请帮我更改yolov5的detect.py函数，将其封装为能够对rtsp流进行检测，同时在模型检测到目标物体之后能够实时返回对应帧的图片以及指定几帧内的视频的函数...

好的，下面是我为您修改后的代码：

import cv2
import torch
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords, plot_one_box
from utils.torch_utils import select_device


def detect_rtsp(rtsp_url, weights, conf_thres=0.25, iou_thres=0.45, device='', imgsz=640, num_of_frames=5):
    # Load model
    device = select_device(device)
    model = attempt_load(weights, map_location=device)  # load FP32 model
    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once

    # Set up RTSP capture
    cap = cv2.VideoCapture(rtsp_url)

    # Initialize variables for storing frames and detections
    frame_count = 0
    detected_frames = []
    detected_images = []

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

        if not ret:
            print("Failed to read frame from RTSP stream.")
            break

        # Resize frame and convert to tensor
        img = letterbox(frame, new_shape=imgsz)[0]
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB
        img = np.ascontiguousarray(img)

        # Detect objects
        img = torch.from_numpy(img).to(device)
        img = img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        pred = model(img)[0]
        pred = non_max_suppression(pred, conf_thres, iou_thres)

        # Store frame and detections
        if len(pred):
            detected_frames.append(frame_count)
            detected_images.append(frame)

        # Plot boxes on frame
        for i, det in enumerate(pred):
            if len(det):
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], frame.shape).round()

                for *xyxy, conf, cls in reversed(det):
                    label = f'{model.names[int(cls)]} {conf:.2f}'
                    plot_one_box(xyxy, frame, label=label, color=colors(int(cls)), line_thickness=3)

        # Show frame
        cv2.imshow("RTSP Stream", frame)

        # Exit if 'q' is pressed or if the number of frames with detections exceeds num_of_frames
        if cv2.waitKey(1) & 0xFF == ord('q') or len(detected_frames) >= num_of_frames:
            break

        frame_count += 1

    cap.release()
    cv2.destroyAllWindows()

    return detected_frames, detected_images

这个函数可以接受一个`rtsp_url`参数，该参数指定了要检测的 RTSP 流的 URL。您还需要指定模型的权重文件路径，以及一些其他参数，例如置信度阈值、IoU 阈值、设备类型、图像大小等。最后，该函数还会返回检测到目标物体的帧以及对应的图像。如果您想要检测多个帧，请将`num_of_frames`参数设置为相应的值。