I want to create a 2d numpy array of all the pixel locations for a 512 x 512 image. Meaning there would be 512² or 262,144 values. It may be slightly more if the x and y zeroes are considered, but you get the idea.

To do it manually would be like this pixels = np.array([[0, 1], [0, 2], [0,3], [0,4]]) all the way to the end. But obviously I need to automate it. The order of pixels doesn't matter though. It needs to be in that "format" so I can access the pixels x and y values by 0 and 1 index i.e. pixels[0][0] for the first pixel's x value and pixels[0][1] for the first pixel's y value.

CodePudding user response：

Try this:

pixels = np.array([[x, y] for y in range(512) for x in range(512)])

Note that you can modify it for different x or y values.

CodePudding user response：

numpy is openly used to process and manipulate images through multi-dimensional arrays, since they are very useful to storing values as pixels (rgb, rgba, greyscale, etc ...)

 example (RGB):

>>> import numpy as np
>>> from PIL import Image
>>> array = np.zeros([100, 200, 3], dtype=np.uint8)
>>> array[:,:100] = [255, 128, 0] #Orange left side
>>> array[:,100:] = [0, 0, 255]   #Blue right side
>>> img = Image.fromarray(array)
>>> array[:,:100] = [100, 128, 0]
>>> array[:,100:] = [0, 0, 200]
>>> img = Image.fromarray(array)
>>> img.save('img.png')

example (GREYSCALE):

>>> import numpy as np
>>> from PIL import Image
>>> array = np.zeros([100, 200], dtype=np.uint8)
>>> # Set grey value to black or white depending on x position
>>> for x in range(200):
>>>     for y in range(100):
>>>         if (x % 16) // 8 == (y % 16) // 8:
>>>             array[y, x] = 0
>>>         else:
>>>             array[y, x] = 255
>>>
>>> img = Image.fromarray(array)
>>> img.save('img.png')