【PYTHON OPENCV】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

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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)