I have an RGB image which I want to convert to a grayscale image, so that I can have one number (maybe between 0 and 1) for each pixel. This gives me a matrix which has the dimensions equal to that of the pixels of the image. Then I want to do some manipulations on this matrix and generate a new grayscale image from this manipulated matrix. How can I do this?
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There is numpy and scipy. Scipy let you load an image and transform it into numpy array. It use PIL behind. With numpy you can "flat" the RGB into a greyscale image. Then you can do anything you want with that array – Pierre Turpin May 29 '14 at 14:34
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Use --> [openCV](http://docs.opencv.org/trunk/doc/py_tutorials/py_tutorials.html) -- or [SimpleCV](http://simplecv.org/) – May 29 '14 at 14:35
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[This answer](http://stackoverflow.com/a/12201744/1595865) should suffice – loopbackbee May 29 '14 at 14:37
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@goncalopp: the conversion is fine, but how do I convert it to a 2D array? – lovespeed May 29 '14 at 14:48
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@lovespeed: Loading an image using the PIL (a la goncalopp's answer) is essentially a 2D array. You'd access a pixel using `img[x,y]`. Is that not sufficient? – rayryeng May 31 '14 at 04:32
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I frequently work with images as NumPy arrays - I do it like so:
import numpy as np
from PIL import Image
x=Image.open('im1.jpg','r')
x=x.convert('L') #makes it greyscale
y=np.asarray(x.getdata(),dtype=np.float64).reshape((x.size[1],x.size[0]))
<manipulate matrix y...>
y=np.asarray(y,dtype=np.uint8) #if values still in range 0-255!
w=Image.fromarray(y,mode='L')
w.save('out.jpg')
If your array values y are no longer in the range 0-255 after the manipulations, you could step up to 16-bit TIFFs or simply rescale.
-Aldo

Aldo
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you can use ```plt.imshow(y, cmap=plt.get_cmap('gray'))``` to display grayscale image – KY Lu Aug 13 '19 at 02:54
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If you plan to go on and work with tensorflow (2.x+) you might find tensorflow.keras.preprocessing.image.img_to_array(image) useful too since this will convert a PIL Image to numpy.ndarray – jtromans Dec 17 '20 at 10:21