Sign languange recognition project

# Load our Libraries

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

# Get our Training and Test Data

train = pd.read_csv('train.csv')

test = pd.read_csv('test.csv')

# Inspect our training data

train.head()

# Get our Training labels

labels= train['label'].values

# plot the quantities in each class

plt.figure(figsize =(18,8))

sns.countplot(x=labels)

# Drop Training Labels from our Training data so we can seperate it

train.drop('label',axis=1,inplace=True)

# Extract the image data from each row in our csv

images = train.values

images= np.array([np.reshape(i, (28, 28))for i in images])

images= np.array([i.flatten() for i in images])

# Extract the image data from each rows in our csv

from sklearn.preprocessing import LabelBinarizer

label_binrizer = LabelBinarizer()

labels = label_binrizer.fit_transform(labels)

# view our labels

labels

# Inspect an image

index = 2

print(labels[index])

plt.imshow(images[index].reshape(28,28))

# Use OpenCv to view 10 random images from our training data

import cv2

import numpy as np

for i in range(0,10):

rand = np.random.randint(0, len(images))

input_im = images[rand]

sample = input_im.reshape(28,28).astype(np.uint8)

sample = cv2.resize(sample, None, fx=10, fy=10, interpolation = cv2.INTER_CUBIC)

cv2.imshow("sample image", sample)

cv2.waitKey(0)

cv2.destroyAllWindows()

# split our data into x_train, x_test, y_train and y_test

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(images, labels, test_size = 0.3, random_state = 101)

# start Loading our tensorflow modules and define our batch size etc

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout

batch_size= 128

num_classes= 24

epochs= 10

from tensorflow.keras.layers import Conv2D, MaxPooling2D

from tensorflow.keras import backend as K

from tensorflow.keras.optimizers import Adam

from keras.models import Sequential

from keras.layers import Dense, Activation, Flatten, Dropout

model = Sequential()

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

model.add(MaxPooling2D(pool_size= (2, 2)))

model.add(Conv2D(64, kernel_size=(3, 3), activation= 'relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, kernel_size=(3, 3), activation= 'relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

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

model.add(Dropout(0.20))

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

# Scale our images

x_train = x_train / 255

x_test = x_test / 255

#Reshape them into the size required by Tensorflow and Keras

x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)

x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)

plt.imshow(x_train[0].reshape(28,28))

# compile our Model

model.compile(loss = 'categorical_crossentropy',

optimizer = Adam(),

metrics=['accuracy'])

print(model.summary())

# Train our Model

history = model.fit(x_train, y_train, validation_data= (x_test, y_test), epochs=epochs, batch_size=batch_size)

# View our Training history graphically

plt.plot(history.history['accuracy'])

plt.plot(history.history['val_accuracy'])

plt.title("Accuracy")

plt.xlabel('epoch')

plt.ylabel('accuracy')

plt.legend(['train', 'test'])

plt.show()

# Save our Model

model.save("sign_mnist_cnn_50_epochs.hs")

print("Model Saved")

# Reshape our test data so that we can evaluvate it's performance on unseen data

test_labels= test['label']

test.drop('label', axis= 1, inplace= True)

test_images= test.values

test_images= np.array([np.reshape(i, (28, 28)) for i in test_images])

test_images= np.array([i.flatten()for i in test_images])

test_labels= label_binrizer.fit_transform(test_labels)

test_images= test_images.reshape(test_images.shape[0], 28, 28, 1)

test_images.shape

y_pred= model.predict(test_images)

# Get our accuracy score

from sklearn.metrics import accuracy_score

accuracy_score(test_labels, y_pred.round())

#Create function to match label to letter

def getLetter(result):

classLabels= {0: 'A',

1: 'B',

2: 'C',

3: 'D',

4: 'E',

5: 'F',

6: 'G',

7: 'H',

8: 'I',

9: 'K',

10: 'L',

11: 'M',

12: 'N',

13: 'O',

14: 'P',

15: 'Q',

16: 'R',

17: 'S',

18: 'T',

19: 'U',

20: 'V',

21: 'W',

22: 'X',

23: 'Y'}

try:

res= int(result)

return classLabels[res]

except:

return "Error"