Upload dlib DNN model and Enrollment & Inference files

Facial_Recognition_Enrollment.py

@@ -0,0 +1,77 @@
+import os
+import dlib
+import cv2
+import sys
+import numpy as np
+
+try:
+  import cPickle  # Python2.
+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'
+
+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/'
+
+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]
+
+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))
+
+  print("{} Face(s) found".format(len(faces)))
+
+  for k, face in enumerate(faces):
+
+    shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
+
+    landmarks = [(p.x, p.y) for p in shape.parts()]
+
+    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:
+      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

@@ -0,0 +1,99 @@
+import os,sys,time
+import dlib
+import cv2
+import numpy as np
+
+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'
+
+SKIP_FRAMES = 10
+THRESHOLD = 0.4
+
+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')
+
+
+cam = cv2.VideoCapture(1)
+
+count = 0
+
+while True:
+  t = time.time()
+  success, im = cam.read()
+
+  if not success:
+    print('cannot capture input from camera')
+    break
+
+
+  if (count % SKIP_FRAMES) == 0:
+
+    img = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+    faces = faceDetector(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+
+    for face in faces:
+
+      shape = shapePredictor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), face)
+
+      x1 = face.left()
+      y1 = face.top()
+      x2 = face.right()
+      y2 = face.bottom()
+
+      faceDescriptor = faceRecognizer.compute_face_descriptor(img, shape)
+
+      # dlib format to list
+      faceDescriptorList = [m for m in faceDescriptor]
+      # to numpy array
+      faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
+      faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]
+
+      # Euclidean distances
+      distances = np.linalg.norm(faceDescriptorsEnrolled - faceDescriptorNdarray, axis=1)
+      
+      # Calculate minimum distance and index of face
+      argmin = np.argmin(distances)    # index
+      minDistance = distances[argmin]  # minimum distance
+
+   
+      if minDistance <= THRESHOLD:
+        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)
+
+      #center = (int((x1 + x2)/2.0), int((y1 + y2)/2.0))
+      #radius = int((y2-y1)/2.0)
+      #color = (0, 255, 0)
+      #cv2.circle(im, center, radius, color, thickness=1, lineType=8, shift=0)
+
+      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)
+
+  k = cv2.waitKey(1) & 0xff
+  if k == 27:
+    break 
+
+  count += 1
+cv2.destroyAllWindows()

Facial_Recognition_Registration.py

@@ -5,7 +5,7 @@ import cv2
 
 
 def register_your_face(label):
-    num_cap = 20
+    num_cap = 60
 
     path = sys.path[0] + '/Facial_images/face_rec/train/' + label