OpenCV与AI深度学习 | 实战 | 基于OpenCV和K-Means聚类实现颜色分割（步骤 + 代码）

 from matplotlib import pyplot as plt

    
 import cv2
    
 import pandas as pd
    
 import numpy as np
    
 from sklearn.cluster import KMeans
    
 %matplotlib inlin
    
    
    
    
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 image = cv2.imread(r'bubbles.jpeg', cv2.IMREAD_UNCHANGED)

    
 plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    
 plt.axis('off')
    
    
    
    
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 image = cv2.imread(r'bubbles.jpeg', cv2.IMREAD_UNCHANGED)

    
 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
 _ , mask = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)
    
 mask = cv2.erode(mask, np.ones((7, 7), np.uint8))
    
    
    
    
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    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
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 filtered_contours = []

    
 df_mean_color = pd.DataFrame()
    
 for idx, contour in enumerate(contours):
    
     area = int(cv2.contourArea(contour))
    
  
    
     # if area is higher than 3000:
    
     if area > 3000:
    
     filtered_contours.append(contour)
    
     # get mean color of contour:
    
     masked = np.zeros_like(image[:, :, 0])  # This mask is used to get the mean color of the specific bead (contour), for kmeans
    
     cv2.drawContours(masked, [contour], 0, 255, -1)
    
  
    
     B_mean, G_mean, R_mean, _ = cv2.mean(image, mask=masked)
    
     df = pd.DataFrame({'B_mean': B_mean, 'G_mean': G_mean, 'R_mean': R_mean}, index=[idx])
    
     df_mean_color = pd.concat([df_mean_color, df])
    
    
    
    
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![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/sOlIGx1uLtNqacfEH5J6Wrz2onpg.png)

 km = KMeans( n_clusters=6)

    
 df_mean_color['label'] = km.fit_predict(df_mean_color)
    
    
    
    
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 def draw_segmented_objects(image, contours, label_cnt_idx, bubbles_count):

    
     mask = np.zeros_like(image[:, :, 0])
    
     cv2.drawContours(mask, [contours[i] for i in label_cnt_idx], -1, (255), -1)
    
     masked_image = cv2.bitwise_and(image, image, mask=mask)
    
     masked_image = cv2.putText(masked_image, f'{bubbles_count} bubbles', (200, 1200), cv2.FONT_HERSHEY_SIMPLEX,
    
                     fontScale = 3, color = (255, 255, 255), thickness = 10, lineType = cv2.LINE_AA)
    
     return masked_image
    
    
    
    
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 img = image.copy()

    
 for label, df_grouped in df_mean_color.groupby('label'):
    
     bubbles_amount = len(df_grouped)
    
     masked_image = draw_segmented_objects(image, contours, df_grouped.index, bubbles_amount)
    
     img = cv2.hconcat([img, masked_image])
    
  
    
 plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB) )
    
    
    
    
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