def get_face_encodings(path_to_image):
#Equalise image using Histogram equalization in cv2
path_to_image = IE.avg_brightness(path_to_image)
# Load image using scipy
image = scipy.misc.imread(path_to_image)
# Detect faces using the face detector
detected_faces_using_SVM = face_detector(image, 1)
#detected_faces_using_CNN = cnn_face_detector(image, 1)
#gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#detected_faces_using_Haar = haar_detector.detectMultiScale(gray, 1.8, 2)
# Get pose/landmarks of those faces
# Will be used as an input to the function that computes face encodings
# This allows the neural network to be able to produce similar numbers for faces of the same people, regardless of camera angle and/or face positioning in the image
shapes_faces = [
shape_predictor(image, face) for face in detected_faces_using_SVM
]
#shapes_faces = [shape_predictor(image, dlib.rectangle(face.rect.left(),face.rect.top(),face.rect.right(),face.rect.bottom())) for face in detected_faces_using_CNN]
#shapes_faces = [shape_predictor(image,dlib.rectangle(x,y,x+w,y+h)) for (x,y,w,h) in detected_faces_using_Haar]
# For every face detected, compute the face encodings
face_encoding = [
np.array(
face_recognition_model.compute_face_descriptor(
image, face_pose, 1)) for face_pose in shapes_faces
]
return face_encoding
def get_face_encodings(path_to_image):
#Equalise image using Histogram equalization in cv2
path_to_image = IE.avg_brightness(path_to_image)
# Load image using scipy
image = scipy.misc.imread(path_to_image)
return haar_detector(image)
return svm_detector(image)
return cnn_detector(image)
