Starwhale Data Types

COCOObjectAnnotation

It provides definitions following the COCO format.

COCOObjectAnnotation(
    id: int,
    image_id: int,
    category_id: int,
    segmentation: Union[t.List, t.Dict],
    area: Union[float, int],
    bbox: Union[BoundingBox, t.List[float]],
    iscrowd: int,
)

Parameter	Description
id	Object id, usually a globally incrementing id
image_id	Image id, usually id of the image
category_id	Category id, usually id of the class in object detection
segmentation	Object contour representation, Polygon (polygon vertices) or RLE format
area	Object area
bbox	Represents bounding box, can be BoundingBox type or list of floats
iscrowd	0 indicates a single object, 1 indicates two unseparated objects

Examples

def _make_coco_annotations(
    self, mask_fpath: Path, image_id: int
) -> t.List[COCOObjectAnnotation]:
    mask_img = PILImage.open(str(mask_fpath))

    mask = np.array(mask_img)
    object_ids = np.unique(mask)[1:]
    binary_mask = mask == object_ids[:, None, None]
    # TODO: tune permute without pytorch
    binary_mask_tensor = torch.as_tensor(binary_mask, dtype=torch.uint8)
    binary_mask_tensor = (
        binary_mask_tensor.permute(0, 2, 1).contiguous().permute(0, 2, 1)
    )

    coco_annotations = []
    for i in range(0, len(object_ids)):
        _pos = np.where(binary_mask[i])
        _xmin, _ymin = float(np.min(_pos[1])), float(np.min(_pos[0]))
        _xmax, _ymax = float(np.max(_pos[1])), float(np.max(_pos[0]))
        _bbox = BoundingBox(
            x=_xmin, y=_ymin, width=_xmax - _xmin, height=_ymax - _ymin
        )

        rle: t.Dict = coco_mask.encode(binary_mask_tensor[i].numpy())  # type: ignore
        rle["counts"] = rle["counts"].decode("utf-8")

        coco_annotations.append(
            COCOObjectAnnotation(
                id=self.object_id,
                image_id=image_id,
                category_id=1,  # PennFudan Dataset only has one class-PASPersonStanding
                segmentation=rle,
                area=_bbox.width * _bbox.height,
                bbox=_bbox,
                iscrowd=0,  # suppose all instances are not crowd
            )
        )
        self.object_id += 1

    return coco_annotations

GrayscaleImage

GrayscaleImage provides a grayscale image type. It is a special case of the Image type, for example the digit images in MNIST.

GrayscaleImage(
    fp: _TArtifactFP = "",
    display_name: str = "",
    shape: Optional[_TShape] = None,
    as_mask: bool = False,
    mask_uri: str = "",
)

Parameter	Description
fp	Image path, IO object, or file content bytes
display_name	Display name shown in Dataset Viewer
shape	Image width and height, default channel is 1
as_mask	Whether used as a mask image
mask_uri	URI of the original image for the mask

Examples

for i in range(0, min(data_number, label_number)):
    _data = data_file.read(image_size)
    _label = struct.unpack(">B", label_file.read(1))[0]
    yield GrayscaleImage(
        _data,
        display_name=f"{i}",
        shape=(height, width, 1),
    ), {"label": _label}

GrayscaleImage Functions

GrayscaleImage.to_types

to_bytes(encoding: str= "utf-8") -> bytes

GrayscaleImage.carry_raw_data

carry_raw_data() -> GrayscaleImage

GrayscaleImage.astype

astype() -> Dict[str, t.Any]

BoundingBox

BoundingBox provides a bounding box type, currently in LTWH format:

• left_x: x-coordinate of left edge
• top_y: y-coordinate of top edge
• width: width of bounding box
• height: height of bounding box

So it represents the bounding box using the coordinates of its left, top, width and height. This is a common format for specifying bounding boxes in computer vision tasks.

