# USAGE
# python color_kmeans.py --image images/jp.png --clusters 3
# or
# python color_kmeans.py -g "images/chase*.png" -c9

# import the necessary packages
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import argparse
import utils
import cv2
from PIL import Image
import glob
import time

current_milli_time = lambda: int(round(time.time() * 1000))

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=False, help= "Path to the image")
ap.add_argument("-g", "--glob", required=False, help= "Glob path")
ap.add_argument("-c", "--clusters", required=True, type=int,help= "# of clusters")
args = vars(ap.parse_args())

def makebar(img):
	outfile = "c%s_%s.gif" %( args["clusters"],current_milli_time() )

	image = cv2.imread( img )
	image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
	# save and show our image
	plt.close('all')
	plt.figure()
	plt.axis("off")
	# show source image
	# plt.imshow(image)

	# reshape the image to be a list of pixels
	image = image.reshape((image.shape[0] * image.shape[1], 3))

	# cluster the pixel intensities
	clt = KMeans(n_clusters = args["clusters"])
	clt.fit(image)

	# build a histogram of clusters and then create a figure
	# representing the number of pixels labeled to each color
	hist = utils.centroid_histogram(clt)
	bar = utils.plot_colors(hist, clt.cluster_centers_)

	im = Image.fromarray(bar)
	im.save(outfile)

	# show our color bar
	plt.figure()
	plt.axis("off")
	plt.imshow(bar)
	plt.show()


# load the image and convert it from BGR to RGB so that
# we can display it with matplotlib
if ( args["image"] ):
	files = glob.glob( args["image"] )
else:
	files = glob.glob( args["glob"] )
	print("{{{{{{{{{{{{{ files }}}}}}}}}}}}}")
	print(files)


for img in files:
	makebar(img)