Sign-Language-Detection/recognise.py at main · Pramod3245/Sign-Language-Detection · GitHub

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
import cv2
import numpy as np
import tensorflow as tf
# from keras.preprocessing.image import load_img
from keras import models
import time as t
# from talk_part import speak_letters


def nothing(x):
    pass

image_x, image_y = 64,64

# from keras.models import load_model
classifier = models.load_model('Trained_model.h5')

def predictor():
       import numpy as np
       from keras.preprocessing import image
       test_image = tf.keras.utils.load_img('1.png', target_size=(64, 64))
       test_image = tf.keras.utils.img_to_array(test_image)
       test_image = np.expand_dims(test_image, axis = 0)
       result = classifier.predict(test_image)
       # t.sleep(5)
       if result[0][0] == 1:
              return 'A'
       elif result[0][1] == 1:
              return 'B'
       elif result[0][2] == 1:
              return 'C'
       elif result[0][3] == 1:
              return 'D'
       elif result[0][4] == 1:
              return 'E'
       elif result[0][5] == 1:
              return 'F'
       elif result[0][6] == 1:
              return 'G'
       elif result[0][7] == 1:
              return 'H'
       elif result[0][8] == 1:
              return 'I'
       elif result[0][9] == 1:
              return 'J'
       elif result[0][10] == 1:
              return 'K'
       elif result[0][11] == 1:
              return 'L'
       elif result[0][12] == 1:
              return 'M'
       elif result[0][13] == 1:
              return 'N'
       elif result[0][14] == 1:
              return 'O'
       elif result[0][15] == 1:
              return 'P'
       elif result[0][16] == 1:
              return 'Q'
       elif result[0][17] == 1:
              return 'R'
       elif result[0][18] == 1:
              return 'S'
       elif result[0][19] == 1:
              return 'T'
       elif result[0][20] == 1:
              return 'U'
       elif result[0][21] == 1:
              return 'V'
       elif result[0][22] == 1:
              return 'W'
       elif result[0][23] == 1:
              return 'X'
       elif result[0][24] == 1:
              return 'Y'
       elif result[0][25] == 1:
              return 'Z'


cam = cv2.VideoCapture(0)

cv2.namedWindow("Trackbars")

cv2.createTrackbar("L - H", "Trackbars", 0, 179, nothing)
cv2.createTrackbar("L - S", "Trackbars", 0, 255, nothing)
cv2.createTrackbar("L - V", "Trackbars", 0, 255, nothing)
cv2.createTrackbar("U - H", "Trackbars", 179, 179, nothing)
cv2.createTrackbar("U - S", "Trackbars", 255, 255, nothing)
cv2.createTrackbar("U - V", "Trackbars", 255, 255, nothing)
cv2.resizeWindow("Trackbars",280,100)

cv2.namedWindow("test")

img_counter = 0

img_text = ''
while True:
    ret, frame = cam.read()
    frame = cv2.flip(frame,1)
    l_h = cv2.getTrackbarPos("L - H", "Trackbars")
    l_s = cv2.getTrackbarPos("L - S", "Trackbars")
    l_v = cv2.getTrackbarPos("L - V", "Trackbars")
    u_h = cv2.getTrackbarPos("U - H", "Trackbars")
    u_s = cv2.getTrackbarPos("U - S", "Trackbars")
    u_v = cv2.getTrackbarPos("U - V", "Trackbars")


    img = cv2.rectangle(frame, (425,100),(625,300), (0,255,0), thickness=2, lineType=8, shift=0)

    lower_blue = np.array([l_h, l_s, l_v])
    upper_blue = np.array([u_h, u_s, u_v])
    imcrop = img[102:298, 427:623]
    hsv = cv2.cvtColor(imcrop, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower_blue, upper_blue)

    cv2.putText(frame, img_text, (30, 400), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (0, 255, 0))
    cv2.imshow("test", frame)
    cv2.imshow("mask", mask)

    #if cv2.waitKey(1) == ord('c'):

    img_name = "1.png"
    save_img = cv2.resize(mask, (image_x, image_y))
    cv2.imwrite(img_name, save_img)
    print("{} written!".format(img_name))
    img_text = predictor()
#     speak_letters(img_text)


#################################################
    import pyttsx3

# initialize the text-to-speech engine
    engine = pyttsx3.init()
    voices = engine.getProperty('voices')
    engine.setProperty('voice', voices[1].id)

    engine.say(predictor())
    engine.runAndWait()
########################################################################

    if cv2.waitKey(1) == 27:
        break


cam.release()
cv2.destroyAllWindows()