import cv2
import numpy as np
from sklearn.cluster import KMeans
from collections import Counter
def get_dominant_color(image, k=4):
image = image.reshape((image.shape[0] * image.shape[1], 3))
clt = KMeans(n_clusters = k)
labels = clt.fit_predict(image)
label_counts = Counter(labels)
dominant_color = clt.cluster_centers_[label_counts.most_common(1)[0][0]]
return list(dominant_color)
bgr_image = cv2.imread('image.png')
hsv_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2HSV)
dom_color = get_dominant_color(hsv_image)
dom_color_hsv = np.full(bgr_image.shape, dom_color, dtype='uint8')
dom_color_bgr = cv2.cvtColor(dom_color_hsv, cv2.COLOR_HSV2BGR)
output_image = np.hstack((bgr_image, dom_color_bgr))
cv2.imshow('Dominant Color', output_image)
cv2.waitKey(0)
from PIL import Image
img = Image.open("img.jpg")
w5 = (img.size[0] // 100) * 5
h5 = (img.size[1] // 100) * 5
croped = img.crop(
(
w5,
h5,
img.size[0] - w5,
img.size[1] - h5
)
)
croped.save("croped.jpg")
from PIL import Image
from PIL import ImageOps
img = Image.open("img.jpg")
w5 = (img.size[0] // 100) * 5
croped = ImageOps.crop(img, w5)
croped.save("croped.jpg")