Integrated Facial Software to Database and Main Program.

Facial_Recognition_Enrollment.py

@@ -5,74 +5,71 @@ import sys
 import numpy as np
 
 try:
-  import cPickle  # Python2.
+    import cPickle  # Python2.
 except ImportError:
-  import _pickle as cPickle  # Python3.
+    import _pickle as cPickle  # Python3.
+
 
 def enroll_face_dataset():
-  pwd = sys.path[0]
+    pwd = sys.path[0]
-  PREDICTOR_PATH = pwd + '/Facial_models/shape_predictor_68_face_landmarks.dat'
+    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'
+    FACE_RECOGNITION_MODEL_PATH = pwd + '/Facial_models/dlib_face_recognition_resnet_model_v1.dat'
 
-  faceDetector = dlib.get_frontal_face_detector()
+    faceDetector = dlib.get_frontal_face_detector()
-  shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
+    shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
-  faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
+    faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
 
+    faceDatasetFolder = pwd + '/Facial_images/face_rec/train/'
 
-  faceDatasetFolder = pwd + '/Facial_images/face_rec/train/'
+    subfolders = []
+    for x in os.listdir(faceDatasetFolder):
+        xpath = os.path.join(faceDatasetFolder, x)
+        if os.path.isdir(xpath):
+            subfolders.append(xpath)
 
-  subfolders = []
+    nameLabelMap = {}
-  for x in os.listdir(faceDatasetFolder):
+    labels = []
-    xpath = os.path.join(faceDatasetFolder, x)
+    imagePaths = []
-    if os.path.isdir(xpath):
+    for i, subfolder in enumerate(subfolders):
-      subfolders.append(xpath)
+        for x in os.listdir(subfolder):
+            xpath = os.path.join(subfolder, x)
+            if x.endswith('jpg'):
+                imagePaths.append(xpath)
+                labels.append(i)
+                nameLabelMap[xpath] = subfolder.split('/')[-1]
 
+    index = {}
+    i = 0
+    faceDescriptors = None
+    for imagePath in imagePaths:
+        # print("processing: {}".format(imagePath))
+        img = cv2.imread(imagePath)
 
-  nameLabelMap = {}
+        faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-  labels = []
-  imagePaths = []
-  for i, subfolder in enumerate(subfolders):
-    for x in os.listdir(subfolder):
-      xpath = os.path.join(subfolder, x)
-      if x.endswith('jpg'):
-        imagePaths.append(xpath)
-        labels.append(i)
-        nameLabelMap[xpath] = subfolder.split('/')[-1]
 
-  index = {}
+        # print("{} Face(s) found".format(len(faces)))
-  i = 0
-  faceDescriptors = None
-  for imagePath in imagePaths:
-    #print("processing: {}".format(imagePath))
-    img = cv2.imread(imagePath)
 
-    faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+        for k, face in enumerate(faces):
 
-    #print("{} Face(s) found".format(len(faces)))
+            shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
 
-    for k, face in enumerate(faces):
+            landmarks = [(p.x, p.y) for p in shape.parts()]
 
-      shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
+            faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
 
-      landmarks = [(p.x, p.y) for p in shape.parts()]
+            faceDescriptorList = [x for x in faceDescriptor]
+            faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
+            faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
 
-      faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
+            if faceDescriptors is None:
+                faceDescriptors = faceDescriptorNdarray
+            else:
+                faceDescriptors = np.concatenate((faceDescriptors, faceDescriptorNdarray), axis=0)
 
+            index[i] = nameLabelMap[imagePath]
+            i += 1
 
-      faceDescriptorList = [x for x in faceDescriptor]
+    # Write descriors and index to disk
-      faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
+    np.save(pwd + '/Facial_models/descriptors.npy', faceDescriptors)
-      faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
+    with open(pwd + '/Facial_models/index.pkl', 'wb') as f:
-
+        cPickle.dump(index, f)
-
-      if faceDescriptors is None:
-        faceDescriptors = faceDescriptorNdarray
-      else:
-        faceDescriptors = np.concatenate((faceDescriptors, faceDescriptorNdarray), axis=0)
-
-      index[i] = nameLabelMap[imagePath]
-      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:
-    cPickle.dump(index, f)

