请给出具体的车牌校正程序

以下是基于Python3.7和OpenCV4.0的车牌定位与畸变校正的完整实现代码：

import cv2
import numpy as np

# 车牌定位
def locate_license_plate(img):
    # 高斯滤波
    img = cv2.GaussianBlur(img, (3, 3), 0)
    # Sobel算子
    sobel = cv2.Sobel(img, cv2.CV_8U, 1, 0, ksize=3)
    # 二值化
    ret, binary = cv2.threshold(sobel, 0, 255, cv2.THRESH_OTSU+cv2.THRESH_BINARY)
    # 闭运算
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (17, 5))
    closed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)

    # 轮廓检测
    contours, hierarchy = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    # 筛选轮廓
    max_area = 0
    best_cnt = None
    for cnt in contours:
        area = cv2.contourArea(cnt)
        if area < 2000 or area > 20000:
            continue
        peri = cv2.arcLength(cnt, True)
        approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
        if len(approx) != 4:
            continue
        if area > max_area:
            max_area = area
            best_cnt = approx

    # 提取车牌轮廓
    if best_cnt is not None:
        x, y, w, h = cv2.boundingRect(best_cnt)
        license_plate = img[y:y+h, x:x+w]
        return license_plate
    else:
        return None

# 畸变校正
def correct_perspective(license_plate_img):
    # 灰度化
    gray = cv2.cvtColor(license_plate_img, cv2.COLOR_BGR2GRAY)

    # 二值化
    ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU+cv2.THRESH_BINARY)

    # 查找轮廓
    contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 找到最大轮廓
    max_area = 0
    best_cnt = None
    for cnt in contours:
        area = cv2.contourArea(cnt)
        if area < 1000 or area > 10000:
            continue
        peri = cv2.arcLength(cnt, True)
        approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
        if len(approx) != 4:
            continue
        if area > max_area:
            max_area = area
            best_cnt = approx

    # 找到四个角点
    rect = cv2.minAreaRect(best_cnt)
    box = cv2.boxPoints(rect)
    box = np.int0(box)

    # 计算变换矩阵
    width = int(np.sqrt((box[0][0]-box[1][0])**2+(box[0][1]-box[1][1])**2))
    height = int(np.sqrt((box[0][0]-box[3][0])**2+(box[0][1]-box[3][1])**2))
    src_pts = box.astype("float32")
    dst_pts = np.array([[0, 0], [width-1, 0], [width-1, height-1], [0, height-1]], dtype="float32")
    M = cv2.getPerspectiveTransform(src_pts, dst_pts)

    # 变换
    warped = cv2.warpPerspective(license_plate_img, M, (width, height))

    return warped

# 主函数
if __name__ == '__main__':
    # 读取图像
    img = cv2.imread('image.jpg')

    # 车牌定位
    license_plate_img = locate_license_plate(img)

    # 畸变校正
    corrected_license_plate_img = correct_perspective(license_plate_img)

    # 显示结果
    cv2.imshow('original image', img)
    cv2.imshow('license plate', license_plate_img)
    cv2.imshow('corrected license plate', corrected_license_plate_img)
    cv2.waitKey()

其中，`locate_license_plate`函数实现了车牌定位，`correct_perspective`函数实现了畸变校正。主函数读取图像，进行车牌定位和畸变校正，并显示结果。