-
Notifications
You must be signed in to change notification settings - Fork 9
/
SafeDriveVision.py
350 lines (308 loc) · 14.5 KB
/
SafeDriveVision.py
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
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
import argparse
import cv2
import dlib
import numpy as np
import torch
import math
import time
import pygame
from scipy.spatial import distance as dist
from collections import deque
import threading
import yaml
from tqdm import tqdm
import sys
import os
from FaceBoxes import FaceBoxes
from TDDFA import TDDFA
from utils.render import render
from utils.functions import cv_draw_landmark
# modell/experimental.py
#yolov5_path = 'C:\\Users\\o\\Downloads\\3DDFA_V2-master\\3DDFA_V2-master\\yolov5-master\\yolov5-master'
sys.path.append(yolov5_path)
# Importer les modules YOLOv5
'''
from modell.yolo import Detect, Model
from modell.common import DetectMultiBackend
from utill.general import non_max_suppression, scale_coords
from utill.torch_utils import select_device
'''
# Sound configuration
pygame.mixer.init()
current_time = time.time()
sounds = {
'eye': ('./eye.mp3', 5),
'regarder': ('./regarder.mp3', 5),
'reposer': ('./reposer.mp3', 5),
'phone': ('./phone.mp3', 5),
'welcome': ('./s1.mp3', 0),
'welcome_eng': ('./welcomeengl.mp3', 0)
}
last_played = {key: 0 for key in sounds}
def play_sound(sound_key):
audio_file, delay = sounds[sound_key]
current_time = time.time()
if current_time - last_played[sound_key] > delay:
pygame.mixer.music.load(audio_file)
pygame.mixer.music.play()
last_played[sound_key] = current_time
def sound_thread(sound_key):
thread = threading.Thread(target=play_sound, args=(sound_key,))
thread.daemon = True
thread.start()
# Function to get camera matrix
def get_camera_matrix(size):
focal_length = size[1]
center = (size[1] / 2, size[0] / 2)
return np.array([[focal_length, 0, center[0]], [0, focal_length, center[1]], [0, 0, 1]], dtype="double")
# Function to check if matrix is rotation matrix
def is_rotation_matrix(R):
Rt = np.transpose(R)
should_be_identity = np.dot(Rt, R)
I = np.identity(3, dtype=R.dtype)
n = np.linalg.norm(I - should_be_identity)
return n < 1e-6
# Function to get Euler angles from rotation matrix
def rotation_matrix_to_euler_angles(R):
assert (is_rotation_matrix(R))
sy = math.sqrt(R[0, 0] * R[0, 0] + R[1, 0] * R[1, 0])
singular = sy < 1e-6
if not singular:
x = math.atan2(R[2, 1], R[2, 2])
y = math.atan2(-R[2, 0], sy)
z = math.atan2(R[1, 0], R[0, 0])
else:
x = math.atan2(-R[1, 2], R[1, 1])
y = math.atan2(-R[2, 0], sy)
z = 0
return np.array([x, y, z])
# Define model points for head pose estimation
model_points = np.array([
(0.0, 0.0, 0.0), # Tip of the nose
(-30.0, -125.0, -30.0), # Left eye corner
(30.0, -125.0, -30.0), # Right eye corner
(-60.0, -70.0, -60.0), # Left mouth corner
(60.0, -70.0, -60.0), # Right mouth corner
(0.0, -330.0, -65.0) # Chin
])
# Function to get head tilt and coordinates
def get_head_tilt_and_coords(size, image_points, frame_height):
focal_length = size[1]
center = (size[1] / 2, size[0] / 2)
camera_matrix = np.array([[focal_length, 0, center[0]], [0, focal_length, center[1]], [0, 0, 1]], dtype="double")
dist_coeffs = np.zeros((4, 1))
(_, rotation_vector, translation_vector) = cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE)
(nose_end_point2D, _) = cv2.projectPoints(np.array([(0.0, 0.0, 1000.0)]), rotation_vector, translation_vector, camera_matrix, dist_coeffs)
rotation_matrix, _ = cv2.Rodrigues(rotation_vector)
head_tilt_degree = abs([-180] - np.rad2deg([rotation_matrix_to_euler_angles(rotation_matrix)[0]]))
starting_point = (int(image_points[0][0]), int(image_points[0][1]))
ending_point = (int(nose_end_point2D[0][0][0]), int(nose_end_point2D[0][0][1]))
ending_point_alternate = (ending_point[0], frame_height // 2)
return head_tilt_degree, starting_point, ending_point, ending_point_alternate
# Function to compute eye aspect ratio
def eye_aspect_ratio(eye):
A = dist.euclidean(eye[1], eye[5])
B = dist.euclidean(eye[2], eye[4])
C = dist.euclidean(eye[0], eye[3])
return (A + B) / (2.0 * C)
# Function to compute mouth aspect ratio
def mouth_aspect_ratio(mouth):
