You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
282 lines
11 KiB
282 lines
11 KiB
from enum import Enum
|
|
from warnings import warn
|
|
|
|
import torch
|
|
|
|
from ..extension import _load_library
|
|
from ..utils import _log_api_usage_once
|
|
|
|
|
|
try:
|
|
_load_library("image")
|
|
except (ImportError, OSError) as e:
|
|
warn(
|
|
f"Failed to load image Python extension: '{e}'"
|
|
f"If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. "
|
|
f"Otherwise, there might be something wrong with your environment. "
|
|
f"Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?"
|
|
)
|
|
|
|
|
|
class ImageReadMode(Enum):
|
|
"""
|
|
Support for various modes while reading images.
|
|
|
|
Use ``ImageReadMode.UNCHANGED`` for loading the image as-is,
|
|
``ImageReadMode.GRAY`` for converting to grayscale,
|
|
``ImageReadMode.GRAY_ALPHA`` for grayscale with transparency,
|
|
``ImageReadMode.RGB`` for RGB and ``ImageReadMode.RGB_ALPHA`` for
|
|
RGB with transparency.
|
|
"""
|
|
|
|
UNCHANGED = 0
|
|
GRAY = 1
|
|
GRAY_ALPHA = 2
|
|
RGB = 3
|
|
RGB_ALPHA = 4
|
|
|
|
|
|
def read_file(path: str) -> torch.Tensor:
|
|
"""
|
|
Reads and outputs the bytes contents of a file as a uint8 Tensor
|
|
with one dimension.
|
|
|
|
Args:
|
|
path (str or ``pathlib.Path``): the path to the file to be read
|
|
|
|
Returns:
|
|
data (Tensor)
|
|
"""
|
|
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
|
|
_log_api_usage_once(read_file)
|
|
data = torch.ops.image.read_file(str(path))
|
|
return data
|
|
|
|
|
|
def write_file(filename: str, data: torch.Tensor) -> None:
|
|
"""
|
|
Writes the contents of an uint8 tensor with one dimension to a
|
|
file.
|
|
|
|
Args:
|
|
filename (str or ``pathlib.Path``): the path to the file to be written
|
|
data (Tensor): the contents to be written to the output file
|
|
"""
|
|
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
|
|
_log_api_usage_once(write_file)
|
|
torch.ops.image.write_file(str(filename), data)
|
|
|
|
|
|
def decode_png(
|
|
input: torch.Tensor, mode: ImageReadMode = ImageReadMode.UNCHANGED, apply_exif_orientation: bool = False
|
|
) -> torch.Tensor:
|
|
"""
|
|
Decodes a PNG image into a 3 dimensional RGB or grayscale Tensor.
|
|
Optionally converts the image to the desired format.
|
|
The values of the output tensor are uint8 in [0, 255].
|
|
|
|
Args:
|
|
input (Tensor[1]): a one dimensional uint8 tensor containing
|
|
the raw bytes of the PNG image.
|
|
mode (ImageReadMode): the read mode used for optionally
|
|
converting the image. Default: ``ImageReadMode.UNCHANGED``.
|
|
See `ImageReadMode` class for more information on various
|
|
available modes.
|
|
apply_exif_orientation (bool): apply EXIF orientation transformation to the output tensor.
|
|
Default: False.
|
|
|
|
Returns:
|
|
output (Tensor[image_channels, image_height, image_width])
|
|
"""
|
|
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
|
|
_log_api_usage_once(decode_png)
|
|
output = torch.ops.image.decode_png(input, mode.value, False, apply_exif_orientation)
|
|
return output
|
|
|
|
|
|
def encode_png(input: torch.Tensor, compression_level: int = 6) -> torch.Tensor:
|
|
"""
|
|
Takes an input tensor in CHW layout and returns a buffer with the contents
|
|
of its corresponding PNG file.
|
|
|
|
Args:
|
|
input (Tensor[channels, image_height, image_width]): int8 image tensor of
|
|
``c`` channels, where ``c`` must 3 or 1.
