def test_float_1_converts_to_uint8_255(self):
assert os.path.exists(self.asset_path) is False
green_uint8 = np.array([[[0, 255, 0]]], dtype="uint8")
green_float32 = np.array([[[0, 1, 0]]], dtype="float32")
a = image(asset_path=self.run_path + "/asset1", data=green_uint8)
b = image(asset_path=self.run_path + "/asset2", data=green_float32)
self.assertEqual(
Image.open(io.BytesIO(open(a.path, "br").read())),
Image.open(io.BytesIO(open(b.path, "br").read())),
)
def test_float32_image(self):
"""Tests that float32 image (pixel values in [0, 1]) are scaled correctly to [0, 255]"""
assert os.path.exists(self.asset_path) is False
event = image(asset_path=self.asset_path,
data=tensor_np(shape=(3, 32, 32)))
assert event.path == self.asset_path
assert os.path.exists(self.asset_path) is True
def test_image_with_one_channel(self):
event = image(
asset_path=self.asset_path,
data=tensor_np(shape=(1, 8, 8)),
dataformats="CHW",
)
assert event.path == self.asset_path
assert os.path.exists(self.asset_path) is True
def test_image_with_four_channel_batched(self):
event = image(
asset_path=self.asset_path,
data=tensor_np(shape=(2, 4, 8, 8)),
dataformats="NCHW",
)
assert event.path == self.asset_path
assert os.path.exists(self.asset_path) is True
def test_uint8_image(self):
"""Tests that uint8 image (pixel values in [0, 255]) is not changed"""
assert os.path.exists(self.asset_path) is False
event = image(
asset_path=self.asset_path,
data=tensor_np(shape=(3, 32, 32), dtype=np.uint8),
)
assert event.path == self.asset_path
assert os.path.exists(self.asset_path) is True
def log_image(self,
data,
name=None,
step=None,
timestamp=None,
rescale=1,
dataformats="CHW"):
"""Logs an image.
```python
>>> log_image(data="path/to/image.png", step=10)
>>> log_image(data=np_array, name="generated_image", step=10)
```
Args:
data: str or numpy.array, a file path or numpy array
name: str, name of the image,
if a path is passed this can be optional and the name of the file will be used
step: int, optional
timestamp: datetime, optional
rescale: int, optional
dataformats: str, optional
"""
self._log_has_events()
is_file = isinstance(data, str) and os.path.exists(data)
ext = "png"
if is_file:
name = name or os.path.basename(data)
ext = get_path_extension(filepath=data) or ext
else:
name = name or "image"
asset_path = get_asset_path(
run_path=self._artifacts_path,
kind=V1ArtifactKind.IMAGE,
name=name,
step=step,
ext=ext,
)
asset_rel_path = os.path.relpath(asset_path, self._artifacts_path)
if is_file:
event_value = events_processors.image_path(from_path=data,
asset_path=asset_path)
elif hasattr(data, "encoded_image_string"):
event_value = events_processors.encoded_image(
asset_path=asset_path,
data=data,
asset_rel_path=asset_rel_path,
)
else:
event_value = events_processors.image(
asset_path=asset_path,
data=data,
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)
