首页用sift使用Python完成特征匹配并缩小最后生成的特征匹配图

用sift使用Python完成特征匹配并缩小最后生成的特征匹配图

时间: 2023-10-09 22:13:09 浏览: 88

使用 Python 实现 SIFT 特征匹配并缩小最终生成的特征匹配图可以遵循以下步骤： 1. 导入必要的库 ```python import cv2 import numpy as np from matplotlib import pyplot as plt ``` 2. 读取图像 ```python img1 = cv2.imread('image1.jpg', 0) img2 = cv2.imread('image2.jpg', 0) ``` 3. 初始化 SIFT 检测器 ```python sift = cv2.xfeatures2d.SIFT_create() ``` 4. 检测关键点和描述符 ```python kp1, des1 = sift.detectAndCompute(img1, None) kp2, des2 = sift.detectAndCompute(img2, None) ``` 5. 匹配特征点 ```python bf = cv2.BFMatcher() matches = bf.knnMatch(des1, des2, k=2) ``` 6. 应用 Lowe's 比率测试，保留最佳匹配 ```python good = [] for m, n in matches: if m.distance < 0.75 * n.distance: good.append([m]) ``` 7. 绘制匹配图 ```python img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=2) ``` 8. 缩小匹配图 ```python scale_percent = 50 # 缩小比例 width = int(img3.shape[1] * scale_percent / 100) height = int(img3.shape[0] * scale_percent / 100) dim = (width, height) resized = cv2.resize(img3, dim, interpolation=cv2.INTER_AREA) ``` 9. 显示并保存匹配图 ```python plt.imshow(resized), plt.show() cv2.imwrite('match.jpg', resized) ```

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