cnn works

application/endpoints.py

@@ -153,12 +153,10 @@ class Camera(Resource):
         try:
             if type == "stream":
                 return flask.Response(self.gen(self.VideoCamera()), mimetype='multipart/x-mixed-replace; boundary=frame')
-            if type == "processed":
+            elif type == "processed":
                 return flask.Response(self.genProcessed(self.VideoCamera()), mimetype='multipart/x-mixed-replace; boundary=frame')
-    
             elif type == "still":
-                lastImage1 = base64.b64decode(lastImage)
-                return flask.Response(lastImage1,  mimetype='image/png')
+                return flask.Response(base64.b64decode(lastImage),  mimetype='image/png')
 
             return flask.make_response(flask.jsonify({'error': "No idea how you got here"}), 404)
         except Exception as e:

application/face_rec.py

@@ -10,19 +10,15 @@ from io import StringIO
 TOLERANCE = 0.6
 FRAME_THICKNESS = 3
 FONT_THICKNESS = 2
-MODEL = "hog"  # default: 'hog', other one can be 'cnn' - CUDA accelerated (if available) deep-learning pretrained model
+MODEL = "cnn"  # default: 'hog', other one can be 'cnn' - CUDA accelerated (if available) deep-learning pretrained model
 
-def readb64(base64_string):
-    sbuf = StringIO()
-    sbuf.write(base64.b64decode(base64_string))
-    pimg = Image.open(sbuf)
-    return cv2.cvtColor(np.array(pimg), cv2.COLOR_RGB2BGR)
 
-print('Loading known faces...')
 known_faces = []
 known_names = []
 
 def initFaceRec():
+    dlib.DLIB_USE_CUDA=True
+    print('Loading known faces...', dlib.DLIB_USE_CUDA)
     session = Session()
     # We oranize known faces as subfolders of KNOWN_FACES_DIR
     # Each subfolder's name becomes our label (name)
@@ -38,7 +34,7 @@ def initFaceRec():
             # Append encodings and name
             known_faces.append(encoding)
             known_names.append(name)
-
+    print('DONE Loading known faces...')
     session.close()
 
 def identifyFace(image):
@@ -58,8 +54,10 @@ def identifyFace(image):
     return res
 
 def identifyFaceVideo(url):
+    
     video = cv2.VideoCapture(url)
     image = video.read()[1]
+    image = cv2.resize(image,None,fx=0.5,fy=0.5)
     ret, image = cv2.imencode(".png", image)
     
     nparr = np.fromstring(image, np.uint8)
@@ -71,16 +69,19 @@ 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()
     for k, v in face_locations.items():
         # Paint frame
         cv2.rectangle(image, v[0], v[1], [255, 0, 0], FRAME_THICKNESS)
         # Wite a name
-        cv2.putText(image, k, v[0], cv2.FONT_HERSHEY_SIMPLEX, 1.5, [255, 0, 255], FONT_THICKNESS)
+        name = " ".join(session.query(Person.fname, Person.lname).filter(Person.person_id == int(k)).first())
+        cv2.putText(image, name, v[0], cv2.FONT_HERSHEY_SIMPLEX, 1.5, [255, 0, 255], FONT_THICKNESS)
 
         # Show image
+    session.close()
     image = cv2.imencode(".jpg", image)[1]
     return image
 
@@ -90,10 +91,9 @@ def compareFace(face_encoding, face_location):
     face_locations = {}
     match = None
     if True in results:  # If at least one is true, get a name of first of found labels
-        match = "name"
-        print(f' - {match} from {results}')
+        match = known_names[results.index(True)]
         top_left = (face_location[3], face_location[0])
         bottom_right = (face_location[1], face_location[2])
 
-        face_locations[match] = (top_left, bottom_right)
+        face_locations[str(match)] = (top_left, bottom_right)
     return face_locations

dlibinstallation.txt

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

run.py

@@ -1,7 +1,8 @@
 from application import app
 from application.face_rec import initFaceRec
+
 initFaceRec()
-app.run(host="localhost", port='5001', debug=True)
+app.run(host="localhost", port='5001', debug=True, threaded=True)