将 CV2 YOLO8s 视频源传递到 Django 应用程序

Question

我有一个预训练的 YOLO8s 模型，并在自定义数据集上对其进行训练，并且我使用 CV2 制作了一个 Python 类来显示结果。如何将来自 CV2 YOLO8 的持续视频源传递到 Django 网页？

这是我当前的代码：

from ultralytics import YOLO
from pathlib import Path
import cv2, onnx, onnxoptimizer,numpy,onnxruntime
import torch.onnx
import torchvision.models as models
from database.db import *
from pprint import pprint as pt
    
    
class Main_field: 
    
    def __init__(self, model, size, source, conf_):
        self.model = self.load_model(model) 
        self.size = size                    
        self.source = source               
        self.conf_ = conf_                  
    
    def __call__(self): 
        self.process_video()
    
    def load_model(self, model):
        model.fuse()                      
        return model
    
    def process_video(self):
        cap = cv2.VideoCapture(self.source) 
        while True:
            ret, frame = cap.read() 
            if not ret: break
                
            results = self.model.predict(frame, conf=self.conf_, imgsz=self.size) 
    
            masks_dict=[result.names for result in results][0] 
            xyxys=[result.boxes.cpu().numpy().xyxy for result in results][0] #xyxy 
            mask_ids=[result.boxes.cpu().numpy().cls.astype(int).tolist() for result in results][0] 
            masks=[masks_dict[itr] for itr in mask_ids] 
    
            db_output=[check_(local_list,str(itr)) for itr in mask_ids if itr] 
                
            video_outp=cv2.imshow("_________", results[0].plot()) 
            pt(mask_ids)
    
            if cv2.waitKey(1) & 0xFF == ord('q'): break
            
        def __del__():
            cap.release()
            cv2.destroyAllWindows()
    
        def init_model(path: str) -> any: return YOLO(path)

Answer 1

您需要使用 djangos 功能来流式传输视频内容（我不是 django 专家），首先您需要使用您的函数制作一个生成器：

    def process_video(self):
        cap = cv2.VideoCapture(self.source) 
        while True:
            ret, frame = cap.read() 
            if not ret: break
                
            results = self.model.predict(frame, conf=self.conf_, imgsz=self.size) 

            # here is your way to annotate frame
            annotated_frame = ...

            # here you convert image to bytes
            image = Image.fromarray(annotated_frame)        
            img_buffer = io.BytesIO()
            image.save(img_buffer, format='JPEG')

            img_bytes = img_buffer.getvalue()
            yield (b'--frame\r\n'b'Content-Type: image/jpeg\r\n\r\n' + img_bytes + b'\r\n')
            

然后你需要创建一些视图（不知道它在 django 中应该如何）

class VideoStream(View):
    def get(self, request, *args, **kwargs):
        return StreamingHttpResponse(process_video(), content_type='multipart/xmixed-replace; boundary=frame')

之后，您需要在 html 页面中创建输出流响应的元素，它看起来有点像这样

    <div class="">
        <img src="{% url 'your_response_root' %}" width="1024" height="768">
    </div>