写一个在Django中显示经过目标检测处理的rtsp服务器视频流的代码

以下是一个Django视图函数，可以用于在网页中显示通过目标检测处理的RTSP服务器视频流：

import cv2
import numpy as np
from django.http import StreamingHttpResponse
from django.views.decorators import gzip
from django.shortcuts import render

# Load YOLOv3 pre-trained model
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Initialize video stream from RTSP server
cap = cv2.VideoCapture("rtsp://<username>:<password>@<ip_address>:<port>/")

# Define function to process each frame of the video stream
def detect_objects(frame):
    # Preprocess image
    blob = cv2.dnn.blobFromImage(frame, scalefactor=0.00392, size=(416, 416), mean=(0, 0, 0), swapRB=True, crop=False)

    # Pass image through YOLOv3 model
    net.setInput(blob)
    outs = net.forward(output_layers)

    # Extract bounding boxes and confidence scores
    boxes = []
    confidences = []
    class_ids = []
    height, width, channels = frame.shape
    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[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-maximum suppression to remove overlapping bounding boxes
    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

    # Draw bounding boxes and labels on image
    font = cv2.FONT_HERSHEY_PLAIN
    colors = np.random.uniform(0, 255, size=(len(classes), 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[class_ids[i]]
            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            cv2.putText(frame, label + " " + confidence, (x, y + 20), font, 2, color, 2)

    # Return processed image
    return frame

# Define a generator function to yield frames from video stream
def video_stream():
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        frame = detect_objects(frame)
        ret, jpeg = cv2.imencode('.jpg', frame)
        yield (b'--frame\r\n'
               b'Content-Type: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n\r\n')

# Define a view function to display video stream in browser
@gzip.gzip_page
def live_video_feed(request):
    return StreamingHttpResponse(video_stream(), content_type='multipart/x-mixed-replace; boundary=frame')

# Define a view function to render the video stream in a template
def live_video(request):
    return render(request, 'live_video.html')

在此代码中，我们首先加载了预训练的YOLOv3模型和COCO数据集的类别列表。然后，我们初始化一个从RTSP服务器获取视频流的视频流对象，并定义了一个名为`detect_objects()`的函数，该函数接收原始图像并返回经过目标检测处理的图像。最后，我们定义了两个视图函数：`live_video_feed()`和`live_video()`。`live_video_feed()`函数是一个生成器函数，它从视频流中读取每个帧并将其传递给`detect_objects()`函数进行处理。然后，它将处理后的帧转换为JPEG格式，并将其作为多部分响应流发送到浏览器。`live_video()`函数是一个简单的视图函数，它仅呈现一个包含视频流的HTML页面。

注意：在代码中，你需要将`<username>`、`<password>`、`<ip_address>`和`<port>`替换为你的实际RTSP服务器的用户名、密码、IP地址和端口号。此外，你还需要在Django项目的静态文件目录中创建一个名为`live_video.html`的模板文件，以便呈现视频流。