yolo v8 webcam 讀出參數

你可以使用YOLOv4或YOLOv5通过Webcam读取参数。首先，确保你已经安装了必要的库，如OpenCV和YOLO。

下面是一个示例代码，用于使用YOLOv4检测Webcam中的对象并打印出参数：

import cv2
import numpy as np

# 加载YOLOv4模型
net = cv2.dnn.readNetFromDarknet('yolov4.cfg', 'yolov4.weights')

# 获取YOLOv4网络输出层的名称
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 打开Webcam
cap = cv2.VideoCapture(0)

while True:
    _, frame = cap.read()

    # 对帧进行预处理
    height, width, channels = frame.shape
    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    
    # 将预处理后的帧输入到YOLOv4模型中
    net.setInput(blob)
    outs = net.forward(output_layers)

    # 解析网络输出并获取参数
    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:  # 设置置信度阈值
                # 对象检测框坐标为相对值，需要乘以帧的宽度和高度
                center_x = int(detection[0] * width)
                center_y = int(detection[1] * height)
                w = int(detection[2] * width)
                h = int(detection[3] * height)

                # 计算对象检测框的左上角坐标
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)

                # 存储参数
                class_ids.append(class_id)
                confidences.append(float(confidence))
                boxes.append([x, y, w, h])

    # 打印参数
    print("Class IDs:", class_ids)
    print("Confidences:", confidences)
    print("Boxes:", boxes)

    # 绘制检测框和类别标签
    class_names = ['class1', 'class2', 'class3']  # 替换为你的类别名称
    colors = np.random.uniform(0, 255, size=(len(class_names), 3))
    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]
            label = f"{class_names[class_ids[i]]}: {confidences[i]:.2f}"
            color = colors[class_ids[i]]
            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            cv2.putText(frame, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

    # 显示帧
    cv2.imshow("YOLOv4", frame)
    if cv2.waitKey(1) == ord('q'):
        break

# 释放资源
cap.release()
cv2.destroyAllWindows()

请注意，你需要将`yolov4.cfg`和`yolov4.weights`替换为你实际的YOLOv4模型文件路径。同时，你也可以在`class_names`列表中替换为你自己的类别名称。

运行这段代码后，控制台将打印出检测到的对象的类别ID、置信度和边界框坐标参数，并且窗口中将显示实时的Webcam画面及检测结果。