ObjectDetectionLabels#

class ObjectDetectionLabels[source]#

Bases: Labels

A set of boxes and associated class_ids and scores.

Implemented using the Tensorflow Object Detection API’s BoxList class.

__init__(npboxes: array, class_ids: array, scores: Optional[array] = None)[source]#

Construct a set of object detection labels.

Parameters

npboxes (array) – float numpy array of size nx4 with cols ymin, xmin, ymax, xmax. Should be in pixel coordinates within the global frame of reference.
class_ids (array) – int numpy array of size n with class ids
scores (Optional[array]) – float numpy array of size n

Methods

`__init__`(npboxes, class_ids[, scores])	Construct a set of object detection labels.
`assert_equal`(expected_labels)
`concatenate`(labels1, labels2)	Return concatenation of labels.
`filter_by_aoi`(aoi_polygons)	Returns a copy of these labels filtered by a given set of AOI polygons
`from_boxlist`(boxlist)	Make ObjectDetectionLabels from BoxList object.
`from_geojson`(geojson[, extent])	Convert GeoJSON to ObjectDetectionLabels object.
`from_predictions`(windows, predictions)	Instantiate from windows and their corresponding predictions.
`get_boxes`()	Return list of Boxes.
`get_class_ids`()
`get_npboxes`()
`get_overlapping`(labels, window[, ...])	Return subset of labels that overlap with window.
`get_scores`()
`global_to_local`(npboxes, window)	Convert from global to local coordinates.
`local_to_global`(npboxes, window)	Convert from local to global coordinates.
`local_to_normalized`(npboxes, window)	Convert from local to normalized coordinates.
`make_empty`()	Instantiate an empty instance of this class.
`normalized_to_local`(npboxes, window)	Convert from normalized to local coordinates.
`prune_duplicates`(labels, score_thresh, ...)	Remove duplicate boxes.
`save`(uri, class_config, crs_transformer)	Save labels as a GeoJSON file.
`to_boxlist`()
`to_dict`()	Returns a dict version of these labels.

__init__(npboxes: array, class_ids: array, scores: Optional[array] = None)[source]#

Construct a set of object detection labels.

Parameters

npboxes (array) – float numpy array of size nx4 with cols ymin, xmin, ymax, xmax. Should be in pixel coordinates within the global frame of reference.
class_ids (array) – int numpy array of size n with class ids
scores (Optional[array]) – float numpy array of size n

assert_equal(expected_labels: ObjectDetectionLabels)[source]#

Parameters: expected_labels (ObjectDetectionLabels) –

static concatenate(labels1: ObjectDetectionLabels, labels2: ObjectDetectionLabels) → ObjectDetectionLabels[source]#

Return concatenation of labels.

Parameters

labels1 (ObjectDetectionLabels) – ObjectDetectionLabels
labels2 (ObjectDetectionLabels) – ObjectDetectionLabels

Return type

ObjectDetectionLabels

filter_by_aoi(aoi_polygons: Iterable[Polygon])[source]#

Returns a copy of these labels filtered by a given set of AOI polygons

Parameters

by (aoi_polygons - A list of AOI polygons to filter) –
coordinates. (in pixel) –
aoi_polygons (Iterable[Polygon]) –

static from_boxlist(boxlist: BoxList)[source]#

Make ObjectDetectionLabels from BoxList object.

Parameters: boxlist (BoxList) –

static from_geojson(geojson: dict, extent: Optional[Box] = None) → ObjectDetectionLabels[source]#

Convert GeoJSON to ObjectDetectionLabels object.

If extent is provided, filter out the boxes that lie “more than a little bit” outside the extent.

Parameters

geojson (dict) – (dict) normalized GeoJSON (see VectorSource)
extent (Optional[Box]) – (Box) in pixel coords

Returns

ObjectDetectionLabels

Return type

ObjectDetectionLabels

classmethod from_predictions(windows: Iterable[Box], predictions: Iterable[Any]) → Labels#

Instantiate from windows and their corresponding predictions.

This makes no assumptions about the type or format of the predictions. Subclasses should implement the __setitem__ method to correctly handle the predictions.

Parameters

windows (Iterable[Box]) – Boxes in pixel coords, specifying chips in the raster.
predictions (Iterable[Any]) – The model predictions for each chip specified by the windows.

Returns

An object of the Label subclass on which this method is: called.

Return type

Labels

get_boxes() → List[Box][source]#

Return list of Boxes.

Return type: List[Box]

get_class_ids() → ndarray[source]#

Return type: ndarray

get_npboxes() → ndarray[source]#

Return type: ndarray

static get_overlapping(labels: ObjectDetectionLabels, window: Box, ioa_thresh: float = 1e-06, clip: bool = False) → ObjectDetectionLabels[source]#

Return subset of labels that overlap with window.

Parameters

labels (ObjectDetectionLabels) – ObjectDetectionLabels
window (Box) – Box
ioa_thresh (float) – the minimum IOA for a box to be considered as overlapping
clip (bool) – if True, clip label boxes to the window

Return type

ObjectDetectionLabels

get_scores() → ndarray[source]#

Return type: ndarray

static global_to_local(npboxes: ndarray, window: Box)[source]#

Convert from global to local coordinates.

The global coordinates are row/col within the extent of a RasterSource. The local coordinates are row/col within the window frame of reference.

Parameters

npboxes (ndarray) –
window (Box) –

static local_to_global(npboxes: ndarray, window: Box)[source]#

Convert from local to global coordinates.

The local coordinates are row/col within the window frame of reference. The global coordinates are row/col within the extent of a RasterSource.

Parameters

npboxes (ndarray) –
window (Box) –

static local_to_normalized(npboxes: ndarray, window: Box)[source]#

Convert from local to normalized coordinates.

The local coordinates are row/col within the window frame of reference. Normalized coordinates range from 0 to 1 on each (height/width) axis.

Parameters

npboxes (ndarray) –
window (Box) –

classmethod make_empty() → ObjectDetectionLabels[source]#

Instantiate an empty instance of this class.

Returns

An object of the Label subclass on which this method is: called.

Return type

Labels

static normalized_to_local(npboxes: ndarray, window: Box)[source]#

Convert from normalized to local coordinates.

Normalized coordinates range from 0 to 1 on each (height/width) axis. The local coordinates are row/col within the window frame of reference.

Parameters

npboxes (ndarray) –
window (Box) –

static prune_duplicates(labels: ObjectDetectionLabels, score_thresh: float, merge_thresh: float) → ObjectDetectionLabels[source]#

Remove duplicate boxes.

Runs non-maximum suppression to remove duplicate boxes that result from sliding window prediction algorithm.

Parameters

labels (ObjectDetectionLabels) – ObjectDetectionLabels
score_thresh (float) – the minimum allowed score of boxes
merge_thresh (float) – the minimum IOA allowed when merging two boxes together

Returns

ObjectDetectionLabels

Return type

ObjectDetectionLabels

save(uri: str, class_config: ClassConfig, crs_transformer: CRSTransformer) → None[source]#

Save labels as a GeoJSON file.

Parameters

uri (str) – URI of the output file.
class_config (ClassConfig) – ClassConfig to map class IDs to names.
crs_transformer (CRSTransformer) – CRSTransformer to convert from pixel-coords to map-coords before saving.

Return type

None

to_boxlist() → BoxList[source]#

Return type: BoxList

to_dict() → dict[source]#

Returns a dict version of these labels.

The Dict has a Box as a key, and a tuple of (class_id, score) as the values.

Return type: dict