Facial_Recognition_Inference.py

@@ -1,13 +1,13 @@
-import os,sys,time
+import os, sys, time
 import dlib
 import cv2
 import numpy as np
+import DBHelper
 
 try:
-  import cPickle  # Python 2
+    import cPickle  # Python 2
 except ImportError:
-  import _pickle as cPickle  # Python 3
+    import _pickle as cPickle  # Python 3
-
 
 pwd = sys.path[0]
 PREDICTOR_PATH = pwd + '/Facial_models/shape_predictor_68_face_landmarks.dat'
@@ -20,75 +20,83 @@ 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)
+index = np.load(pwd + '/Facial_models/index.pkl', allow_pickle=True)
-faceDescriptorsEnrolled = np.load(pwd+'/Facial_models/descriptors.npy')
+faceDescriptorsEnrolled = np.load(pwd + '/Facial_models/descriptors.npy')
 
-
+cam = cv2.VideoCapture(0)
-cam = cv2.VideoCapture(1)
 count = 0
 
 x1 = x2 = y1 = y2 = 0
 
-while True:
+cond = False
-  t = time.time()
-  success, im = cam.read()
 
-  if not success:
+while DBHelper.get_power() == "on":
-    print('cannot capture input from camera')
+    t = time.time()
-    break
+    success, im = cam.read()
 
+    if not success:
+        print('cannot capture input from camera')
+        break
 
-  if (count % SKIP_FRAMES) == 0:
+    if (count % SKIP_FRAMES) == 0:
 
-    img = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+        img = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
-    faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+        faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
 
-    for face in faces:
+        for face in faces:
 
-      shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
+            shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
 
-      x1 = face.left()
+            x1 = face.left()
-      y1 = face.top()
+            y1 = face.top()
-      x2 = face.right()
+            x2 = face.right()
-      y2 = face.bottom()
+            y2 = face.bottom()
 
-      faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
+            faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
 
-      # dlib format to list
+            # dlib format to list
-      faceDescriptorList = [m for m in faceDescriptor]
+            faceDescriptorList = [m for m in faceDescriptor]
-      # to numpy array
+            # to numpy array
-      faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
+            faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
-      faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
+            faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
 
-      # Euclidean distances
+            # Euclidean distances
-      distances = np.linalg.norm(faceDescriptorsEnrolled - faceDescriptorNdarray, axis=1)
+            distances = np.linalg.norm(faceDescriptorsEnrolled - faceDescriptorNdarray, axis=1)
 
-      # Calculate minimum distance and index of face
+            # Calculate minimum distance and index of face
-      argmin = np.argmin(distances)    # index
+            argmin = np.argmin(distances)  # index
-      minDistance = distances[argmin]  # minimum distance
+            minDistance = distances[argmin]  # minimum distance
 
+            if minDistance <= THRESHOLD:
+                label = DBHelper.get_firstname(index[argmin]) + "_" + DBHelper.get_lastname(index[argmin])
+                cond = True
+            else:
+                label = 'unknown'
+                cond = False
 
-      if minDistance <= THRESHOLD:
+            # print("time taken = {:.3f} seconds".format(time.time() - t))
-        label = index[argmin]
-      else:
-        label = 'unknown'
 
-      #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.imshow('img', im)
 
-  cv2.rectangle(im, (x1, y1), (x2, y2), (0, 255, 0), 2)
+    k = cv2.waitKey(1) & 0xff
-  font_face = cv2.FONT_HERSHEY_SIMPLEX
+    if k == 27:
-  font_scale = 0.8
+        break
-  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)
 
+    count += 1
+    if cond:
+        DBHelper.set_motor("on")
+        DBHelper.set_alarm("off")
+    elif not cond:
+        DBHelper.set_motor("off")
+        DBHelper.set_alarm("on")
 
-  cv2.imshow('img', im)
+DBHelper.set_alarm("off")
-
+DBHelper.set_motor("off")
-  k = cv2.waitKey(1) & 0xff
-  if k == 27:
-    break 
-
-  count += 1
 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_Inference
-    Facial_Recognition_Wrapper.face_recognition_inference("LBPH")
 
 
 if __name__ == "__main__":