match(descriptors1, descriptors2, None)
 
    # Sort matches by score
    # matches.sort(key=lambda x: x.distance, reverse=False)
    matches = tuple(sorted(list(matches), key=lambda x: x.distance, reverse=False))
 
    # Remove not so good matches
    num_good_matches = int(len(matches) * good_match_percent)
    matches = matches[:num_good_matches]
 
    # Extract location of good matches
    points1 = np.zeros((len(matches), 2), dtype=np.float32)
    points2 = np.zeros((len(matches), 2), dtype=np.float32)
 
    for i, match in enumerate(matches):
        points1[i, :] = keypoints1[match.queryIdx].pt
        points2[i, :] = keypoints2[match.trainIdx].pt
 
    # Find homography
    h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
 
    # Use homography to align images
    height, width, channels = img2.shape
    img1_aligned = cv2.warpPerspective(img1, h, (width, height))
 
    return img1_aligned, img2
 
 
def find_differences(img1, img2, threshold=30):
    # Convert images to grayscale
    img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
    img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
 
    # Compute absolute difference between the two images
    diff = cv2.absdiff(img1_gray, img2_gray)
 
    # Threshold the difference image
    _, thresh = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)
 
    return thresh
 
 
if __name__ == '__main__':
    # Load images
    img1 = cv2.imread('img/img01.jpg')
    img2 = cv2.imread('img/img02.jpg')
 
    # Align the images
    img1_aligned, img2 = align_images(img1, img2)
 
    # Find differences between the aligned images
    differences = find_differences(img1_aligned, img2)
 
    # Save the result
    cv2.imwrite('img/differences.jpg', differences)
 
    # Show the result
    cv2.imshow('Differences', differences)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

这个脚本首先使用ORB特征检测器来检测两张图片的关键点，并计算描述符。然后，匹配特征点并使用RANSAC算法找到一个透视变换矩阵，以便将第一张图片与第二张图片对齐。最后，计算两张图片之间的差异，并将结果保存为一个新的图片。你也可以更改阈值参数以获得更好的差异检测结果。