二次多项式图像配准并对配准精度进行定量评价代码
时间: 2023-08-11 15:07:45 浏览: 58
下面是基于Python的二次多项式图像配准并对配准精度进行定量评价的代码示例:
```python
import cv2
import numpy as np
import math
# 读取待配准的两幅图像
img1 = cv2.imread('img1.jpg')
img2 = cv2.imread('img2.jpg')
# 提取图像特征点
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
# 对特征点进行匹配
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(matches, key=lambda x: x.distance)
# 选取最佳匹配的前50个特征点
good_matches = matches[:50]
src_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)
# 计算相对位移量
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC)
h, w = img1.shape[:2]
img2_aligned = cv2.warpPerspective(img2, M, (w, h), flags=cv2.INTER_LINEAR)
# 计算配准精度
diff = cv2.absdiff(img1, img2_aligned)
diff_gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
mse = np.mean(diff_gray ** 2)
psnr = 10 * math.log10((255 ** 2) / mse)
# 打印配准精度
print("MSE: ", mse)
print("PSNR: ", psnr)
```
需要注意的是,这只是一个简单的示例代码,实际应用中需要根据具体情况进行适当的调整和改进。