color_kmeans.py

# USAGE
# python color_kmeans.py --image images/jp.png --clusters 3

# import the necessary packages
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import argparse
import utils
import cv2
import collections
import numpy as np

def rgbtohsv(color):
    R = color[0] / 255
    G = color[1] / 255
    B = color[2] / 255

    MAX = max(R, G, B)
    MIN = min(R, G, B)

    V = MAX
    if V == 0:
        S = 0
    else:
        S = (V - MIN) / V

    if G == B:
        H = 0
    else:
        if V == R:
            H = (60 * (G - B)) / (V - MIN)
        elif V == G:
            H = 120 + ((60 * (B - R)) / (V - MIN))
        elif V == B:
            H = 240 + ((60 * (R - G)) / (V - MIN))

    if (H < 0):
        H = H + 360

    return round(H), round(S * 100), round(V * 100)


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

# load the image and convert it from BGR to RGB so that
# we can dispaly it with matplotlib
image = cv2.imread(args["image"])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# show our image
plt.figure()
plt.axis("off")
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_)

d = {}
for (percent, color) in zip(hist, clt.cluster_centers_):
    p = round(percent, 2)
    colors = [int(color[0]), int(color[1]), int(color[2])]  # R: color[0], G: color[1], B: color[2]
    d[p] = colors

od = collections.OrderedDict(sorted(d.items(), reverse=True))
print(od)
count = 1
for percent in od:
    if count > 3: break
    color = od[percent]
    # suppose white or black is background
    if (color[0] < 5 and color[1] < 5 and color[2] < 5) or (color[0] > 250 and color[1] > 250 and color[2] > 250):
        print("background")
        continue
    print(str(count) + ": " + "R (" + str(color[0]) + "), G (" + str(color[1]) + "), B (" + str(color[2]) + ")")
    # convert rgb to hsv
    h, s, v = rgbtohsv(color)
    print("hsv : " + str(h) + ", " + str(s) + ", " + str(v))
    count += 1

# show our color bar
hist_len = 300
bar = np.zeros((50, hist_len, 3), dtype="uint8")
startX = 0
for percent in od:
    color = np.array(od[percent])
    # print(color.astype("uint8").tolist())
    endX = startX + (percent * hist_len)
    cv2.rectangle(bar, (int(startX), 0), (int(endX), 50),
                  color.astype("uint8").tolist(), -1)
    startX = endX

plt.figure()
plt.axis("off")
plt.imshow(bar)
plt.show()