|
|
compression_level (int): Compression factor for the resulting file, it must be a number
|
|
between 0 and 9. Default: 6
|
|
|
|
Returns:
|
|
Tensor[1]: A one dimensional int8 tensor that contains the raw bytes of the
|
|
PNG file.
|
|
"""
|
|
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
|
|
_log_api_usage_once(encode_png)
|
|
output = torch.ops.image.encode_png(input, compression_level)
|
|
return output
|
|
|
|
|
|
def write_png(input: torch.Tensor, filename: str, compression_level: int = 6):
|
|
"""
|
|
Takes an input tensor in CHW layout (or HW in the case of grayscale images)
|
|
and saves it in a PNG file.
|
|
|
|
Args:
|
|
input (Tensor[channels, image_height, image_width]): int8 image tensor of
|
|
``c`` channels, where ``c`` must be 1 or 3.
|
|
filename (str or ``pathlib.Path``): Path to save the image.
|
|
compression_level (int): Compression factor for the resulting file, it must be a number
|
|
between 0 and 9. Default: 6
|
|
"""
|
|
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
|
|
_log_api_usage_once(write_png)
|
|
output = encode_png(input, compression_level)
|
|
write_file(filename, output)
|
|
|
|
|
|
def decode_jpeg(
|
|
input: torch.Tensor,
|
|
mode: ImageReadMode = ImageReadMode.UNCHANGED,
|
|
device: str = "cpu",
|
|
apply_exif_orientation: bool = False,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Decodes a JPEG image into a 3 dimensional RGB or grayscale Tensor.
|
|
Optionally converts the image to the desired format.
|
|
The values of the output tensor are uint8 between 0 and 255.
|
|
|
|
Args:
|
|
input (Tensor[1]): a one dimensional uint8 tensor containing
|
|
the raw bytes of the JPEG image. This tensor must be on CPU,
|
|
regardless of the ``device`` parameter.
|
|
mode (ImageReadMode): the read mode used for optionally
|
|
converting the image. The supported modes are: ``ImageReadMode.UNCHANGED``,
|
|
``ImageReadMode.GRAY`` and ``ImageReadMode.RGB``
|
|
Default: ``ImageReadMode.UNCHANGED``.
|
|
See ``ImageReadMode`` class for more information on various
|
|
available modes.
|
|
device (str or torch.device): The device on which the decoded image will
|
|
be stored. If a cuda device is specified, the image will be decoded
|
|
with `nvjpeg <https://developer.nvidia.com/nvjpeg>`_. This is only
|
|
supported for CUDA version >= 10.1
|
|
|
|
.. betastatus:: device parameter
|
|
|
|
.. warning::
|
|
There is a memory leak in the nvjpeg library for CUDA versions < 11.6.
|
|
Make sure to rely on CUDA 11.6 or above before using ``device="cuda"``.
|
|
apply_exif_orientation (bool): apply EXIF orientation transformation to the output tensor.
|
|
Default: False. Only implemented for JPEG format on CPU.
|
|
|
|
Returns:
|
|
output (Tensor[image_channels, image_height, image_width])
|
|
"""
|
|
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
|
|
_log_api_usage_once(decode_jpeg)
|
|
device = torch.device(device)
|
|
if device.type == "cuda":
|
|
output = torch.ops.image.decode_jpeg_cuda(input, mode.value, device)
|
|
else:
|
|
output = torch.ops.image.decode_jpeg(input, mode.value, apply_exif_orientation)
|
|
return output
|
|
|
|
|
|
def encode_jpeg(input: torch.Tensor, quality: int = 75) -> torch.Tensor:
|
|
"""
|
|
Takes an input tensor in CHW layout and returns a buffer with the contents
|
|
of its corresponding JPEG file.
|
|
|
|
Args:
|
|
input (Tensor[channels, image_height, image_width])): int8 image tensor of
|
|
``c`` channels, where ``c`` must be 1 or 3.
