车牌字符识别yolo

基于YOLO V8的车牌识别系统可以准确识别正面水平放置的蓝底白字车牌。然而，该系统目前还不能实现对汉字的识别，因为数据不够多[^1]。如果您有自己的图像，可以尝试使用该系统进行测试。以下是一个基于YOLO V8的车牌字符识别的示例代码：

# 导入所需的库
import cv2
import numpy as np

# 加载YOLO V8模型
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

# 加载车牌字符类别标签
classes = []
with open("classes.txt", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# 加载图像
image = cv2.imread("car_plate.jpg")

# 对图像进行预处理
blob = cv2.dnn.blobFromImage(image, 1/255, (416, 416), (0, 0, 0), True, crop=False)

# 将预处理后的图像输入到模型中进行推理
net.setInput(blob)
outs = net.forward()

# 解析模型输出
class_ids = []
confidences = []
boxes = []
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            center_x = int(detection * image.shape)
            center_y = int(detection * image.shape)
            width = int(detection * image.shape)
            height = int(detection * image.shape)
            left = int(center_x - width / 2)
            top = int(center_y - height / 2)
            class_ids.append(class_id)
            confidences.append(float(confidence))
            boxes.append([left, top, width, height])

# 非最大抑制，筛选出最可能的车牌字符区域
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# 在图像上绘制识别结果
for i in indices:
    i = i
    box = boxes[i]
    left = box
    top = box[1]
    width = box
    height = box
    cv2.rectangle(image, (left, top), (left + width, top + height), (0, 255, 0), 2)
    cv2.putText(image, classes[class_ids[i]], (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# 显示识别结果
cv2.imshow("Car Plate Recognition", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，上述代码中的模型权重文件（yolov3.weights）、模型配置文件（yolov3.cfg）和类别标签文件（classes.txt）需要根据实际情况进行替换。此外，您还需要将待识别的车牌图像（car_plate.jpg）放在相应的路径下。