Integrated Facial Software to Database and Main Program.

Facial_Recognition_Enrollment.py

@@ -9,6 +9,7 @@ try:
 except ImportError:
     import _pickle as cPickle  # Python3.
 
+
 def enroll_face_dataset():
     pwd = sys.path[0]
     PREDICTOR_PATH = pwd + '/Facial_models/shape_predictor_68_face_landmarks.dat'
@@ -18,7 +19,6 @@ def enroll_face_dataset():
     shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
     faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
 
-
     faceDatasetFolder = pwd + '/Facial_images/face_rec/train/'
 
     subfolders = []
@@ -27,7 +27,6 @@ def enroll_face_dataset():
         if os.path.isdir(xpath):
             subfolders.append(xpath)
 
-
     nameLabelMap = {}
     labels = []
     imagePaths = []
@@ -43,12 +42,12 @@ def enroll_face_dataset():
     i = 0
     faceDescriptors = None
     for imagePath in imagePaths:
-    #print("processing: {}".format(imagePath))
+        # print("processing: {}".format(imagePath))
         img = cv2.imread(imagePath)
 
         faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
 
-    #print("{} Face(s) found".format(len(faces)))
+        # print("{} Face(s) found".format(len(faces)))
 
         for k, face in enumerate(faces):
 
@@ -58,12 +57,10 @@ def enroll_face_dataset():
 
             faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
 
-    
             faceDescriptorList = [x for x in faceDescriptor]
             faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
             faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
 
-
             if faceDescriptors is None:
                 faceDescriptors = faceDescriptorNdarray
             else:
@@ -73,6 +70,6 @@ def enroll_face_dataset():
             i += 1
 
     # Write descriors and index to disk
-  np.save(pwd+'/Facial_models/descriptors.npy', faceDescriptors)
-  with open(pwd+'/Facial_models/index.pkl', 'wb') as f:
+    np.save(pwd + '/Facial_models/descriptors.npy', faceDescriptors)
+    with open(pwd + '/Facial_models/index.pkl', 'wb') as f:
         cPickle.dump(index, f)

Facial_Recognition_Inference.py

@@ -1,14 +1,14 @@
-import os,sys,time
+import os, sys, time
 import dlib
 import cv2
 import numpy as np
+import DBHelper
 
 try:
     import cPickle  # Python 2
 except ImportError:
     import _pickle as cPickle  # Python 3
 
-
 pwd = sys.path[0]
 PREDICTOR_PATH = pwd + '/Facial_models/shape_predictor_68_face_landmarks.dat'
 FACE_RECOGNITION_MODEL_PATH = pwd + '/Facial_models/dlib_face_recognition_resnet_model_v1.dat'
@@ -20,16 +20,17 @@ faceDetector = dlib.get_frontal_face_detector()
 shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
 faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
 
-index = np.load(pwd+'/Facial_models/index.pkl', allow_pickle=True)
-faceDescriptorsEnrolled = np.load(pwd+'/Facial_models/descriptors.npy')
+index = np.load(pwd + '/Facial_models/index.pkl', allow_pickle=True)
+faceDescriptorsEnrolled = np.load(pwd + '/Facial_models/descriptors.npy')
 
-
-cam = cv2.VideoCapture(1)
+cam = cv2.VideoCapture(0)
 count = 0
 
 x1 = x2 = y1 = y2 = 0
 
-while True:
+cond = False
+
+while DBHelper.get_power() == "on":
     t = time.time()
     success, im = cam.read()
 
@@ -37,7 +38,6 @@ while True:
         print('cannot capture input from camera')
         break
 
-
     if (count % SKIP_FRAMES) == 0:
 
         img = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
@@ -67,22 +67,21 @@ while True:
             argmin = np.argmin(distances)  # index
             minDistance = distances[argmin]  # minimum distance
 
-   
             if minDistance <= THRESHOLD:
-        label = index[argmin]
+                label = DBHelper.get_firstname(index[argmin]) + "_" + DBHelper.get_lastname(index[argmin])
+                cond = True
             else:
                 label = 'unknown'
+                cond = False
 
-      #print("time taken = {:.3f} seconds".format(time.time() - t))
-
+            # print("time taken = {:.3f} seconds".format(time.time() - t))
 
     cv2.rectangle(im, (x1, y1), (x2, y2), (0, 255, 0), 2)
     font_face = cv2.FONT_HERSHEY_SIMPLEX
     font_scale = 0.8
     text_color = (0, 255, 0)
     printLabel = '{} {:0.4f}'.format(label, minDistance)
-  cv2.putText(im, printLabel, (int(x1), int(y1)) , font_face, font_scale, text_color, thickness=2)
-
+    cv2.putText(im, printLabel, (int(x1), int(y1)), font_face, font_scale, text_color, thickness=2)
 
     cv2.imshow('img', im)
 
@@ -91,4 +90,13 @@ while True:
         break
 
     count += 1
+    if cond:
+        DBHelper.set_motor("on")
+        DBHelper.set_alarm("off")
+    elif not cond:
+        DBHelper.set_motor("off")
+        DBHelper.set_alarm("on")
+
+DBHelper.set_alarm("off")
+DBHelper.set_motor("off")
 cv2.destroyAllWindows()

Facial_Recognition_Registration.py

@@ -4,6 +4,7 @@ import math
 import cv2
 import Facial_Recognition_Enrollment
 
+
 def register_your_face(label):
     num_cap = 50
 
@@ -37,6 +38,6 @@ if __name__ == "__main__":
     register_your_face(label)
     print("Data saved! Starting enrollment...")
     print()
-    Facial_Recognition_Enrollment.enroll_face_dataset()  #Need discuss and modify after intergrate with database. 
+    Facial_Recognition_Enrollment.enroll_face_dataset()  # Need discuss and modify after intergrate with database.
     print("Face registration completed!")
     print()

Upload_Face.py

@@ -1,5 +1,6 @@
 import DBHelper
 import Facial_Recognition_Registration
+import Facial_Recognition_Enrollment
 
 
 def upload_your_face(firstname, lastname, email, phone):
@@ -13,6 +14,7 @@ def upload_your_face(firstname, lastname, email, phone):
             count += 1
         DBHelper.upload_data("User_" + str(count), firstname, lastname, email, phone)
         Facial_Recognition_Registration.register_your_face("User_" + str(count))
+        Facial_Recognition_Enrollment.enroll_face_dataset()
         for i in range(20):
             DBHelper.upload_user_photo("User_" + str(count) + "/" + str(i) + ".jpg")
     except:

start_engine.py

@@ -1,6 +1,6 @@
 import os
 import DBHelper
-import Facial_Recognition_Wrapper
+import Facial_Recognition_Inference
 
 
 def start():
@@ -32,8 +32,7 @@ def start():
         print("Success.")
     except:
         print("No Thieves are registered.")
-    Facial_Recognition_Wrapper.training_recognizer("LBPH")
-    Facial_Recognition_Wrapper.face_recognition_inference("LBPH")
+    Facial_Recognition_Inference
 
 
 if __name__ == "__main__":