将值分配到图像数组的随机块中的有效方法? [英] Efficient way to assign values into random blocks of an array of images?

问题描述

我有一个3d的numpy数组.它是一个二维正方形图像的数组，所有大小均相同.我的任务是屏蔽图像的随机正方形补丁(将所有像素值设置为0).我可以弄清楚在只有1张图像的情况下该怎么做

I have a 3d numpy array. It's an array of 2d square images, all of the same size. my task is to block out a random square patch of the images (set all the pixel values to 0). I can figure out how to do it in the case where i just have 1 image as follows

x = np.random.randint(image_width - size)
y = np.random.randint(image_width - size)
image[x:x + size, y:y + size] = 0

其中size是被屏蔽区域的大小.我不确定如何有效地对二维图像阵列执行此技术，在该二维图像阵列中，为每个图像随机生成了被遮挡的补丁(不一定是在阵列中的每个图像中被遮挡的同一补丁).我在必须处理的所有新索引工具(广播，花式索引等)中感到有点迷茫，并且想知道是否有一种快速简单的方法来完成此任务.目前，我的方法是使用for循环简单地遍历每个图像，一次执行一个图像，但是我想知道numpy是否足够强大，以某种方式一次完成所有操作.我确实意识到我需要使用

where size is the size of the blocked out area. I'm not sure how to efficiently do this technique to an array of 2d images, where the blocked out patch is randomly generated for each one (not necessarily the same patch blocked out in each image in the array). I'm feeling a bit lost in all the new indexing tools I have to deal with (broadcasting, fancy indexing etc) and was wondering if there was a quick and simple way to do this thanks. My approach at the moment is to simply iterate over each image using a for loop, doing one image at a time but I wondered if numpy is powerful enough to somehow do them all in one fell swoop. I do realize that I need to use

x_array = np.random.randint(image_width - size, size=len(image_array))
y_array = np.random.randint(image_width - size, size=len(image_array))

在某个时候，但我不知道在哪里.

at some point but I don't know where.

推荐答案

我们可以利用 np.lib.stride_tricks.as_strided 基于基于 view_as_windows 的 as_strided 使用的更多信息.

We can leverage np.lib.stride_tricks.as_strided based scikit-image's view_as_windows to get sliding windows. More info on use of as_strided based view_as_windows.

from skimage.util.shape import view_as_windows

# Get sliding windows as views
w = view_as_windows(image_array,(1,size,size))[...,0,:,:]

# Index with advanced-indexing into windows array and
# assign into it, thus reassigning into input array directly being views
w[np.arange(len(x_array)),x_array,y_array] = 0

样品运行

设置输入-

In [60]: image_array # input array
Out[60]: 
array([[[54, 57, 74, 77, 77],
        [19, 93, 31, 46, 97],
        [80, 98, 98, 22, 68],
        [75, 49, 97, 56, 98]],

       [[91, 47, 35, 87, 82],
        [19, 30, 90, 79, 89],
        [57, 74, 92, 98, 59],
        [39, 29, 29, 24, 49]],

       [[42, 75, 19, 67, 42],
        [41, 84, 33, 45, 85],
        [65, 38, 44, 10, 10],
        [46, 63, 15, 48, 27]]])

In [68]: size
Out[68]: 2

# x and y starting indices for 0s assignments 
In [65]: x_array
Out[65]: array([1, 0, 1])

In [66]: y_array
Out[66]: array([2, 2, 0])

使用建议的解决方案-

In [62]: w = view_as_windows(a,(1,size,size))[...,0,:,:]

In [63]: w[np.arange(len(x_array)),x_array,y_array] = 0

In [64]: image_array # verify
Out[64]: 
array([[[54, 57, 74, 77, 77], # start at (1,2)
        [19, 93,  0,  0, 97],
        [80, 98,  0,  0, 68],
        [75, 49, 97, 56, 98]],

       [[91, 47,  0,  0, 82], # start at (0,2)
        [19, 30,  0,  0, 89],
        [57, 74, 92, 98, 59],
        [39, 29, 29, 24, 49]],

       [[42, 75, 19, 67, 42], # start at (1,0)
        [ 0,  0, 33, 45, 85],
        [ 0,  0, 44, 10, 10],
        [46, 63, 15, 48, 27]]])

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