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Python - greyscale image to 3 channels

I have code that looks like this

from skimage import io as sio
test_image = imread('/home/username/pat/file.png')
test_image = skimage.transform.resize(test_image, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)
print test_image.shape # prints (128,128)
print test_image.max(), test_image.min() # prints 65535.0 0.0
sio.imshow(test_image)


More importantly, I need to make this image be in 3 channels, so I can feed it into a neural network that expects such input, any idea how to do that?

I want to transform a 1-channel image into a 3-channel image that looks reasonable when I plot it, makes sense, etc. How?

I tried padding with 0s, I tried copying the same values 3 times for the 3 channels, but then when I try to display the image, it looks like gibberish. So how can I transform the image into 3 channels, even if it becomes something like, bluescale instead of greyscale, but still be able to visualize it in a meaningful way?

Edit:
if I try

test_image = skimage.color.gray2rgb(test_image)


I get all white image, with some black dots.

I get the same all white, rare small black dots if I try

convert Test1_PC_1.tif -colorspace sRGB -type truecolor Test1_PC_1_new.tif


Before the attempted transform with gray2rgb

print type(test_image[0,0])
<type 'numpy.uint16'>


After

print type(test_image[0,0,0])
<type 'numpy.float64'>


gray2rgb

3 Answers
3

You need to convert the array from 2D to 3D, where the third dimension is the color.

You can use the gray2rgb function function provided by skimage:

test_image = skimage.color.gray2rgb(test_image)


Alternatively, you can write your own conversion -- which gives you some flexibility to tweak the pixel values:

# basic conversion from gray to RGB encoding
test_image = np.array([[[s,s,s] for s in r] for r in test_image],dtype="u1")

# conversion from gray to RGB encoding -- putting the image in the green channel
test_image = np.array([[[0,s,0] for s in r] for r in test_image],dtype="u1")


I notice from your max() value, that you're using 16-bit sample values (which is uncommon). You'll want a different dtype, maybe "u16" or "int32". Also, you may need to play some games to make the image display with the correct polarity (it may appear with black/white reversed).

max()

dtype

"u16"

"int32"

One way to get there is to just invert all of the pixel values:

test_image = 65535-test_image ## invert 16-bit pixels


Or you could look into the norm parameter to imshow, which appears to have an inverse function.

norm

imshow

inverse

dtype

print type(test_image[0,0])

print type(test_image[0,0,0])

cmap is ignored if X is 3-D, directly specifying RGB(A) values.

Your conversion from gray-value to RGB by replicating the gray-value three times such that R==G==B is correct.

R==G==B

The strange displayed result is likely caused by assumptions made during display. You will need to scale your data before display to fix it.

Usually, a uint8 image has values 0-255, which are mapped to min-max scale of display. Uint16 has values 0-65535, with 65535 mapped to max. Floating-point images are very often assumed to be in the range 0-1, with 1 mapped to max. Any larger value will then also be mapped to max. This is why you see so much white in your output image.

If you divide each output sample by the maximum value in your image you’ll be able to display it properly.

Well, imshow is using by default, a kind of heatmap to display the image intensities. To display a grayscale image just specify the colormap as above:

plt.imshow(image, cmap="gray")


Now, i think you can get the channel of an image by doing:

image[:,:,i] where i is in 0,1,2


To extract an image for a specific channel:

red_image = image.copy()
red_image[:,:,1] = 0
red_image[:,:,2] = 0


Edit:
Do you definitely have to use skimage? What about python-opencv module?
Have you tried the following example?

import cv2
import cv

color_img = cv2.cvtColor(gray_img, cv.CV_GRAY2RGB)


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