How to slice an image into red, green and blue channels with misc.imread

Question

I am trying to slice an image into RGB and I have a problem with plotting these images. I obtain all images from a certain folder with this function:

def get_images(path, image_type):
image_list = []
for filename in glob.glob(path + '/*'+ image_type):
    im=misc.imread(filename, mode='RGB')
    image_list.append(im)
return image_list

This function creates 4d array (30, 1536, 2048, 3) and I am quite sure that the first value represents number of images, second and third are dimensions and third are RGB values.

After I obtained all the images, I stored them as a numpy array

image_list = get_images('C:\HDR\images', '.jpg')
temp = np.array(image_list)

After that I tried to use simple slicing on order to take specific colors from these images:

red_images = temp[:,:,:,0]
green_images = temp[:,:,:,1]
blue_images = temp[:,:,:,2]

When I print out the values, everything seems to be fine.

print(temp[11,125,311,:])
print(red_images[11,125,311])
print(green_images[11,125,311])
print(blue_images[11,125,311])

And I get the following:

[105  97  76]
105
97
76

So far, everything seems to be fine, but the problem arises when I try to display the image. I used matplotlib.pyplot.imshow to display it and I get the image like:

Which is reasonable, because I choose red:

 plt.imshow(temp[29,:,:,0])

But when I change it to different color channel, like this:

plt.imshow(temp[29,:,:,2])

I get the image like this:

My question is simple. What is happening here?

Answer 1

So, you want to show in different colors the different RGB channels of an image...

import matplotlib.pyplot as plt
from matplotlib.cbook import get_sample_data

image = plt.imread(get_sample_data('grace_hopper.jpg'))

titles = ['Grace Hopper', 'Red channel', 'Green channel', 'Blue channel']
cmaps = [None, plt.cm.Reds_r, plt.cm.Greens_r, plt.cm.Blues_r]

fig, axes = plt.subplots(1, 4, figsize=(13,3))
objs = zip(axes, (image, *image.transpose(2,0,1)), titles, cmaps)

for ax, channel, title, cmap in objs:
    ax.imshow(channel, cmap=cmap)
    ax.set_title(title)
    ax.set_xticks(())
    ax.set_yticks(())

plt.savefig('RGB1.png')

Note that when you have a dark room with a red pen on a dark table, if you turn on a red lamp you percept the pen as almost white...

Another possibility is to create a different image for each color, with the pixel values for the other colors turned to zero. Starting from where we left we define a function to extract a channel into an otherwise black image

...
from numpy import array, zeros_like
def channel(image, color):
    if color not in (0, 1, 2): return image
    c = image[..., color]
    z = zeros_like(c)
    return array([(c, z, z), (z, c, z), (z, z, c)][color]).transpose(1,2,0)

and finally use it...

colors = range(-1, 3)
fig, axes = plt.subplots(1, 4, figsize=(13,3))
objs = zip(axes, titles, colors)
for ax, title, color in objs:
    ax.imshow(channel(image, color))
    ax.set_title(title)
    ax.set_xticks(())
    ax.set_yticks(())

plt.savefig('RGB2.png')

I can't tell which is the version that I like better, perhaps the 1st one is looking more realistic to me (maybe it looks less artificial) but it's quite subjective...