[SOLVED] Slicing the channels of image and storing the channels into numpy array(same size as image). Plotting the numpy array not giving the original image

Slicing the channels of image and storing the channels into numpy array(same size as image). Plotting the numpy array not giving the original image

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.

Solution

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)