init.py

import cv2
import face_recognition
import glob
import os
import numpy as np


class FaceRecognition:
    def __init__(self):
        self.known_faces = []
        self.known_face_encodings = []
        self.known_face_names = []

        self.bootstrap()

    @staticmethod
    def get_known_faces():
        known_faces = dict()
        images_list = glob.glob('./faces/*')

        for image_path in images_list:
            face_name = os.path.basename(os.path.splitext(image_path)[0])

            image = face_recognition.load_image_file(image_path)

            known_faces[face_name] = face_recognition.face_encodings(image)[0]

        return known_faces

    def load_faces_library(self):
        known_faces = self.get_known_faces()
        self.known_face_encodings = list(known_faces.values())
        self.known_face_names = list(known_faces.keys())
        return

    def process_faces_encoded(self, face_encodings):
        """
        Processes the faces encoded in the current frame, comparing them with the known faces

        :param face_encodings:
        :return:
        """

        face_names = []
        for face_encoding in face_encodings:
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding)
            name = "Unknown"

            # # If a match was found in known_face_encodings, just use the first one.
            # if True in matches:
            #     first_match_index = matches.index(True)
            #     name = known_face_names[first_match_index]

            # Or instead, use the known face with the smallest distance to the new face
            face_distances = face_recognition.face_distance(self.known_face_encodings, face_encoding)
            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = self.known_face_names[best_match_index]

            face_names.append(name)

        return face_names

    def display_names(self, face_locations, face_names, frame):
        for (top, right, bottom, left), name in zip(face_locations, face_names):
            # Scale back up face locations since the frame we detected in was scaled to 1/4 size
            top *= 4
            right *= 4
            bottom *= 4
            left *= 4

            # Draw a box around the face
            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

            # Draw a label with a name below the face
            cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
        return

    def bootstrap(self):
        video_capture = cv2.VideoCapture(0)

        self.load_faces_library()

        # Initialize some variables
        process_this_frame = True

        while True:
            # Grab a single frame of video
            ret, frame = video_capture.read()

            # Resize frame of video to 1/4 size for faster face recognition processing
            small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

            # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
            rgb_small_frame = small_frame[:, :, ::-1]

            # Only process every other frame of video to save time
            if process_this_frame:
                # Find all the faces and face encodings in the current frame of video
                face_locations = face_recognition.face_locations(rgb_small_frame)
                face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

                face_names = self.process_faces_encoded(face_encodings)

            process_this_frame = not process_this_frame

            # Display the results
            self.display_names(face_locations, face_names, frame)

            # Display the resulting image
            cv2.imshow('Video', frame)

            # Hit 'q' on the keyboard to quit!
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break

        # Release handle to the webcam
        video_capture.release()
        cv2.destroyAllWindows()
    pass


FaceRecognition()