dominant colors.py

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

# # 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 display it with matplotlib
# image = cv2.imread(args["image"])
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
IMAGES_FOLDER_PATH = os.path.join(CURRENT_DIR, "Images")
IMAGE_FILE = os.path.join(IMAGES_FOLDER_PATH, "image2.jpg")

n = 5

image = cv2.imread(IMAGE_FILE)
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=n)
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_)

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