def testSingleImageDetectionMaskExport(self):
masks = np.array([[[
1,
1,
], [1, 1]], [[0, 0], [0, 1]], [[0, 0], [0, 0]]],
dtype=np.uint8)
classes = np.array([1, 2, 3], dtype=np.int32)
scores = np.array([0.8, 0.2, 0.7], dtype=np.float32)
coco_annotations = coco_tools.ExportSingleImageDetectionMasksToCoco(
image_id='first_image',
category_id_set=set([1, 2, 3]),
detection_classes=classes,
detection_scores=scores,
detection_masks=masks)
expected_counts = ['04', '31', '4']
for i, mask_annotation in enumerate(coco_annotations):
self.assertEqual(mask_annotation['segmentation']['counts'],
expected_counts[i])
self.assertTrue(
np.all(
np.equal(mask.decode(mask_annotation['segmentation']),
masks[i])))
self.assertEqual(mask_annotation['image_id'], 'first_image')
self.assertEqual(mask_annotation['category_id'], classes[i])
self.assertAlmostEqual(mask_annotation['score'], scores[i])
def add_single_detected_image_info(self,
image_id,
detections_dict):
"""Adds detections for a single image to be used for evaluation.
If a detection has already been added for this image id, a warning is
logged, and the detection is skipped.
Args:
image_id: A unique string/integer identifier for the image.
detections_dict: A dictionary containing -
DetectionResultFields.detection_scores: float32 numpy array of shape
[num_boxes] containing detection scores for the boxes.
DetectionResultFields.detection_classes: integer numpy array of shape
[num_boxes] containing 1-indexed detection classes for the boxes.
DetectionResultFields.detection_masks: optional uint8 numpy array of
shape [num_boxes, image_height, image_width] containing instance
masks corresponding to the boxes. The elements of the array must be
in {0, 1}.
Raises:
ValueError: If groundtruth for the image_id is not available or if
spatial shapes of groundtruth_instance_masks and detection_masks are
incompatible.
"""
if image_id not in self._image_id_to_mask_shape_map:
raise ValueError('Missing groundtruth for image id: {}'.format(image_id))
if image_id in self._image_ids_with_detections:
tf.logging.warning('Ignoring detection with image id %s since it was '
'previously added', image_id)
return
groundtruth_masks_shape = self._image_id_to_mask_shape_map[image_id]
detection_masks = detections_dict[standard_fields.DetectionResultFields.
detection_masks]
if groundtruth_masks_shape[1:] != detection_masks.shape[1:]:
raise ValueError('Spatial shape of groundtruth masks and detection masks '
'are incompatible: {} vs {}'.format(
groundtruth_masks_shape,
detection_masks.shape))
_check_mask_type_and_value(standard_fields.DetectionResultFields.
detection_masks,
detection_masks)
self._detection_masks_list.extend(
coco_tools.ExportSingleImageDetectionMasksToCoco(
image_id=image_id,
category_id_set=self._category_id_set,
detection_masks=detection_masks,
detection_scores=detections_dict[standard_fields.
DetectionResultFields.
detection_scores],
detection_classes=detections_dict[standard_fields.
DetectionResultFields.
detection_classes]))
self._image_ids_with_detections.update([image_id])
