q1594的博客

import cv2import numpy as npimport matplotlib.pyplot as plt#第一步 准备数据#表现为A级的员工的笔试、面试成绩a = np.random.randint(95,100,(20,2)).astype(np.float32)#表现为B级的员工的笔试、面试成绩b = np.random.randint(90,95,(20,2)).astype(np.float32)#合并数据data = np.vstack((a,b))d

一维数据的K均值聚类import cv2import numpy as npimport matplotlib.pyplot as plt#随机生成两组数组#生成60个值在[0,50]内的xiaoMI直径数据xiaoMI = np.random.randint(0,50,60)#生成60个值在[200,250]内的daMI直径数据daMI = np.random.randint(200,250,60)#将xiaoMI和daMI组合为MIMI = np.hstack((xiaoMI,da

import cv2import numpy as npimport matplotlib.pyplot as pltimg = cv2.imread("23.png")#获取XML文件，加载人脸检测器faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#调用函数detecMultiScalefaces =

一种图像添加椒盐噪音的方法import cv2import numpy as npimg=cv2.imread("1.png",0)rows,cols=img.shapeimg1=np.zeros((rows,cols,1),dtype=np.uint8)M=np.random.randint(0,256,size=[rows,cols],dtype=np.uint8)for i in range(rows): for j in range(cols): if M[i,