def __init__(self, data_or_path, mode=None, caption=None, grouping=None):
"""
Accepts numpy array of image data, or a PIL image. The class attempts to infer
the data format and converts it.
If grouping is set to a number the interface combines N images.
"""
self._grouping = grouping
self._caption = caption
self._width = None
self._height = None
self._image = None
if isinstance(data_or_path, six.string_types):
super(Image, self).__init__(data_or_path, is_tmp=False)
else:
data = data_or_path
PILImage = util.get_module(
"PIL.Image",
required=
'wandb.Image needs the PIL package. To get it, run "pip install pillow".'
)
if util.is_matplotlib_typename(util.get_full_typename(data)):
buf = six.BytesIO()
util.ensure_matplotlib_figure(data).savefig(buf)
self._image = PILImage.open(buf)
elif isinstance(data, PILImage.Image):
self._image = data
elif util.is_pytorch_tensor_typename(util.get_full_typename(data)):
vis_util = util.get_module(
"torchvision.utils",
"torchvision is required to render images")
if hasattr(data, "requires_grad") and data.requires_grad:
data = data.detach()
data = vis_util.make_grid(data, normalize=True)
self._image = PILImage.fromarray(
data.mul(255).clamp(0,
255).byte().permute(1, 2,
0).cpu().numpy())
else:
if hasattr(data, "numpy"): # TF data eager tensors
data = data.numpy()
if data.ndim > 2:
data = data.squeeze(
) # get rid of trivial dimensions as a convenience
self._image = PILImage.fromarray(self.to_uint8(data),
mode=mode
or self.guess_mode(data))
self._width, self._height = self._image.size
tmp_path = os.path.join(MEDIA_TMP.name,
util.generate_id() + '.png')
self._image.save(tmp_path, transparency=None)
super(Image, self).__init__(tmp_path, is_tmp=True)
def __init__(self, data, mode=None, caption=None, grouping=None):
"""
Accepts numpy array of image data, or a PIL image. The class attempts to infer
the data format and converts it.
If grouping is set to a number the interface combines N images.
"""
PILImage = util.get_module(
"PIL.Image",
required=
"wandb.Image requires the PIL package, to get it run: pip install pillow"
)
if util.is_matplotlib_typename(util.get_full_typename(data)):
buf = six.BytesIO()
util.ensure_matplotlib_figure(data).savefig(buf)
self.image = PILImage.open(buf)
elif isinstance(data, PILImage.Image):
self.image = data
elif util.is_pytorch_tensor_typename(util.get_full_typename(data)):
vis_util = util.get_module(
"torchvision.utils",
"torchvision is required to render images")
if hasattr(data, "requires_grad") and data.requires_grad:
data = data.detach()
data = vis_util.make_grid(data, normalize=True)
self.image = PILImage.fromarray(
data.mul(255).clamp(0, 255).byte().permute(1, 2,
0).cpu().numpy())
else:
# Handle TF eager tensors
if hasattr(data, "numpy"):
data = data.numpy()
data = data.squeeze(
) # get rid of trivial dimensions as a convenience
self.image = PILImage.fromarray(self.to_uint8(data),
mode=mode or self.guess_mode(data))
self.grouping = grouping
self.caption = caption
