opencv车牌识别参考代码

这里提供一个基于OpenCV的车牌识别参考代码，包含车牌定位、字符分割和字符识别三个部分：

import cv2
import numpy as np
import pytesseract

# 车牌定位
def locate_car_plate(image):
    # 将图像转换为灰度图
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # 运用高斯滤波平滑图像
    blur = cv2.GaussianBlur(gray, (5, 5), 0)

    # 进行图像二值化
    thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

    # 找到图像中所有的轮廓
    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # 找到所有矩形轮廓
    rects = [cv2.boundingRect(contour) for contour in contours]

    # 筛选出符合条件的矩形轮廓
    candidates = []
    for rect in rects:
        x, y, w, h = rect
        if 0.8 <= w / h <= 4 and 1000 <= w * h <= 8000:
            candidates.append(rect)

    # 找到最大的矩形轮廓作为车牌区域
    car_plate = image.copy()
    if candidates:
        car_plate_rect = max(candidates, key=lambda x: x[2] * x[3])
        x, y, w, h = car_plate_rect
        car_plate = image[y:y + h, x:x + w]

    return car_plate

# 字符分割
def segment_characters(image):
    # 将图像转换为灰度图
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # 进行图像二值化
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

    # 找到图像中所有的轮廓
    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 找到所有矩形轮廓
    rects = [cv2.boundingRect(contour) for contour in contours]

    # 筛选出符合条件的矩形轮廓
    chars = []
    for rect in rects:
        x, y, w, h = rect
        if 5 <= w <= 200 and 10 <= h <= 200 and 0.35 <= w / h <= 1.5:
            chars.append(rect)

    # 将字符按照位置排序
    chars = sorted(chars, key=lambda x: x[0])

    return chars

# 字符识别
def recognize_characters(image, chars):
    # 将图像转换为灰度图
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # 进行图像二值化
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

    # 将字符按照位置排序
    chars = sorted(chars, key=lambda x: x[0])

    # 识别每个字符
    result = ''
    for rect in chars:
        x, y, w, h = rect
        char = thresh[y:y + h, x:x + w]
        char = cv2.resize(char, (36, 36))
        char = cv2.copyMakeBorder(char, 2, 2, 2, 2, cv2.BORDER_CONSTANT, value=0)
        char = cv2.cvtColor(char, cv2.COLOR_GRAY2BGR)
        char = cv2.resize(char, (36, 36), interpolation=cv2.INTER_AREA)
        char = cv2.dnn.blobFromImage(char, scalefactor=1 / 255.0, size=(36, 36), mean=(0, 0, 0), swapRB=True, crop=False)
        net.setInput(char)
        preds = net.forward()
        index = np.argmax(preds)
        result += chars_dict[index]

    return result

# 加载字符识别模型
net = cv2.dnn.readNet('char_recognition_model.weights', 'char_recognition_model.cfg')

# 字符编码表
chars_dict = {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B',
              12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M',
              23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X',
              34: 'Y', 35: 'Z'}

# 读取车辆图片
image = cv2.imread('car.jpg')

# 车牌定位
car_plate = locate_car_plate(image)

# 字符分割
chars = segment_characters(car_plate)

# 字符识别
result = recognize_characters(car_plate, chars)

# 显示结果
print(result)

# 使用pytesseract进行OCR识别
text = pytesseract.image_to_string(car_plate, lang='chi_sim')
print(text)

在该代码中，首先进行车牌定位，然后对车牌进行字符分割，最后使用深度学习模型进行字符识别。在字符识别部分，我们将每个字符resize到36x36，然后作为输入传入深度学习模型进行预测，得到最终的车牌字符识别结果。除了使用深度学习模型进行字符识别外，我们还可以使用OCR库pytesseract进行车牌识别。