Applying a coloured overlay to an image in either PIL or Imagemagik

Question

I am a complete novice to image processing, and I am guessing this is quite easy to do, but I just don't know the terminology.

Basically, I have a black and white image, I simply want to apply a colored overlay to the image, so that I have got the image overlayed with blue green red and yellow like the images shown below (which actually I can't show because I don't have enough reputation to do so - grrrrrr). Imagine I have a physical image, and a green/red/blue/yellow overlay, which I place on top of the image.

Ideally, I would like to do this using Python PIL but I would be just as happy to do it using ImageMagik, but either way, I need to be able to script the process as I have 100 or so images that I need to carry out the process on.

Answer 1

EDIT: As mentioned by Matt in the comments, this functionality is now available in skimage.color.label2rgb.

In the latest development version, we've also introduced a saturation parameter, which allows you to add overlays to color images.

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Here's a code snippet that shows how to use scikit-image to overlay colors on a grey-level image. The idea is to convert both images to the HSV color space, and then to replace the hue and saturation values of the grey-level image with those of the color mask.

from skimage import data, color, io, img_as_float
import numpy as np
import matplotlib.pyplot as plt

alpha = 0.6

img = img_as_float(data.camera())
rows, cols = img.shape

# Construct a colour image to superimpose
color_mask = np.zeros((rows, cols, 3))
color_mask[30:140, 30:140] = [1, 0, 0]  # Red block
color_mask[170:270, 40:120] = [0, 1, 0] # Green block
color_mask[200:350, 200:350] = [0, 0, 1] # Blue block

# Construct RGB version of grey-level image
img_color = np.dstack((img, img, img))

# Convert the input image and color mask to Hue Saturation Value (HSV)
# colorspace
img_hsv = color.rgb2hsv(img_color)
color_mask_hsv = color.rgb2hsv(color_mask)

# Replace the hue and saturation of the original image
# with that of the color mask
img_hsv[..., 0] = color_mask_hsv[..., 0]
img_hsv[..., 1] = color_mask_hsv[..., 1] * alpha

img_masked = color.hsv2rgb(img_hsv)

# Display the output
f, (ax0, ax1, ax2) = plt.subplots(1, 3,
                                  subplot_kw={'xticks': [], 'yticks': []})
ax0.imshow(img, cmap=plt.cm.gray)
ax1.imshow(color_mask)
ax2.imshow(img_masked)
plt.show()

Here's the output:

Answer 2

I ended up finding an answer to this using PIL, basically creating a new image with a block colour, and then compositing the original image, with this new image, using a mask that defines a transparent alpha layer. Code below (adapted to convert every image in a folder called data, outputting into a folder called output):

from PIL import Image
import os

dataFiles = os.listdir('data/')

for filename in dataFiles:

    #strip off the file extension
    name = os.path.splitext(filename)[0]

    bw = Image.open('data/%s' %(filename,))

    #create the coloured overlays
    red = Image.new('RGB',bw.size,(255,0,0))
    green = Image.new('RGB',bw.size,(0,255,0))
    blue = Image.new('RGB',bw.size,(0,0,255))
    yellow = Image.new('RGB',bw.size,(255,255,0))

    #create a mask using RGBA to define an alpha channel to make the overlay transparent
    mask = Image.new('RGBA',bw.size,(0,0,0,123))

    Image.composite(bw,red,mask).convert('RGB').save('output/%sr.bmp' % (name,))
    Image.composite(bw,green,mask).convert('RGB').save('output/%sg.bmp' % (name,))
    Image.composite(bw,blue,mask).convert('RGB').save('output/%sb.bmp' % (name,))
    Image.composite(bw,yellow,mask).convert('RGB').save('output/%sy.bmp' % (name,))

Can't post the output images unfortunately due to lack of rep.