decode_image¶

torchvision.io.decode_image(input: Union[Tensor, str], mode: ImageReadMode = ImageReadMode.UNCHANGED, apply_exif_orientation: bool = False) → Tensor[source]¶

Decode an image into a uint8 tensor, from a path or from raw encoded bytes.

Currently supported image formats are jpeg, png, gif and webp.

The values of the output tensor are in uint8 in [0, 255] for most cases.

If the image is a 16-bit png, then the output tensor is uint16 in [0, 65535] (supported from torchvision 0.21). Since uint16 support is limited in pytorch, we recommend calling torchvision.transforms.v2.functional.to_dtype() with scale=True after this function to convert the decoded image into a uint8 or float tensor.

Note

decode_image() doesn’t work yet on AVIF or HEIC images. For these formats, directly call decode_avif() or decode_heic().

Parameters:

input (Tensor or str or pathlib.Path) – The image to decode. If a tensor is passed, it must be one dimensional uint8 tensor containing the raw bytes of the image. Otherwise, this must be a path to the image file.
mode (str or ImageReadMode) – The mode to convert the image to, e.g. “RGB”. Default is “UNCHANGED”. See ImageReadMode for available modes.
apply_exif_orientation (bool) – apply EXIF orientation transformation to the output tensor. Only applies to JPEG and PNG images. Default: False.

Returns: