def image(tag, tensor, rescale=1, dataformats="CHW"):
"""Outputs a `Summary` protocol buffer with images. The summary has up to
`max_images` summary values containing images. The images are built from
`tensor` which must be 3-D with shape `[height, width, channels]` and where
`channels` can be:
* 1: `tensor` is interpreted as Grayscale.
* 3: `tensor` is interpreted as RGB.
* 4: `tensor` is interpreted as RGBA.
Args:
tag: A name for the generated node. Will also serve as a series name in
TensorBoard.
tensor: A 3-D `uint8` or `float32` `Tensor` of shape `[height, width,
channels]` where `channels` is 1, 3, or 4.
'tensor' can either have values in [0, 1] (float32) or [0, 255] (uint8).
The image() function will scale the image values to [0, 255] by applying
a scale factor of either 1 (uint8) or 255 (float32).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
tag = tbxsummary._clean_tag(tag)
tensor = tbxmake_np(tensor)
tensor = convert_to_HWC(tensor, dataformats)
# Do not assume that user passes in values in [0, 255], use data type to detect
if tensor.dtype != np.uint8:
tensor = (tensor * 255.0).astype(np.uint8)
image = tbxsummary.make_image(tensor, rescale=rescale)
return TBXSummary(value=[TBXSummary.Value(tag=tag, image=image)])
def tensor_to_video(tensor, fps=4):
tensor = tbxmake_np(tensor)
tensor = tbx_prepare_video(tensor)
# If user passes in uint8, then we don't need to rescale by 255
if tensor.dtype != np.uint8:
tensor = (tensor * 255.0).astype(np.uint8)
return tbxsummary.make_video(tensor, fps)
def tensor_to_clip(tensor, fps=4):
tensor = tbxmake_np(tensor)
tensor = tbx_prepare_video(tensor)
# If user passes in uint8, then we don't need to rescale by 255
if tensor.dtype != np.uint8:
tensor = (tensor * 255.0).astype(np.uint8)
t, h, w, c = tensor.shape
clip = mpy.ImageSequenceClip(list(tensor), fps=fps)
return clip, (h, w, c)