PIL Best Way To Replace Color?

Question

I am trying to remove a certain color from my image however it's not working as well as I'd hoped. I tried to do the same thing as seen here http://stackoverflow.com/questions/765736/using-pil-to-make-all-white-pixels-transparent however the image quality is a bit lossy so it leaves a little ghost of odd colored pixels around where what was removed. I tried doing something like change pixel if all three values are below 100 but because the image was poor quality the surrounding pixels weren't even black.

Does anyone know of a better way with PIL in Python to replace a color and anything surrounding it? This is probably the only sure fire way I can think of to remove the objects completely however I can't think of a way to do this.

The picture has a white background and text that is black. Let's just say I want to remove the text entirely from the image without leaving any artifacts behind.

Would really appreciate someone's help! Thanks

Answer 1

You'll need to represent the image as a 2-dimensional array. This means either making a list of lists of pixels, or viewing the 1-dimensional array as a 2d one with some clever math. Then, for each pixel that is targeted, you'll need to find all surrounding pixels. You could do this with a python generator thus:

def targets(x,y):
    yield (x,y) # Center
    yield (x+1,y) # Left
    yield (x-1,y) # Right
    yield (x,y+1) # Above
    yield (x,y-1) # Below
    yield (x+1,y+1) # Above and to the right
    yield (x+1,y-1) # Below and to the right
    yield (x-1,y+1) # Above and to the left
    yield (x-1,y-1) # Below and to the left

So, you would use it like this:

for x in range(width):
    for y in range(height):
        px = pixels[x][y]
        if px[0] == 255 and px[1] == 255 and px[2] == 255:
            for i,j in targets(x,y):
                newpixels[i][j] = replacementColor

Answer 2

If the pixels are not easily identifiable e.g you say (r < 100 and g < 100 and b < 100) also doesn't match correctly the black region, it means you have lots of noise.

Best way would be to identify a region and fill it with color you want, you can identify the region manually or may be by edge detection e.g. http://bitecode.co.uk/2008/07/edge-detection-in-python/

or more sophisticated approach would be to use library like opencv (http://opencv.willowgarage.com/wiki/) to identify objects.

Answer 3

Using numpy and PIL:

This loads the image into a numpy array of shape (W,H,3), where W is the width and H is the height. The third axis of the array represents the 3 color channels, R,G,B.

import Image
import numpy as np

orig_color=(255,255,255)
replacement_color=(0,0,0)
img = Image.open(filename).convert('RGB')
arr = np.array(np.asarray(img))
masks=[]
for i in range(3):
    band=arr[:,:,i]
    masks.append(band==orig_color[i])
mask=np.logical_and.reduce(masks)
for i in range(3):
    arr[mask,i]=replacement_color[i]
img = Image.fromarray(arr,mode='RGB')
img.save('out.png')

Answer 4

#!/usr/bin/python
from PIL import Image
import sys

img = Image.open(sys.argv[1])
img = img.convert("RGBA")

pixdata = img.load()

# Clean the background noise, if color != white, then set to black.
# change with your color
for y in xrange(img.size[1]):
    for x in xrange(img.size[0]):
        if pixdata[x, y] == (255, 255, 255, 255):
            pixdata[x, y] = (0, 0, 0, 255)