segiddins/ImageColors.py

## ImageColors.py
#!/usr/bin/python

from collections import namedtuple
from math import sqrt
import random
import os
import sys
import time
from PIL import Image

Point = namedtuple('Point', ('coords', 'n', 'ct'))
Cluster = namedtuple('Cluster', ('points', 'center', 'n'))
filetoanalyze = sys.argv[1]
if len(sys.argv) > 2:
    numcolors = int(sys.argv[2])
else:
    numcolors = 3


def get_points(img):
    points = []
    w, h = img.size
    for count, color in img.getcolors(w * h):
        points.append(Point(color, 3, count))
    return points

rtoh = lambda rgb: '#%s' % ''.join(('%02x' % p for p in rgb))


def colorz(filename, n=3):
    img = Image.open(filename)
    img.thumbnail((200, 200))
    w, h = img.size
    points = get_points(img)
    clusters = kmeans(points, n, 1)
    rgbs = [map(int, c.center.coords) for c in clusters]
    return map(rtoh, rgbs)


def euclidean(p1, p2):
    return sqrt(sum([
        (p1.coords[i] - p2.coords[i]) ** 2 for i in range(p1.n)
    ]))


def calculate_center(points, n):
    vals = [0.0 for i in range(n)]
    plen = 0
    for p in points:
        plen += p.ct
        for i in range(n):
            vals[i] += (p.coords[i] * p.ct)
    return Point([(v / plen) for v in vals], n, 1)


def kmeans(points, k, min_diff):
    clusters = [Cluster([p], p, p.n) for p in random.sample(points, k)]

    while 1:
        plists = [[] for i in range(k)]

        for p in points:
            smallest_distance = float('Inf')
            for i in range(k):
                distance = euclidean(p, clusters[i].center)
                if distance < smallest_distance:
                    smallest_distance = distance
                    idx = i
            plists[idx].append(p)

        diff = 0
        for i in range(k):
            old = clusters[i]
            center = calculate_center(plists[i], old.n)
            new = Cluster(plists[i], center, old.n)
            clusters[i] = new
            diff = max(diff, euclidean(old.center, new.center))

        if diff < min_diff:
            break

    return clusters


colors = colorz(filetoanalyze, numcolors)
print colors
fobj_out = open("myFile.html", "w")
myFile = '''
<!DOCTYPE HTML>
<html>
    <head>
        <title>ImageColors</title>
        <style>span {dispay: inline; width:185px; height:185px; float:left; margin:15px; padding:15px}</style>
    </head>
    <body>
    <img src='%s' width="350px" height="350px" />
    <div>
    ''' % (filetoanalyze)
for x in xrange(0, numcolors):
    myFile += '''<span style='background-color: %s'>%s</span>''' % (colors[x], colors[x])
myFile += '''</div>
</body>
</html>
'''
fobj_out.write(myFile)
fobj_out.close()

os.system('open myFile.html')
time.sleep(4.0)
os.system('rm myFile.html')
	#!/usr/bin/python

	from collections import namedtuple
	from math import sqrt
	import random
	import os
	import sys
	import time
	from PIL import Image

	Point = namedtuple('Point', ('coords', 'n', 'ct'))
	Cluster = namedtuple('Cluster', ('points', 'center', 'n'))
	filetoanalyze = sys.argv[1]
	if len(sys.argv) > 2:
	numcolors = int(sys.argv[2])
	else:
	numcolors = 3


	def get_points(img):
	points = []
	w, h = img.size
	for count, color in img.getcolors(w * h):
	points.append(Point(color, 3, count))
	return points

	rtoh = lambda rgb: '#%s' % ''.join(('%02x' % p for p in rgb))


	def colorz(filename, n=3):
	img = Image.open(filename)
	img.thumbnail((200, 200))
	w, h = img.size
	points = get_points(img)
	clusters = kmeans(points, n, 1)
	rgbs = [map(int, c.center.coords) for c in clusters]
	return map(rtoh, rgbs)


	def euclidean(p1, p2):
	return sqrt(sum([
	(p1.coords[i] - p2.coords[i]) ** 2 for i in range(p1.n)
	]))


	def calculate_center(points, n):
	vals = [0.0 for i in range(n)]
	plen = 0
	for p in points:
	plen += p.ct
	for i in range(n):
	vals[i] += (p.coords[i] * p.ct)
	return Point([(v / plen) for v in vals], n, 1)


	def kmeans(points, k, min_diff):
	clusters = [Cluster([p], p, p.n) for p in random.sample(points, k)]

	while 1:
	plists = [[] for i in range(k)]

	for p in points:
	smallest_distance = float('Inf')
	for i in range(k):
	distance = euclidean(p, clusters[i].center)
	if distance < smallest_distance:
	smallest_distance = distance
	idx = i
	plists[idx].append(p)

	diff = 0
	for i in range(k):
	old = clusters[i]
	center = calculate_center(plists[i], old.n)
	new = Cluster(plists[i], center, old.n)
	clusters[i] = new
	diff = max(diff, euclidean(old.center, new.center))

	if diff < min_diff:
	break

	return clusters


	colors = colorz(filetoanalyze, numcolors)
	print colors
	fobj_out = open("myFile.html", "w")
	myFile = '''
	<!DOCTYPE HTML>
	<html>
	<head>
	<title>ImageColors</title>
	<style>span {dispay: inline; width:185px; height:185px; float:left; margin:15px; padding:15px}</style>
	</head>
	<body>
	<img src='%s' width="350px" height="350px" />
	<div>
	''' % (filetoanalyze)
	for x in xrange(0, numcolors):
	myFile += '''<span style='background-color: %s'>%s</span>''' % (colors[x], colors[x])
	myFile += '''</div>
	</body>
	</html>
	'''
	fobj_out.write(myFile)
	fobj_out.close()

	os.system('open myFile.html')
	time.sleep(4.0)
	os.system('rm myFile.html')