def testBoundingBoxParser(self):
bounding_boxes = np.array([[0.0, 0.5, 0.3], [0.0, 0.1, 0.6],
[1.0, 0.6, 0.8], [1.0, 0.6, 0.7]]).transpose()
features = {
'ymin': self._FloatFeature(bounding_boxes[:, 0]),
'xmin': self._FloatFeature(bounding_boxes[:, 1]),
'ymax': self._FloatFeature(bounding_boxes[:, 2]),
'xmax': self._FloatFeature(bounding_boxes[:, 3])
}
example = tf.train.Example(features=tf.train.Features(feature=features))
parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax', 'ymax')
result = parser.parse(example)
self.assertIsNotNone(result)
np_testing.assert_almost_equal(result, bounding_boxes)
parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax',
'another_ymax')
result = parser.parse(example)
self.assertIsNone(result)
def __init__(self):
self.items_to_handlers = {
fields.DetectionResultFields.key:
StringParser(fields.TfExampleFields.source_id),
# Object detections.
fields.DetectionResultFields.detection_boxes:
(tf_example_parser.BoundingBoxParser(
fields.TfExampleFields.detection_bbox_xmin,
fields.TfExampleFields.detection_bbox_ymin,
fields.TfExampleFields.detection_bbox_xmax,
fields.TfExampleFields.detection_bbox_ymax)),
fields.DetectionResultFields.detection_classes:
(tf_example_parser.Int64Parser(
fields.TfExampleFields.detection_class_label)),
fields.DetectionResultFields.detection_scores:
(tf_example_parser.FloatParser(
fields.TfExampleFields.detection_score)),
}
self.optional_items_to_handlers = {}