ImageMask

Format image masks or overlays for logging to W&B.

ImageMask(
    val: dict,
    key: str
) -> None

Args
val	(dictionary) One of these two keys to represent the image: mask_data : (2D numpy array) The mask containing an integer class label for each pixel in the image path : (string) The path to a saved image file of the mask class_labels : (dictionary of integers to strings, optional) A mapping of the integer class labels in the mask to readable class names. These will default to class_0, class_1, class_2, etc.
key	(string) The readable name or id for this mask type (e.g. predictions, ground_truth)

Examples:

Logging a single masked image

import numpy as np
import wandb

wandb.init()
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
predicted_mask = np.empty((100, 100), dtype=np.uint8)
ground_truth_mask = np.empty((100, 100), dtype=np.uint8)

predicted_mask[:50, :50] = 0
predicted_mask[50:, :50] = 1
predicted_mask[:50, 50:] = 2
predicted_mask[50:, 50:] = 3

ground_truth_mask[:25, :25] = 0
ground_truth_mask[25:, :25] = 1
ground_truth_mask[:25, 25:] = 2
ground_truth_mask[25:, 25:] = 3

class_labels = {0: "person", 1: "tree", 2: "car", 3: "road"}

masked_image = wandb.Image(
    image,
    masks={
        "predictions": {"mask_data": predicted_mask, "class_labels": class_labels},
        "ground_truth": {"mask_data": ground_truth_mask, "class_labels": class_labels},
    },
)
wandb.log({"img_with_masks": masked_image})

Log a masked image inside a Table

import numpy as np
import wandb

wandb.init()
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
predicted_mask = np.empty((100, 100), dtype=np.uint8)
ground_truth_mask = np.empty((100, 100), dtype=np.uint8)

predicted_mask[:50, :50] = 0
predicted_mask[50:, :50] = 1
predicted_mask[:50, 50:] = 2
predicted_mask[50:, 50:] = 3

ground_truth_mask[:25, :25] = 0
ground_truth_mask[25:, :25] = 1
ground_truth_mask[:25, 25:] = 2
ground_truth_mask[25:, 25:] = 3

class_labels = {0: "person", 1: "tree", 2: "car", 3: "road"}

class_set = wandb.Classes(
    [
        {"name": "person", "id": 0},
        {"name": "tree", "id": 1},
        {"name": "car", "id": 2},
        {"name": "road", "id": 3},
    ]
)

masked_image = wandb.Image(
    image,
    masks={
        "predictions": {"mask_data": predicted_mask, "class_labels": class_labels},
        "ground_truth": {"mask_data": ground_truth_mask, "class_labels": class_labels},
    },
    classes=class_set,
)

table = wandb.Table(columns=["image"])
table.add_data(masked_image)
wandb.log({"random_field": table})

Methods

type_name

View source

@classmethod
type_name() -> str

validate

View source

validate(
    val: dict
) -> bool