def _create_generators(self):
"""
check if we have the img & lbls name. and create in case we need it.
:return:
"""
fn_prefix = './file_names/' + self.dataset_name + '_'
x_trains_path = fn_prefix + 'x_train_fns.npy'
x_validations_path = fn_prefix + 'x_val_fns.npy'
y_trains_path = fn_prefix + 'y_train_fns.npy'
y_validations_path = fn_prefix + 'y_val_fns.npy'
tf_utils = TFRecordUtility(number_of_landmark=self.num_landmark)
if os.path.isfile(x_trains_path) and os.path.isfile(x_validations_path) \
and os.path.isfile(y_trains_path) and os.path.isfile(y_validations_path):
x_train_filenames = load(x_trains_path)
x_val_filenames = load(x_validations_path)
y_train = load(y_trains_path)
y_val = load(y_validations_path)
else:
filenames, labels = tf_utils.create_image_and_labels_name(
dataset_name=self.dataset_name)
filenames_shuffled, y_labels_shuffled = shuffle(filenames, labels)
x_train_filenames, x_val_filenames, y_train, y_val = train_test_split(
filenames_shuffled,
y_labels_shuffled,
test_size=0.05,
random_state=1)
save(x_trains_path, x_train_filenames)
save(x_validations_path, x_val_filenames)
save(y_trains_path, y_train)
save(y_validations_path, y_val)
return x_train_filenames, x_val_filenames, y_train, y_val
def _create_generators(self):
fn_prefix = './file_names/' + self.dataset_name + '_'
# x_trains_path = fn_prefix + 'x_train_fns.npy'
# x_validations_path = fn_prefix + 'x_val_fns.npy'
tf_utils = TFRecordUtility(number_of_landmark=self.num_landmark)
filenames, labels = tf_utils.create_image_and_labels_name(
img_path=self.img_path, annotation_path=self.annotation_path)
filenames_shuffled, y_labels_shuffled = shuffle(filenames, labels)
x_train_filenames, x_val_filenames, y_train, y_val = train_test_split(
filenames_shuffled,
y_labels_shuffled,
test_size=LearningConfig.batch_size,
random_state=1)
# save(x_trains_path, filenames_shuffled)
# save(x_validations_path, y_labels_shuffled)
# save(x_trains_path, x_train_filenames)
# save(x_validations_path, x_val_filenames)
# save(y_trains_path, y_train)
# save(y_validations_path, y_val)
# return filenames_shuffled, y_labels_shuffled
return x_train_filenames, x_val_filenames, y_train, y_val
def _create_generators(self):
tf_utils = TFRecordUtility()
if os.path.isfile('x_train_filenames.npy') and \
os.path.isfile('x_val_filenames.npy') and \
os.path.isfile('y_train_filenames.npy') and \
os.path.isfile('y_val_filenames.npy'):
x_train_filenames = load('x_train_filenames.npy')
x_val_filenames = load('x_val_filenames.npy')
y_train = load('y_train_filenames.npy')
y_val = load('y_val_filenames.npy')
else:
filenames, labels = tf_utils.create_image_and_labels_name()
filenames_shuffled, y_labels_shuffled = shuffle(filenames, labels)
x_train_filenames, x_val_filenames, y_train, y_val = train_test_split(
filenames_shuffled,
y_labels_shuffled,
test_size=0.1,
random_state=1)
save('x_train_filenames.npy', x_train_filenames)
save('x_val_filenames.npy', x_val_filenames)
save('y_train_filenames.npy', y_train)
save('y_val_filenames.npy', y_val)
return x_train_filenames, x_val_filenames, y_train, y_val
def _create_generators(self):
tf_utils = TFRecordUtility()
if self.point_wise:
if True or os.path.isfile('npy/' +'x_train_filenames_pw.npy') and \
os.path.isfile('npy/' +'x_val_filenames_pw.npy') and \
os.path.isfile('npy/' +'y_train_filenames_pw.npy') and \
os.path.isfile('npy/' +'y_val_filenames_pw.npy'):
x_train_filenames = load('npy/x_train_filenames_pw.npy')
x_val_filenames = load('npy/x_val_filenames_pw.npy')
y_train = load('npy/y_train_filenames_pw.npy')
y_val = load('npy/y_val_filenames_pw.npy')
else:
for i in range(68):
filenames, labels = tf_utils.create_fused_images_and_labels_name(
)
filenames_shuffled, y_labels_shuffled = shuffle(
filenames, labels)
x_train_filenames, x_val_filenames, y_train, y_val = train_test_split(
filenames_shuffled,
y_labels_shuffled,
test_size=0.1,
random_state=1)
save('npy/' + 'x_train_filenames_pw_.npy',
x_train_filenames)
save('npy/' + 'x_val_filenames_pw_.npy', x_val_filenames)
save('npy/' + 'y_train_filenames_pw_.npy', y_train)
save('npy/' + 'y_val_filenames_pw_.npy', y_val)
else:
if os.path.isfile('x_train_filenames.npy') and \
os.path.isfile('x_val_filenames.npy') and \
os.path.isfile('y_train_filenames.npy') and \
os.path.isfile('y_val_filenames.npy'):
x_train_filenames = load('x_train_filenames.npy')
x_val_filenames = load('x_val_filenames.npy')
y_train = load('y_train_filenames.npy')
y_val = load('y_val_filenames.npy')
else:
filenames, labels = tf_utils.create_image_and_labels_name(
self.point_wise)
filenames_shuffled, y_labels_shuffled = shuffle(
filenames, labels)
x_train_filenames, x_val_filenames, y_train, y_val = train_test_split(
filenames_shuffled,
y_labels_shuffled,
test_size=0.1,
random_state=1)
save('x_train_filenames.npy', x_train_filenames)
save('x_val_filenames.npy', x_val_filenames)
save('y_train_filenames.npy', y_train)
save('y_val_filenames.npy', y_val)
return x_train_filenames, x_val_filenames, y_train, y_val
def _create_generators(self, img_path=None, annotation_path=None):
# x_trains_path = fn_prefix + 'x_train_fns.npy'
# x_validations_path = fn_prefix + 'x_val_fns.npy'
tf_utils = TFRecordUtility(number_of_landmark=self.num_landmark)
if img_path is None:
filenames, labels = tf_utils.create_image_and_labels_name(
img_path=self.img_path, annotation_path=self.annotation_path)
else:
filenames, labels = tf_utils.create_image_and_labels_name(
img_path=img_path, annotation_path=annotation_path)
filenames_shuffled, y_labels_shuffled = shuffle(filenames, labels)
# x_train_filenames, x_val_filenames, y_train, y_val = train_test_split(
# filenames_shuffled, y_labels_shuffled, test_size=LearningConfig.batch_size, random_state=1)
return filenames_shuffled, y_labels_shuffled
def _create_generators(self):
tf_utils = TFRecordUtility(self.output_len)
filenames, labels = tf_utils.create_image_and_labels_name()
filenames_shuffled, y_labels_shuffled = shuffle(filenames, labels)
x_train_filenames, x_val_filenames, y_train, y_val = train_test_split(
filenames_shuffled,
y_labels_shuffled,
test_size=0.05,
random_state=100,
shuffle=True)
save('x_train_filenames.npy', x_train_filenames)
save('x_val_filenames.npy', x_val_filenames)
save('y_train_filenames.npy', y_train)
save('y_val_filenames.npy', y_val)
return x_train_filenames, x_val_filenames, y_train, y_val
