119 lines
4.2 KiB
Python
119 lines
4.2 KiB
Python
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import face_recognition
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import os
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import cv2
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import numpy as np
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import math
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import sys
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import time
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def face_confidence(face_distance, face_match_threshold=0.6):
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range = (1.0 - face_match_threshold)
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linear_val = (1.0 - face_distance) / (range * 2.0)
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if face_distance < face_match_threshold:
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return str(round(linear_val * 100, 2)) + "%"
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else:
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value = (linear_val + ((1.0 - linear_val) * math.pow((linear_val - 0.5) * 2, 0.2))) * 100
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return str(round(value, 2)) + "%"
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class FaceRecognition:
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face_locations = []
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face_encodings = []
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face_names = []
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known_face_encodings = []
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known_face_names = []
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process_current_frame = True
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def __init__(self):
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self.encode_faces()
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def encode_faces(self):
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for image in os.listdir("faces"):
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face_image = face_recognition.load_image_file(f'faces/{image}')
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face_encoding = face_recognition.face_encodings(face_image)[0]
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self.known_face_encodings.append(face_encoding)
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self.known_face_names.append(image.split(".")[0])
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print(self.known_face_names)
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def run_recognition(self):
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video_capture = cv2.VideoCapture(0)
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# Set the window to normal so it can be resized
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cv2.namedWindow('Face Attendance', cv2.WINDOW_NORMAL)
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# Set the window to full screen
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cv2.setWindowProperty('Face Attendance', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
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if not video_capture.isOpened():
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sys.exit('Video capture is not opened')
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no_face_timer = 0
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no_face_threshold = 2 # Adjust this to your desired timeout in seconds
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while True:
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ret, frame = video_capture.read()
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if self.process_current_frame:
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small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
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rgb_small_frame = small_frame[:, :, ::-1]
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self.face_locations = face_recognition.face_locations(rgb_small_frame)
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if not self.face_locations:
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no_face_timer += 1
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else:
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no_face_timer = 0
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if no_face_timer >= no_face_threshold * 30: # 30 frames per second
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# Close the OpenCV window after the timeout
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break
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self.face_encodings = face_recognition.face_encodings(rgb_small_frame, self.face_locations)
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self.face_names = []
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for face_encoding in self.face_encodings:
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matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding)
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name = "Unknown"
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confidence = 'Unknown'
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face_distances = face_recognition.face_distance(self.known_face_encodings, face_encoding)
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best_match_index = np.argmin(face_distances)
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if matches[best_match_index]:
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confidence = face_confidence(face_distances[best_match_index])
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if float(confidence.split("%")[0]) > 55:
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name = self.known_face_names[best_match_index]
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confidence = face_confidence(face_distances[best_match_index])
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else:
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name = "Unknown"
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confidence = 'Unknown'
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self.face_names.append(f'{name} {confidence}')
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self.process_current_frame = not self.process_current_frame
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for (top, right, bottom, left), name in zip(self.face_locations, self.face_names):
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top *= 4
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right *= 4
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bottom *= 4
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left *= 4
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cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
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cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), -1)
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font = cv2.FONT_HERSHEY_DUPLEX
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cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.8, (255, 255, 255), 1)
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cv2.imshow('Face Attendance', frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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video_capture.release()
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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fr = FaceRecognition()
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fr.run_recognition()
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