BoundingBox(
    x: float,
    y: float,
    width: float,
    height: float
)

Parameter	Description
x	x-coordinate of left edge (left_x)
y	y-coordinate of top edge (top_y)
width	Width of bounding box
height	Height of bounding box

ClassLabel

Describe labels.

ClassLabel(
     names: List[Union[int, float, str]]
)

Image

Image Type.

Image(
    fp: _TArtifactFP = "",
    display_name: str = "",
    shape: Optional[_TShape] = None,
    mime_type: Optional[MIMEType] = None,
    as_mask: bool = False,
    mask_uri: str = "",
)

Parameter	Description
fp	Image path, IO object, or file content bytes
display_name	Display name shown in Dataset Viewer
shape	Image width, height and channels
mime_type	MIMEType supported types
as_mask	Whether used as a mask image
mask_uri	URI of the original image for the mask

The main difference from GrayscaleImage is that Image supports multi-channel RGB images by specifying shape as (W, H, C).

Examples

import io
import typing as t
import pickle
from PIL import Image as PILImage
from starwhale import Image, MIMEType

def _iter_item(paths: t.List[Path]) -> t.Generator[t.Tuple[t.Any, t.Dict], None, None]:
    for path in paths:
        with path.open("rb") as f:
            content = pickle.load(f, encoding="bytes")
            for data, label, filename in zip(
                content[b"data"], content[b"labels"], content[b"filenames"]
            ):
                annotations = {
                    "label": label,
                    "label_display_name": dataset_meta["label_names"][label],
                }

                image_array = data.reshape(3, 32, 32).transpose(1, 2, 0)
                image_bytes = io.BytesIO()
                PILImage.fromarray(image_array).save(image_bytes, format="PNG")

                yield Image(
                    fp=image_bytes.getvalue(),
                    display_name=filename.decode(),
                    shape=image_array.shape,
                    mime_type=MIMEType.PNG,
                ), annotations

Image Functions

Image.to_types

to_bytes(encoding: str= "utf-8") -> bytes

Image.carry_raw_data

carry_raw_data() -> GrayscaleImage

Image.astype

astype() -> Dict[str, t.Any]

Video

Video type.

Video(
    fp: _TArtifactFP = "",
    display_name: str = "",
    mime_type: Optional[MIMEType] = None,
)

Parameter	Description
fp	Video path, IO object, or file content bytes
display_name	Display name shown in Dataset Viewer
mime_type	MIMEType supported types

Examples

import typing as t
from pathlib import Path

from starwhale import Video, MIMEType

root_dir = Path(__file__).parent.parent
dataset_dir = root_dir / "data" / "UCF-101"
test_ds_path = [root_dir / "data" / "test_list.txt"]

def iter_ucf_item() -> t.Generator:
    for path in test_ds_path:
        with path.open() as f:
            for line in f.readlines():
                _, label, video_sub_path = line.split()

                data_path = dataset_dir / video_sub_path
                data = Video(
                    data_path,
                    display_name=video_sub_path,
                    shape=(1,),
                    mime_type=MIMEType.WEBM,
                )

                yield f"{label}_{video_sub_path}", {
                    "video": data,
                    "label": label,
                }

Audio

Audio type.

Audio(
    fp: _TArtifactFP = "",
    display_name: str = "",
    mime_type: Optional[MIMEType] = None,
)

Parameter	Description
fp	Audio path, IO object, or file content bytes
display_name	Display name shown in Dataset Viewer
mime_type	MIMEType supported types

Examples

import typing as t
from starwhale import Audio

def iter_item() -> t.Generator[t.Tuple[t.Any, t.Any], None, None]:
    for path in validation_ds_paths:
        with path.open() as f:
            for item in f.readlines():
                item = item.strip()
                if not item:
                    continue

                data_path = dataset_dir / item
                data = Audio(
                    data_path, display_name=item, shape=(1,), mime_type=MIMEType.WAV
                )

                speaker_id, utterance_num = data_path.stem.split("_nohash_")
                annotations = {
                    "label": data_path.parent.name,
                    "speaker_id": speaker_id,
                    "utterance_num": int(utterance_num),
                }
                yield data, annotations

Audio Functions

Audio.to_types

to_bytes(encoding: str= "utf-8") -> bytes

Audio.carry_raw_data

carry_raw_data() -> Audio

Audio.astype

astype() -> Dict[str, t.Any]

Text

Text type, the default encode type is utf-8.

