写一个有关相机标定的整个过程代码
时间: 2023-06-26 13:07:16 浏览: 46
以下是相机标定的整个过程代码,包括图像采集、角点检测、角点亚像素精确化、标定参数计算和结果可视化等步骤:
```python
import numpy as np
import cv2
# 定义棋盘格参数
BOARD_SIZE = (9, 6) # 棋盘格大小
SQUARE_SIZE = 2.5 # 棋盘格单格边长,单位为cm
# 定义图像采集函数
def capture_images():
camera = cv2.VideoCapture(0) # 打开摄像头
images = []
while len(images) < 15: # 采集15张图像
ret, frame = camera.read()
if ret:
images.append(frame)
cv2.imshow('Captured Image', frame)
cv2.waitKey(100)
camera.release()
cv2.destroyAllWindows()
return images
# 定义角点检测函数
def find_corners(images):
corners_list = []
for img in images:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, BOARD_SIZE, None)
if ret:
corners_list.append(corners)
cv2.drawChessboardCorners(img, BOARD_SIZE, corners, ret)
cv2.imshow('Detected Corners', img)
cv2.waitKey(100)
cv2.destroyAllWindows()
return corners_list
# 定义角点亚像素精确化函数
def refine_corners(images, corners_list):
refined_corners_list = []
for i in range(len(images)):
gray = cv2.cvtColor(images[i], cv2.COLOR_BGR2GRAY)
corners = cv2.cornerSubPix(gray, corners_list[i], (11, 11), (-1, -1),
(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001))
refined_corners_list.append(corners)
return refined_corners_list
# 定义标定函数
def calibrate_camera(refined_corners_list):
object_points = np.zeros((len(refined_corners_list), BOARD_SIZE[0] * BOARD_SIZE[1], 3), np.float32)
object_points[:, :, :2] = np.mgrid[0:BOARD_SIZE[0], 0:BOARD_SIZE[1]].T.reshape(-1, 2) * SQUARE_SIZE
image_points = np.array(refined_corners_list)
ret, camera_matrix, distortion_coefficients, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points,
(gray.shape[1], gray.shape[0]),
None, None)
return ret, camera_matrix, distortion_coefficients, rvecs, tvecs
# 定义结果可视化函数
def visualize_results(images, camera_matrix, distortion_coefficients):
for img in images:
h, w = img.shape[:2]
new_camera_matrix, roi = cv2.getOptimalNewCameraMatrix(camera_matrix, distortion_coefficients, (w, h), 1, (w, h))
undistorted = cv2.undistort(img, camera_matrix, distortion_coefficients, None, new_camera_matrix)
cv2.imshow('Original Image', img)
cv2.imshow('Undistorted Image', undistorted)
cv2.waitKey(1000)
cv2.destroyAllWindows()
# 主函数
if __name__ == '__main__':
images = capture_images()
corners_list = find_corners(images)
refined_corners_list = refine_corners(images, corners_list)
ret, camera_matrix, distortion_coefficients, rvecs, tvecs = calibrate_camera(refined_corners_list)
visualize_results(images, camera_matrix, distortion_coefficients)
```
使用该代码,可以对摄像头进行标定,并将结果可视化,从而消除畸变效应,得到更准确的图像。
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