以下是一个基于深度学习的车牌识别的代码示例，使用了Python和OpenCV库：
```python
import cv2
import numpy as np
def preprocess(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
_, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
return thresh
def find_contours(thresh):
contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
return contours
def extract_plate(image, contour):
x, y, w, h = cv2.boundingRect(contour)
plate = image[y:y + h, x:x + w]
return plate
def resize_image(image):
plate = cv2.resize(image, (144, 33))
return plate
def recognize_plate(image, model):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
plate = preprocess(gray)
contours = find_contours(plate)
if contours:
contour = max(contours, key=cv2.contourArea)
extracted_plate = extract_plate(image, contour)
resized_plate = resize_image(extracted_plate)
plate_image = np.expand_dims(resized_plate, axis=0)
plate_image = plate_image / 255.0
predictions = model.predict(plate_image)
plate_number = np.argmax(predictions)
return plate_number
return None
# 加载训练好的模型
model = cv2.dnn.readNet('model.weights', 'model.cfg')
# 加载图像
image = cv2.imread('car_plate.jpg')
# 进行车牌识别
plate_number = recognize_plate(image, model)
if plate_number is not None:
print("车牌号码：", plate_number)
else:
print("未能识别车牌号码")
```
请注意，这只是一个示例代码，需要您提供一个训练好的模型文件(model.weights)和模型配置文件(model.cfg)才能正确运行。您可以使用深度学习框架（如TensorFlow或PyTorch）训练您自己的模型，然后将其加载到代码中进行车牌识别。