def test_image_with_boxes(self):
event = image_boxes(
asset_path=self.asset_path,
tensor_image=tensor_np(shape=(3, 32, 32)),
tensor_boxes=np.array([[10, 10, 40, 40]]),
)
assert event.path == self.asset_path
assert os.path.exists(self.asset_path) is True
def log_image_with_boxes(
self,
tensor_image,
tensor_boxes,
name=None,
step=None,
timestamp=None,
rescale=1,
dataformats="CHW",
):
"""Logs an image with bounding boxes.
```python
>>> log_image_with_boxes(
>>> name="my_image",
>>> tensor_image=np.arange(np.prod((3, 32, 32)), dtype=float).reshape((3, 32, 32)),
>>> tensor_boxes=np.array([[10, 10, 40, 40]]),
>>> )
```
Args:
tensor_image: numpy.array or str: Image data or file name
tensor_boxes: numpy.array or str: Box data (for detected objects)
box should be represented as [x1, y1, x2, y2]
name: str, name of the image
step: int, optional
timestamp: datetime, optional
rescale: int, optional
dataformats: str, optional
"""
self._log_has_events()
name = name or "figure"
asset_path = get_asset_path(
run_path=self._artifacts_path,
kind=V1ArtifactKind.IMAGE,
name=name,
step=step,
)
asset_rel_path = os.path.relpath(asset_path, self._artifacts_path)
event_value = events_processors.image_boxes(
asset_path=asset_path,
tensor_image=tensor_image,
tensor_boxes=tensor_boxes,
rescale=rescale,
dataformats=dataformats,
asset_rel_path=asset_rel_path,
)
if event_value == UNKNOWN:
return
logged_event = LoggedEventSpec(
name=name,
kind=V1ArtifactKind.IMAGE,
event=V1Event.make(timestamp=timestamp,
step=step,
image=event_value),
)
self._event_logger.add_event(logged_event)