用Scipy进行图像侵蚀和扩张 [英] Image erosion and dilation with Scipy

问题描述

我正在尝试使用scipy来执行数组。我使用 flatten = true 将原始图像转换为灰度，如下所示：

  im = scipy.misc.imread（'flower.png'，flatten = True）.astype（np.uint8）
  

然后调用：

  im1 = ndimage.grey_erosion（im，size =（15,15））
  

虽然它是灰度的，却得到了很好的侵蚀图片。

I am trying to use scipy to do erosion and dilation of an image. It seems pretty straightforward using scipy -> binary_erosion / dialation. However, the output is not at all what is expected.

Here is my basic code:

import scipy
from scipy import ndimage
import matplotlib.pyplot as plt
import numpy as np
import Image

#im = Image.open('flower.png')
im = ndimage.imread('flower.png')
im = ndimage.binary_erosion(im).astype(np.float32)
scipy.misc.imsave('erosion.png', im)


im2 = Image.open('flower.png')
im2 = ndimage.binary_dilation(im2)
scipy.misc.imsave('dilation.png', im2)

This is the output:

The output for dilation is just a completely white image for the original "flower.png"

I believe that I must specify a better kernel or mask but am not really sure why I am getting a green output for erosion and completely white output for dilation.

解决方案

I was using the binary erosion instead of the grey erosion array. I converted the original image to greyscale by using flatten=true like so:

im = scipy.misc.imread('flower.png', flatten=True).astype(np.uint8)

then called:

im1 = ndimage.grey_erosion(im, size=(15,15))

And got a nicely eroded picture, although it is greyscale.

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