归一化互相关匹配算法代码
时间: 2023-10-13 18:07:59 浏览: 53
以下是一个简单的归一化互相关匹配算法的 Python 代码实现:
```python
import numpy as np
def normxcorr2(template, image):
# 计算模板和图像的平均值
temp_mean = np.mean(template)
img_mean = np.mean(image)
# 将模板和图像的值归一化为零均值
template_norm = template - temp_mean
image_norm = image - img_mean
# 计算模板和图像的标准差
temp_std = np.sqrt(np.sum(template_norm ** 2))
img_std = np.sqrt(np.sum(image_norm ** 2))
# 计算互相关系数
corr = np.zeros((image.shape[0] - template.shape[0] + 1, image.shape[1] - template.shape[1] + 1))
for i in range(corr.shape[0]):
for j in range(corr.shape[1]):
image_patch = image_norm[i:i+template.shape[0], j:j+template.shape[1]]
image_patch_std = np.sqrt(np.sum(image_patch ** 2))
corr[i, j] = np.sum(template_norm * image_patch) / (temp_std * image_patch_std)
return corr
```
该函数接受两个参数,分别是模板和图像。它首先计算模板和图像的平均值,然后将它们的值归一化为零均值。接下来,它计算模板和图像的标准差,并计算互相关系数。最后,该函数返回一个二维数组,表示模板在图像中的匹配程度。