imagee.py

#!/usr/bin/python

import os,sys
from PIL import Image
from PIL import ImageFilter
from pprint import pprint as pp
import os
import glob
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from path import Path
import time

d = Path("/Users/joyhsu0504/Dropbox/CalHacks2016/faces/")
while len(d.files()) == 0:
	time.sleep(20)
imgpath = d.files()[0]
img = Image.open(imgpath)

img = img.convert("RGBA")
pic = img.load()
datas = img.getdata()

C_BLACK = 0
C_WHITE = 1

def _isbw(col):
	c = 240 
	if col[0] < c and col[1] < c and col[2] < c:
		col = C_BLACK
	else:
		col = C_WHITE

	return col

def _getcoord( size, pos ):
	x,y = pos
	w,h = size
	i = (y * w) + x
	return i

def _getbw( imgdata, size, pos ):
	return imgdata[ _getcoord(size,pos) ]

def _setbw( imgdata, size, pos, col ):
	imgdata[ _getcoord(size,pos) ] = col

def _getbwdata( img ):
	d = list(img.getdata())
	for i, c in enumerate(d):
		d[ i ] = _isbw( c )
		# print i, c, d[ i ]
	return d


step1_func = lambda parr: parr[0] + parr[2] + parr[4] > 0 and parr[2] + parr[4] + parr[6] > 0
step2_func = lambda parr: parr[0] + parr[2] + parr[6] > 0 and parr[0] + parr[4] + parr[6] > 0

def do_step(imgdata, size, func):
	was_modified = False
	for j in range(1,h-1):
		for i in range(1,w-1):
			p1 = _getbw( imgdata, size, ( i,  j   ) )
			p2 = _getbw( imgdata, size, ( i,  j-1 ) )
			p3 = _getbw( imgdata, size, ( i+1,j-1 ) )
			p4 = _getbw( imgdata, size, ( i+1,j   ) )
			p5 = _getbw( imgdata, size, ( i+1,j+1 ) )
			p6 = _getbw( imgdata, size, ( i,  j+1 ) )
			p7 = _getbw( imgdata, size, ( i,  j+1 ) )
			p8 = _getbw( imgdata, size, ( i-1,j   ) )
			p9 = _getbw( imgdata, size, ( i-1,j-1 ) )

			A_Val  = (p2 == 0 and p3 == 1) + (p3 == 0 and p4 == 1) 
			A_Val += (p4 == 0 and p5 == 1) + (p5 == 0 and p6 == 1) 
			A_Val += (p6 == 0 and p7 == 1) + (p7 == 0 and p8 == 1)
			A_Val += (p8 == 0 and p9 == 1) + (p9 == 0 and p2 == 1)

			B_Val = sum([p2,p3,p4,p5,p6,p7,p8,p9])
			parr = [p2,p3,p4,p5,p6,p7,p8,p9,p2]

			if p1 == C_BLACK:
				if 2 <= B_Val <= 6:
					if A_Val == 1:
						if func(parr):
							_setbw( imgdata, size, (i,j), C_WHITE )
							was_modified = True
	return (imgdata, was_modified)

#difference function to calculate between two pixels
def difference(item, item2):
	reddif = abs(item[0] - item2[0])
	greendif = abs(item[1] - item2[1])
	bluedif = abs(item[2] - item2[2])
	maxdif = max(reddif, greendif, bluedif)
	return maxdif

#check if pixel is in bounds of image
def inBounds(x, y, width, height):
	if x < 0 or x > width-1 or y < 0 or y > height-1:
		return False
	return True

#get list of neighbors in a 5 x 5 block
def getSquareNeighbors(x, y, width, height):
	ans = []
	for i in range(x, x+6):
		for j in range(y, y+6):
			if(inBounds(i, j, width, height)):
				ans.append(newPic[i, j])
	return ans