Text(
    content: str,
    encoding: str = "utf-8",
)

Parameter	Description
content	The text content
encoding	Encoding format of the text

Examples

import typing as t
from pathlib import Path
from starwhale import Text

def iter_item(self) -> t.Generator[t.Tuple[t.Any, t.Any], None, None]:
    root_dir = Path(__file__).parent.parent / "data"

    with (root_dir / "fra-test.txt").open("r") as f:
        for line in f.readlines():
            line = line.strip()
            if not line or line.startswith("CC-BY"):
                continue

            _data, _label, *_ = line.split("\t")
            data = Text(_data, encoding="utf-8")
            annotations = {"label": _label}
            yield data, annotations

Text Functions

to_types

to_bytes(encoding: str= "utf-8") -> bytes

Text.carry_raw_data

carry_raw_data() -> Text

Text.astype

astype() -> Dict[str, t.Any]

Text.to_str

to_str() -> str

Binary

Binary provides a binary data type, stored as bytes.

Binary(
    fp: _TArtifactFP = "",
    mime_type: MIMEType = MIMEType.UNDEFINED,
)

Parameter	Description
fp	Path, IO object, or file content bytes
mime_type	MIMEType supported types

Binary Functions

Binary.to_types

to_bytes(encoding: str= "utf-8") -> bytes

Binary.carry_raw_data

carry_raw_data() -> Binary

Binary.astype

astype() -> Dict[str, t.Any]

Link

Link provides a link type to create remote-link datasets in Starwhale.

Link(
    uri: str,
    auth: Optional[LinkAuth] = DefaultS3LinkAuth,
    offset: int = 0,
    size: int = -1,
    data_type: Optional[BaseArtifact] = None,
)

Parameter	Description
uri	URI of the original data, currently supports localFS and S3 protocols
auth	Link auth information
offset	Data offset relative to file pointed by uri
size	Data size
data_type	Actual data type pointed by the link, currently supports Binary, Image, Text, Audio and Video

Link Functions

Link.astype

astype() -> Dict[str, t.Any]

MIMEType

MIMEType describes the multimedia types supported by Starwhale, implemented using Python Enum. It is used in the mime_type attribute of Image, Video etc to enable better Dataset Viewer support.

class MIMEType(Enum):
    PNG = "image/png"
    JPEG = "image/jpeg"
    WEBP = "image/webp"
    SVG = "image/svg+xml"
    GIF = "image/gif"
    APNG = "image/apng"
    AVIF = "image/avif"
    PPM = "image/x-portable-pixmap"
    MP4 = "video/mp4"
    AVI = "video/avi"
    WEBM = "video/webm"
    WAV = "audio/wav"
    MP3 = "audio/mp3"
    PLAIN = "text/plain"
    CSV = "text/csv"
    HTML = "text/html"
    GRAYSCALE = "x/grayscale"
    UNDEFINED = "x/undefined"

Line

from starwhale import ds, Point, Line

with dataset("collections") as ds:
    line_points = [
        Point(x=0.0, y=1.0),
        Point(x=0.0, y=100.0)
    ]
    ds.append({"line": line_points})
    ds.commit()

Point

from starwhale import ds, Point

with dataset("collections") as ds:
    ds.append(Point(x=0.0, y=100.0))
    ds.commit()

Polygon

from starwhale import ds, Point, Polygon

with dataset("collections") as ds:
    polygon_points = [
        Point(x=0.0, y=1.0),
        Point(x=0.0, y=100.0),
        Point(x=2.0, y=1.0),
        Point(x=2.0, y=100.0),
    ]
    ds.append({"polygon": polygon_points})
    ds.commit()