def decode_tiff(contents, index=0, name=None):
"""
Decode a TIFF-encoded image to a uint8 tensor.
Args:
contents: A `Tensor` of type `string`. 0-D. The TIFF-encoded image.
index: A `Tensor` of type int64. 0-D. The 0-based index of the frame
inside TIFF-encoded image.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `uint8` and shape of `[height, width, 4]` (RGBA).
"""
return core_ops.io_decode_tiff(contents, index, name=name)
def __init__(self,
filename,
internal=False):
with tf.name_scope("TIFFIOTensor"):
# TIFF can fit into memory so load TIFF first
data = tf.io.read_file(filename)
info = core_ops.io_decode_tiff_info(data)
spec = tuple([
tf.TensorSpec(tf.TensorShape(
e.numpy().tolist() + [4]), tf.uint8) for e in info])
columns = [i for i, _ in enumerate(spec)]
elements = [
io_tensor_ops.TensorIOTensor(
core_ops.io_decode_tiff(data, i),
internal=internal) for i in columns]
super(TIFFIOTensor, self).__init__(
spec, columns, elements,
internal=internal)
def __init__(self, filename, internal=True):
if not internal:
raise ValueError(
"TIFFIODataset constructor is private; please use one "
"of the factory methods instead (e.g., "
"IODataset.from_pcap())")
with tf.name_scope("TIFFIODataset"):
content = tf.io.read_file(filename)
_, dtype = core_ops.io_decode_tiff_info(content)
# use dtype's rank to find out the number of elements
dataset = tf.data.Dataset.range(
tf.cast(tf.shape(dtype)[0], tf.int64))
dataset = dataset.map(
lambda index: core_ops.io_decode_tiff(content, index))
self._dataset = dataset
self._content = content
super(TIFFIODataset, self).__init__(self._dataset._variant_tensor) # pylint: disable=protected-access
def __init__(self, filename, internal=False):
with tf.name_scope("TIFFIOTensor"):
# TIFF can fit into memory so load TIFF first
data = tf.io.read_file(filename)
shapes, dtypes = core_ops.io_decode_tiff_info(data)
# NOTE: While shapes returned correctly handles 3 or 4 channels
# we can only handle RGBA so fix shape as 4 for now,
# until decode_tiff is updated.
spec = tuple([
tf.TensorSpec(tf.TensorShape(shape.tolist()[0:2] + [4]), dtype)
for (shape, dtype) in zip(shapes.numpy(), dtypes.numpy())
])
columns = [i for i, _ in enumerate(spec)]
elements = [
io_tensor_ops.TensorIOTensor(core_ops.io_decode_tiff(data, i),
internal=internal)
for i in columns
]
super().__init__(spec, columns, elements, internal=internal)
