Python script that accepts a user inputted path/image, analyzes the colors used, creates a copy of the original image with circles showing the three most cominant colors in the image, and saves it as the original file name appended with "_primarycolors.png"

Most_Used_Colors.py

import cv2
import numpy as np
from sklearn.cluster import KMeans

# function to draw small circles at the bottom right corner of the image
def draw_circles(image, colors):
  height, width, _ = image.shape
  circle_radius = int(min(height, width) * 0.05) # radius of the circles is 5% of the minimum of height and width
  for i, color in enumerate(colors):
    center_x = int(width * 0.9)
    center_y = int(height * 0.9) - i * (circle_radius // 2)
    if center_x - circle_radius >= 0 and center_x + circle_radius < width and center_y - circle_radius >= 0 and center_y + circle_radius < height: # check if the center of the circle is within the bounds of the image
      cv2.circle(image, (center_x, center_y), circle_radius, color, -1) # draw the circle with the given color

# function to get the dominant colors of the image
def get_dominant_colors(image, k=3):
  image = image.reshape((image.shape[0] * image.shape[1], 3)) # flatten the image into a list of pixels
  clt = KMeans(n_clusters=k, n_init=10) # create a KMeans object with k clusters and n_init=10
  clt.fit(image) # fit the model to the pixels
  return clt.cluster_centers_ # return the center of each cluster as the dominant colors

# read the image file
image_path = input("Enter the path to the image file: ")
try:
  image = cv2.imread(image_path)
except:
  print("Error: Invalid image file")
  exit()

# get the dominant colors of the image
colors = get_dominant_colors(image)

# draw the circles with the dominant colors
draw_circles(image, colors)

# save the modified image
output_path = image_path.split('.')[0] + "_primarycolors.png" # create the output path by adding "_primarycolors.png" to the original image path
cv2.imwrite(output_path, image)