python基于K-means聚类算法的图像分割

import numpy as npimport random

def loss_function(present_center, pre_center):  '''  损失函数，计算上一次与当前聚类中的差异（像素差的平方和）  :param present_center: 当前聚类中心  :param pre_center: 上一次聚类中心  :return:　损失值  '''  present_center = np.array(present_center)  pre_center = np.array(pre_center)  return np.sum((present_center - pre_center)**2)

def classifer(intput_signal, center):  '''  分类器（通过当前的聚类中心，给输入图像分类）  :param intput_signal: 输入图像  :param center: 聚类中心  :return:　标签矩阵  '''  input_row, input_col= intput_signal.shape # 输入图像的尺寸  pixls_labels = np.zeros((input_row, input_col)) # 储存所有像素标签  pixl_distance_t = [] # 单个元素与所有聚类中心的距离，临时用  for i in range(input_row):    for j in range(input_col):      # 计算每个像素与所有聚类中心的差平方      for k in range(len(center)):        distance_t = np.sum(abs((intput_signal[i, j]).astype(int) - center[k].astype(int))**2)        pixl_distance_t.append(distance_t)      # 差异最小则为该类      pixls_labels[i, j] = int(pixl_distance_t.index(min(pixl_distance_t)))      # 清空该list，为下一个像素点做准备      pixl_distance_t = []  return pixls_labels

def k_means(input_signal, center_num, threshold):  '''  基于k-means算法的图像分割（适用于灰度图）  :param input_signal:　输入图像  :param center_num:　聚类中心数目  :param threshold:　迭代阈值  :return:  '''  input_signal_cp = np.copy(input_signal) # 输入信号的副本  input_row, input_col = input_signal_cp.shape # 输入图像的尺寸  pixls_labels = np.zeros((input_row, input_col)) # 储存所有像素标签  # 随机初始聚类中心行标与列标  initial_center_row_num = [i for i in range(input_row)]  random.shuffle(initial_center_row_num)  initial_center_row_num = initial_center_row_num[:center_num]  initial_center_col_num = [i for i in range(input_col)]  random.shuffle(initial_center_col_num)  initial_center_col_num = initial_center_col_num[:center_num]  # 当前的聚类中心  present_center = []  for i in range(center_num):    present_center.append(input_signal_cp[initial_center_row_num[i], initial_center_row_num[i]])  pixls_labels = classifer(input_signal_cp, present_center)  num = 0 # 用于记录迭代次数  while True:    pre_centet = present_center.copy() # 储存前一次的聚类中心    # 计算当前聚类中心    for n in range(center_num):      temp = np.where(pixls_labels == n)      present_center[n] = sum(input_signal_cp[temp].astype(int)) / len(input_signal_cp[temp])    # 根据当前聚类中心分类    pixls_labels = classifer(input_signal_cp, present_center)    # 计算上一次聚类中心与当前聚类中心的差异    loss = loss_function(present_center, pre_centet)    num = num + 1    print("Step:"+ str(num) + "  Loss:" + str(loss))    # 当损失小于迭代阈值时，结束迭代    if loss <= threshold:      break  return pixls_labels