def __init__(self, filename, internal=False):
with tf.name_scope("EXRIOTensor"):
data = tf.io.read_file(filename)
shapes, dtypes, channels = core_ops.io_decode_exr_info(data)
parts = []
index = 0
for (shape, dtypes, channels) in zip(shapes.numpy(),
dtypes.numpy(),
channels.numpy()):
# Remove trailing 0 from dtypes
while dtypes[-1] == 0:
dtypes.pop()
channels.pop()
spec = tuple([
tf.TensorSpec(tf.TensorShape(shape), dtype)
for dtype in dtypes
])
columns = [channel.decode() for channel in channels]
elements = [
io_tensor_ops.TensorIOTensor(core_ops.io_decode_exr(
data, index, channel, dtype=dtype),
internal=internal)
for (channel, dtype) in zip(columns, dtypes)
]
parts.append(
EXRPartIOTensor(spec, columns, elements,
internal=internal))
index += 1
spec = tuple([part.spec for part in parts])
columns = [i for i, _ in enumerate(parts)]
super(EXRIOTensor, self).__init__(spec,
columns,
parts,
internal=internal)
def decode_exr(contents, index, channel, dtype, name=None):
"""
Decode a EXR-encoded image to a uint8 tensor.
Args:
contents: A `Tensor` of type `string`. 0-D. The EXR-encoded image.
index: A `Tensor` of type int64. 0-D. The 0-based index of the frame
inside EXR-encoded image.
channel: A `Tensor` of type string. 0-D. The channel inside the 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_exr(
contents, index=index, channel=channel, dtype=dtype, name=name)