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e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | # Copyright 2020 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Dict, Optional, Sequence, Union
import numpy as np
import torch
from monai.transforms import rescale_array
from monai.utils import optional_import
PIL, _ = optional_import("PIL")
GifImage, _ = optional_import("PIL.GifImagePlugin", name="Image")
if TYPE_CHECKING:
from tensorboard.compat.proto.summary_pb2 import Summary
from torch.utils.tensorboard import SummaryWriter
else:
Summary, _ = optional_import("tensorboard.compat.proto.summary_pb2", name="Summary")
SummaryWriter, _ = optional_import("torch.utils.tensorboard", name="SummaryWriter")
def _image3_animated_gif(tag: str, image: Union[np.ndarray, torch.Tensor], scale_factor: float = 1.0) -> Summary:
"""Function to actually create the animated gif.
Args:
tag: Data identifier
image: 3D image tensors expected to be in `HWD` format
scale_factor: amount to multiply values by. if the image data is between 0 and 1, using 255 for this value will
scale it to displayable range
"""
assert len(image.shape) == 3, "3D image tensors expected to be in `HWD` format, len(image.shape) != 3"
ims = [(np.asarray((image[:, :, i])) * scale_factor).astype(np.uint8) for i in range(image.shape[2])]
ims = [GifImage.fromarray(im) for im in ims]
img_str = b""
for b_data in PIL.GifImagePlugin.getheader(ims[0])[0]:
img_str += b_data
img_str += b"\x21\xFF\x0B\x4E\x45\x54\x53\x43\x41\x50" b"\x45\x32\x2E\x30\x03\x01\x00\x00\x00"
for i in ims:
for b_data in PIL.GifImagePlugin.getdata(i):
img_str += b_data
img_str += b"\x3B"
summary_image_str = Summary.Image(height=10, width=10, colorspace=1, encoded_image_string=img_str)
image_summary = Summary.Value(tag=tag, image=summary_image_str)
return Summary(value=[image_summary])
def make_animated_gif_summary(
tag: str,
image: Union[np.ndarray, torch.Tensor],
max_out: int = 3,
animation_axes: Sequence[int] = (3,),
image_axes: Sequence[int] = (1, 2),
other_indices: Optional[Dict] = None,
scale_factor: float = 1.0,
) -> Summary:
"""Creates an animated gif out of an image tensor in 'CHWD' format and returns Summary.
Args:
tag: Data identifier
image: The image, expected to be in CHWD format
max_out: maximum number of slices to animate through
animation_axes: axis to animate on (not currently used)
image_axes: axes of image (not currently used)
other_indices: (not currently used)
scale_factor: amount to multiply values by.
if the image data is between 0 and 1, using 255 for this value will scale it to displayable range
"""
if max_out == 1:
suffix = "/image"
else:
suffix = "/image/{}"
if other_indices is None:
other_indices = {}
axis_order = [0] + list(animation_axes) + list(image_axes)
slicing = []
for i in range(len(image.shape)):
if i in axis_order:
slicing.append(slice(None))
else:
other_ind = other_indices.get(i, 0)
slicing.append(slice(other_ind, other_ind + 1))
image = image[tuple(slicing)]
for it_i in range(min(max_out, list(image.shape)[0])):
one_channel_img: Union[torch.Tensor, np.ndarray] = (
image[it_i, :, :, :].squeeze(dim=0) if torch.is_tensor(image) else image[it_i, :, :, :]
)
summary_op = _image3_animated_gif(tag + suffix.format(it_i), one_channel_img, scale_factor)
return summary_op
def add_animated_gif(
writer: SummaryWriter,
tag: str,
image_tensor: Union[np.ndarray, torch.Tensor],
max_out: int,
scale_factor: float,
global_step: Optional[int] = None,
) -> None:
"""Creates an animated gif out of an image tensor in 'CHWD' format and writes it with SummaryWriter.
Args:
writer: Tensorboard SummaryWriter to write to
tag: Data identifier
image_tensor: tensor for the image to add, expected to be in CHWD format
max_out: maximum number of slices to animate through
scale_factor: amount to multiply values by. If the image data is between 0 and 1, using 255 for this value will
scale it to displayable range
global_step: Global step value to record
"""
writer._get_file_writer().add_summary(
make_animated_gif_summary(
tag, image_tensor, max_out=max_out, animation_axes=[1], image_axes=[2, 3], scale_factor=scale_factor
),
global_step,
)
def add_animated_gif_no_channels(
writer: SummaryWriter,
tag: str,
image_tensor: Union[np.ndarray, torch.Tensor],
max_out: int,
scale_factor: float,
global_step: Optional[int] = None,
) -> None:
"""Creates an animated gif out of an image tensor in 'HWD' format that does not have
a channel dimension and writes it with SummaryWriter. This is similar to the "add_animated_gif"
after inserting a channel dimension of 1.
Args:
writer: Tensorboard SummaryWriter to write to
tag: Data identifier
image_tensor: tensor for the image to add, expected to be in CHWD format
max_out: maximum number of slices to animate through
scale_factor: amount to multiply values by. If the image data is between 0 and 1,
using 255 for this value will scale it to displayable range
global_step: Global step value to record
"""
writer._get_file_writer().add_summary(
make_animated_gif_summary(
tag, image_tensor, max_out=max_out, animation_axes=[1], image_axes=[1, 2], scale_factor=scale_factor
),
global_step,
)
def plot_2d_or_3d_image(
data: Union[torch.Tensor, np.ndarray],
step: int,
writer: SummaryWriter,
index: int = 0,
max_channels: int = 1,
max_frames: int = 64,
tag: str = "output",
) -> None:
"""Plot 2D or 3D image on the TensorBoard, 3D image will be converted to GIF image.
Note:
Plot 3D or 2D image(with more than 3 channels) as separate images.
Args:
data: target data to be plotted as image on the TensorBoard.
The data is expected to have 'NCHW[D]' dimensions, and only plot the first in the batch.
step: current step to plot in a chart.
writer: specify TensorBoard SummaryWriter to plot the image.
index: plot which element in the input data batch, default is the first element.
max_channels: number of channels to plot.
max_frames: number of frames for 2D-t plot.
tag: tag of the plotted image on TensorBoard.
"""
d = data[index].detach().cpu().numpy() if torch.is_tensor(data) else data[index]
if d.ndim == 2:
d = rescale_array(d, 0, 1)
dataformats = "HW"
writer.add_image(f"{tag}_{dataformats}", d, step, dataformats=dataformats)
return
if d.ndim == 3:
if d.shape[0] == 3 and max_channels == 3: # RGB
dataformats = "CHW"
writer.add_image(f"{tag}_{dataformats}", d, step, dataformats=dataformats)
return
for j, d2 in enumerate(d[:max_channels]):
d2 = rescale_array(d2, 0, 1)
dataformats = "HW"
writer.add_image(f"{tag}_{dataformats}_{j}", d2, step, dataformats=dataformats)
return
if d.ndim >= 4:
spatial = d.shape[-3:]
for j, d3 in enumerate(d.reshape([-1] + list(spatial))[:max_channels]):
d3 = rescale_array(d3, 0, 255)
add_animated_gif(writer, f"{tag}_HWD_{j}", d3[None], max_frames, 1.0, step)
return
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