python相机标定

采用的是张正友方法，参考以下文章：https://github.com/Nocami/PythonComputerVision-6-CameraCalibration
在该方法的基础上实现了批量的定标，便于软件设计。

# -*- coding: utf-8 -*-
from cv2 import cv2
import numpy as np
import glob
class Cphoto_pre_work:

    def __init__(self):
        self.obj_points = []  # 存储3D点
        self.img_points = []  # 存储2D点
        self.size=[]
        self.ret=[]
        self.mtx=[]
        self.dist=[]
        self.rvecs=[]
        self.tvecs=[]
        self.list1=['ret','mtx','dist','rvecs','tvecs']
        self.savesrc5=[]

    def get_grid(self,src,src_corner):
        #进行标定
        #src:图像路径
        #src_corner:棋盘格坐标路径
        images = glob.glob(src)
        # 设置寻找亚像素角点的参数，采用的停止准则是最大循环次数30和最大误差容限0.001
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

        # 获取标定板角点的位置
        objp = np.zeros((4*6,3), np.float32)
        objp[:,:2] = np.mgrid[0:6,0:4].T.reshape(-1,2)  # 将世界坐标系建在标定板上，所有点的Z坐标全部为0，所以只需要赋值x和y
        i=0
        for fname in images:
            img = cv2.imread(fname)
            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            self.size = gray.shape[::-1]
            ret, corners = cv2.findChessboardCorners(gray, (6, 4), None)
            
            if ret:
                self.obj_points.append(objp)
                corners2 = cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1), criteria)  # 在原角点的基础上寻找亚像素角点

                if [corners2]:
                    self.img_points.append(corners2)
                else:
                    self.img_points.append(corners)
                i+=1
                cv2.drawChessboardCorners(img, (6, 4), corners, ret)  # 记住，OpenCV的绘制函数一般无返回值
                cv2.imwrite(src_corner+fname.rsplit("\\",1)[1],img)
                # cv2.imwrite('./outimg/conimg'+str(i)+'.jpg', img)
                # cv2.waitKey(4000)
        if self.img_points:
            return True
        else:
                return False
    
    def get_parameter5(self,savesrc):
        # 保存相应5个标定参数，并存储
        self.savesrc5=savesrc
        self.ret, self.mtx, self.dist, self.rvecs, self.tvecs = cv2.calibrateCamera(self.obj_points, self.img_points,self.size, None, None)
        np.save(savesrc+ self.list1[0],self.ret)
        np.save(savesrc+ self.list1[1],self.mtx)
        np.save(savesrc+ self.list1[2],self.dist)
        np.save(savesrc+ self.list1[3],self.rvecs)
        np.save(savesrc+ self.list1[4],self.tvecs)
    def last_photo(self,imgsrc,src_par5,last_src):
        #imgsrc:原始影像路径
        #src_part5:5参数路径
        #last_src:最后保存路径
        #选择指定文件夹即可直接读入文件夹内保存的.npy数据,生成相片
        origin_images = glob.glob(imgsrc+'\*.[jp][pn]g')
        for tempfname in origin_images:
            tempimg=cv2.imread(tempfname)
            h,w=tempimg.shape[:2]
            # print(tempfname.rsplit("\\",1)[1])
            newcameramtx, roi=cv2.getoptimalNewCameraMatrix(np.load(src_par5+self.list1[1]+'.npy'),np.load(src_par5+self.list1[2]+'.npy'),(w,h),1,(w,h))#显示更大范围的图片（正常重映射之后会删掉一部分图像）
            print (newcameramtx)
            print("------------------使用undistort函数-------------------")
            temp_dst=cv2.undistort(tempimg,np.load(src_par5+self.list1[1]+'.npy'),np.load(src_par5+ self.list1[2]+'.npy'),None,newcameramtx)
            x,y,w,h = roi
            tempdst1 = temp_dst[y:y+h,x:x+w]
            cv2.imwrite(last_src+tempfname.rsplit("\\",1)[1], tempdst1)
            print ("方法一:dst的大小为:", tempdst1.shape)


if __name__ == '__main__':
    temp=Cphoto_pre_work()
    temp.get_grid('.\images4\*.jpg','./savecorner'+'/')
    srccc=r'E:\canshu'+'\\'
    temp.get_parameter5(srccc)
    temp.last_photo('.\images4',srccc,'./saveimg'+'/')