em_3d

generate_tiles

This method to crop the image into smaller crops.

Parameters:

Name	Type	Description	Default
input_image		numpy array of input image	required
tile_size		The crop size	256
overlap		Overlap between two crops	128

Returns:

Name	Type	Description
tiles		numpy array of image crops

Source code in fastestimator/fastestimator/dataset/data/em_3d.py

def generate_tiles(input_image, tile_size=256, overlap=128):
    """
        This method to crop the image into smaller crops.

        Args:
            input_image: numpy array of input image
            tile_size: The crop size
            overlap: Overlap between two crops

        Returns:
            tiles: numpy array of image crops
    """
    stride = tile_size - overlap
    _, height, width = input_image.shape
    tiles = []
    for i in range(0, height, stride):
        for j in range(0, width, stride):
            if i + tile_size < height and j + tile_size < width:
                tiles.append(input_image[:, i:i + tile_size, j:j + tile_size])
    return np.array(tiles)

get_encode_label

One hot encode the input mask

Parameters:

Name	Type	Description	Default
label_data		Color encoded input label	required

Returns:

Name	Type	Description
encoded_label		one hot encoded label

Source code in fastestimator/fastestimator/dataset/data/em_3d.py

def get_encode_label(label_data):
    """
        One hot encode the input mask

        Args:
            label_data: Color encoded input label

        Returns:
            encoded_label: one hot encoded label
    """
    encoded_label = np.zeros(label_data.shape[:-1], np.uint8)
    for i, value in color_mapping.items():
        encoded_label[np.all(label_data == value, axis=-1)] = i
    return encoded_label

load_data

Load and return the 3d electron microscope platelet dataset.

Sourced from https://bio3d-vision.github.io/platelet-description. Electronic Microscopy 3D cell dataset, consists of 2 3D images, one 800x800x50 and the other 800x800x24. The 800x800x50 is used as training dataset and 800x800x24 is used for validation. If tile is True, then instead of using the entire 800x800 images, the 800x800x50 is tiled into 256x256x24 tiles with an overlap of 128 producing around 75 training images and similarly the 800x800x24 image is tiled to produce 25 validation images.

The method downloads the dataset from google drive and provides train and validation NumpyDataset. While the dataset contains encoded value 0 as background, its omitted in the one hot encoded class label provided by this method. Below indexes represent the labels in channel layer. Index Class name 0 Cell 1 Mitochondria 2 Alpha granule 3 Canalicular vessel 4 Dense granule body 5 Dense granule core

Parameters:

Name	Type	Description	Default
root_dir	Optional[str]	The path to store the downloaded data. When path is not provided, the data will be saved into fastestimator_data under the user's home directory.	None
image_key	str	The key for image.	'image'
label_key	str	The key for label.	'label'
tile	bool	Whether to tile the image into multiple smaller images, or to return the individual volumes directly.	True

Returns:

Type	Description
Tuple[NumpyDataset, NumpyDataset]	(train_data, eval_data)

Source code in fastestimator/fastestimator/dataset/data/em_3d.py

def load_data(root_dir: Optional[str] = None, image_key: str = "image", label_key: str = "label",
              tile: bool = True) -> Tuple[NumpyDataset, NumpyDataset]:
    """Load and return the 3d electron microscope platelet dataset.


    Sourced from https://bio3d-vision.github.io/platelet-description.
    Electronic Microscopy 3D cell dataset, consists of 2 3D images, one 800x800x50 and the other 800x800x24.
    The 800x800x50 is used as training dataset and 800x800x24 is used for validation. If `tile` is True, then instead
    of using the entire 800x800 images, the 800x800x50 is tiled into 256x256x24 tiles with an overlap of 128 producing
    around 75 training images and similarly the 800x800x24 image is tiled to produce 25 validation images.

