Commented most relevant files for submission

face_recognition/Dockerfile

@@ -1,9 +1,11 @@
 FROM python:3.6
+#copy requirements file and install required libraries. CMake is installed seperately because it is needed for face recognition and the way pip installs the requirements file fails as CMake isnt installed in time
 COPY ./requirements.txt /app/requirements.txt
 WORKDIR /app
 RUN pip install CMake
 RUN pip install -r requirements.txt
 RUN rm requirements.txt
+#make new user so the container duesnt run using root, then copy the api and start it
 RUN useradd -ms /bin/bash admin
 USER admin
 COPY /app  .

face_recognition/app/app.py

@@ -11,6 +11,7 @@ import cv2
 import numpy as np
 import imutils
 app = Flask(__name__)
+#establish connection to minio for this instance of the api
 minioClient = Minio('minio:9000', access_key=os.environ['s3-name'], secret_key=os.environ['s3-password'], secure=False)
  
 @app.route('/')
@@ -27,25 +28,29 @@ def access_minio():
 def new_user_id():
 #save demo object to reserve id in minio
     id = None
+    #five tries to generate unique id
     for limited_try in range(0,5):
         id = str(uuid.uuid4())
         if check_id(id) == False:
             break
     demo_object = ['test']
+    #dump demo object and save it like its a face encoding to reserve id
     with open('/tmp/demo_object.pkl', 'wb') as f:
         pickle.dump(demo_object, f)
     minioClient.fput_object('users', str(id), '/tmp/demo_object.pkl')
     return jsonify({'id':id})
   
 @app.route('/init_face')
-#call like https://face.sguba.de/init_face?id=123&encoded_string=abc
+#call like https://face.sguba.de/init_face?id=123&encoded_string=base64encoded_urlsafe_string
 def new_face():
     id  = request.args.get('id', None)
     img = request.args.get('encoded_string', None)
     temporary_img_path = '/tmp/'+str(id)+'.jpg'
     temporary_pkl_path = '/tmp/'+str(id)+'.pkl'
+    #transcode base64_string to img and generate encoding
     save_img(img, temporary_img_path)
     face_encoding_response = encode_face(temporary_img_path)
+    #check if encoding was successfull, if yes save encoding in minio
     if face_encoding_response['success']==True:
     	with open(temporary_pkl_path, 'wb') as file:
         	pickle.dump(face_encoding_response['encoding'], file)
@@ -59,8 +64,10 @@ def check_face():
     img = request.args.get('encoded_string', None)
     temporary_img_path = '/tmp/'+str(id)+'.jpg'
     temporary_pkl_path = '/tmp/'+str(id)+'.pkl'
+    #transcode base64_string to img and generate encoding
     save_img(img, temporary_img_path)
     face_encoding_response = encode_face(temporary_img_path)
+    #check if encoding was successfull, if yes get initial encoding from minio and compare encodings
     if face_encoding_response['success']==True:
         minioClient.fget_object('users', str(id), temporary_pkl_path)
         with open(temporary_pkl_path, 'rb') as file:
@@ -84,7 +91,9 @@ def check_id(id):
     return known
 
 def save_img(encoded_data, filename):
+    #length of base64 string is always a multiple of 4, if not its padded with =, here we add padding that might have been lost so there are no errors when decoding the img
     encoded_data += "=" * ((4 - len(encoded_data) % 4) % 4)
+    #convert string to binary, decode base64, decode image and rotate it 90 degree because android studio rotates the camera, then save image to temp
     encoded_data = bytes(encoded_data, encoding='utf-8')
     nparr = np.fromstring(base64.urlsafe_b64decode(encoded_data), np.uint8)
     img = cv2.imdecode(nparr, cv2.IMREAD_ANYCOLOR)
@@ -92,18 +101,18 @@ def save_img(encoded_data, filename):
     return cv2.imwrite(filename, img)
   
 def encode_face(path):
+    #create face encoding from data, and check wether it was succesfull. If it was unsuccesfull the encodings list is empty
     success = None
     face = face_recognition.load_image_file(path)
     face_encoding = face_recognition.face_encodings(face)
     if len(face_encoding)==0:
-        success = False
+        response = {'success':False, 'encoding':None}
-        response = {'success':success, 'encoding':None}
     else:
-        success = True
+        response = {'success':True, 'encoding':face_encoding[0]}
-        response = {'success':success, 'encoding':face_encoding[0]}
     return response
           
 def setup():
+    #create minio buckets that are used at startup if they dont exist already
     try:
         minioClient.make_bucket("users")
     except BucketAlreadyOwnedByYou as err:
@@ -113,7 +122,8 @@ def setup():
     except ResponseError as err:
         raise
   
- 
+ #check if the script is run primarily and not imported etc.
 if __name__ == '__main__':
     setup()
+    #start running the api
     app.run(host='0.0.0.0')

face_recognition/docker-compose.yml

@@ -1,5 +1,6 @@
 version: "2"
 
+#customized for the environment. Requires traefik reverse proxy
 networks:
   face-recognition:
     external: false
@@ -20,7 +21,7 @@ services:
     environment:
       - s3-adress=minio
       - s3-name=face-minio
-      - s3-password = testpw1
+      - s3-password=testpw123
     restart: unless-stopped
     networks:
       - face-recognition
@@ -30,7 +31,7 @@ services:
     labels:
       - traefik.enable=true
       - traefik.frontend.rule=Host:face.sguba.de
-      - traefik.port=5555
+      - traefik.port=5000
       - traefik.docker.network=proxy
     depends_on:
       - minio
@@ -40,19 +41,19 @@ services:
     restart: unless-stopped
     container_name: minio
     ports: 
-      - "9999:9000"
+      - "9009:9000"
     environment:
       - MINIO_ACCESS_KEY=face-minio
-      - MINIO_ACCESS_KEY=testpw1
+      - MINIO_SECRET_KEY=testpw123
     networks:
       - face-recognition
       - proxy
     labels:
       - traefik.enable=true
-      - treafik.frontend.rule=face-minio.sguba.de
+      - traefik.frontend.rule=Host:face-minio.sguba.de
-      - traefik.port=9999
+      - traefik.port=9000
-      - traeffik.docker.network=proxy
+      - traefik.docker.network=proxy
     volumes:
       - face_minio_data:/data
       - face_minio_config:/root/.minio
-    command: server /mlflow-minio/storage
+    command: minio server /data