def _parsing_covering_helper(
groundtruth_category_array, groundtruth_instance_array,
predicted_category_array, predicted_instance_array, num_classes,
max_instances_per_category, ignored_label, offset, normalize_by_image_size):
"""Helper function to compute parsing covering."""
pc = parsing_covering.ParsingCovering(num_classes, ignored_label,
max_instances_per_category, offset,
normalize_by_image_size)
pc.compare_and_accumulate(groundtruth_category_array,
groundtruth_instance_array,
predicted_category_array, predicted_instance_array)
return pc.weighted_iou_per_class, pc.gt_area_per_class
def _build_metric(metric,
num_categories,
ignored_label,
max_instances_per_category,
intersection_offset=None,
normalize_by_image_size=True):
"""Creates a metric aggregator objet of the given name."""
if metric == 'pq':
logging.warning('One should check Panoptic Quality results against the '
'official COCO API code. Small numerical differences '
'(< 0.1%) can be magnified by rounding.')
return panoptic_quality.PanopticQuality(num_categories, ignored_label,
max_instances_per_category,
intersection_offset)
elif metric == 'pc':
return parsing_covering.ParsingCovering(
num_categories, ignored_label, max_instances_per_category,
intersection_offset, normalize_by_image_size)
else:
raise ValueError('No implementation for metric "%s"' % metric)
def test_matches_expected_normalize_by_size(self):
pred_classes = test_utils.read_segmentation_with_rgb_color_map(
'team_pred_class.png', _CLASS_COLOR_MAP)
pred_instances = test_utils.read_test_image('team_pred_instance.png',
mode='L')
instance_class_map = {
0: 0,
47: 1,
97: 1,
133: 1,
150: 1,
174: 1,
198: 2,
215: 1,
244: 1,
255: 1,
}
gt_instances, gt_classes = test_utils.panoptic_segmentation_with_class_map(
'team_gt_instance.png', instance_class_map)
pc = parsing_covering.ParsingCovering(num_categories=3,
ignored_label=0,
max_instances_per_category=256,
offset=256 * 256,
normalize_by_image_size=True)
pc.compare_and_accumulate(gt_classes, gt_instances, pred_classes,
pred_instances)
np.testing.assert_array_almost_equal(
pc.weighted_iou_per_class, [0.0, 0.5002088756, 0.03935002196],
decimal=4)
np.testing.assert_array_almost_equal(
pc.gt_area_per_class, [0.0, 0.7135955832, 0.07277746408],
decimal=4)
# Note that the per-category and overall PCs are identical to those without
# normalization in the previous test, because we only have a single image.
np.testing.assert_array_almost_equal(pc.result_per_category(),
[0.0, 0.70097, 0.54069],
decimal=4)
self.assertAlmostEqual(pc.result(), 0.6208296732)
def test_matches_expected(self):
pred_classes = test_utils.read_segmentation_with_rgb_color_map(
'team_pred_class.png', _CLASS_COLOR_MAP)
pred_instances = test_utils.read_test_image('team_pred_instance.png',
mode='L')
instance_class_map = {
0: 0,
47: 1,
97: 1,
133: 1,
150: 1,
174: 1,
198: 2,
215: 1,
244: 1,
255: 1,
}
gt_instances, gt_classes = test_utils.panoptic_segmentation_with_class_map(
'team_gt_instance.png', instance_class_map)
pc = parsing_covering.ParsingCovering(num_categories=3,
ignored_label=0,
max_instances_per_category=256,
offset=256 * 256,
normalize_by_image_size=False)
pc.compare_and_accumulate(gt_classes, gt_instances, pred_classes,
pred_instances)
np.testing.assert_array_almost_equal(pc.weighted_iou_per_class,
[0.0, 39864.14634, 3136],
decimal=4)
np.testing.assert_array_equal(pc.gt_area_per_class, [0.0, 56870, 5800])
np.testing.assert_array_almost_equal(pc.result_per_category(),
[0.0, 0.70097, 0.54069],
decimal=4)
self.assertAlmostEqual(pc.result(), 0.6208296732)
def test_totally_wrong(self):
categories = np.zeros([6, 6], np.uint16)
gt_instances = np.array([
[0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
],
dtype=np.uint16)
pred_instances = 1 - gt_instances
pc = parsing_covering.ParsingCovering(num_categories=2,
ignored_label=0,
max_instances_per_category=1,
offset=16,
normalize_by_image_size=False)
pc.compare_and_accumulate(categories, gt_instances, categories,
pred_instances)
np.testing.assert_array_equal(pc.weighted_iou_per_class, [0.0, 0.0])
np.testing.assert_array_equal(pc.gt_area_per_class, [0.0, 10.0])
np.testing.assert_array_equal(pc.result_per_category(), [0.0, 0.0])
self.assertEqual(pc.result(), 0.0)
def test_perfect_match(self):
categories = np.zeros([6, 6], np.uint16)
instances = np.array([
[2, 2, 2, 2, 2, 2],
[2, 4, 4, 4, 4, 2],
[2, 4, 4, 4, 4, 2],
[2, 4, 4, 4, 4, 2],
[2, 4, 4, 2, 2, 2],
[2, 4, 2, 2, 2, 2],
],
dtype=np.uint16)
pc = parsing_covering.ParsingCovering(num_categories=3,
ignored_label=2,
max_instances_per_category=2,
offset=16,
normalize_by_image_size=False)
pc.compare_and_accumulate(categories, instances, categories, instances)
np.testing.assert_array_equal(pc.weighted_iou_per_class,
[0.0, 21.0, 0.0])
np.testing.assert_array_equal(pc.gt_area_per_class, [0.0, 21.0, 0.0])
np.testing.assert_array_equal(pc.result_per_category(),
[0.0, 1.0, 0.0])
self.assertEqual(pc.result(), 1.0)
