从图像中提取 N 个补丁

Question

I have an image of dimension 155 x 240 . Like the following:

I want to extract certain shape of patchs (25 x 25) . I don't want to patch from the whole image .

I want to extract N number of patch from non-zero (not background) area of the image . How can I do that? Any idea or suggestion or implementation will be appreciated. You can try with either Matlab or Python.

Note: I have generated a random image so that you can process it for patching. image_process variable is that image in this code.

import numpy as np
from scipy.ndimage.filters import convolve
import matplotlib.pyplot as plt

background = np.ones((155,240))
background[78,120] = 2
n_d = 50
y,x = np.ogrid[-n_d: n_d+1, -n_d: n_d+1]
mask = x**2+y**2 <= n_d**2
mask = 254*mask.astype(float)


image_process = convolve(background, mask)-sum(sum(mask))+1
image_process[image_process==1] = 0
image_process[image_process==255] = 1

plt.imshow(image_process)

Answer 1

Lets assume that the pixels values you want to omit is 0.

In this case what you could do, is first find the indices of the non-zero values, then slice the image in the min / max position to get only the desired area, and then simply apply extract_patches_2d with the desired window size and number of patches.

For example, given the dummy image you supplied:

import numpy as np
from scipy.ndimage.filters import convolve
import matplotlib.pyplot as plt

background = np.ones((155,240))
background[78,120] = 2
n_d = 50
y,x = np.ogrid[-n_d: n_d+1, -n_d: n_d+1]
mask = x**2+y**2 <= n_d**2
mask = 254*mask.astype(float)


image_process = convolve(background, mask)-sum(sum(mask))+1
image_process[image_process==1] = 0
image_process[image_process==255] = 1
plt.figure()
plt.imshow(image_process)
plt.show()

from sklearn.feature_extraction.image import extract_patches_2d
x, y = np.nonzero(image_process)
xl,xr = x.min(),x.max()
yl,yr = y.min(),y.max()
only_desired_area = image_process[xl:xr+1, yl:yr+1]

window_shape = (25, 25)
B = extract_patches_2d(only_desired_area, window_shape, max_patches=100)  # B shape will be (100, 25, 25)

If you plot the only_desired_area you will get the following image:

This is the main logic if you wish an even tighter bound you should adjust the slicing properly.