Program for license plate recognition based on convolutional neural network using Python and Keras

The following is a sample code for a license plate recognition program based on a convolutional neural network (CNN), written using Python and the Keras library:

import numpy as np
import cv2
from keras.models import load_model

# 加载训练好的CNN模型
model = load_model('license_plate_cnn_model.h5')

# 定义车牌字符集
characters = '0123456789ABCDEFGHJKLMNPQRSTUVWXYZ'

# 加载车牌图像并进行预处理
def preprocess_plate_image(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
    return thresh

# 对单个字符图像进行预处理和预测
def predict_character_image(image):
    processed_image = cv2.resize(image, (30, 30))
    processed_image = np.expand_dims(processed_image, axis=2)
    processed_image = np.expand_dims(processed_image, axis=0)
    prediction = model.predict(processed_image)
    character_index = np.argmax(prediction)
    character = characters[character_index]
    return character

# 对整个车牌图像进行字符识别
def recognize_license_plate(plate_image):
    # 将车牌图像划分成字符图像
    contours, _ = cv2.findContours(plate_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    contours = sorted(contours, key=lambda c: cv2.boundingRect(c)[0])
    character_images = []
    for contour in contours:
        x, y, w, h = cv2.boundingRect(contour)
        if w > 10 and h > 10:
            character_image = plate_image[y:y+h, x:x+w]
            character_image = preprocess_character_image(character_image)
            character_images.append(character_image)

    # 对每个字符图像进行预测并组合成车牌号码
    license_plate = ''.join([predict_character_image(character_image) for character_image in character_images])
    return license_plate

This program first loads a trained CNN model and then defines the license plate character set. In the preprocess_plate_image() function, the program preprocesses the license plate image, including converting it into a grayscale image, applying Gaussian blur, adaptive threshold binarization, etc. In the predict_character_image() function, the program preprocesses a single character image and uses the CNN model to predict, and returns the predicted character. In the recognize_license_plate() function, the program divides the license plate image into individual character images, predicts each character image and combines it into a license plate number.

This program is just an example, actual license plate recognition tasks may require more pre- and post-processing steps, as well as more complex models and algorithms.