Smart Image Color Palette Extractor

import cv2

import numpy as np

from sklearn.cluster import KMeans

from tkinter import Tk, filedialog

from PIL import Image

import matplotlib.pyplot as plt

def rgb_to_hex(rgb):

    """Convert RGB tuple to HEX string."""

    return "#{:02x}{:02x}{:02x}".format(int(rgb[0]), int(rgb[1]), int(rgb[2]))

def extract_colors(image_path, num_colors=6):

    """Extract dominant colors from an image using KMeans clustering."""

    # Read and convert image

    image = cv2.imread(image_path)

    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    

    # Reshape image data for clustering

    pixels = image.reshape((-1, 3))

    pixels = np.float32(pixels)

    # Apply K-Means

    kmeans = KMeans(n_clusters=num_colors, random_state=42)

    kmeans.fit(pixels)

    

    colors = np.round(kmeans.cluster_centers_).astype(int)

    return colors

def display_palette(colors):

    """Display color palette using matplotlib."""

    plt.figure(figsize=(10, 2))

    plt.axis("off")

    # Create palette blocks

    for i, color in enumerate(colors):

        plt.subplot(1, len(colors), i + 1)

        plt.imshow(np.ones((100, 100, 3), dtype=np.uint8) * np.uint8(color))

        plt.title(rgb_to_hex(color), fontsize=10)

        plt.axis("off")

    plt.tight_layout()

    plt.show()

def main():

    # GUI File Picker

    root = Tk()

    root.withdraw()

    image_path = filedialog.askopenfilename(title="Select an Image",

                                            filetypes=[("Image Files", "*.jpg *.jpeg *.png *.bmp")])

    if not image_path:

        print("❌ No file selected.")

        return

    print(f"📸 Analyzing: {image_path}")

    colors = extract_colors(image_path, num_colors=6)

    

    print("\n🎨 Dominant Colors:")

    for color in colors:

        print(f"RGB: {tuple(color)} | HEX: {rgb_to_hex(color)}")

    # Show palette visually

    display_palette(colors)

if __name__ == "__main__":

    main()