Image

class Image

A class for logging images to W&B.

See https://pillow.readthedocs.io/en/stable/handbook/concepts.html#modes for more information on modes.

Args:

• data_or_path: Accepts numpy array of image data, or a PIL image. The class attempts to infer the data format and converts it.
• mode: The PIL mode for an image. Most common are “L”, “RGB”, “RGBA”.
• caption: Label for display of image.

When logging a torch.Tensor as a wandb.Image, images are normalized. If you do not want to normalize your images, 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})

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/ref/python/data-types/boundingboxes2d/
• masks: A dictionary containing mask information for the image.
• see: https://docs.wandb.ai/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 ```python

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 ```python

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) ```python

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})
``` 

method Image.guess_mode

guess_mode(
    data: Union[ForwardRef('np.ndarray'), ForwardRef('torch.Tensor')],
    file_type: Optional[str] = None
) → str

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