Please use `shift` from the `scipy.ndimage` namespace, the `scipy.ndimage.interpolation` namespace is deprecated. from scipy.ndimage.interpolation import shift
时间: 2023-08-16 22:11:29 浏览: 266
Sure, you can use the `shift` function from the `scipy.ndimage` namespace instead of the deprecated `scipy.ndimage.interpolation` namespace. Here's how you can import and use it:
```python
from scipy.ndimage import shift
# Example usage
arr = [[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]
shifted_arr = shift(arr, (1, 1))
print(shifted_arr)
```
This will shift the array `arr` by one position in both the horizontal and vertical directions. The output will be:
```
[[0 0 0]
[0 1 2]
[0 4 5]]
```
Note that the `shift` function takes the array as the first argument and a tuple `(shift_x, shift_y)` as the second argument to specify the amount of shift in each direction.
相关问题
Please use `zoom` from the `scipy.ndimage` namespace, the `scipy.ndimage.interpolation` namespace is deprecated.
Sure, I understand. To use `zoom` from the `scipy.ndimage` namespace, you can import it with the following code:
```python
from scipy import ndimage
output = ndimage.zoom(input, zoom, output=None, order=3, mode='constant', cval=0.0, prefilter=True)
```
Here, `input` is the input array you want to zoom, `zoom` is the zoom factor for each dimension, and `output` is an optional output array. The `order` parameter specifies the order of the spline interpolation used for zooming, and `mode` specifies how the input array is extended beyond its boundaries. Finally, `cval` and `prefilter` are optional parameters that specify the value to use for points outside the boundaries and whether to prefilter the input before interpolation, respectively.
I hope this helps! Let me know if you have any other questions.
DeprecationWarning: Please use `convolve` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.
这个警告是因为你正在使用`scipy.ndimage.filters`命名空间中的滤波器函数,而该命名空间已经弃用。建议改用`scipy.ndimage`命名空间中的`convolve`函数。
例如,如果你要使用高斯滤波器,可以像这样:
```
import scipy.ndimage as ndimage
import numpy as np
# 创建一个 5x5 的高斯滤波器
sigma = 1.0
kernel = np.zeros((5, 5))
for i in range(5):
for j in range(5):
x, y = i - 2, j - 2
kernel[i, j] = np.exp(-(x**2 + y**2) / (2 * sigma**2))
kernel /= kernel.sum()
# 对图像进行滤波
image_filtered = ndimage.convolve(image, kernel)
```
这里我使用了`ndimage.convolve`函数来代替`ndimage.filters.gaussian_filter`函数。注意,我在定义高斯滤波器时使用的是二维数组`kernel`,所以在卷积时也要使用`ndimage.convolve`函数。
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