单目相机内参标定（python）
单目相机内参标定（python）
标定原理网上一大堆，就不在这赘述了，直接上代码。
import cv2
import numpy as np
import glob
# 找棋盘格角点标定并且写入文件
# 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) # 阈值
# 棋盘格模板规格
w = 8 # 9 - 1
h = 6 # 7 - 1
# 世界坐标系中的棋盘格点,例如(0,0,0), (1,0,0), (2,0,0) ....,(8,5,0)，去掉Z坐标，记为二维矩阵
objp = np.zeros((w*h, 3), np.float32)
objp[:, :2] = np.mgrid[0:w, 0:h].T.reshape(-1, 2)
objp = objp * 21 # 棋盘方块边长21 mm
# 储存棋盘格角点的世界坐标和图像坐标对
objpoints = [] # 在世界坐标系中的三维点
imgpoints = [] # 在图像平面的二维点
images = glob.glob('E:/code/1_21mm_2/*.jpg') # 拍摄的十几张棋盘图片所在目录
i = 1
for fname in images:
img = cv2.imread(fname)
# 获取画面中心点
h1, w1 = img.shape[0], img.shape[1]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
u, v = img.shape[:2]
print(u, v)
# 找到棋盘格角点
ret, corners = cv2.findChessboardCorners(gray, (w, h), None)
# 如果找到足够点对，将其存储起来
if ret == True:
print("i:", i)
i = i+1
# 对检测到的角点作进一步的优化计算，可使角点的精度达到亚像素级别
cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
objpoints.append(objp)
imgpoints.append(corners)
# 将角点在图像上显示
cv2.drawChessboardCorners(img, (w, h), corners, ret)
cv2.namedWindow('findCorners', cv2.WINDOW_NORMAL)
cv2.resizeWindow('findCorners', 640, 480)
cv2.imshow('findCorners', img)
cv2.waitKey(200)
cv2.destroyAllWindows()
# 标定
print('正在计算')
ret, mtx, dist, rvecs, tvecs = \
cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
cv_file = cv2.FileStorage("E:/code/1_21mm_2/camera.yaml", cv2.FILE_STORAGE_WRITE)
cv_file.write("camera_matrix", mtx)
cv_file.write("dist_coeff", dist)
# 请注意，*释放*不会关闭（）FileStorage对象
cv_file.release()
print("ret:", ret)
print("mtx:\n", mtx) # 内参数矩阵
print("dist畸变值:\n", dist) # 畸变系数 distortion cofficients = (k_1,k_2,p_1,p_2,k_3)
print("rvecs旋转（向量）外参:\n", rvecs) # 旋转向量 # 外参数
print("tvecs平移（向量）外参:\n", tvecs) # 平移向量 # 外参数
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (u, v), 0, (u, v))
print('newcameramtx外参', newcameramtx)
camera = cv2.VideoCapture(0)
while True:
(grabbed, frame) = camera.read()
h1, w1 = frame.shape[:2]
# 打开标定文件
cv_file = cv2.FileStorage("E:/code/1_21mm_2/camera.yaml", cv2.FILE_STORAGE_READ)
camera_matrix = cv_file.getNode("camera_matrix").mat()
dist_matrix = cv_file.getNode("dist_coeff").mat()
cv_file.release()
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(camera_matrix, dist_matrix, (u, v), 0, (u, v))
# 纠正畸变
dst1 = cv2.undistort(frame, camera_matrix, dist_matrix, None, newcameramtx)
mapx, mapy = cv2.initUndistortRectifyMap(camera_matrix, dist_matrix, None, newcameramtx, (w1, h1), 5)
dst2 = cv2.remap(frame, mapx, mapy, cv2.INTER_LINEAR)
# 裁剪图像，输出纠正畸变以后的图片
x, y, w1, h1 = roi
dst1 = dst1[y:y + h1, x:x + w1]
cv2.imshow('dst1', dst1)
if cv2.waitKey(1) & 0xFF == ord('q'): # 按q保存一张图片
cv2.imwrite("E:/code/1_21mm_2/frame.jpg", dst1)
break
camera.release()
cv2.destroyAllWindows()
相机标定的棋盘生成程序见：https://blog.csdn.net/qq_42598221/article/details/119212743