我正在编写一段部分归一化输入图像强度的代码。由于我找不到NumPy库的相应函数，我创建了以下代码：
```python
def arealNorm(img, kernel_size=7): #图像应为灰度图
    img_norm = np.zeros(img.shape)
    for v in range(kernel_size, img.shape[0] - kernel_size):
        for u in range(kernel_size, img.shape[1] - kernel_size):
            MaxV = np.max(img[v - kernel_size:v + kernel_size, u - kernel_size:u + kernel_size])
            minV = np.min(img[v - kernel_size:v + kernel_size, u - kernel_size:u + kernel_size])
            newval = (img[v, u] - minV) / (MaxV - minV)
            img_norm[v, u] = newval
    return img_norm

<details>
<summary>英文:</summary>
I am making a code that partially normalizes intensity of an input image. Since I could not find corresponding function of Numpy library, I made a code like following: 

Since it should access every pixel of the image this code works very slowly. 
Is there any numpy-like way to convert my code to work faster ? 
</details>
# 答案1
**得分**: 1
使用内置函数scipy.ndimage.filters.generic_filter来加速这段代码的一种方法。该函数将用户定义的函数应用于输入图像中的每个邻域。
以下是使用generic_filter的示例实现：
```python
from scipy.ndimage.filters import generic_filter
def arealNorm(img, kernel_size=7):
  def local_norm(patch):
    max_val = np.max(patch)
    min_val = np.min(patch)
    return (img - min_val) / (max_val - min_val)
  img_norm = generic_filter(img, local_norm, size=kernel_size)
  return img_norm

def arealNorm(img, kernel_size=7):  # Img should me gray
    # img_norm = np.zeros(img.shape)
    img_max = ndi.filters.maximum_filter(img, size=kernel_size)
    img_min = ndi.filters.minimum_filter(img, size=kernel_size)
    img_norm = (img - img_min) / (img_max - img_min)
    return img_norm