opencv车牌识别完整代码

这是一个基于OpenCV的车牌识别完整代码，包括车牌定位、车牌字符分割和字符识别等步骤，你可以根据自己的需求进行修改和优化。

import cv2
import numpy as np
import pytesseract

# 车牌定位
def locate_license_plate(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 高斯滤波
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    # Canny边缘检测
    canny = cv2.Canny(blur, 50, 150)
    # 轮廓检测
    contours, hierarchy = cv2.findContours(canny, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    # 遍历所有轮廓
    for contour in contours:
        # 计算轮廓面积
        area = cv2.contourArea(contour)
        if area < 1000:  # 过滤面积较小的轮廓
            continue
        # 计算轮廓周长
        perimeter = cv2.arcLength(contour, True)
        # 多边形拟合
        approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
        if len(approx) == 4:  # 选取4个顶点的轮廓
            x, y, w, h = cv2.boundingRect(contour)
            # 过滤不符合长宽比的轮廓
            if w / h > 2.5 or h / w > 2.5:
                continue
            # 裁剪出车牌区域
            license_plate = img[y:y + h, x:x + w]
            return license_plate

# 车牌字符分割
def segment_license_plate(license_plate):
    gray = cv2.cvtColor(license_plate, cv2.COLOR_BGR2GRAY)
    # 二值化
    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    # 腐蚀
    kernel = np.ones((3, 3), np.uint8)
    erosion = cv2.erode(binary, kernel, iterations=1)
    # 轮廓检测
    contours, hierarchy = cv2.findContours(erosion, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    char_imgs = []
    # 遍历所有轮廓
    for contour in contours:
        # 计算轮廓面积
        area = cv2.contourArea(contour)
        if area < 50:  # 过滤面积较小的轮廓
            continue
        # 计算轮廓周长
        perimeter = cv2.arcLength(contour, True)
        # 多边形拟合
        approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
        x, y, w, h = cv2.boundingRect(contour)
        # 过滤不符合长宽比的轮廓
        if w / h > 3 or h / w > 3:
            continue
        # 裁剪出字符图片
        char_img = license_plate[y:y + h, x:x + w]
        char_imgs.append(char_img)
    return char_imgs

# 字符识别
def recognize_chars(char_imgs):
    chars = []
    # 遍历所有字符图片
    for char_img in char_imgs:
        # 二值化
        gray = cv2.cvtColor(char_img, cv2.COLOR_BGR2GRAY)
        _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
        # 调整大小
        resized = cv2.resize(binary, (20, 30), interpolation=cv2.INTER_AREA)
        # 转换为字符串
        char = pytesseract.image_to_string(resized, config="--psm 10")
        chars.append(char)
    return chars

# 加载图片
img = cv2.imread("car.jpg")

# 车牌定位
license_plate = locate_license_plate(img)

# 车牌字符分割
char_imgs = segment_license_plate(license_plate)

# 字符识别
chars = recognize_chars(char_imgs)

# 显示结果
cv2.imshow("License Plate", license_plate)
for i, char in enumerate(chars):
    cv2.imshow("Char %d" % i, char)
cv2.waitKey(0)
cv2.destroyAllWindows()

这段代码首先使用 `locate_license_plate` 函数对输入的图片进行车牌定位，然后使用 `segment_license_plate` 函数对车牌进行字符分割，最后使用 `recognize_chars` 函数对每个字符进行识别。最终会显示车牌和每个字符的识别结果。