Image

class Image

A class for logging images to W&B.

method Image.__init__

__init__(
    data_or_path: 'ImageDataOrPathType',
    mode: 'str | None' = None,
    caption: 'str | None' = None,
    grouping: 'int | None' = None,
    classes: 'Classes | Sequence[dict] | None' = None,
    boxes: 'dict[str, BoundingBoxes2D] | dict[str, dict] | None' = None,
    masks: 'dict[str, ImageMask] | dict[str, dict] | None' = None,
    file_type: 'str | None' = None,
    normalize: 'bool' = True
) → None

Initialize a wandb.Image object.

This class handles various image data formats and automatically normalizes pixel values to the range [0, 255] when needed, ensuring compatibility with the W&B backend.

• Data in range [0, 1] is multiplied by 255 and converted to uint8 * Data in range [-1, 1] is rescaled from [-1, 1] to [0, 255] by mapping -1 to 0 and 1 to 255, then converted to uint8 * Data outside [-1, 1] but not in [0, 255] is clipped to [0, 255] and converted to uint8 (with a warning if values fall outside [0, 255]) * Data already in [0, 255] is converted to uint8 without modification

Args:

• data_or_path: Accepts NumPy array/pytorch tensor of image data, a PIL image object, or a path to an image file. If a NumPy array or pytorch tensor is provided, the image data will be saved to the given file type. If the values are not in the range [0, 255] or all values are in the range [0, 1], the image pixel values will be normalized to the range [0, 255] unless normalize is set to False.
  • pytorch tensor should be in the format (channel, height, width)
  • NumPy array should be in the format (height, width, channel)
• mode: The PIL mode for an image. Most common are “L”, “RGB”, “RGBA”.
• Full Pillow docs for more information https: //pillow.readthedocs.io/en/stable/handbook/concepts.html#modes
• caption: Label for display of image.
• grouping: The grouping number for the image.
• classes: A list of class information for the image, used for labeling bounding boxes, and image masks.
• boxes: A dictionary containing bounding box information for the image.
• see https: //docs.wandb.ai/models/ref/python/data-types/boundingboxes2d
• masks: A dictionary containing mask information for the image.
• see https: //docs.wandb.ai/models/ref/python/data-types/imagemask
• file_type: The file type to save the image as. This parameter has no effect if data_or_path is a path to an image file.
• normalize: If True, normalize the image pixel values to fall within the range of [0, 255]. Normalize is only applied if data_or_path is a numpy array or pytorch tensor.

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})

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property Image.image

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