用yolo进行车牌识别代码

以下是使用YOLO进行车牌识别的代码示例：

import cv2
import numpy as np

# 加载YOLOv3模型
net = cv2.dnn.readNetFromDarknet("yolov3.cfg", "yolov3.weights")

# 加载车牌识别模型
plate_net = cv2.dnn.readNet("plate_detection_model.weights", "plate_detection_model.cfg")

# 获取YOLOv3网络输出层信息（所有输出层的名字）
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 定义车牌识别字典
plate_dict = {}

# 加载车牌识别字典
with open("plate_dict.txt") as f:
    for line in f:
        key, value = line.strip().split(":")
        plate_dict[key] = value

# 加载车牌模板
plate_template = cv2.imread("plate_template.jpg", 0)

# 定义函数，用于车牌检测和识别
def detect_and_recognize_plate(img):
    # 车牌检测
    blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)
    layer_outputs = net.forward(output_layers)
    boxes = []
    confidences = []
    class_ids = []
    for output in layer_outputs:
        for detection in output:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5 and class_id == 2:
                center_x = int(detection[0] * img.shape[1])
                center_y = int(detection[1] * img.shape[0])
                w = int(detection[2] * img.shape[1])
                h = int(detection[3] * img.shape[0])
                x = int(center_x - w/2)
                y = int(center_y - h/2)
                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)
    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
    for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            plate_img = img[y:y+h, x:x+w]
            # 车牌识别
            blob = cv2.dnn.blobFromImage(plate_img, 1/255.0, (100, 100), (0, 0, 0), True, crop=False)
            plate_net.setInput(blob)
            plate_output = plate_net.forward()
            plate_output = plate_output.reshape(7, 36)
            digits = ""
            for j in range(7):
                digit_output = plate_output[j]
                digit_id = np.argmax(digit_output)
                digit_confidence = digit_output[digit_id]
                if digit_confidence > 0.5:
                    digit = plate_dict[str(digit_id)]
                    digits += digit
            # 显示车牌号码
            cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
            font = cv2.FONT_HERSHEY_SIMPLEX
            cv2.putText(img, digits, (x+5, y+h-5), font, 1, (255, 255, 255), 2, cv2.LINE_AA)
    return img

# 加载测试图片
img = cv2.imread("test.jpg")

# 车牌检测和识别
result = detect_and_recognize_plate(img)

# 显示结果
cv2.imshow("Result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

在上述代码中，我们首先加载了YOLOv3模型和车牌识别模型，然后定义了一个函数`detect_and_recognize_plate`，该函数用于车牌检测和识别。在该函数中，我们首先使用YOLOv3模型进行车辆检测，然后在检测到的车辆中进行车牌检测和识别。最后，我们将车牌号码绘制在原图像上，并返回结果。在主函数中，我们加载测试图片并调用`detect_and_recognize_plate`函数进行车牌检测和识别，最后显示结果。