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wandb.data_types.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
Text
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/4.2.x/handbook/concepts.html#concept-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})

Methods

all_boxes

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

all_captions

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

all_masks

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

guess_mode

guess_mode(
data: "np.ndarray"
) -> str
Guess what type of image the np.array is representing

to_uint8

@classmethod
to_uint8(
data: "np.ndarray"
) -> "np.ndarray"
Converts floating point image on the range [0,1] and integer images on the range [0,255] to uint8, clipping if necessary.
Class Variables
Text
MAX_DIMENSION
65500
MAX_ITEMS
108