from sklearn.cluster import KMeans
import cv2
import numpy as np
import matplotlib.pyplot as plt

# Load the image
img = cv2.imread('/mnt/data/arthur-hinton-9Y1iOQ4rvtw-unsplash.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# Resize image to speed up k-means
img = cv2.resize(img, (64, 64), interpolation=cv2.INTER_AREA)

# Reshape the image to be a list of RGB pixels
pixels = img.reshape(-1, 3)

# Perform k-means clustering to find the most dominant colors
kmeans = KMeans(n_clusters=5)
kmeans.fit(pixels)

# Get the RGB values of the cluster centers
colors = kmeans.cluster_centers_

# Convert colors to integer
colors = colors.round(0).astype(int)

colors

array([[ 73,  78, 107],
       [233, 163, 158],
       [ 39,  48,  77],
       [198, 136, 141],
       [120, 113, 135]])

# Plotting the colors as a palette
palette = np.zeros((50, 300, 3), dtype=np.uint8)

steps = 300 // 5
for i, color in enumerate(colors):
    palette[:, i*steps:(i+1)*steps, :] = color
    
plt.figure(figsize=(5, 2))
plt.imshow(palette)
plt.axis('off')
plt.show()

An experimental model that can solve tasks by generating Python code and executing it in a Jupyter notebook.
You can upload any kind of file, and ask model to analyse it, or produce a new file which you can download.

Color Palette from Image