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server/__pycache__/
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# IoI Interaktion ohne Interaktion
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##### (Demo)
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Dieses Projekt enthält alle Komponenten eines Systems zu überwachung und Analyse mehrerer Videostreams und der Steuerung von Geräten auf Basis der Analyse.
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### Struktur:
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Pfad | Beschreibung
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----|---
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./FrontEnd/ | Enthält den Front-End Client. Geschrieben in C#.
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./client/ | Ein simpler Flask (Python) Endpoint der als Demo Endpoint dient
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./server/ | Enthält alle Dateien für Das Backend
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./server/cams.json | Entält Informationen über die genutzen Videostreams, kann als Teil der Datenbank verstanden werden
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./server/clients.json | Entält Informationen über die möglichen Endpoints, kann als Teil der Datenbank verstanden werden
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./server/Dockerfile | Enthält die Deployment Beschreibung. Kann als Prod. Env genutzt werden. Nutzt in diesem Fall aber nur die CPU
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./server/model.pb | Enthält das genutzte vortrainierte Neuronale Netz
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./server/motion_detector_old.py | Ein erster Prototyp eines rein OpenCV basierten Ansatzes
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./server/requirments.txt | Enthält die pip Abhängigkeiten, wird von Dockerfile genutzt
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./server/detector.py | Stellt die detection API die vom Server genutzt wird. Hier wird das NN ausgeführt
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./server/app.py | Ein Flask Server. Nutzt detector.py um Personen in Videos zu erkennen. Bietet mehrere Routen, mehr hierzu weiter unten.
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### Routes
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/client/ returns client list as json
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/client/<num>/info returns client information as json
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/cam/ returns camera list as json
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/cam/<num>/stream returns a unedited mjpeg stream
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/cam/<num>/processed returns a mjpeg stream with persons highlighted
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/info returns camera infromation
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### Architektur
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FROM python:3.7
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RUN apt-get update
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RUN apt-get install -y gcc libevent-dev python-dev
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COPY ./requirements.txt /
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RUN pip install -r /requirements.txt
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COPY ./ /app
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WORKDIR /app
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EXPOSE 5000
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CMD ["gunicorn", "-b", "0.0.0.0:5000","--worker-class", "gthread", "-w","4", "--threads", "100", "app"]
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66
mock/app.py
66
mock/app.py
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import os
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import random
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import json
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from importlib import import_module
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import cv2
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from flask import Flask, jsonify, Response
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application = Flask(__name__)
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clients = []
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cams = []
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with open("./clients.json", 'r', encoding='utf-8') as f:
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array = f.read()
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clients = json.loads(array)
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with open("./cams.json", 'r', encoding='utf-8') as f:
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array = f.read()
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cams = json.loads(array)
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class VideoCamera(object):
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def __init__(self, url):
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self.video = cv2.VideoCapture(url)
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def __del__(self):
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self.video.release()
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def get_frame(self):
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success, image = self.video.read()
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ret, jpeg = cv2.imencode('.jpg', image)
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return jpeg.tobytes()
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def gen(camera):
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"""Video streaming generator function."""
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while True:
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frame = camera.get_frame()
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yield (b'--frame\r\n'
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b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
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@application.route('/client/')
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def client_list():
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json = clients
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return jsonify(json)
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@application.route('/client/<num>/info')
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def client_info(num):
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json = clients[int(num)]
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return jsonify(json)
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@application.route('/cam/')
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def cam_list():
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json = cams
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return jsonify(json)
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@application.route('/cam/<num>/info')
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def cam_info(num):
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json = cams[int(num)]
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return jsonify(json)
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@application.route('/cam/<num>/stream')
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def cam_stream(num):
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return Response(gen(VideoCamera(cams[int(num)]["ip"])),
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mimetype='multipart/x-mixed-replace; boundary=frame')
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if __name__ == '__main__':
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print(clients[0])
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application.run(host='127.0.0.1', port=80, threaded=True)
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[
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{
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"id": 0,
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"label": "cam1",
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"ip": "http://89.29.108.38:80/mjpg/video.mjpg",
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"client_id": 0,
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"status": true,
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"x":0.2,
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"y":0.8,
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"angle": 130
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},
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{
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"id": 1,
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"label": "cam2",
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"ip": "http://89.29.108.38:80/mjpg/video.mjpg",
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"client_id": 1,
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"status": false,
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"x":0.4,
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"y":0.2,
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"angle": 190
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},
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{
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"id": 2,
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"label": "cam3",
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"ip": "http://62.99.80.154:81/mjpg/video.mjpg",
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"client_id": 2,
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"status": true,
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"x":0.9,
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"y":0.1,
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"angle": 270
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}
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]
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[
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{
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"id": 0,
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"label": "name1",
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"ip": "http://127.0.0.2",
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"status": true,
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"x":0.11,
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"y":0.8
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},
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{
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"id": 1,
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"label": "name2",
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"ip": "http://127.0.0.2",
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"status": false,
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"x":0.3,
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"y":0.3
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},
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{
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"id": 2,
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"label": "name3",
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"ip": "http://127.0.0.2",
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"status": true,
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"x":0.5,
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"y":0.6
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}
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]
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requests
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flask
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gunicorn
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opencv-python
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