自动驾驶——自动泊车之AVM环视系统算法的视频校正相关代码

AVM环视系统的视频校正主要包括对视频中的每一帧进行畸变矫正和图像拼接，这里提供一些相关的OpenCV代码供参考：

import cv2
import numpy as np

# 标定板格点数
board_w = 9
board_h = 6

# 棋盘格角点坐标
objp = np.zeros((board_w * board_h, 3), np.float32)
objp[:, :2] = np.mgrid[0:board_w, 0:board_h].T.reshape(-1, 2)

# 存储棋盘格角点坐标和图像中对应点坐标的数组
objpoints = []  # 3D points in real world space
imgpoints = []  # 2D points in image plane.

# 读取棋盘格标定图片
img = cv2.imread('calibration.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 棋盘格角点检测
ret, corners = cv2.findChessboardCorners(gray, (board_w, board_h), None)

# 如果检测到棋盘格角点，进行标定
if ret == True:
    objpoints.append(objp)
    imgpoints.append(corners)
    img = cv2.drawChessboardCorners(img, (board_w, board_h), corners, ret)

    # 相机内参、畸变系数等标定参数计算
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

    # 视频校正
    cap = cv2.VideoCapture('test.mp4')
    fourcc = cv2.VideoWriter_fourcc(*'XVID')
    out = cv2.VideoWriter('output.avi', fourcc, 30.0, (1280, 720))

    while cap.isOpened():
        ret, frame = cap.read()
        if ret == True:
            # 畸变矫正
            h, w = frame.shape[:2]
            newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
            dst = cv2.undistort(frame, mtx, dist, None, newcameramtx)

            # 图像拼接
            dst1 = cv2.warpPerspective(dst, H, (w, h))
            dst2 = cv2.warpPerspective(dst, H, (w, h))
            dst3 = cv2.warpPerspective(dst, H, (w, h))
            dst4 = cv2.warpPerspective(dst, H, (w, h))
            dst = cv2.add(dst1, dst2)
            dst = cv2.add(dst, dst3)
            dst = cv2.add(dst, dst4)

            # 写入视频文件
            out.write(dst)

            # 显示结果
            cv2.imshow('frame', dst)
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
        else:
            break

    # 释放资源
    cap.release()
    out.release()
    cv2.destroyAllWindows()

这里H是通过图像校正算法（如SIFT、SURF等）计算出来的单应性矩阵，四个 `dst` 分别对应四个摄像头采集的图像。最终将四个 `dst` 进行图像拼接，得到完整的车辆周围环境全景图像，并将校正后的视频帧写入输出视频文件。