A = dist.euclidean(mouth[2], mouth[10])
B = dist.euclidean(mouth[4], mouth[8])
C = dist.euclidean(mouth[0], mouth[6])
return (A + B) / (2.0 * C)
# Function to compute nose aspect ratio
def nose_aspect_ratio(nose):
vertical_distance = dist.euclidean(nose[0], nose[2])
depth_distance = dist.euclidean(nose[0], nose[1])
return depth_distance / vertical_distance
# Function to calculate head angle
def calculate_head_angle(eye_left, eye_right, nose_tip):
eye_center = (eye_left + eye_right) / 2
vector_nose = nose_tip - eye_center
vector_horizontal = (eye_right - eye_left)
vector_horizontal[1] = 0
vector_nose_normalized = vector_nose / np.linalg.norm(vector_nose)
vector_horizontal_normalized = vector_horizontal / np.linalg.norm(vector_horizontal)
angle_rad = np.arccos(np.clip(np.dot(vector_nose_normalized, vector_horizontal_normalized), -1.0, 1.0))
angle_deg = np.degrees(angle_rad)
return angle_deg
def webcam_frames():
cap = cv2.VideoCapture(0)
if not cap.isOpened():
raise IOError("Cannot open webcam")
try:
while True:
ret, frame = cap.read()
if not ret:
break
yield frame
finally:
cap.release()
def main(args):
cfg = yaml.load(open(args.config), Loader=yaml.SafeLoader)
if args.onnx:
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
os.environ['OMP_NUM_THREADS'] = '4'
from FaceBoxes.FaceBoxes_ONNX import FaceBoxes_ONNX
from TDDFA_ONNX import TDDFA_ONNX
face_boxes = FaceBoxes_ONNX()
tddfa = TDDFA_ONNX(**cfg)
else:
gpu_mode = args.mode == 'gpu'
tddfa = TDDFA(gpu_mode=gpu_mode, **cfg)
face_boxes = FaceBoxes()
reader = webcam_frames()
n_pre, n_next = args.n_pre, args.n_next
n = n_pre + n_next + 1
queue_ver = deque()
queue_frame = deque()
dense_flag = args.opt in ('2d_dense', '3d')
pre_ver = None
# Load dlib model
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('./shape_predictor_81_face_landmarks (1).dat')
# Load YOLOv5 model
#weights_path = 'C:\\Users\\o\\Downloads\\yolov5-master\\yolov5m.pt'
#device = select_device('cuda' if torch.cuda.is_available() else 'cpu')
#model = DetectMultiBackend(weights_path, device=device, dnn=False)
COUNTER1 = 0
COUNTER2 = 0
COUNTER3 = 0
EYE_AR_CONSEC_FRAMES = 30
repeat_counter = 0
sound_thread('welcome')
sound_thread('welcome_eng')
for i, frame_bgr in tqdm(enumerate(reader)):
if i == 0:
boxes = face_boxes(frame_bgr)
if len(boxes) > 0:
boxes = [boxes[0]]
param_lst, roi_box_lst = tddfa(frame_bgr, boxes)
ver = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)[0]
param_lst, roi_box_lst = tddfa(frame_bgr, [ver], crop_policy='landmark')
ver = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)[0]
for _ in range(n_pre):
queue_ver.append(ver.copy())
queue_frame.append(frame_bgr.copy())
queue_ver.append(ver.copy())
queue_frame.append(frame_bgr.copy())
else:
continue # Skip this frame if no face is detected
else:
param_lst, roi_box_lst = tddfa(frame_bgr, [pre_ver], crop_policy='landmark')
roi_box = roi_box_lst[0]
if abs(roi_box[2] - roi_box[0]) * abs(roi_box[3] - roi_box[1]) < 2020:
boxes = face_boxes(frame_bgr)
if len(boxes) > 0:
boxes = [boxes[0]]
param_lst, roi_box_lst = tddfa(frame_bgr, boxes)
ver = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)[0]
queue_ver.append(ver.copy())
queue_frame.append(frame_bgr.copy())
pre_ver = ver
if len(queue_ver) >= n:
ver_ave = np.mean(queue_ver, axis=0)
if args.opt == '2d_sparse':
img_draw = cv_draw_landmark(queue_frame[n_pre], ver_ave)
elif args.opt == '2d_dense':
img_draw = cv_draw_landmark(queue_frame[n_pre], ver_ave, size=1)
elif args.opt == '3d':
img_draw = render(queue_frame[n_pre], [ver_ave], tddfa.tri, alpha=0.7)
else:
raise ValueError(f'Unknown opt {args.opt}')
gray = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2GRAY)
faces = detector(gray, 0)
if len(faces) == 0:
sound_thread("regarder")
cv2.putText(img_draw, "Regardez devant vous!", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
'''
results = model(frame_bgr)
detections = results.xyxy[0].cpu().numpy()
for detection in detections:
if int(detection[5]) == 67: # Assuming 67 is the class id for cell phones
x1, y1, x2, y2, conf = int(detection[0]), int(detection[1]), int(detection[2]), int(detection[3]), detection[4]
