摄像头畸变处理

摄像机标定是机器人视觉进行目标定位跟踪的首要环节，通过标定板标定好摄像机的内外参数，将参数返回给摄像机，然后进行后续的定位识别工作。

下面是利用python语言结合OpenCV进行摄像机标定的代码：

import cv2  
import numpy as np  
import glob  

# 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_MAX_ITER | cv2.TERM_CRITERIA_EPS, 30, 0.001)  

# 获取标定板角点的位置（9*6分别为竖着的畸变点和横向的畸变点）
objp = np.zeros((9*6,3), np.float32)  
objp[:,:2] = np.mgrid[0:9,0:6].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  

obj_points = []    # 存储3D点  
img_points = []    # 存储2D点
images = glob.glob("img\jibian1.jpeg")  
for fname in images:  
    img = cv2.imread(fname)  
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  
    size = gray.shape[::-1]
    print('size')
    print(size)
    ret, corners = cv2.findChessboardCorners(gray, (9,6), None)  
    print(ret)
    print(corners)
    if ret:  
        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.any():  
            img_points.append(corners2)  
        else:  
            img_points.append(corners)  

        cv2.drawChessboardCorners(img, (9,6), corners, ret)   # 记住，OpenCV的绘制函数一般无返回值  
        cv2.imshow('img', img)  
        cv2.waitKey(500000)  

print(len(img_points))  
cv2.destroyAllWindows()  

# 标定  
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points,size, None, None)  

print("ret:",ret)  
print("mtx:\n",mtx )       # 内参数矩阵  
print("dist:\n",dist )     # 畸变系数   distortion cofficients = (k_1,k_2,p_1,p_2,k_3)  
print("rvecs:\n",rvecs )   # 旋转向量  # 外参数  
print("tvecs:\n",tvecs)    # 平移向量  # 外参数  

print("-----------------------------------------------------")  
# 畸变校正  
img = cv2.imread(images[0])  
h, w = img.shape[:2]  
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx,dist,(w,h),1,(w,h))  
print(newcameramtx)  
print("------------------使用undistort函数-------------------")  
dst = cv2.undistort(img,mtx,dist,None,newcameramtx)  
x,y,w,h = roi  
dst1 = dst[y:y+h,x:x+w]  
cv2.imwrite('calibresult11.jpg', dst1)  
print("方法一:dst的大小为:", dst1.shape)  

# undistort方法二  
print("-------------------使用重映射的方式-----------------------")  
mapx,mapy = cv2.initUndistortRectifyMap(mtx,dist,None,newcameramtx,(w,h),5)  # 获取映射方程  
#dst = cv2.remap(img,mapx,mapy,cv2.INTER_LINEAR)      # 重映射  
dst = cv2.remap(img,mapx,mapy,cv2.INTER_CUBIC)        # 重映射后，图像变小了  
x,y,w,h = roi  
dst2 = dst[y:y+h,x:x+w]  
cv2.imwrite('calibresult11_2.jpg', dst2)  
print("方法二:dst的大小为:", dst2.shape)        # 图像比方法一的小  

print("-------------------计算反向投影误差-----------------------")  
tot_error = 0  
for i in range(len(obj_points)):  
    img_points2, _ = cv2.projectPoints(obj_points[i],rvecs[i],tvecs[i],mtx,dist)  
    error = cv2.norm(img_points[i],img_points2, cv2.NORM_L2)/len(img_points2)  
    tot_error += error  

mean_error = tot_error/len(obj_points)  
print("total error: ", tot_error)  
print("mean error: ", mean_error)
cv2.waitKey(0)

http://blog.csdn.net/firemicrocosm/article/details/48594897