main.py

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

def face_confidence(face_distance, face_match_threshold=0.6):
    # Calculate probability of face match
    range = (1.0 - face_match_threshold)
    linear_val = (1.0 - face_distance) / (range * 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, 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()

    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.split('.')[0])
            
        print(self.known_face_names)

    def run_recognition(self): 
        video_capture = cv2.VideoCapture(0)
        
        if not video_capture.isOpened():
            print("Error: Could not open video device")
            sys.exit()
            
        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 faces 
                self.face_locations = face_recognition.face_locations(cv2.resize(frame, (0, 0), fx=0.25, fy=0.25))
                self.face_encodings = face_recognition.face_encodings(cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB), 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 = "Unknown"
                    
                    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
                bottom *= 4
                left *= 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_SIMPLEX, 0.8, (255, 255, 255), 1)

            cv2.imshow('Face Recognition', frame)
                
            if cv2.waitKey(1) == ord('q'):
                break
                
        print('Exiting...')       
        video_capture.release()
        cv2.destroyAllWindows()


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