kmeans图像分割(python)_python画kmeans_python_分割_图像分割_聚类

import numpy as np import 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