ASL.AI/data_training.py at main · codedbyishika/ASL.AI · 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
import pandas as pd
import numpy as np
import cv2
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout


train = pd.read_csv('sign_mnist_train.csv')
test = pd.read_csv('sign_mnist_test.csv')

x = train.drop('label',axis=1).values
y=train['label'].values

x=x.reshape(-1,28,28,1)

x=x/255.0
y = to_categorical(y, num_classes=26)

X_train, X_temp, y_train, y_temp = train_test_split(x, y, test_size=0.3, random_state=42, stratify=y)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)

model = Sequential()

model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(2, 2))

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(2, 2))

model.add(Conv2D(64, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(2, 2))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(26, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

batch_size = 32
epochs = 10

history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_val, y_val))

test_loss, test_accuracy = model.evaluate(X_test, y_test)
print(f'Test accuracy: {test_accuracy * 100}%')

X_test = test.drop('label', axis=1).values
y_test = test['label'].values

X_test = X_test.reshape(-1, 28, 28, 1)
X_test = X_test / 255.0

y_test = to_categorical(y_test, num_classes=26)

test_loss, test_accuracy = model.evaluate(X_test, y_test)
print(f'Test accuracy: {test_accuracy * 100}%')

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray = cv2.resize(gray, (28, 28))
    gray = gray / 255.0
    gray = gray.reshape(1, 28, 28, 1)

    prediction = model.predict(gray)
    predicted_label = np.argmax(prediction)

    cv2.putText(frame, f'Predicted: {chr(predicted_label + 65)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

    cv2.imshow('Sign Language Recognition', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break


cap.release()
cv2.destroyAllWindows()