def test_load_directory_data():
invalid_directory_path = 'invalid_directory_path'
valid_dummy_directory = './resources/dummy_data_directory'
empty_dummy_directory = './resources/dummy_empty_data_directory'
channels = 1
# should raise error when receives an invalid directory path
with pytest.raises(NotADirectoryError):
DataLoader(from_csv=False, datapath=invalid_directory_path)
# should raise error when tries to load empty directory
data_loader = DataLoader(from_csv=False, datapath=empty_dummy_directory)
with pytest.raises(AssertionError):
data_loader.get_data()
# should assign an image's parent directory name as its label
data_loader = DataLoader(from_csv=False, datapath=valid_dummy_directory)
images, labels, label_index_map = data_loader.get_data()
label_count = len(label_index_map.keys())
label = [0] * label_count
label[label_index_map['happiness']] = 1
assert label == labels[0]
data_loader = DataLoader(from_csv=False, datapath=valid_dummy_directory)
images, labels, label_index_map = data_loader.get_data()
# should return non-empty image and label arrays when given valid arguments
assert len(images) > 0 and len(labels) > 0
# should return same number of labels and images when given valid arguments
assert len(images) == len(labels)
# should reshape image to contain channel_axis in channel_last format
assert images.shape[-1] == channels
def test_load_csv_data():
invalid_csv_file_path = 'invalid_csv_file_path'
channels = 1
invalid_image_dimensions = (50, 77)
invalid_target_labels = [8, 9, 10]
# should raise error when not given csv column indices for images and labels
with pytest.raises(ValueError):
DataLoader(from_csv=True, target_labels=valid_target_labels, datapath=valid_csv_file_path,
image_dimensions=valid_image_dimensions, csv_label_col=csv_label_col)
# should raise error when given invalid csv file path
with pytest.raises(FileNotFoundError):
DataLoader(from_csv=True, target_labels=valid_target_labels, datapath=invalid_csv_file_path,
image_dimensions=valid_image_dimensions, csv_label_col=csv_label_col, csv_image_col=csv_image_col)
# should raise error when given invalid csv column indices
with pytest.raises(ValueError):
DataLoader(from_csv=True, target_labels=valid_target_labels, datapath=valid_csv_file_path,
image_dimensions=valid_image_dimensions, csv_label_col=csv_label_col, csv_image_col=10)
# should raise error when given empty target_labels list
with pytest.raises(ValueError):
DataLoader(from_csv=True, datapath=valid_csv_file_path, image_dimensions=valid_image_dimensions,
csv_label_col=csv_label_col, csv_image_col=csv_image_col)
# should raise error when not given image dimensions
with pytest.raises(ValueError):
DataLoader(from_csv=True, target_labels=valid_target_labels, datapath=valid_csv_file_path,
csv_label_col=csv_label_col, csv_image_col=csv_image_col)
# should raise error when given invalid image dimensions
with pytest.raises(ValueError):
DataLoader(from_csv=True, target_labels=valid_target_labels, datapath=valid_csv_file_path,
image_dimensions=invalid_image_dimensions, csv_label_col=csv_label_col, csv_image_col=csv_image_col)
# should raise error if no image samples found in csv file
with pytest.raises(AssertionError):
data_loader = DataLoader(from_csv=True, target_labels=invalid_target_labels, datapath=valid_csv_file_path,
image_dimensions=valid_image_dimensions, csv_label_col=csv_label_col,
csv_image_col=csv_image_col)
data_loader.get_data()
data_loader = DataLoader(from_csv=True, target_labels=valid_target_labels, datapath=valid_csv_file_path,
image_dimensions=valid_image_dimensions, csv_label_col=csv_label_col,
csv_image_col=csv_image_col)
images, labels = data_loader.get_data()
# should return non-empty image and label arrays when given valid arguments
assert len(images) > 0 and len(labels) > 0
# should return same number of labels and images when given valid arguments
assert len(images) == len(labels)
# should reshape the images to given valid image_dimensions
assert list(images.shape[1:]) == list(valid_image_dimensions) + [channels]
def test_should_generate_images_based_on_out_channels_parameter():
