File size: 4,946 Bytes
29572ad
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import sys
sys.path.append("C:/ProgramData/Anaconda3/envs/facerecog/Lib/site-packages")
# import required modules
from pydub import AudioSegment
from pydub.playback import play

import time, cv2
import mediapipe as mp
import numpy as np


global z
global time1

mp_face_mesh = mp.solutions.face_mesh
face_mesh = mp_face_mesh.FaceMesh(min_detection_confidence=0.5, min_tracking_confidence=0.5)

mp_drawing = mp.solutions.drawing_utils

drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)

left, right, up, down = 0, 0, 0, 0
leftState, rightState, upState, downState = 1, 1, 1, 1
v1, v2, v3, v4, v5 = 0, 0, 0, 0, 0  # Initialize v1, v2, v3, v4, and v5

#mixer.init()
#mixer.init('alsa')
def Faces(frame):
    global z
    global time1
    global left, right, up, down, leftState, rightState, upState, downState, v1, v2, v3, v4, v5

    man = 0
    end = 0
    start = 0
    str5 = 'Time in minutes : '

    a = []
    m = 0
    min = 0
    rik = 0
    rik1 = 0
    z1 = 0
    z2 = 0
    preval = 0
    starttime = time.perf_counter()
    time1 = time.perf_counter() - starttime

    success = True
    image = frame

    image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
    image.flags.writeable = False
    results = face_mesh.process(image)
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    img_h, img_w, img_c = image.shape
    face_3d = []
    face_2d = []

    if results.multi_face_landmarks:
        time1 = time.perf_counter() - starttime

        if int(man) == 1:
            time1 = time1 - (end - m) + 1

        for face_landmarks in results.multi_face_landmarks:
            for idx, lm in enumerate(face_landmarks.landmark):
                if idx == 33 or idx == 263 or idx == 1 or idx == 61 or idx == 291 or idx == 199:
                    if idx == 1:
                        nose_2d = (lm.x * img_w, lm.y * img_h)
                        nose_3d = (lm.x * img_w, lm.y * img_h, lm.z * 3000)

                    x, y = int(lm.x * img_w), int(lm.y * img_h)
                    face_2d.append([x, y])
                    face_3d.append([x, y, lm.z])

            face_2d = np.array(face_2d, dtype=np.float64)
            face_3d = np.array(face_3d, dtype=np.float64)
            focal_length = 1 * img_w
            cam_matrix = np.array([[focal_length, 0, img_h / 2],
                                    [0, focal_length, img_w / 2],
                                    [0, 0, 1]])

            dist_matrix = np.zeros((4, 1), dtype=np.float64)
            success, rot_vec, trans_vec = cv2.solvePnP(face_3d, face_2d, cam_matrix, dist_matrix)
            rmat, jac = cv2.Rodrigues(rot_vec)
            angles, mtxR, mtxQ, Qx, Qy, Qz = cv2.RQDecomp3x3(rmat)

            x = angles[0] * 360
            y = angles[1] * 360
            z = angles[2] * 360

            if y < -10:
                v1 = time.perf_counter()
                if (v1 - v5) > 1:
                    song = AudioSegment.from_mp3('./faceRecognize/x.mpeg')
                    play(song)
                    print('play song from face')
                if leftState:
                    left = left + 1
                    leftState = 0
                    rightState = 1

                text = "Looking Left"
            elif y > 10:
                v2 = time.perf_counter()
                if (v2 - v5) > 20:
                    song = AudioSegment.from_mp3('./faceRecognize/x.mpeg')
                    play(song)
                if rightState:
                    leftState = 1
                    upState = 1
                    downState = 1
                    rightState = 0
                    right = right + 1
                text = "Looking Right"
            else:
                v5 = time.perf_counter()
                leftState = 1
                rightState = 1
                upState = 1
                downState = 1
                text = "Forward"

            nose_3d_projection, jacobian = cv2.projectPoints(nose_3d, rot_vec, trans_vec, cam_matrix, dist_matrix)

            p1 = (int(nose_2d[0]), int(nose_2d[1]))
            p2 = (int(nose_2d[0] + y * 10), int(nose_2d[1] - x * 10))

            cv2.line(image, p1, p2, (255, 0, 0), 3)

            cv2.putText(image, "Left: " + str(np.round(left, 2)), (500, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
            cv2.putText(image, "Right: " + str(np.round(right, 2)), (500, 100), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

        cv2.putText(image, f'time: {int(time1)} sec', (300, 450), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 255, 0), 2)

        mp_drawing.draw_landmarks(
            image=image,
            landmark_list=face_landmarks,
            connections=mp_face_mesh.FACEMESH_TESSELATION,
            landmark_drawing_spec=drawing_spec,
            connection_drawing_spec=drawing_spec)
    else:
        end = time.perf_counter() - starttime
        man = 1
        m = time1

    return image