wandb.data_types.ImageMask
Format image masks or overlays for logging to W&B.
ImageMask(
val: dict,
key: str
) -> None
Arguments | Text |
|---|---|
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) |
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})
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})
@classmethod
type_name() -> str
validate(
val: dict
) -> bool
Last modified 2mo ago