基于像素特征的kmeas聚类的图像分割方案

kmeans聚类代码

将像素进行聚类，得到每个像素的聚类标签，默认聚类簇数为3

def seg_kmeans(img,clusters=3):
    img_flat=img.reshape((-1,3))
    # print(img_flat.shape)
 
    img_flat=np.float32(img_flat)
    criteria=(cv.TERM_CRITERIA_MAX_ITER+cv.TERM_CRITERIA_EPS,20,0.5)
    flags=cv.KMEANS_RANDOM_CENTERS
    ret,labels,centers=cv.kmeans(data=img_flat,K=clusters,bestLabels=None,criteria=criteria,attempts=10,flags=flags)
    return labels

为显示分割后的图像，将标签进行颜色映射，下述代码中，将第一类标签映射为绿色，第二类标签映射为蓝色，第三类标签映射为红色等：

def img_res(labels):
    color = np.uint8([[255, 0, 0],
                      [0, 0, 255],
                      [0, 255, 0],
                      [255,255,0]])
    res = color[labels.flatten()]  # 像素映射
    result = res.reshape((img.shape))
    return result

为探究不同聚类簇数的影响，分别采用聚类簇数clusters为2、3、4进行结果显示

RGB颜色模型下的聚类效果

HSV颜色模型下的聚类效果

完整源码

import cv2 as cv
import matplotlib.pyplot as plt
import os
import numpy as np
 
def img_read(pattern=cv.IMREAD_GRAYSCALE):#默认显示灰度图
    path=os.path.abspath(".")
    img_path=path+r"\data\twins.jpg"
    img=cv.imread(img_path,pattern)
    return img
 
def seg_kmeans(img,clusters=3):
    img_flat=img.reshape((-1,3))
    # print(img_flat.shape)
 
    img_flat=np.float32(img_flat)
    criteria=(cv.TERM_CRITERIA_MAX_ITER+cv.TERM_CRITERIA_EPS,20,0.5)
    flags=cv.KMEANS_RANDOM_CENTERS
    ret,labels,centers=cv.kmeans(data=img_flat,K=clusters,bestLabels=None,criteria=criteria,attempts=10,flags=flags)
    return labels
 
def img_res(labels):
    color = np.uint8([[255, 0, 0],
                      [0, 0, 255],
                      [0, 255, 0],
                      [255,255,0]])
    res = color[labels.flatten()]  # 像素映射
    result = res.reshape((img.shape))
    return result
 
def plot_res(img):
    clusters = [2, 3, 4]  # 聚类簇数
    img_list = [img] * len(clusters)
    # 采取不同的聚类簇数进行聚类
    labels = map(seg_kmeans, img_list, clusters)#map函数返回一个迭代器对象
    labels = [np.array(label) for label in labels]
    # 对结果进行颜色映射，便于输出
    results = map(img_res, labels)
    plt.subplot(2, 2, 1), plt.imshow(img)
    for i, res in enumerate(results):
        plt.subplot(2, 2, i + 2)
        plt.imshow(res)
        plt.title('k=' + str(i + 2))
    plt.subplots_adjust(hspace=0.5)
    plt.show()
 
if __name__=="__main__":
    img=img_read(pattern=None)
    img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)
    img_HSV=cv.cvtColor(img,cv.COLOR_BGR2HSV)
    plot_res(img_RGB)
    plot_res(img_HSV)