|
|
quality (int): Quality of the resulting JPEG file, it must be a number between
|
|
1 and 100. Default: 75
|
|
|
|
Returns:
|
|
output (Tensor[1]): A one dimensional int8 tensor that contains the raw bytes of the
|
|
JPEG file.
|
|
"""
|
|
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
|
|
_log_api_usage_once(encode_jpeg)
|
|
if quality < 1 or quality > 100:
|
|
raise ValueError("Image quality should be a positive number between 1 and 100")
|
|
|
|
output = torch.ops.image.encode_jpeg(input, quality)
|
|
return output
|
|
|
|
|
|
def write_jpeg(input: torch.Tensor, filename: str, quality: int = 75):
|
|
"""
|
|
Takes an input tensor in CHW layout and saves it in a JPEG file.
|
|
|
|
Args:
|
|
input (Tensor[channels, image_height, image_width]): int8 image tensor of ``c``
|
|
channels, where ``c`` must be 1 or 3.
|
|
filename (str or ``pathlib.Path``): Path to save the image.
|
|
quality (int): Quality of the resulting JPEG file, it must be a number
|
|
between 1 and 100. Default: 75
|
|
"""
|
|
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
|
|
_log_api_usage_once(write_jpeg)
|
|
output = encode_jpeg(input, quality)
|
|
write_file(filename, output)
|
|
|
|
|
|
def decode_image(
|
|
input: torch.Tensor, mode: ImageReadMode = ImageReadMode.UNCHANGED, apply_exif_orientation: bool = False
|
|
) -> torch.Tensor:
|
|
"""
|
|
Detects whether an image is a JPEG or PNG and performs the appropriate
|
|
operation to decode the image into a 3 dimensional RGB or grayscale Tensor.
|
|
|
|
Optionally converts the image to the desired format.
|
|
The values of the output tensor are uint8 in [0, 255].
|
|
|
|
Args:
|
|
input (Tensor): a one dimensional uint8 tensor containing the raw bytes of the
|
|
PNG or JPEG image.
|
|
mode (ImageReadMode): the read mode used for optionally converting the image.
|
|
Default: ``ImageReadMode.UNCHANGED``.
|
|
See ``ImageReadMode`` class for more information on various
|
|
available modes.
|
|
apply_exif_orientation (bool): apply EXIF orientation transformation to the output tensor.
|
|
Default: False.
|
|
|
|
Returns:
|
|
output (Tensor[image_channels, image_height, image_width])
|
|
"""
|
|
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
|
|
_log_api_usage_once(decode_image)
|
|
output = torch.ops.image.decode_image(input, mode.value, apply_exif_orientation)
|
|
return output
|
|
|
|
|
|
def read_image(
|
|
path: str, mode: ImageReadMode = ImageReadMode.UNCHANGED, apply_exif_orientation: bool = False
|
|
) -> torch.Tensor:
|
|
"""
|
|
Reads a JPEG or PNG image into a 3 dimensional RGB or grayscale Tensor.
|
|
Optionally converts the image to the desired format.
|
|
The values of the output tensor are uint8 in [0, 255].
|
|
|
|
Args:
|
|
path (str or ``pathlib.Path``): path of the JPEG or PNG image.
|
|
mode (ImageReadMode): the read mode used for optionally converting the image.
|
|
Default: ``ImageReadMode.UNCHANGED``.
|
|
See ``ImageReadMode`` class for more information on various
|
|
available modes.
|
|
apply_exif_orientation (bool): apply EXIF orientation transformation to the output tensor.
|
|
Default: False.
|
|
|
|
Returns:
|
|
output (Tensor[image_channels, image_height, image_width])
|
|
"""
|
|
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
|
|
_log_api_usage_once(read_image)
|
|
data = read_file(path)
|
|
return decode_image(data, mode, apply_exif_orientation=apply_exif_orientation)
|
|
|
|
|
|
def _read_png_16(path: str, mode: ImageReadMode = ImageReadMode.UNCHANGED) -> torch.Tensor:
|
|
data = read_file(path)
|
|
return torch.ops.image.decode_png(data, mode.value, True)
|