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版本:0.6.11

Starwhale 数据类型 SDK

COCOObjectAnnotation

提供COCO类型的定义。

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,
)
参数说明
idobject id,一般为全局object的递增id
image_idimage id,一般为图片id
category_idcategory id,一般为目标检测中类别的id
segmentation物体轮廓表示,Polygon(多边形的点)或RLE格式
areaobject面积
bbox表示bounding box,可以为BoundingBox类型或float的列表
iscrowd0表示是一个单独的object,1表示两个没有分开的object

使用示例

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

提供灰度图类型,比如MNIST中数字手写体图片,是 Image 类型的一个特例。

GrayscaleImage(
fp: _TArtifactFP = "",
display_name: str = "",
shape: Optional[_TShape] = None,
as_mask: bool = False,
mask_uri: str = "",
)
参数说明
fp图片的路径、IO对象或文件内容的bytes
display_nameDataset Viewer上展示的名字
shape图片的Width和Height,channel默认为1
as_mask是否作为Mask图片
mask_uriMask原图的URI

使用示例

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函数

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

提供边界框类型,目前为 LTWH 格式,即 left_x, top_y, widthheight

BoundingBox(
x: float,
y: float,
width: float,
height: float
)
参数说明
xleft_x的坐标
ytop_y的坐标
width图片的宽度
height图片的高度

ClassLabel

描述label的数量和类型。

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

Image

图片类型。

Image(
fp: _TArtifactFP = "",
display_name: str = "",
shape: Optional[_TShape] = None,
mime_type: Optional[MIMEType] = None,
as_mask: bool = False,
mask_uri: str = "",
)
参数说明
fp图片的路径、IO对象、numpy对象、pillow image对象或文件内容的bytes
display_nameDataset Viewer上展示的名字
shape图片的Width、Height和channel
mime_typeMIMEType支持的类型
as_mask是否作为Mask图片
mask_uriMask原图的URI

使用示例

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函数

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

视频类型。

Video(
fp: _TArtifactFP = "",
display_name: str = "",
mime_type: Optional[MIMEType] = None,
)
参数说明
fp视频的路径、IO对象或文件内容的bytes
display_nameDataset Viewer上展示的名字
mime_typeMIMEType支持的类型

使用示例

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

音频类型。

Audio(
fp: _TArtifactFP = "",
display_name: str = "",
mime_type: Optional[MIMEType] = None,
)
参数说明
fp音频文件的路径、IO对象或文件内容的bytes
display_nameDataset Viewer上展示的名字
mime_typeMIMEType支持的类型

使用示例

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函数

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

文本类型,默认为 utf-8 格式。

Text(
content: str,
encoding: str = "utf-8",
)
参数说明
contenttext内容
encodingtext的编码格式

使用示例

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函数

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

二进制类型,用bytes存储。

Binary(
fp: _TArtifactFP = "",
mime_type: MIMEType = MIMEType.UNDEFINED,
)
参数说明
fp路径、IO对象或文件内容的bytes
mime_typeMIMEType支持的类型

Binary函数

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类型,用来制作 remote-link 类型的数据集。

Link(
uri: str,
auth: Optional[LinkAuth] = DefaultS3LinkAuth,
offset: int = 0,
size: int = -1,
data_type: Optional[BaseArtifact] = None,
)
参数说明
uri原始数据的uri地址,目前支持localFS和S3两种协议
authLink Auth信息
offset数据相对uri指向的文件偏移量
size数据大小
data_typeLink指向的实际数据类型,目前支持 Binary, Image, Text, AudioVideo 类型

Link函数

Link.astype

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

MIMEType

描述Starwhale支持的多媒体类型,用Python Enum类型实现,用在 ImageVideo 等类型的mime_type 属性上,能更好的进行Dataset Viewer。

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

描述直线。

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

描述点。

from starwhale import ds, Point

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

Polygon

描述多边形。

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()