"""
This script makes used of dlib library to calculate the 128D descriptor to be used for face recognition
and compare the faces using some distance metrics
"""
# Import required packages:
import cv2
import dlib
import numpy as np
# Load shape predictor, face enconder and face detector using dlib library:
pose_predictor_5_point = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")
face_encoder = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")
detector = dlib.get_frontal_face_detector()
def compare_faces_ordered(encodings, face_names, encoding_to_check):
"""Returns the ordered distances and names when comparing a list of face encodings against a candidate to check"""
distances = list(np.linalg.norm(encodings - encoding_to_check, axis=1))
return zip(*sorted(zip(distances, face_names)))
def compare_faces(encodings, encoding_to_check):
"""Returns the distances when comparing a list of face encodings against a candidate to check"""
return list(np.linalg.norm(encodings - encoding_to_check, axis=1))
def face_encodings(face_image, number_of_times_to_upsample=1, num_jitters=1):
"""Returns the 128D descriptor for each face in the image"""
# Detect faces:
face_locations = detector(face_image, number_of_times_to_upsample)
# Detected landmarks:
raw_landmarks = [pose_predictor_5_point(face_image, face_location) for face_location in face_locations]
# Calculate the face encoding for every detected face using the detected landmarks for each one:
return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for
raw_landmark_set in raw_landmarks]
# Load images:
known_image_1 = cv2.imread("jared_1.jpg")
known_image_2 = cv2.imread("jared_2.jpg")
known_image_3 = cv2.imread("jared_3.jpg")
known_image_4 = cv2.imread("obama.jpg")
unknown_image = cv2.imread("jared_4.jpg")
# Convert image from BGR (OpenCV format) to RGB (dlib format):
known_image_1 = known_image_1[:, :, ::-1]
known_image_2 = known_image_2[:, :, ::-1]
known_image_3 = known_image_3[:, :, ::-1]
known_image_4 = known_image_4[:, :, ::-1]
unknown_image = unknown_image[:, :, ::-1]
# Crate names for each loaded image:
names = ["jared_1.jpg", "jared_2.jpg", "jared_3.jpg", "obama.jpg"]
# Create the encodings:
known_image_1_encoding = face_encodings(known_image_1)[0]
known_image_2_encoding = face_encodings(known_image_2)[0]
known_image_3_encoding = face_encodings(known_image_3)[0]
known_image_4_encoding = face_encodings(known_image_4)[0]
known_encodings = [known_image_1_encoding, known_image_2_encoding, known_image_3_encoding, known_image_4_encoding]
unknown_encoding = face_encodings(unknown_image)[0]
# Compare faces:
computed_distances = compare_faces(known_encodings, unknown_encoding)
computed_distances_ordered, ordered_names = compare_faces_ordered(known_encodings, names, unknown_encoding)
# Print obtained results:
print(computed_distances)
print(computed_distances_ordered)
print(ordered_names)
