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