ObjectDetectionRandomWindowGeoDataset#

class ObjectDetectionRandomWindowGeoDataset[source]#

Bases: RandomWindowGeoDataset

Attributes

max_size

min_size

__init__(*args, **kwargs)[source]#

Constructor.

Parameters:

*args – See RandomWindowGeoDataset.__init__().

Keyword Arguments:

  • bbox_params – Optional bbox_params to use when resizing windows. Defaults to None.

  • ioa_thresh – Minimum IoA of a bounding box with a given window for it to be included in the labels for that window. Defaults to 0.9.

  • clip – Clip bounding boxes to window limits when retrieving labels for a window. Defaults to False.

  • neg_ratio – Ratio of sampling probabilities of negative windows (windows w/o bboxes) vs positive windows (windows w/ at least 1 bbox). E.g. neg_ratio=2 means 2/3 probability of sampling a negative window. If None, the default sampling behavior of RandomWindowGeoDataset is used, without taking bboxes into account. Defaults to None.

  • neg_ioa_thresh – A window will be considered negative if its max IoA with any bounding box is less than this threshold. Defaults to 0.2.

  • **kwargs – See RandomWindowGeoDataset.__init__().

Methods

__init__(*args, **kwargs)

Constructor.

append_resize_transform(transform, out_size)

Get transform to use for resizing windows to out_size.

from_uris(image_uri[, label_vector_uri, ...])

Create an instance of this class from image and label URIs.

sample_window()

Sample a window with random size and location within the AOI.

sample_window_loc(h, w)

Randomly sample coordinates of the top left corner of the window.

sample_window_size()

Randomly sample the window size.
__add__(other: Dataset[T_co]) → ConcatDataset[T_co]#
Parameters:

other (Dataset[T_co]) –

Return type:

ConcatDataset[T_co]

__getitem__(idx: int)#
Parameters:

idx (int) –

__init__(*args, **kwargs)[source]#

Constructor.

Parameters:

*args – See RandomWindowGeoDataset.__init__().

Keyword Arguments:

  • bbox_params – Optional bbox_params to use when resizing windows. Defaults to None.

  • ioa_thresh – Minimum IoA of a bounding box with a given window for it to be included in the labels for that window. Defaults to 0.9.

  • clip – Clip bounding boxes to window limits when retrieving labels for a window. Defaults to False.

  • neg_ratio – Ratio of sampling probabilities of negative windows (windows w/o bboxes) vs positive windows (windows w/ at least 1 bbox). E.g. neg_ratio=2 means 2/3 probability of sampling a negative window. If None, the default sampling behavior of RandomWindowGeoDataset is used, without taking bboxes into account. Defaults to None.

  • neg_ioa_thresh – A window will be considered negative if its max IoA with any bounding box is less than this threshold. Defaults to 0.2.

  • **kwargs – See RandomWindowGeoDataset.__init__().

append_resize_transform(transform: BasicTransform, out_size: tuple[int, int]) → BasicTransform[source]#

Get transform to use for resizing windows to out_size.

Parameters:

  • transform (BasicTransform) –

  • out_size (tuple[int, int]) –

Return type:

BasicTransform

classmethod from_uris(image_uri: str | list[str], label_vector_uri: str | None = None, class_config: ClassConfig | None = None, aoi_uri: str | list[str] = [], label_vector_default_class_id: int | None = None, image_raster_source_kw: dict = {}, label_vector_source_kw: dict = {}, label_source_kw: dict = {}, **kwargs)#

Create an instance of this class from image and label URIs.

This is a convenience method. For more fine-grained control, it is recommended to use the default constructor.

Parameters:

  • image_uri (str | list[str]) – URI or list of URIs of GeoTIFFs to use as the source of image data.

  • label_vector_uri (str | None) – URI of GeoJSON file to use as the source of label. Defaults to None.

  • class_config (ClassConfig | None) – The ClassConfig. Must be non-None if creating a scene without a LabelSource. Defaults to None.

  • aoi_uri (str | list[str]) – URI or list of URIs of GeoJSONs that specify the area-of-interest. If provided, the dataset will only access data from this area. Defaults to [].

  • label_vector_default_class_id (int | None) – If using label_vector_uri and all polygons in that file belong to the same class and they do not contain a class_id property, then use this argument to map all of the polygons to the appropriate class ID. See docs for ClassInferenceTransformer for more details. Defaults to None.

  • image_raster_source_kw (dict) – Additional arguments to pass to the RasterioSource used for image data. See docs for RasterioSource for more details. Defaults to {}.

  • label_vector_source_kw (dict) – Additional arguments to pass to the GeoJSONVectorSourceConfig used for label data, if label_vector_uri is set. See docs for GeoJSONVectorSourceConfig for more details. Defaults to {}.

  • label_source_kw (dict) – Additional arguments to pass to the ObjectDetectionLabelSourceConfig used for label data, if label_vector_uri is set. See docs for ObjectDetectionLabelSourceConfig for more details. Defaults to {}.

  • **kwargs – All other keyword args are passed to the default constructor for this class.

Returns:

An instance of this GeoDataset subclass.

sample_window() → Box#

Sample a window with random size and location within the AOI.

If the scene has AOI polygons, try to find a random window that is within the AOI. Otherwise, just return the first sampled window.

Raises:

StopIteration – If unable to find a valid window within self.max_sample_attempts attempts.

Returns:

The sampled window.

Return type:

Box

sample_window_loc(h: int, w: int) → tuple[int, int]#

Randomly sample coordinates of the top left corner of the window.

Parameters:
Return type:

tuple[int, int]

sample_window_size() → tuple[int, int]#

Randomly sample the window size.

Return type:

tuple[int, int]

property max_size#
property min_size#