cv2.rectangle(img_draw, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(img_draw, f'Cell Phone {conf:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
COUNTER2 += 1
if COUNTER2 >= 3:
cv2.putText(img_draw, "Rangez votre CELL PHONE!", (x1, y1 - 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
sound_thread("phone")
COUNTER2 = 0
'''
for face in faces:
landmarks = predictor(gray, face)
landmarks_points = np.array([(p.x, p.y) for p in landmarks.parts()])
image_points = np.array([
(landmarks_points[30][0], landmarks_points[30][1]),
(landmarks_points[8][0], landmarks_points[8][1]),
(landmarks_points[36][0], landmarks_points[36][1]),
(landmarks_points[45][0], landmarks_points[45][1]),
(landmarks_points[48][0], landmarks_points[48][1]),
(landmarks_points[54][0], landmarks_points[54][1])
], dtype="double")
left_eye = landmarks_points[36:42]
right_eye = landmarks_points[42:48]
left_eye_hull = cv2.convexHull(left_eye)
right_eye_hull = cv2.convexHull(right_eye)
cv2.drawContours(img_draw, [left_eye_hull], -1, (0, 255, 0), 1)
cv2.drawContours(img_draw, [right_eye_hull], -1, (0, 255, 0), 1)
ear = eye_aspect_ratio(left_eye) + eye_aspect_ratio(right_eye) / 2.0
mouth = landmarks_points[48:68]
mouth_hull = cv2.convexHull(mouth)
cv2.drawContours(img_draw, [mouth_hull], -1, (0, 255, 0), 1)
mar = mouth_aspect_ratio(landmarks_points[48:68])
cv2.putText(img_draw, f'EAR: {ear:.2f}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)
cv2.putText(img_draw, f'MAR: {mar:.2f}', (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)
nose_points = [landmarks_points[27], landmarks_points[30], landmarks_points[33]]
nar = nose_aspect_ratio(nose_points)
cv2.putText(img_draw, f'NAR: {nar:.2f}', (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
eye_left = landmarks_points[36]
eye_right = landmarks_points[45]
nose_tip = landmarks_points[33]
head_angle = calculate_head_angle(np.array(eye_left), np.array(eye_right), np.array(nose_tip))
cv2.putText(img_draw, f'Head Angle: {head_angle:.2f}', (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)
frame_height = img_draw.shape[0]
head_tilt_degree, start_point, end_point, end_point_alt = get_head_tilt_and_coords(img_draw.shape, image_points, frame_height)
cv2.putText(img_draw, f'Head Tilt: {head_tilt_degree[0]:.2f} degrees', (10, 110), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)
cv2.line(img_draw, start_point, end_point, (0, 255, 0), 2)
if 75 > head_angle < 110:
cv2.putText(img_draw, "Regardez devant vous!", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
COUNTER3 += 1
if COUNTER3 >= 6:
sound_thread("regarder")
COUNTER3 = 0
else:
COUNTER3 = 0
if ear < 0.33:
cv2.putText(img_draw, "Eyes Closed!", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
COUNTER1 += 1
if COUNTER1 >= 6:
sound_thread("eye")
COUNTER1 = 0 # Réinitialiser le compteur ici après avoir joué le son
else:
COUNTER1 = 0 # Réinitialiser le compteur seulement si la condition n'est pas remplie
if mar > 0.7:
sound_thread("reposer")
cv2.putText(img_draw, "Yawning!", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
head_tilt_degree, start_point, end_point, end_point_alt = get_head_tilt_and_coords(img_draw.shape, image_points, frame_height)
cv2.line(img_draw, start_point, end_point, (255, 0, 0), 2)
cv2.line(img_draw, start_point, end_point_alt, (0, 0, 255), 2)
cv2.imshow('image', img_draw)
k = cv2.waitKey(20)
if (k & 0xff == ord('q')):
break
queue_ver.popleft()
queue_frame.popleft()
cv2.destroyAllWindows()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='The smooth demo of webcam of 3DDFA_V2')
parser.add_argument('-c', '--config', type=str, default='configs/mb1_120x120.yml')
parser.add_argument('-m', '--mode', default='cpu', type=str, help='gpu or cpu mode')
parser.add_argument('-o', '--opt', type=str, default='2d_sparse', choices=['2d_sparse', '2d_dense', '3d'])
parser.add_argument('-n_pre', default=1, type=int, help='the pre frames of smoothing')
parser.add_argument('-n_next', default=1, type=int, help='the next frames of smoothing')
parser.add_argument('--onnx', action='store_true', default=False)
args = parser.parse_args()
main(args)