    The method downloads the dataset from google drive and provides train and validation NumpyDataset.
    While the dataset contains encoded value 0 as background, its omitted in the one hot encoded class label provided
    by this method. Below indexes represent the labels in channel layer.
        Index	Class name
        0		Cell
        1		Mitochondria
        2		Alpha granule
        3		Canalicular vessel
        4		Dense granule body
        5		Dense granule core

    Args:
        root_dir: The path to store the downloaded data. When `path` is not provided,
            the data will be saved into `fastestimator_data` under the user's home directory.
        image_key: The key for image.
        label_key: The key for label.
        tile: Whether to tile the image into multiple smaller images, or to return the individual volumes directly.

    Returns:
        (train_data, eval_data)
    """
    home = str(Path.home())

    if root_dir is None:
        root_dir = os.path.join(home, 'fastestimator_data', 'electronmicroscopy')
    else:
        root_dir = os.path.join(os.path.abspath(root_dir), 'electronmicroscopy')
    os.makedirs(root_dir, exist_ok=True)

    data_compressed_path = os.path.join(root_dir, 'images_and_labels_rgba.zip')
    data_folder_path = os.path.join(root_dir, 'platelet-em/images')

    download_file_from_google_drive('1OMVY1bkfssYdH11xuFdxv7nhqEjzCY7L', data_compressed_path)
    shutil.unpack_archive(data_compressed_path, root_dir)

    train_data = tifffile.imread(os.path.join(root_dir, 'platelet-em/images/50-images.tif'))
    val_data = tifffile.imread(os.path.join(root_dir, 'platelet-em/images/24-images.tif'))
    train_label_data = tifffile.imread(os.path.join(root_dir, 'platelet-em/labels-semantic/50-semantic.tif'))
    val_label_data = tifffile.imread(os.path.join(root_dir, 'platelet-em/labels-semantic/24-semantic.tif'))

    encoded_train_label = get_encode_label(train_label_data)
    encoded_val_label = get_encode_label(val_label_data)

    if not tile:
        train_data = np.expand_dims(np.moveaxis(train_data, 0, -1), 0)
        train_labels = np.expand_dims(np.moveaxis(encoded_train_label, 0, -1), 0)
        train_labels = np.eye(7, dtype=np.float32)[train_labels].take(indices=range(1, 7), axis=-1)

        val_data = np.expand_dims(np.moveaxis(val_data, 0, -1), 0)
        val_labels = np.expand_dims(np.moveaxis(encoded_val_label, 0, -1), 0)
        val_labels = np.eye(7, dtype=np.float32)[val_labels].take(indices=range(1, 7), axis=-1)

        train_data = NumpyDataset({image_key: train_data, label_key: train_labels})
        eval_data = NumpyDataset({image_key: val_data, label_key: val_labels})
        return train_data, eval_data

    train_data_slices = [train_data[0:24], train_data[13:37], train_data[26:50]]
    train_label_slices = [encoded_train_label[0:24], encoded_train_label[13:37], encoded_train_label[26:50]]

    training_data_tiles = np.moveaxis(
        np.concatenate([generate_tiles(slice_data) for slice_data in train_data_slices], axis=0), 1, -1)
    training_label_tiles = np.moveaxis(
        np.concatenate([generate_tiles(slice_data) for slice_data in train_label_slices], axis=0), 1, -1)

    val_data_tiles = np.moveaxis(generate_tiles(val_data), 1, -1)
    val_label_tiles = np.moveaxis(generate_tiles(encoded_val_label), 1, -1)

    val_label_tiles = np.eye(7, dtype=np.float32)[val_label_tiles].take(indices=range(1, 7), axis=-1)
    training_label_tiles = np.eye(7, dtype=np.float32)[training_label_tiles].take(indices=range(1, 7), axis=-1)

    train_data = NumpyDataset({image_key: training_data_tiles, label_key: training_label_tiles})
    eval_data = NumpyDataset({image_key: val_data_tiles, label_key: val_label_tiles})

    return train_data, eval_data