Integrate face registration and enrollment

Facial_Recognition_Enrollment.py

@@ -9,69 +9,70 @@ try:
 except ImportError:
   import _pickle as cPickle  # Python3.
 
-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'
+def enroll_face_dataset():
+  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'
 
-faceDetector = dlib.get_frontal_face_detector()
-shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
-faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)
+  faceDetector = dlib.get_frontal_face_detector()
+  shapePredictor = dlib.shape_predictor(PREDICTOR_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 = []
+  for x in os.listdir(faceDatasetFolder):
+    xpath = os.path.join(faceDatasetFolder, x)
+    if os.path.isdir(xpath):
+      subfolders.append(xpath)
 
 
-nameLabelMap = {}
-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]
+  nameLabelMap = {}
+  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 = {}
-i = 0
-faceDescriptors = None
-for imagePath in imagePaths:
-  print("processing: {}".format(imagePath))
-  img = cv2.imread(imagePath)
+  index = {}
+  i = 0
+  faceDescriptors = None
+  for imagePath in imagePaths:
+    print("processing: {}".format(imagePath))
+    img = cv2.imread(imagePath)
 
-  faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+    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):
+    for k, face in enumerate(faces):
 
-    shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
+      shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
 
-    landmarks = [(p.x, p.y) for p in shape.parts()]
+      landmarks = [(p.x, p.y) for p in shape.parts()]
 
-    faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
+      faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
 
     
-    faceDescriptorList = [x for x in faceDescriptor]
-    faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
-    faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
+      faceDescriptorList = [x for x in faceDescriptor]
+      faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
+      faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
 
 
-    if faceDescriptors is None:
-      faceDescriptors = faceDescriptorNdarray
-    else:
-      faceDescriptors = np.concatenate((faceDescriptors, faceDescriptorNdarray), axis=0)
+      if faceDescriptors is None:
+        faceDescriptors = faceDescriptorNdarray
+      else:
+        faceDescriptors = np.concatenate((faceDescriptors, faceDescriptorNdarray), axis=0)
 
-    index[i] = nameLabelMap[imagePath]
-    i += 1
+      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)
+  # 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_Registration.py

@@ -2,10 +2,11 @@ import sys
 import os
 import math
 import cv2
+import Facial_Recognition_Enrollment
 
 
 def register_your_face(label):
-    num_cap = 60
+    num_cap = 50
 
     path = sys.path[0] + '/Facial_images/face_rec/train/' + label
 
@@ -14,7 +15,7 @@ def register_your_face(label):
     if not folder:
         os.makedirs(path)
 
-    cap = cv2.VideoCapture(0)
+    cap = cv2.VideoCapture(1)
     c = 0
     while c < num_cap:
         ret, frame = cap.read()
@@ -31,5 +32,6 @@ def register_your_face(label):
 
 
 if __name__ == "__main__":
-    label = input('Enter a label:')
+    label = input('Enter a label: ')
     register_your_face(label)
+    Facial_Recognition_Enrollment.enroll_face_dataset()