Catatan untuk Belajar MTCRE Mikrotik Certified Routing Engineer

 

	import cv2
	import numpy as np
	import requests
	from urllib.request import urlopen
	from io import BytesIO
	from IPython.display import Image, display
	# Function to detect dominant colors in an image
	def detect_colors(image_url, num_colors=5):
	# Download the image from the URL
	response = urlopen(image_url)
	image_data = BytesIO(response.read())
	image = cv2.imdecode(np.asarray(bytearray(image_data.read()), dtype=np.uint8), -1)
	# Convert image to RGB
	image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
	# Reshape the image to a list of pixels
	pixels = image_rgb.reshape((-1, 3))
	# Perform k-means clustering to find dominant colors
	k = num_colors
	criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, 0.2)
	_, labels, centers = cv2.kmeans(pixels.astype(np.float32), k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
	# Convert the color centers to uint8
	centers = np.uint8(centers)
	# Sort the colors by frequency
	unique_labels, counts = np.unique(labels, return_counts=True)
	color_counts = list(zip(unique_labels, counts, centers))
	color_counts.sort(key=lambda x: x[1], reverse=True)
	# Extract the dominant colors
	dominant_colors = [tuple(color[2]) for color in color_counts]
	return dominant_colors
	# Input image URL
	image_url = input("Enter the image URL: ")
	# Detect dominant colors
	num_colors = 5 # You can adjust this number as needed
	dominant_colors = detect_colors(image_url, num_colors)
	# Display the dominant colors
	print("Dominant Colors:")
	for i, color in enumerate(dominant_colors, start=1):
	print(f"Color {i}: RGB {color}")
	# Display the image
	display(Image(url=image_url))

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