ImageMask
2 minute read
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
run = 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,
},
},
)
run.log({"img_with_masks": masked_image})
Log a masked image inside a Table
import numpy as np
import wandb
run = 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)
run.log({"random_field": table})
Methods
type_name
@classmethod
type_name() -> str
validate
validate(
val: dict
) -> bool
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