The easiest way is to use the numpy interface for this, since it allows you to do operations on the whole image:

from PIL import Image
import numpy as np

def _colour_mask(img, colour):
    """Finds all indices of a single colour in a PIL.Image"""
    return (img == colour).all(axis=2).nonzero()


def _convert_colour(img, incolour, outcolour):
    """Replaces incolour with outcolour in a PIL.Image.
    
    Returns a new PIL.Image.
    Assumes that img has as many channels as len(incolour) and len(outcolour).
    """ 
    img = np.array(img)
    img[_colour_mask(img, incolour)] = outcolour
    return Image.fromarray(img)


def convert_colour(region_number, incolour, outcolour):
    file_name = region_list.regions_d[region_number][0]
    img = Image.open(file_name)
    new_img = _convert_colour(img, incolour, outcolour)
    new_img.save(file_name, "PNG")
    colour_change_single(region_number, outcolour)

The easiest way is to use the numpy interface for this, since it allows you to do operations on the whole image:

from PIL import Image
import numpy as np

def _colour_mask(img, colour):
    """Finds all indices of a single colour in a PIL.Image"""
    if len(img.shape) == 3:
        return (img == colour).all(axis=2).nonzero()
    elif len(image.shape) == 2:
        return (img == colour).nonzero()
    else:
        raise ValueError("Invalid image shape {}".format(img.shape))


def _convert_colour(img, incolour, outcolour):
    """Replaces incolour with outcolour in a PIL.Image.
    
    Returns a new PIL.Image.
    Assumes that img has as many channels as len(incolour) and len(outcolour).
    """ 
    img = np.array(img)
    img[_colour_mask(img, incolour)] = outcolour
    return Image.fromarray(img)


def convert_colour(region_number, incolour, outcolour):
    file_name = region_list.regions_d[region_number][0]
    img = Image.open(file_name)
    new_img = _convert_colour(img, incolour, outcolour)
    new_img.save(file_name, "PNG")
    colour_change_single(region_number, outcolour)

The easiest way is to use the numpy interface for this, since it allows you to do operations on the whole image:

from PIL import Image
import numpy as np

def _colour_mask(img, colour):
    """Finds all indices of a single colour in a PIL.Image"""
    return (img == colour).all(axis=2).nonzero()


def _convert_colour(img, incolour, outcolour):
    """Replaces incolour with outcolour in a PIL.Image.
    
    Returns a new PIL.Image.
    Assumes that img has as many channels as len(incolour) and len(outcolour).
    """ 
    img = np.array(img)
    img[_colour_mask(incolour)] = outcolour
    return Image.fromarray(img)


def convert_colour(region_number, incolour, outcolour):
    file_name = region_list.regions_d[region_number][0]
    img = Image.open(file_name)
    new_img = _convert_colour(img, incolour, outcolour)
    new_img.save(file_name, "PNG")

The easiest way is to use the numpy interface for this, since it allows you to do operations on the whole image:

from PIL import Image
import numpy as np

def _colour_mask(img, colour):
    """Finds all indices of a single colour in a PIL.Image"""
    return (img == colour).all(axis=2).nonzero()


def _convert_colour(img, incolour, outcolour):
    """Replaces incolour with outcolour in a PIL.Image.
    
    Returns a new PIL.Image.
    Assumes that img has as many channels as len(incolour) and len(outcolour).
    """ 
    img = np.array(img)
    img[_colour_mask(img, incolour)] = outcolour
    return Image.fromarray(img)


def convert_colour(region_number, incolour, outcolour):
    file_name = region_list.regions_d[region_number][0]
    img = Image.open(file_name)
    new_img = _convert_colour(img, incolour, outcolour)
    new_img.save(file_name, "PNG")
    colour_change_single(region_number, outcolour)