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I separated the 3 channels of an colour image. I created a new NumPy array of the same size as the image, and stored the 3 channels of the image into 3 slices of the 3D NumPy array. After plotting the NumPy array, the plotted image is not same as original image. Why is this happening?

Both img and new_img array have same elements, but image is different.

    import matplotlib.image as mpimg
    import matplotlib.pyplot as plt
    import numpy as np

    img=mpimg.imread('/storage/emulated/0/1sumint/kali5.jpg')

    new_img=np.empty(img.shape)

    new_img[:,:,0]=img[:,:,0]
    new_img[:,:,1]=img[:,:,1]
    new_img[:,:,2]=img[:,:,2]

    plt.imshow(new_img)
    plt.show()

Expect the same image as original image.

Cris Luengo
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Shivam Das
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1 Answers1

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The problem is that your new image will be created with the default data type of float64 on this line:

new_img=np.empty(img.shape)

unless you specify a different dtype.

You can either (best) copy the original image's dtype like this:

new_img = np.empty(im.shape, dtype=img.dtype)

or use something like this:

new_img = np.zeros_like(im)

or (worst) specify one you happen to know matches your data, like this, 

new_img = np.empty(im.shape, dtype=np.uint8)

I presume you have some reason for copying one channel at a time, but if not, you can avoid all the foregoing issues and just do:

new_img = np.copy(img)
Mark Setchell
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