main.py

import sys

import face_recognition
import os
import cv2
import numpy as np
import math


def face_confidence(face_distance, face_match_threshold=0.6):
    range_val = (1.0 - face_match_threshold)
    linear_val = (1.0 - face_distance) / (range_val * 2.0)

    if face_distance > face_match_threshold:
        return str(round(linear_val * 100, 2)) + '%'
    else:
        value = (linear_val + ((1.0 - linear_val) * math.pow((linear_val - 0.5) * 2, 0.2))) * 100
        return str(round(value * 100, 2)) + '%'


class FaceRecognition:
    face_locations = []
    face_encodings = []
    face_names = []
    known_face_encodings = []
    known_face_names = []
    process_current_frame = True

    def __init__(self):
        self.encode_faces()
        # encode frames

    def encode_faces(self):
        for image in os.listdir('faces'):
            face_image = face_recognition.load_image_file(f'faces/{image}')
            face_encoding = face_recognition.face_encodings(face_image)[0]

            self.known_face_encodings.append(face_encoding)
            self.known_face_names.append(image)

        print(self.known_face_names)

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

        if not video_capture.isOpened():
            sys.exit("video source not found...")

        while True:
            ret, frame = video_capture.read()

            if self.process_current_frame:
                small_frame = cv2.resize(frame, (0,0), fx=0.25, fy=0.25)
                rgb_small_frame = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)

                #find all the faces in the current frame
                self.face_locations = face_recognition.face_locations(rgb_small_frame)
                self.face_encodings = face_recognition.face_encodings(rgb_small_frame,self.face_locations)

                self.face_names = []
                for face_encoding in self.face_encodings:
                    matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding)
                    name = " unknown"
                    confidence = "unknwon"

                    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]
                        confidence = face_confidence(face_distances[best_match_index])

                    self.face_names.append(f'{name} ({confidence}')

            self.process_current_frame = not self.process_current_frame


            #Display annotations
            for (top, right, bottom, left), name in zip(self.face_locations, self.face_names):
                top *= 4
                right *= 4
                left *= 4
                bottom *= 4

                cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
                cv2.rectangle(frame, (left, bottom-35), (right, bottom), (0, 0, 255), -1)
                cv2.putText(frame, name, (left + 6, bottom-6), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 255, 255), 1)

            cv2.imshow('Face Recognition', frame)
            if cv2.waitKey(1) == ord('q'):
                break

        video_capture.release()
        cv2.destroyAllWindows()







if __name__ == '__main__':
    fr = FaceRecognition()
    fr.run_recognition()

'''




import threading
import cv2
from deepface import DeepFace

cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)

cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

counter = 0
face_match = False

reference_img = cv2.imread("reference.jpg")


def check_face(frame):
    global face_match
    try:
        if DeepFace.verify(frame, reference_img.copy())["verified"]:
            face_match = True
        else:
            face_match = False

    except ValueError:
        face_match = False


while True:
    ret, frame = cap.read()

    if ret:
        if counter % 30 == 0:
            try:
                threading.Thread(target=check_face, args=(frame.copy(),)).start()
            except ValueError:
                pass
        counter += 1

        if face_match:
            cv2.putText(frame, "MATCH!", (20, 450), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 3)
        else:
            cv2.putText(frame, "NO MATCH!", (20, 450), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 3)
        cv2.imshow("video", frame)
    key = cv2.waitKey(1)
    if key == ord("q"):
        break

cv2.destroyAllWindows()
'''