OpenCV ORB 特征点检测与匹配 - Python 代码示例

OpenCV ORB 特征点检测与匹配 - Python 代码示例

本示例展示如何使用 OpenCV 的 ORB 算法检测图像中的特征点，并使用 BFMatcher 进行特征点匹配，以识别图像之间的相似性。

1. 读取图像

import cv2

# 读取图像
img1 = cv2.imread('1.png')
img2 = cv2.imread('2.png')
img3 = cv2.imread('3.png')
img4 = cv2.imread('4.png')

2. 创建 ORB 对象

# 创建 ORB 对象
 orb = cv2.ORB_create()

3. 检测特征点

# 检测特征点
keypoints1 = orb.detect(img1, None)
keypoints2 = orb.detect(img2, None)
keypoints3 = orb.detect(img3, None)
keypoints4 = orb.detect(img4, None)

# 绘制特征点
img_with_keypoints1 = cv2.drawKeypoints(img1, keypoints1, None)
img_with_keypoints2 = cv2.drawKeypoints(img2, keypoints2, None)
img_with_keypoints3 = cv2.drawKeypoints(img3, keypoints3, None)
img_with_keypoints4 = cv2.drawKeypoints(img4, keypoints4, None)

cv2.imshow('ORB keypoints1', img_with_keypoints1)
cv2.imshow('ORB keypoints2', img_with_keypoints2)
cv2.imshow('ORB keypoints3', img_with_keypoints3)
cv2.imshow('ORB keypoints4', img_with_keypoints4)
cv2.waitKey(0)

4. 特征点匹配

要匹配这四张图像的特征点，可以使用 OpenCV 中的 BFMatcher（暴力匹配器）或 FLANNMatcher（快速最近邻匹配器）。

以下是使用 BFMatcher 进行特征点匹配的示例代码：

# 创建 BFMatcher 对象
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

# 计算特征描述符
keypoints1, descriptors1 = orb.detectAndCompute(img1, None)
keypoints2, descriptors2 = orb.detectAndCompute(img2, None)
keypoints3, descriptors3 = orb.detectAndCompute(img3, None)
keypoints4, descriptors4 = orb.detectAndCompute(img4, None)

# 匹配特征点
matches1_2 = bf.match(descriptors1, descriptors2)
matches1_3 = bf.match(descriptors1, descriptors3)
matches1_4 = bf.match(descriptors1, descriptors4)

# 绘制匹配结果
img_matches1_2 = cv2.drawMatches(img1, keypoints1, img2, keypoints2, matches1_2, None)
img_matches1_3 = cv2.drawMatches(img1, keypoints1, img3, keypoints3, matches1_3, None)
img_matches1_4 = cv2.drawMatches(img1, keypoints1, img4, keypoints4, matches1_4, None)

cv2.imshow('Matches 1-2', img_matches1_2)
cv2.imshow('Matches 1-3', img_matches1_3)
cv2.imshow('Matches 1-4', img_matches1_4)
cv2.waitKey(0)

以上代码将计算每对图像之间的特征描述符，并使用 BFMatcher 进行匹配。然后，使用 cv2.drawMatches 函数绘制匹配结果。