リアルタイムビデオ予測のための遅延ソリューション

最近、炎のリアルタイムビデオ予測を行うと、数秒から10秒、または数十秒の範囲の遅延の問題が常に発生しますが、これは受け入れがたいものです。
次のスキームでは、1秒以内に遅延を制御できます。

全体的なアイデア：kafkaを採用する

kafkaインストールリファレンスインストールkafka
プロデューサーはビデオストリームを取得し、各フレームを取得し、処理アルゴリズムモデルを呼び出し、処理されたフレームをkafkaに取り込み、
コンシューマーはkafkaニュースビデオをサブスクライブします。
プロデューサーコードは次のとおりですkafka_producer.py

# -*- coding: utf-8 -*-
from kafka import KafkaProducer
from imutils.video import VideoStream
import requests
import imutils
import numpy as np
import time
import cv2
from PIL import Image
from io import BytesIO
import base64
import json


def publish_video(server, topic, rtsp, api_url):
   producer = KafkaProducer(bootstrap_servers=server)
   vs = VideoStream(src=rtsp).start()
   time.sleep(2.0)

   print("publishing video...")

   while True:
      frame = vs.read()
      frame = process(frame, api_url)
      frame = imutils.resize(frame, width=400)
      # send to kafka topic
      producer.send(topic, frame.tobytes())

   vs.stop()


def process(frame, api_url):
   img = Image.fromarray(frame)
   output_buffer = BytesIO()  # 创建一个BytesIO
   img.save(output_buffer, format='JPEG')  # 写入output_buffer
   byte_data = output_buffer.getvalue()  # 在内存中读取
   image_base64 = base64.b64encode(byte_data).decode()  # 转为BASE64
   data = json.dumps(
      {"image": image_base64, "visual_result": "True", "username": "xsrt", "password": "dGVzdC1wd2QxMjM="})
   headers = {"Content-Type": "application/json"}
   response = requests.post(api_url, data=data, headers=headers)
   result = response.text
   result = json.loads(result)
   try:
      base64_img = result["base64"]
      process_img = base64.b64decode(base64_img)
      img = cv2.imdecode(np.frombuffer(process_img, np.uint8), cv2.IMREAD_COLOR)
      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
   except:
      img = frame

   return img


def process_list(result):
   for i in result:
      result_i = i
      result_i_bbox = result_i["bbox"]
      for i in range(len(result_i_bbox)):
         result_i_bbox[i] = result_i_bbox[i] * 2
   return result


if __name__ == '__main__':
   server = '192.168.20.99:9092'
   topic = 'run'
   # rtsp = 'rtsp://admin:[email protected]:554/h264/ch1/main/av_stream'
   rtsp = 0
   api_url = 'http://192.168.20.99:8191/fire/json'
   publish_video(server, topic, rtsp, api_url)

コンシューマーコードは次のとおりです kafka_consumer.py

from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import time
import cv2
from kafka import KafkaConsumer
import sys

def showCam(server, topic, window_name):

    consumer = KafkaConsumer(
        topic,
        bootstrap_servers=[server])

    fps = FPS().start()

    for msg in consumer:
        # print(msg)
        decoded = np.frombuffer(msg.value, np.uint8)
        # print(decoded.size)
        # decoded = decoded.reshape(225, 400, 3)
        decoded = decoded.reshape((int)(decoded.size / (400 * 3)), 400, 3)
        cv2.namedWindow(window_name, flags=cv2.WINDOW_FREERATIO)
        cv2.imshow(window_name, decoded)

        key = cv2.waitKey(1) & 0xFF
        if key == ord("q"):
            break
        fps.update()

    fps.stop()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    server = '192.168.20.99:9092'
    topic = 'run'
    window_name = 'fire'
    showCam(server, topic, window_name)

---

さらなる最適化

kafka_producer.py

# -*- coding: utf-8 -*-
from kafka import KafkaProducer
from imutils.video import VideoStream
import requests
import imutils
import numpy as np
import time
import cv2
from PIL import Image
from io import BytesIO
import base64
import json


def publish_video(server, topic, rtsp, api_url):
   producer = KafkaProducer(bootstrap_servers=server)
   vs = VideoStream(src=rtsp).start()
   time.sleep(2.0)

   print("publishing video...")

   i = 0
   a = 5
   while True:
      i = i +1
      if i % a == 0:
         frame = vs.read()
         if q.qsize() > 1:
            q.get()
         else:
            q.put(frame)
            # frame = process(frame)
            # frame = imutils.resize(frame, width=400)
            frame = q.get()
            if frame is not None:
               frame = imutils.resize(frame, width=400)
               # frame = detection(frame, 'pretrained.prototxt.txt', 'pretrained.caffemodel')
               frame = process(frame)
               # send to kafka topic
               producer.send(topic, frame.tobytes())

   vs.stop()


def process(frame, api_url):
   img = Image.fromarray(frame)
   output_buffer = BytesIO()  # 创建一个BytesIO
   img.save(output_buffer, format='JPEG')  # 写入output_buffer
   byte_data = output_buffer.getvalue()  # 在内存中读取
   image_base64 = base64.b64encode(byte_data).decode()  # 转为BASE64
   data = json.dumps(
      {"image": image_base64, "visual_result": "True", "username": "xsrt", "password": "dGVzdC1wd2QxMjM="})
   headers = {"Content-Type": "application/json"}
   response = requests.post(api_url, data=data, headers=headers)
   result = response.text
   result = json.loads(result)
   try:
      base64_img = result["base64"]
      process_img = base64.b64decode(base64_img)
      img = cv2.imdecode(np.frombuffer(process_img, np.uint8), cv2.IMREAD_COLOR)
      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
   except:
      img = frame

   return img


def process_list(result):
   for i in result:
      result_i = i
      result_i_bbox = result_i["bbox"]
      for i in range(len(result_i_bbox)):
         result_i_bbox[i] = result_i_bbox[i] * 2
   return result


if __name__ == '__main__':
   server = '192.168.20.99:9092'
   topic = 'run'
   # rtsp = 'rtsp://admin:[email protected]:554/h264/ch1/main/av_stream'
   rtsp = 0
   api_url = 'http://192.168.20.99:8191/fire/json'
   publish_video(server, topic, rtsp, api_url)

---

最適化することもできます。次のスキームを参照して、複数の（Haikang \ Dahua）Webカメラのビデオストリームを
読み取り（opencv-pythonを使用）、リアルタイムの読み取り遅延の問題を解決します。