#get list of specifically adjacent neighbors
def getSpecNeighbors(x, y, width, height):
	ans = []
	if(inBounds(x + 1, y, width, height)):
		ans.append(newPic[x + 1, y])
	if(inBounds(x, y + 1, width, height)):
		ans.append(newPic[x, y + 1])
	if(inBounds(x - 1, y, width, height)):
		ans.append(newPic[x - 1, y])
	if(inBounds(x, y - 1, width, height)):
		ans.append(newPic[x, y - 1])
	return ans

#get list of surrounding pixels
def getNeighbors(x, y, width, height):
	ans = []
	for i in range(x-1, x+2):
		for j in range(y-1, y+2):
			if(inBounds(i, j, width, height)):
				ans.append(pic[i, j])
	return ans

newData = []
threshold = 15
width, height = img.size

newImage = Image.new("RGBA", img.size)

#change to black and white based on difference and threshold
for i in range(width):
    for j in range(height):
		temp = getNeighbors(i, j, width, height)
		found = False
		for n in range(0, len(temp)):
			diff = difference(pic[i, j], temp[n])
			if diff > threshold:
				newImage.putpixel((i, j), (0, 0, 0, 255))
				found = True
				break
		if not found:
			newImage.putpixel((i, j), (255, 255, 255, 255))

#take out small colored pixels
tempVar = False
newPic = newImage.load()
for i in range(width):
	for j in range(height):	
		temp = getSpecNeighbors(i, j, width, height)
		for n in range(len(temp)):
			if newPic[i, j][1] == temp[n][1]:
				tempVar = True
		if not tempVar:
			newImage.putpixel((i, j), (255, 255, 255, 255))
		tempVar = False

#take out random splotches of black
for i in range(0, width, 5):
	for j in range(0, height, 5):	
		count = 0
		temp2 = getSquareNeighbors(i, j, width, height)
		for n in range(len(temp2)):
			if temp2[n][1] == 0:
				count+=1
		if count <= 15:
			for q in range(i, i+6):
				for a in range(j, j+6):
					if(inBounds(q, a, width, height)):
						newImage.putpixel((q, a), (255, 255, 255, 255))

size = width *2, height *2
newImage.thumbnail(size, Image.ANTIALIAS)
imageFin = newImage.filter(ImageFilter.SMOOTH_MORE)
imageFin = imageFin.filter(ImageFilter.SMOOTH_MORE)
imageFin = imageFin.filter(ImageFilter.SMOOTH_MORE)
imageFin.thumbnail(size, Image.ANTIALIAS)
			
imageFin = imageFin.filter(ImageFilter.SMOOTH_MORE)
imageFin = imageFin.filter(ImageFilter.SMOOTH_MORE)
#imageFin = imageFin.filter(ImageFilter.BLUR)


#imageFin.show()
#imageFin.save("hahah.jpg")
imageFin.show()

imageFin.save('/Users/joyhsu0504/Dropbox/CalHacks2016/processed/photo.jpg', 'JPG')
imgpath.remove()

#newImage.show()
#newImage.save("hahah", "PNG")

'''if __name__ == '__main__':
	imgname = 'hahah.jpg'
	img = Image.open(imgname)
	w, h = img.size

	""" The data is returned as a single array """
	pixels = list(img.getdata())

	# Create black and white pixel bitmap image
	nimg = Image.new('1', img.size, -1 )

	# Convert source image to black and white pixels
	bwdata = _getbwdata( img )

	# Run the algorithm until no further modifications are required
	is_modified = True
	while is_modified:

		bwdata, modified1 = do_step(bwdata,img.size,step1_func)
		bwdata, modified2 = do_step(bwdata,img.size,step2_func)

		is_modified = modified1 | modified2

		print is_modified, modified1, modified2

	# Push the data to image
	nimg.putdata( bwdata )
	nimg.show()

	## And save
	fp = open('.abcd_output.jpg','w')
	nimg.save(fp)
	fp.close()'''