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from sklearn.cluster import KMeans
from collections import Counter
import numpy as np
import cv2
def get_image(pil_image):
nimg = np.array(pil_image)
image = cv2.cvtColor(nimg, cv2.COLOR_RGB2BGR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def get_labels(rimg):
clf = KMeans(n_clusters=5)
labels = clf.fit_predict(rimg)
return labels, clf
def get_closest_color(colors):
white = (255, 255, 255)
closest_color = min(colors, key=lambda c: np.linalg.norm(np.array(c) - white))
return closest_color
def RGB2HEX(color):
return "#{:02x}{:02x}{:02x}".format(int(color[0]), int(color[1]), int(color[2]))
def extract_colors_and_closest_to_white(image_path):
img = cv2.imread(image_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
reshaped_img = img.reshape(img.shape[0] * img.shape[1], img.shape[2])
labels, clf = get_labels(reshaped_img)
counts = Counter(labels)
center_colors = clf.cluster_centers_
ordered_colors = [center_colors[i] for i in counts.keys()]
hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts.keys()]
closest_color_to_white = get_closest_color(center_colors)
hex_closest_color_to_white = RGB2HEX(closest_color_to_white)
return hex_colors, hex_closest_color_to_white
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