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signLanguageRecognition.py
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# @fileName signLanguageRecognition.py
# @author Melih Altun @2023
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import categorical_crossentropy
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Model
from tensorflow.keras.applications import imagenet_utils
from tensorflow.keras.layers import Flatten
from tensorflow.keras.models import load_model
from sklearn.metrics import confusion_matrix
import itertools
import matplotlib.pyplot as plt
import random
import os
import shutil
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print('Num GPUs Available: ', len(physical_devices))
#tf.config.experimental.set_memory_growth(physical_devices[0], True)
os.chdir('./Sign-Language-Digits-Dataset')
if os.path.isdir('./train/0/') is False:
os.mkdir('./train')
os.mkdir('./test')
os.mkdir('./valid')
for i in range(0, 10):
shutil.move(f'{i}', './train')
os.mkdir(f'./valid/{i}')
os.mkdir(f'./test/{i}')
valid_samples = random.sample(os.listdir(f'./train/{i}'), 30)
for j in valid_samples:
shutil.move(f'./train/{i}/{j}', f'./valid/{i}')
test_samples = random.sample(os.listdir(f'./train/{i}'), 5)
for j in test_samples:
shutil.move(f'./train/{i}/{j}', f'./test/{i}')
os.chdir('../')
test_path = ('./Sign-Language-Digits-Dataset/test')
test_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet.preprocess_input).flow_from_directory(directory=test_path, target_size=(224, 224), batch_size=10, shuffle=False)
#delete the model file if you want the model retrained
if os.path.isfile('models/sign_language_model.h5') is False:
train_path = ('./Sign-Language-Digits-Dataset/train')
valid_path = ('./Sign-Language-Digits-Dataset/valid')
train_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet.preprocess_input).flow_from_directory(directory=train_path, target_size=(224, 224), batch_size=10)
valid_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet.preprocess_input).flow_from_directory(directory=valid_path, target_size=(224, 224), batch_size=10)
assert train_batches.n == 1712
assert valid_batches.n == 300
assert test_batches.n == 50
assert test_batches.num_classes == valid_batches.num_classes == test_batches.num_classes == 10
mobile = tf.keras.applications.mobilenet.MobileNet()
mobile.summary()
partialModel1 = mobile.layers[-5].output # was [-6] on tutorial
partialModel2 = Flatten()(partialModel1)
output = Dense(units=10, activation='softmax')(partialModel2)
model = Model(inputs=mobile.input, outputs = output)
for layer in model.layers[:-23]:
layer.trainable = False
model.summary()
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x=train_batches, validation_data=valid_batches, epochs=10) # , verbose=2)
if os.path.isdir('./models/') is False:
os.mkdir('./models')
model.save('./models/sign_language_model.h5')
else:
model = load_model('./models/sign_language_model.h5')
test_labels = test_batches.classes
predictions = model.predict(x=test_batches, verbose=0)
cm = confusion_matrix(y_true=test_labels, y_pred=predictions.argmax(axis=1))
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalizes Confusion Matrix")
else:
print("Confusion Matrix, without normalization")
print(cm)
thresh = cm.max()/2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
test_batches.class_indices
cm_plot_labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
plot_confusion_matrix(cm=cm, classes=cm_plot_labels, title='Confusion Matrix')