with pytest.raises(ValueError) as e:
DataLoader(out_channels=0)
assert "Out put channel should be either 3(RGB) or 1(Grey) but got 0" == str(e.value)
# Should generate images with single channel
channels = 1
data_loader = DataLoader(from_csv=True, target_labels=valid_target_labels, datapath=valid_csv_file_path,
image_dimensions=valid_image_dimensions, csv_label_col=csv_label_col,
csv_image_col=csv_image_col, out_channels=channels)
images, labels = data_loader.get_data()
assert list(images.shape[1:]) == list(valid_image_dimensions) + [channels]
# Should generate images with 3 channel
channels = 3
data_loader = DataLoader(from_csv=True, target_labels=valid_target_labels, datapath=valid_csv_file_path,
image_dimensions=valid_image_dimensions, csv_label_col=csv_label_col,
csv_image_col=csv_image_col, out_channels=channels)
images, labels = data_loader.get_data()
assert list(images.shape[1:]) == list(valid_image_dimensions) + [channels]
def test_load_time_series_directory_data():
invalid_directory_path = 'invalid_directory_path'
valid_dummy_directory = './resources/dummy_time_series_data_directory'
empty_dummy_directory = './resources/dummy_empty_data_directory'
valid_time_steps = 4
channels = 1
# should raise error when receives an invalid directory path
with pytest.raises(NotADirectoryError):
DataLoader(from_csv=False, datapath=invalid_directory_path, time_steps=4)
# should raise error when tries to load empty directory
data_loader = DataLoader(from_csv=False, datapath=empty_dummy_directory, time_steps=4)
with pytest.raises(AssertionError):
data_loader.get_data()
# should raise error when given time_step argument that is less than 1
with pytest.raises(ValueError):
DataLoader(from_csv=False, datapath=valid_dummy_directory, time_steps=-4)
# should raise error when given time_step argument that not an integer
with pytest.raises(ValueError):
DataLoader(from_csv=False, datapath=valid_dummy_directory, time_steps=4.7)
# should raise error when tries to load time series sample
# containing a quantity of images less than the time_steps argument
with pytest.raises(ValueError):
data_loader = DataLoader(from_csv=False, datapath=valid_dummy_directory, time_steps=10)
data_loader.get_data()
# should assign an image's parent directory name as its label
data_loader = DataLoader(from_csv=False, datapath=valid_dummy_directory, time_steps=valid_time_steps)
samples, labels, label_index_map = data_loader.get_data()
label_count = len(label_index_map.keys())
label = [0] * label_count
label[label_index_map['happiness']] = 1
assert label == labels[0]
data_loader = DataLoader(from_csv=False, datapath=valid_dummy_directory, time_steps=valid_time_steps)
samples, labels, label_index_map = data_loader.get_data()
# should return non-empty image and label arrays when given valid arguments
assert len(samples) > 0 and len(labels) > 0
# should return same number of labels and images when given valid arguments
assert len(samples) == len(labels)
# should reshape image to contain channel_axis in channel_last format
assert samples.shape[1] == valid_time_steps
# should reshape image to contain channel_axis in channel_last format
assert samples.shape[-1] == channels
from sklearn.model_selection import train_test_split
time_delay = 2
raw_dimensions = (48, 48)
target_dimensions = (64, 64)
channels = 1
verbose = True
using_feature_extraction = True
print('--------------- Convolutional LSTM Model -------------------')
print('Loading data...')
directory_path = "image_data/sample_image_series_directory"
data_loader = DataLoader(from_csv=False,
datapath=directory_path,
time_steps=time_delay)
image_data, labels, emotion_map = data_loader.get_data()
if verbose:
print('raw image data shape: ' + str(image_data.shape))
label_count = len(labels[0])
print('Training net...')
validation_split = 0.15
X_train, X_test, y_train, y_test = train_test_split(image_data,
labels,
test_size=validation_split,
random_state=42,
stratify=labels)
train_gen = DataGenerator(time_delay=time_delay).fit(X_train, y_train)
test_gen = DataGenerator(time_delay=time_delay).fit(X_test, y_test)