File size: 8,874 Bytes
0e2accf 0c36783 0e2accf 0c36783 0e2accf 0c36783 0e2accf 0c36783 0e2accf 0c36783 c59fa34 0c36783 c59fa34 0c36783 0e2accf 0c36783 0e2accf 0c36783 26504b5 0c36783 0e2accf 0c36783 0e2accf 0c36783 26504b5 0c36783 26504b5 0c36783 26504b5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 |
import cv2
import dlib
import numpy as np
import os
import time
import mediapipe as mp
from skimage import feature
class AntiSpoofingSystem:
def __init__(self):
self.detector = dlib.get_frontal_face_detector()
self.anti_spoofing_completed = False
self.blink_count = 0
self.image_captured = False
self.captured_image = None
self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
self.mp_hands = mp.solutions.hands
self.hands = self.mp_hands.Hands(static_image_mode=False, max_num_hands=1, min_detection_confidence=0.7)
self.cap = cv2.VideoCapture(0)
self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
self.save_directory = "Person"
if not os.path.exists(self.save_directory):
os.makedirs(self.save_directory)
self.net_smartphone = cv2.dnn.readNet('yolov4.weights', 'Pretrained_yolov4 (1).cfg')
with open('PreTrained_coco.names', 'r') as f:
self.classes_smartphone = f.read().strip().split('\n')
self.EAR_THRESHOLD = 0.25
self.BLINK_CONSEC_FRAMES = 4
self.left_eye_state = False
self.right_eye_state = False
self.left_blink_counter = 0
self.right_blink_counter = 0
self.smartphone_detected = False
self.smartphone_detection_frame_interval = 30
self.frame_count = 0
# New attributes for student data
self.student_id = None
self.student_name = None
def calculate_ear(self, eye):
A = np.linalg.norm(eye[1] - eye[5])
B = np.linalg.norm(eye[2] - eye[4])
C = np.linalg.norm(eye[0] - eye[3])
return (A + B) / (2.0 * C)
def analyze_texture(self, face_region):
gray_face = cv2.cvtColor(face_region, cv2.COLOR_BGR2GRAY)
lbp = feature.local_binary_pattern(gray_face, P=8, R=1, method="uniform")
lbp_hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, 58), range=(0, 58))
lbp_hist = lbp_hist.astype("float")
lbp_hist /= (lbp_hist.sum() + 1e-5)
return np.sum(lbp_hist[:10]) > 0.3
def detect_hand_gesture(self, frame):
results = self.hands.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
return results.multi_hand_landmarks is not None
def detect_smartphone(self, frame):
if self.frame_count % self.smartphone_detection_frame_interval == 0:
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
self.net_smartphone.setInput(blob)
output_layers_names = self.net_smartphone.getUnconnectedOutLayersNames()
detections = self.net_smartphone.forward(output_layers_names)
for detection in detections:
for obj in detection:
scores = obj[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5 and self.classes_smartphone[class_id] == 'cell phone':
center_x = int(obj[0] * frame.shape[1])
center_y = int(obj[1] * frame.shape[0])
width = int(obj[2] * frame.shape[1])
height = int(obj[3] * frame.shape[0])
left = int(center_x - width / 2)
top = int(center_y - height / 2)
cv2.rectangle(frame, (left, top), (left + width, top + height), (0, 0, 255), 2)
cv2.putText(frame, 'Smartphone Detected', (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
self.smartphone_detected = True
self.left_blink_counter = 0
self.right_blink_counter = 0
return
self.frame_count += 1
self.smartphone_detected = False
def access_verified_image(self):
ret, frame = self.cap.read()
if not ret:
return None
# Perform anti-spoofing checks
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = self.detector(gray)
# Check if a face is detected
if len(faces) == 0:
return None
# Assume the first detected face is the subject
face = faces[0]
landmarks = self.predictor(gray, face)
# Check for blink detection (assuming you have this method correctly implemented)
leftEye = np.array([(landmarks.part(n).x, landmarks.part(n).y) for n in range(36, 42)])
rightEye = np.array([(landmarks.part(n).x, landmarks.part(n).y) for n in range(42, 48)])
ear_left = self.calculate_ear(leftEye)
ear_right = self.calculate_ear(rightEye)
if not self.detect_blink(ear_left, ear_right):
return None
# Check for hand gesture (assuming you have this method correctly implemented)
if not self.detect_hand_gesture(frame):
return None
# Check if a smartphone is detected
self.detect_smartphone(frame)
if self.smartphone_detected:
return None
# Check texture for liveness (assuming you have this method correctly implemented)
(x, y, w, h) = (face.left(), face.top(), face.width(), face.height())
expanded_region = frame[max(y - h // 2, 0):min(y + 3 * h // 2, frame.shape[0]),
max(x - w // 2, 0):min(x + 3 * w // 2, frame.shape[1])]
if not self.analyze_texture(expanded_region):
return None
return frame
def detect_blink(self, left_ear, right_ear):
if self.smartphone_detected:
self.left_eye_state = False
self.right_eye_state = False
self.left_blink_counter = 0
self.right_blink_counter = 0
return False
if left_ear < self.EAR_THRESHOLD:
if not self.left_eye_state:
self.left_eye_state = True
else:
if self.left_eye_state:
self.left_eye_state = False
self.left_blink_counter += 1
if right_ear < self.EAR_THRESHOLD:
if not self.right_eye_state:
self.right_eye_state = True
else:
if self.right_eye_state:
self.right_eye_state = False
self.right_blink_counter += 1
if self.left_blink_counter > 0 and self.right_blink_counter > 0:
self.left_blink_counter = 0
self.right_blink_counter = 0
return True
else:
return False
def run(self):
ret, frame = self.cap.read()
if not ret:
return None
# Detect smartphone in the frame
self.detect_smartphone(frame)
if self.smartphone_detected:
cv2.putText(frame, "Mobile phone detected, can't record attendance", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
else:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = self.detector(gray)
for face in faces:
landmarks = self.predictor(gray, face)
leftEye = np.array([(landmarks.part(n).x, landmarks.part(n).y) for n in range(36, 42)])
rightEye = np.array([(landmarks.part(n).x, landmarks.part(n).y) for n in range(42, 48)])
ear_left = self.calculate_ear(leftEye)
ear_right = self.calculate_ear(rightEye)
if self.detect_blink(ear_left, ear_right):
self.blink_count += 1
cv2.putText(frame, f"Blink Count: {self.blink_count}", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
# Check if conditions for image capture are met
if self.blink_count >= 5 and not self.image_captured:
# Capture the image and reset blink count
self.save_image(frame)
self.blink_count = 0
self.image_captured = True
return frame
def save_image(self, frame):
# Implement logic to save the frame as an image
timestamp = int(time.time())
image_name = f"captured_{timestamp}.png"
cv2.imwrite(os.path.join(self.save_directory, image_name), frame)
self.captured_image = frame
print(f"Image captured and saved as {image_name}")
def get_captured_image(self):
# Return the captured image with preprocessing applied (if necessary)
captured_frame = self.captured_image
if captured_frame is not None:
return captured_frame
return None
if __name__ == "__main__":
anti_spoofing_system = AntiSpoofingSystem()
anti_spoofing_system.run()
|