from typing import (Any, List, Optional)
import numpy as np
from rastervision.core.box import Box
from rastervision.core.data import ClassConfig
from rastervision.core.data.label import SemanticSegmentationLabels
from rastervision.core.data.label_source.label_source import LabelSource
from rastervision.core.data.raster_source import RasterSource
[docs]def fill_edge(label_arr: np.ndarray, window: Box, extent: Box,
fill_value: int) -> np.ndarray:
"""If window goes over the edge of the extent, buffer with fill_value."""
if window.ymax <= extent.ymax and window.xmax <= extent.xmax:
return label_arr
x = np.full(window.size, fill_value)
ylim = extent.ymax - window.ymin
xlim = extent.xmax - window.xmin
x[0:ylim, 0:xlim] = label_arr[0:ylim, 0:xlim]
return x
[docs]class SemanticSegmentationLabelSource(LabelSource):
"""A read-only label source for semantic segmentation."""
[docs] def __init__(self, raster_source: RasterSource, class_config: ClassConfig):
"""Constructor.
Args:
raster_source (RasterSource): A raster source that returns a single
channel raster with class_ids as values.
null_class_id (int): the null class id used as fill values for when
windows go over the edge of the label array. This can be
retrieved using class_config.null_class_id.
"""
self.raster_source = raster_source
self.class_config = class_config
[docs] def enough_target_pixels(self, window: Box, target_count_threshold: int,
target_classes: List[int]) -> bool:
"""Check if window contains enough pixels of the given target classes.
Args:
window: The larger window from-which the sub-window will
be clipped.
target_count_threshold: Minimum number of target pixels.
target_classes: The classes of interest. The given
window is examined to make sure that it contains a
sufficient number of target pixels.
Returns:
True (the window does contain interesting pixels) or False.
"""
label_arr = self.get_label_arr(window)
target_count = 0
for class_id in target_classes:
target_count += (label_arr == class_id).sum()
return target_count >= target_count_threshold
[docs] def get_labels(self,
window: Optional[Box] = None) -> SemanticSegmentationLabels:
"""Get labels for a window.
Args:
window (Optional[Box], optional): Window to get labels for. If
None, returns labels covering the full extent of the scene.
Returns:
SemanticSegmentationLabels: The labels.
"""
if window is None:
window = self.extent
label_arr = self.get_label_arr(window)
labels = SemanticSegmentationLabels.make_empty(
extent=self.extent,
num_classes=len(self.class_config),
smooth=False)
labels[window] = label_arr
return labels
[docs] def get_label_arr(self, window: Optional[Box] = None) -> np.ndarray:
"""Get labels for a window.
The returned array will be the same size as the input window. If window
overflows the extent, the overflowing region will be filled with the ID
of the null class as defined by the class_config.
Args:
window (Optional[Box], optional): Window to get labels for. If
None, returns a label array covering the full extent of the scene.
Returns:
np.ndarray: Label array.
"""
if window is None:
window = self.extent
label_arr = self.raster_source.get_chip(window)
if label_arr.ndim == 3:
label_arr = np.squeeze(label_arr, axis=2)
label_arr = fill_edge(label_arr, window, self.extent,
self.class_config.null_class_id)
return label_arr
@property
def extent(self) -> Box:
return self.raster_source.extent
def __getitem__(self, key: Any) -> Any:
if isinstance(key, Box):
return self.get_label_arr(key)
else:
return super().__getitem__(key)