python 深度学习车牌识别代码

以下是一个简单的基于CNN的车牌字符识别代码示例：

import numpy as np
import cv2
import tensorflow as tf

# 加载训练好的模型
model = tf.keras.models.load_model('model.h5')

# 字符集
char_set = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']

# 车牌字符识别函数
def recognize_character(img):
    # 图像预处理
    img = cv2.resize(img, (32, 40))
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img = np.expand_dims(img, axis=-1)
    img = np.expand_dims(img, axis=0)
    img = img / 255.0
    # 使用模型进行预测
    pred = model.predict(img)
    pred = np.argmax(pred, axis=-1)
    return char_set[int(pred)]

# 车牌字符分割函数
def segment_characters(img):
    # 转换为灰度图像
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 进行二值化处理
    ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    # 进行形态学处理
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
    # 查找轮廓
    contours, hierarchy = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    # 遍历轮廓，分割字符
    characters = []
    for contour in contours:
        x, y, w, h = cv2.boundingRect(contour)
        area = w * h
        if area > 200 and area < 800:
            roi = img[y:y+h, x:x+w]
            character = recognize_character(roi)
            characters.append((character, (x, y, w, h)))
    return characters

# 测试代码
img = cv2.imread('test.jpg')
characters = segment_characters(img)
print(characters)

以上代码实现了车牌字符分割和识别，其中模型文件`model.h5`需提前训练好。测试时，将测试图像`test.jpg`放入代码所在目录并运行即可。