Skip to main content

​
class Image

A class for logging images to W&B.

​
method Image.__init__

__init__(
    data_or_path: 'ImageDataOrPathType',
    mode: Optional[str] = None,
    caption: Optional[str] = None,
    grouping: Optional[int] = None,
    classes: Optional[ForwardRef('Classes'), Sequence[dict]] = None,
    boxes: Optional[Dict[str, ForwardRef('BoundingBoxes2D')], Dict[str, dict]] = None,
    masks: Optional[Dict[str, ForwardRef('ImageMask')], Dict[str, dict]] = None,
    file_type: Optional[str] = None,
    normalize: bool = True
) → None
Initialize a wandb.Image object. 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 explanation at 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})

​
property Image.image

⌘I