Sign Language Interpreter using Deep Learning

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# Model Traiining using CNNimport numpy as npimport pickleimport cv2, osfrom glob import globfrom keras import optimizersfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.layers import Dropoutfrom keras.layers import Flattenfrom keras.layers.convolutional import Conv2Dfrom keras.layers.convolutional import MaxPooling2Dfrom keras.utils import np_utilsfrom keras.callbacks import ModelCheckpointfrom keras import backend as KK.set_image_dim_ordering('tf')os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'def get_image_size():img = cv2.imread('gestures/1/100.jpg', 0)return img.shapedef get_num_of_classes():return len(glob('gestures/*'))image_x, image_y = get_image_size()def cnn_model():num_of_classes = get_num_of_classes()model = Sequential()model.add(Conv2D(16, (2,2), input_shape=(image_x, image_y, 1), activation='relu'))model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))model.add(Conv2D(32, (3,3), activation='relu'))model.add(MaxPooling2D(pool_size=(3, 3), strides=(3, 3), padding='same'))model.add(Conv2D(64, (5,5), activation='relu'))model.add(MaxPooling2D(pool_size=(5, 5), strides=(5, 5), padding='same'))model.add(Flatten())model.add(Dense(128, activation='relu'))model.add(Dropout(0.2))model.add(Dense(num_of_classes, activation='softmax'))sgd = optimizers.SGD(lr=1e-2)model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])filepath="cnn_model_keras2.h5"checkpoint1 = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')callbacks_list = [checkpoint1]#from keras.utils import plot_model#plot_model(model, to_file='model.png', show_shapes=True)return model, callbacks_listdef train():with open("train_images", "rb") as f:train_images = np.array(pickle.load(f))with open("train_labels", "rb") as f:train_labels = np.array(pickle.load(f), dtype=np.int32)with open("val_images", "rb") as f:val_images = np.array(pickle.load(f))with open("val_labels", "rb") as f:val_labels = np.array(pickle.load(f), dtype=np.int32)train_images = np.reshape(train_images, (train_images.shape[0], image_x, image_y, 1))val_images = np.reshape(val_images, (val_images.shape[0], image_x, image_y, 1))train_labels = np_utils.to_categorical(train_labels)val_labels = np_utils.to_categorical(val_labels)print(val_labels.shape)model, callbacks_list = cnn_model()model.summary()model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=15, batch_size=500, callbacks=callbacks_list)scores = model.evaluate(val_images, val_labels, verbose=0)print("CNN Error: %.2f%%" % (100-scores[1]*100))#model.save('cnn_model_keras2.h5')train()K.clear_session();

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