Split And Join Images In Python

I am trying to split an image using image slicer in python and then apply histogram equalization on each of them and combine them back. I am able to split the images into smaller b

Solution 1:

After looking into the image_slicer code, I can see the confusion. The main problem is that each Tile object, contains both the image data and metadata, such as filename and position in final image. However, the image data is not updated when the files pointed to are updated.

Thus, when updating the files pointed to by the metadata also the image object of the tile needs to be updated. I imagine the simplest way to do this, is to reopen the image in the tile whenever the file on disk is changed. This is likely to do the trick:

import cv2
import numpy as np
from matplotlib import pyplot as plt
from scipy.misc import imsave
from scipy import ndimage
from scipy import misc
import scipy.misc
import scipy
import image_slicer
from image_slicer import join
from PIL import Image

img = 'watch.png'
num_tiles = 64
tiles = image_slicer.slice(img, num_tiles)

for tile in tiles:
    img = scipy.misc.imread(tile.filename)
    hist,bins = np.histogram(img.flatten(),256,[0,256])
    cdf = hist.cumsum()
    cdf_normalized = cdf *hist.max()/ cdf.max()  
    plt.plot(cdf_normalized, color = 'g')
    plt.hist(img.flatten(),256,[0,256], color = 'g')
    plt.xlim([0,256])
    plt.legend(('cdf','histogram'), loc = 'upper left')
    cdf_m = np.ma.masked_equal(cdf,0)
    cdf_o = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
    cdf = np.ma.filled(cdf_o,0).astype('uint8')
    img3 = cdf[img]
    cv2.imwrite(tile.filename,img3)
    tile.image = Image.open(tile.filename)

image = join(tiles)
image.save('watch-join.png')

Thus, the main change is to add tile.image = Image.open(tile.filename) at the end of the loop. Note also that I have updated your code slightly, by removing the first loop that generates the filenames, and instead the second loop is over the tiles directly, as they contain the needed information all ready.

Solution 2:

This is the source code for image_slicer.join():

defjoin(tiles):
    """
    @param ``tiles`` - Tuple of ``Image`` instances.
    @return ``Image`` instance.
    """
    im = Image.new('RGB', get_combined_size(tiles), None)
    columns, rows = calc_columns_rows(len(tiles))
    for tile in tiles:
        im.paste(tile.image, tile.coords)
    return im

As you can see, it uses the Tile objects stored in the program (in your case, inside the list tiles), which haven't changed. You need to either change the objects in memory instead of loading from the file and rewriting, or load the files into tiles as well.

The easy way in my opinion is to modify your for loop (I hope I got the syntax right):

for i in range(0, num_tiles):
    img = tiles[i].image
    hist, bins = np.histogram(img.flatten(), 256, [0, 256])
    cdf = hist.cumsum()
    cdf_normalized = cdf * hist.max() / cdf.max()  
    plt.plot(cdf_normalized, color = 'g')
    plt.hist(img.flatten(), 256, [0, 256], color='g')
    plt.xlim([0, 256])
    plt.legend(('cdf', 'histogram'), loc='upper left')
    cdf_m = np.ma.masked_equal(cdf, 0)
    cdf_o = (cdf_m - cdf_m.min()) * 255 / (cdf_m.max() - cdf_m.min())
    cdf = np.ma.filled(cdf_o, 0).astype('uint8')
    img3 = cdf[img]
    tiles[i].image = img3