from PIL import Image
from PIL import ImageChops
from os import listdir
from os.path import isfile, join
import numpy as np
import sys

def pixel_diff(image_a, image_b):
    diff = ImageChops.difference(image_a.convert('L'), image_b.convert('L'))
    diff = diff.convert('L')

    return diff


def total_histogram_diff(pixel_diff):
    return sum(i * n for i, n in enumerate(pixel_diff.histogram()))

def image_diff(image_a, image_b):
    if isinstance(image_a, str):
        image_a = Image.open(image_a)

    if isinstance(image_b, str):
        image_b = Image.open(image_b)
    histogram_diff = total_histogram_diff(pixel_diff(image_a, image_b))
    return histogram_diff

def is_equal(image_a, image_b, tolerance=0.0):
    return image_diff_percent(image_a, image_b) <= tolerance

def image_diff_percent(image_a, image_b):

    if isinstance(image_a, str):
        image_a = Image.open(image_a)

    if isinstance(image_b, str):
        image_b = Image.open(image_b)

    input_images_histogram_diff = image_diff(image_a, image_b)

    black_reference_image = Image.new('RGB', image_a.size, (0, 0, 0))
    white_reference_image = Image.new('RGB', image_a.size, (255, 255, 255))

    worst_bw_diff = image_diff(black_reference_image, white_reference_image)

    percentage_histogram_diff = (input_images_histogram_diff / float(worst_bw_diff)) * 100

    return percentage_histogram_diff


def hash_to_string(x):
	ret = ''
	for i in x.reshape(-1):
		if i == True:
			ret+='1'
		else:
			ret+='0'
	return ret
def diff(image_a, image_b):
    res = 0
    if isinstance(image_a, str):
        image_a = Image.open(image_a)
    if isinstance(image_b, str):
        image_b = Image.open(image_b)

    a_hash = average_hash(image_a)
    b_hash = average_hash(image_b)

    for i in range(len(a_hash)):
        if(a_hash[i] != b_hash[i]):
            res +=1
    return res
def average_hash(image, hash_size=8):
	image = image.convert("RGB").resize((hash_size, hash_size), Image.ANTIALIAS)
	pixels = np.asarray(image)
	r = np.zeros(shape = (hash_size, hash_size), dtype = float)
	g = np.copy(r)
	b = np.copy(r)
	for i,j in np.ndindex(r.shape):
			r[i][j] = pixels[i][j][0]
			g[i][j] = pixels[i][j][1]
			b[i][j] = pixels[i][j][2]
	avg_r = r.mean()
	avg_g = g.mean()
	avg_b = b.mean()
	diff_r = r > avg_r
	diff_g = g > avg_b
	diff_b = b > avg_b
	return hash_to_string(diff_r) + hash_to_string(diff_g) + hash_to_string(diff_b)

if ("--path" not in sys.argv):
	print("Arguments:")
	print("")
	print("\t\t--path PATH\t specifies path to images")
	print("\t\t--help, -h \t get some help")
	print("\n")
	exit()

pathIndex = sys.argv.index("--path") + 1
if ((pathIndex != len(sys.argv)) & (len(sys.argv) > 2)):
	pathToImages = sys.argv[pathIndex] + "\\"
else:
    exit()

imagesNames = [f for f in listdir(pathToImages) if isfile(join(pathToImages, f))]

images = []
for imageName in imagesNames:
    images.append(Image.open(pathToImages + imageName))

    results = []
for i in range(len(images)):
    for j in range(i + 1, len(images)):
        if (is_equal(images[i], images[j])):
            results.append([i, j])
        elif (diff(images[i], images[j]) <=10 ):
            results.append([i,j])


for result in results:
    print(imagesNames[result[0]] + " " + imagesNames[result[1]])