Skip to main content

Image

Format images for logging to W&B.

Image(
data_or_path: "ImageDataOrPathType",
mode: Optional[str] = None,
caption: Optional[str] = None,
grouping: Optional[int] = None,
classes: Optional[Union['Classes', Sequence[dict]]] = None,
boxes: Optional[Union[Dict[str, 'BoundingBoxes2D'], Dict[str, dict]]] = None,
masks: Optional[Union[Dict[str, 'ImageMask'], Dict[str, dict]]] = None
) -> None
Arguments
data_or_path(numpy array, string, io) Accepts numpy array of image data, or a PIL image. The class attempts to infer the data format and converts it.
mode(string) The PIL mode for an image. Most common are "L", "RGB", "RGBA". Full explanation at https://pillow.readthedocs.io/en/stable/handbook/concepts.html#modes.
caption(string) Label for display of image.

Note : When logging a torch.Tensor as a wandb.Image, images are normalized. If you do not want to normalize your images, please convert your tensors to a PIL Image.

Examples:

Create a wandb.Image from a numpy array

import numpy as np
import wandb

wandb.init()
examples = []
for i in range(3):
pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
image = wandb.Image(pixels, caption=f"random field {i}")
examples.append(image)
wandb.log({"examples": examples})

Create a wandb.Image from a PILImage

import numpy as np
from PIL import Image as PILImage
import wandb

wandb.init()
examples = []
for i in range(3):
pixels = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
pil_image = PILImage.fromarray(pixels, mode="RGB")
image = wandb.Image(pil_image, caption=f"random field {i}")
examples.append(image)
wandb.log({"examples": examples})
Attributes

Methods

all_boxes

View source

@classmethod
all_boxes(
images: Sequence['Image'],
run: "LocalRun",
run_key: str,
step: Union[int, str]
) -> Union[List[Optional[dict]], bool]

all_captions

View source

@classmethod
all_captions(
images: Sequence['Media']
) -> Union[bool, Sequence[Optional[str]]]

all_masks

View source

@classmethod
all_masks(
images: Sequence['Image'],
run: "LocalRun",
run_key: str,
step: Union[int, str]
) -> Union[List[Optional[dict]], bool]

guess_mode

View source

guess_mode(
data: "np.ndarray"
) -> str

Guess what type of image the np.array is representing.

to_uint8

View source

@classmethod
to_uint8(
data: "np.ndarray"
) -> "np.ndarray"

Convert image data to uint8.

Convert floating point image on the range [0,1] and integer images on the range [0,255] to uint8, clipping if necessary.

Class Variables
MAX_DIMENSION65500
MAX_ITEMS108
Was this page helpful?👍👎