started Doku

Dockerfile

@@ -1,7 +0,0 @@
-FROM python
-COPY ./certs /certs
-
-COPY ./ /app
-RUN pip install -r /app/requirements.txt
-
-CMD python /app/run.py

README.md

@@ -1,7 +1,104 @@
-BioSys
+# Gesichtserkennung Demo
+
+## Quick Start
+
+In diesem Abschnitt finden Sie alle nötigen Informationen zum schnellen Deployen der Applikation.
+
+### Anforderungen
+
+ - Python 3.x
+ - pip (für Python 3)
+
+### Deployment
+#### ohne CUDA
+
+    pip install -r requirements.txt
+    python run.py
+
+#### mit CUDA
+
+    original stackOverflow Antwort: 
+    https://stackoverflow.com/questions/49731346/compile-dlib-with-cuda/57592670#57592670
+
+    Install Intel Performance Library: https://software.seek.intel.com/performance-libraries
+    Install cuDNN: https://developer.nvidia.com/cudnn
+
+    pip install cmake
+    git clone https://github.com/davisking/dlib.git
+    cd dlib
+    git submodule init
+    git submodule update
+    mkdir build
+    cd build
+    cmake  -D DLIB_USE_CUDA=1 -D USE_AVX_INSTRUCTIONS=1 ../
+    cmake --build . --config Release
+    cd ../
+    python setup.py install
+    cd ../
+
+    pip install -r requirements.txt
+    python run.py
 
 
+### Konfiguration
 
-# Notes
+Die Konfigurationsdatei finden Sie im Ordner "application".  
+In *./application/config.py* können Sie die relevanten Parameter konfigurieren.
 
-FingerprintID: 0 is the left pinky 9 is the right pinky, people with more than 10 fingers may use another system.
+
+    # Database config
+    databaseFile    = "./test.sqlite"
+    echoDatabase    = False
+
+    # Web Server config
+    debug           = True
+    port            = '5001'
+
+    # Face recognition config
+    model           = "hog" # hog or cnn
+    tolerance       = 0.6
+    useCUDA         = True 
+    videoSource     = "http://192.168.178.56:8080/video" 
+
+Video Source kann eine Videodatei, ein live stream oder eine Webcam sein:  
+https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_gui/py_video_display/py_video_display.html#goal
+
+
+## Komponenten
+
+## API
+Die API ist RESTful, besitzt 2 Endpoints mit dem Präfix */api/v1*.  
+
+Struktur:
+
+    /person/id?useFace
+        POST:
+            legt neues Person Objekt an
+        GET:
+            Parameter:
+                id: URL Parameter, id des gesuchten Objektes, optional
+                useFace: Query Parameter, letztes gespeicherte Bild wird zum identifizieren genutzt , optional
+
+                    Get: wenn nur id gesetzt ist
+                    Verifizierung: wenn id und useFace gesetzt sind 
+                    Identifizierung: wenn useFace gesetzt ist 
+
+        PUT:
+            disabled
+        DELETE:
+            Löscht Objekt per ID
+
+    /camera/type
+        POST:
+            speichert den aktuellen Frame
+        GET:
+            type - still (default): gibt letzten gespeicherten Frame
+            type - stream: gibt einen mjpeg Video Stream aus, welcher als Bild eingebettet werden kann
+            type - processed: mjpeg Video Stream mit umrandeten Gesichtern und eingezeichneten Namen
+        PUT:
+            disabled
+        DELETE:
+            disabled
+
+
+## Datenstruktur  

application/__init__.py

@@ -12,7 +12,6 @@ app = Flask(__name__)
 api = Api(app, version='1', contact={"name":""}, license={"name":"Online Dienst Dokumentation"}, api_spec_url='/api/swagger')
 
 
-
 api.add_resource(endpoints.PersonList,'/api/v1/person/<string:id>', '/api/v1/person/')
 api.add_resource(endpoints.Camera,'/api/v1/camera/<string:type>', "/api/v1/camera/")
 

application/config.py

@@ -1,2 +1,17 @@
 
+# Database config
+databaseFile = "./test.sqlite"
+echoDatabase = False
+
+
+# Web Server config
 debug = True
+port = '5001'
+
+
+# Face recognition config
+model = "hog" # can be hog or cnn
+tolerance = 0.6
+useCUDA = True # is only relevant if dlib installer glitched out during installatzion
+videoSource = "http://192.168.178.56:8080/video" # used by openCV, can use webcams or videostreams
+

application/db.py

@@ -6,8 +6,9 @@ from sqlalchemy.ext.declarative import declarative_base
 import enum
 from flask_sqlalchemy import SQLAlchemy
 from flask import Flask
+import application.config as config
 
-engine = db.create_engine('sqlite:///./test.sqlite', echo=False)
+engine = db.create_engine('sqlite:///' + config.databaseFile, echo=config.echoDatabase)
 connection = engine.connect()
 Base = declarative_base()
 Session = sessionmaker(bind=engine)

application/endpoints.py

@@ -53,8 +53,6 @@ class PersonList(Resource):
             parser.add_argument('useFace', type=bool, required=False)
             args = parser.parse_args()
 
-            
-
             # this indicates that the captured face should be use for identification / validation
             if "useFace" in args and args["useFace"]:
                 Camera().post()
@@ -112,6 +110,7 @@ class PersonList(Resource):
             data = session.query(Person).filter_by(person_id=id).delete()
             session.commit()
             session.close()
+            fr.initFaceRec()
             return flask.make_response(flask.jsonify({'data': data}), 204)
 
         except Exception as e:
@@ -122,7 +121,7 @@ class Camera(Resource):
     # provides the function used for the live streams
     class VideoCamera(object):
         """Video stream object"""
-        url = "http://192.168.178.56:8080/video"
+        url = config.videoSource
         def __init__(self):
             self.video = cv2.VideoCapture(self.url)
 
@@ -143,7 +142,7 @@ class Camera(Resource):
 
     def genProcessed(self, url=None):
         """Video streaming generator function."""
-        url = "http://192.168.178.56:8080/video"
+        url = config.videoSource
         while True:
             frame = fr.identifyFaceVideo(url).tobytes()
             yield (b'--frame\r\n'

application/face_rec.py

@@ -6,18 +6,18 @@ from application.db import Session, Person
 import base64
 import numpy as np
 from io import StringIO
+import application.config as config
 
-TOLERANCE = 0.6
+TOLERANCE = config.tolerance
 FRAME_THICKNESS = 3
 FONT_THICKNESS = 2
-MODEL = "cnn"  # default: 'hog', other one can be 'cnn' - CUDA accelerated (if available) deep-learning pretrained model
+MODEL = config.model  # default: 'hog', other one can be 'cnn' - CUDA accelerated (if available) deep-learning pretrained model
-
 
 known_faces = []
 known_names = []
 
 def initFaceRec():
-    dlib.DLIB_USE_CUDA=True
+    dlib.DLIB_USE_CUDA = config.useCUDA
     print('Loading known faces...', dlib.DLIB_USE_CUDA)
     session = Session()
     for face, name in session.query(Person.face, Person.person_id).all():
@@ -67,7 +67,6 @@ def identifyFaceVideo(url):
     face_locations = {} #face locations to be drawn
 
     for face_encoding, face_location in zip(encodings, locations):
-
         face_locations.update(compareFace(face_encoding, face_location))
 
     session = Session()

dlib

@@ -1 +0,0 @@
-Subproject commit c7062aa3631ba4912255a9bcc0563d404d3bdd5d

dlibinstallation.txt

@@ -1 +0,0 @@
-https://stackoverflow.com/a/57592670/10785079

run.py

@@ -1,8 +1,9 @@
 from application import app
 from application.face_rec import initFaceRec
+import application.config as config
 
 initFaceRec()
-app.run(host="localhost", port='5001', debug=True, threaded=True)
+app.run(host="localhost", port=config.port, debug=config.debug, threaded=True)