def _make_chip_pos_windows(image_extent, label_store, chip_size):
chip_size = chip_size
pos_windows = []
boxes = label_store.get_labels().get_boxes()
done_boxes = set()
# Get a random window around each box. If a box was previously included
# in a window, then it is skipped.
for box in boxes:
if box.tuple_format() not in done_boxes:
# If this object is bigger than the chip,
# don't use this box.
if chip_size < box.get_width() or chip_size < box.get_height():
log.warning('Label is larger than chip size: {} '
'Skipping this label'.format(box.tuple_format()))
continue
window = box.make_random_square_container(chip_size)
pos_windows.append(window)
# Get boxes that lie completely within window
window_boxes = label_store.get_labels(window=window)
window_boxes = ObjectDetectionLabels.get_overlapping(
window_boxes, window, ioa_thresh=1.0)
window_boxes = window_boxes.get_boxes()
window_boxes = [box.tuple_format() for box in window_boxes]
done_boxes.update(window_boxes)
return pos_windows
def get_train_labels(self, window, scene):
window_labels = scene.ground_truth_label_source.get_labels(
window=window)
return ObjectDetectionLabels.get_overlapping(
window_labels,
window,
ioa_thresh=self.config.chip_options.ioa_thresh,
clip=True)
def make_neg_windows(raster_source, label_store, chip_size, nb_windows,
max_attempts, filter_windows):
extent = raster_source.get_extent()
neg_windows = []
for _ in range(max_attempts):
for _ in range(max_attempts):
window = extent.make_random_square(chip_size)
if any(filter_windows([window])):
break
chip = raster_source.get_chip(window)
labels = ObjectDetectionLabels.get_overlapping(
label_store.get_labels(), window, ioa_thresh=0.2)
# If no labels and not blank, append the chip
if len(labels) == 0 and np.sum(chip.ravel()) > 0:
neg_windows.append(window)
if len(neg_windows) == nb_windows:
break
return list(neg_windows)
def get_labels(self, window=None):
if window is None:
return self.labels
return ObjectDetectionLabels.get_overlapping(self.labels, window)