请生成调用摄像头并应用yolo v4进行检测的python代码

以下是调用摄像头并应用yolo v4进行检测的Python代码示例：

import cv2
import numpy as np

# Load Yolo
net = cv2.dnn.readNet("yolov4.weights", "yolov4.cfg")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# Initialize webcam
cap = cv2.VideoCapture(0)

while True:
    # Read frame from webcam
    _, frame = cap.read()

    # Detect objects in the frame
    height, width, _ = frame.shape
    blob = cv2.dnn.blobFromImage(frame, 1/255, (416, 416), (0, 0, 0), swapRB=True, crop=False)
    net.setInput(blob)
    output_layers_names = net.getUnconnectedOutLayersNames()
    layerOutputs = net.forward(output_layers_names)

    # Get bounding boxes for detected objects
    boxes = []
    confidences = []
    class_ids = []
    for output in layerOutputs:
        for detection in output:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                center_x = int(detection[0] * width)
                center_y = int(detection[1] * height)
                w = int(detection[2] * width)
                h = int(detection[3] * height)
                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)

    # Apply non-max suppression to remove overlapping bounding boxes
    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

    # Draw bounding boxes and labels for detected objects
    font = cv2.FONT_HERSHEY_PLAIN
    colors = np.random.uniform(0, 255, size=(len(boxes), 3))
    if len(indexes) > 0:
        for i in indexes.flatten():
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])
            confidence = str(round(confidences[i], 2))
            color = colors[i]
            cv2.rectangle(frame, (x, y), (x+w, y+h), color, 2)
            cv2.putText(frame, label + " " + confidence, (x, y+20), font, 2, (255,255,255), 2)

    # Display output
    cv2.imshow("Object Detection", frame)

    # Exit loop by pressing 'q'
    if cv2.waitKey(1) == ord('q'):
        break

# Release resources
cap.release()
cv2.destroyAllWindows()

在此代码中，我们使用了OpenCV的dnn模块来加载yolo v4模型及其配置文件，并从COCO数据集中获取类名。然后，我们初始化了摄像头，并在循环中读取摄像头帧。我们将每一帧输入到yolo v4模型中，以检测其中的物体，并获取每个物体的边界框，置信度和类别ID。然后，我们对这些边界框应用非最大抑制（NMS）算法，以消除重叠的边界框。最后，我们在每个检测到的物体周围绘制边界框和标签，并将输出显示在屏幕上。我们通过按下“q”键退出循环，并释放摄像头资源。