opencv车牌识别python完整全面代码

以下是一份OpenCV车牌识别的Python完整代码。请注意，这只是一个基本示例，您可能需要根据您的具体需求进行更改和优化。

import cv2
import numpy as np

# 车牌识别函数
def plate_recognition(image):
    # 图像灰度化
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # 高斯滤波
    blur = cv2.GaussianBlur(gray, (5, 5), 0)

    # Sobel算子处理
    sobel = cv2.Sobel(blur, cv2.CV_8U, 1, 0, ksize=3)

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

    # 膨胀和腐蚀操作
    element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 9))
    element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (24, 6))

    dilation = cv2.dilate(binary, element2, iterations=1)
    erosion = cv2.erode(dilation, element1, iterations=1)
    dilation2 = cv2.dilate(erosion, element2, iterations=3)

    # 查找轮廓
    contours, hierarchy = cv2.findContours(dilation2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # 筛选车牌轮廓
    car_plate = None
    for i in range(len(contours)):
        cnt = contours[i]
        area = cv2.contourArea(cnt)

        # 面积筛选
        if area < 2000 or area > 50000:
            continue

        # 轮廓近似
        approx = cv2.approxPolyDP(cnt, 4, True)

        # 判断是否为矩形
        if len(approx) != 4:
            continue

        # 判断矩形边长比例是否符合要求
        x, y, w, h = cv2.boundingRect(cnt)
        ratio = float(w) / float(h)
        if ratio > 8 or ratio < 2:
            continue

        # 符合要求的轮廓
        car_plate = cnt
        break

    if car_plate is None:
        return None

    # 提取车牌图像
    x, y, w, h = cv2.boundingRect(car_plate)
    car_plate_image = image[y:y+h, x:x+w]

    return car_plate_image

# 读取图像
image = cv2.imread('car.jpg')

# 车牌识别
car_plate_image = plate_recognition(image)

# 显示结果
cv2.imshow('Image', image)
cv2.imshow('Car Plate', car_plate_image)
cv2.waitKey(0)

在运行此代码之前，请将示例图像保存为“car.jpg”并将其放入与代码相同的目录中。运行代码后，您将看到原始图像和提取的车牌图像。