verification and identification works

application/endpoints.py

@@ -6,7 +6,7 @@ import json
 import cv2
 import base64
 from application.db import Session, Person, Fingerprint
-
+import application.face_rec as fr
 lastImage = ""
 
 class PersonList(Resource):
@@ -58,17 +58,19 @@ class PersonList(Resource):
                 if id is not None:
                     # validate
                     data = list(session.query(Person).filter_by(person_id=id))[0].serialize()
-                    data["matching_score"] = 0.95
+                    results = fr.identifyFace(lastImage)
+                    data["matching_score"] = 1 - results[int(id)]
                     # return identified person object + matching score
                     return flask.make_response(flask.jsonify({'data': data}), 200)
                 else:
                     # replace by Biometric function
                     # identify
                     # return identified person object + matching score
+                    results = fr.identifyFace(lastImage)
                     data = []
                     for x in list(session.query(Person).all()):
                         ser = x.serialize()
-                        ser["matching_score"] = 0.95
+                        ser["matching_score"] = 1 - results[x.person_id]
                         data.append(ser)
                     
                     return flask.make_response(flask.jsonify({'data': data}), 200)

application/face_rec.py

@@ -1,7 +1,7 @@
 import face_recognition
 import os
 import cv2
-from db import Session, Person
+from application.db import Session, Person
 import base64
 import numpy as np
 from base64 import decodestring
@@ -34,77 +34,51 @@ def readb64(base64_string):
 print('Loading known faces...')
 known_faces = []
 known_names = []
-session = Session()
-
-# We oranize known faces as subfolders of KNOWN_FACES_DIR
-# Each subfolder's name becomes our label (name)
-for face, name in session.query(Person.face, Person.lname).all():
-        # Load an image
-        nparr = np.fromstring(base64.b64decode(face), np.uint8)
-        image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-        
-        # Get 128-dimension face encoding
-        # Always returns a list of found faces, for this purpose we take first face only (assuming one face per image as you can't be twice on one image)
-        encoding = face_recognition.face_encodings(image)[0]
-
-        # Append encodings and name
-        known_faces.append(encoding)
-        known_names.append(name)
 
 
-print('Processing unknown faces...')
-# Now let's loop over a folder of faces we want to label
 
-image = face_recognition.load_image_file('C:/Users/ofjok/Desktop/1.png')
+def initFaceRec():
+    session = Session()
+    # We oranize known faces as subfolders of KNOWN_FACES_DIR
+    # Each subfolder's name becomes our label (name)
+    for face, name in session.query(Person.face, Person.person_id).all():
+            # Load an image
+            nparr = np.fromstring(base64.b64decode(face), np.uint8)
+            image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+            
+            # Get 128-dimension face encoding
+            # Always returns a list of found faces, for this purpose we take first face only (assuming one face per image as you can't be twice on one image)
+            encoding = face_recognition.face_encodings(image)[0]
 
-# This time we first grab face locations - we'll need them to draw boxes
+            # Append encodings and name
-locations = face_recognition.face_locations(image, model=MODEL)
+            known_faces.append(encoding)
+            known_names.append(name)
 
-# Now since we know loctions, we can pass them to face_encodings as second argument
+def identifyFace(imgage):
-# Without that it will search for faces once again slowing down whole process
+    print('Processing unknown faces...')
-encodings = face_recognition.face_encodings(image, locations)
+    image = face_recognition.load_image_file('C:/Users/ofjok/Desktop/1.png')
 
-# We passed our image through face_locations and face_encodings, so we can modify it
+    locations = face_recognition.face_locations(image, model=MODEL)
-# First we need to convert it from RGB to BGR as we are going to work with cv2
+    encodings = face_recognition.face_encodings(image, locations)
-image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
 
-# But this time we assume that there might be more faces in an image - we can find faces of dirrerent people
 
-for face_encoding, face_location in zip(encodings, locations):
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
 
-    # We use compare_faces (but might use face_distance as well)
+    res = {}
-    # Returns array of True/False values in order of passed known_faces
-    results = face_recognition.compare_faces(known_faces, face_encoding, TOLERANCE)
 
-    # Since order is being preserved, we check if any face was found then grab index
+    for face_encoding, face_location in zip(encodings, locations):
-    # then label (name) of first matching known face withing a tolerance
-    match = None
-    if True in results:  # If at least one is true, get a name of first of found labels
-        match = known_names[results.index(True)]
-        print(f' - {match} from {results}')
 
-        # Each location contains positions in order: top, right, bottom, left
+        # We use compare_faces (but might use face_distance as well)
-        top_left = (face_location[3], face_location[0])
+        # Returns array of True/False values in order of passed known_faces
-        bottom_right = (face_location[1], face_location[2])
+        results = face_recognition.face_distance(known_faces, face_encoding)
 
-        # Get color by name using our fancy function
+        # Since order is being preserved, we check if any face was found then grab index
-        color = name_to_color(match)
+        # then label (name) of first matching known face withing a tolerance       
+          # If at least one is true, get a name of first of found labels
+        res = {known_names[i]: results[i] for i in range(0, len(results)) }
+    
+    return res
 
-        # Paint frame
-        cv2.rectangle(image, top_left, bottom_right, color, FRAME_THICKNESS)
 
-        # Now we need smaller, filled grame below for a name
+initFaceRec()
-        # This time we use bottom in both corners - to start from bottom and move 50 pixels down
+identifyFace("")
-        top_left = (face_location[3], face_location[2])
-        bottom_right = (face_location[1], face_location[2] + 22)
-
-        # Paint frame
-        cv2.rectangle(image, top_left, bottom_right, color, cv2.FILLED)
-
-        # Wite a name
-        cv2.putText(image, match, (face_location[3] + 10, face_location[2] + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200, 200, 200), FONT_THICKNESS)
-
-    # Show image
-    cv2.imshow("1", image)
-    cv2.waitKey(0)
-    cv2.destroyWindow("1")