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,
    file_type: Optional[str] = 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

with wandb.init() as run:
    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)
    run.log({"examples": examples})

Create a wandb.Image from a PILImage

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

with wandb.init() as run:
    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)
    run.log({"examples": examples})

log .jpg rather than .png (default)

import numpy as np
import wandb

with wandb.init() as run:
    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}", file_type="jpg")
        examples.append(image)
    run.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_DIMENSION	65500
MAX_ITEMS	108