wandb.data_types.ImageMask
Wandb class for image masks or overlays, useful for tasks like semantic segmentation.
ImageMask(val: dict,key: str) -> None
Arguments | |
| (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. |
| (string) The readable name or id for this mask type (e.g. predictions, ground_truth) |
Log a mask overlay for a given image
predicted_mask = np.array([[1, 2, 2, ... , 3, 2, 1], ...])ground_truth_mask = np.array([[1, 1, 1, ... , 2, 3, 1], ...])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})
Prepare an image mask to be added to a wandb.Table
raw_image_path = "sample_image.png"predicted_mask_path = "predicted_mask.png"class_set = wandb.Classes([{"name" : "person", "id" : 0},{"name" : "tree", "id" : 1},{"name" : "car", "id" : 2},{"name" : "road", "id" : 3}])masked_image = wandb.Image(raw_image_path, classes=class_set,masks={"prediction" : {"path" : predicted_mask_path}})
@classmethodtype_name() -> str
validate(val: dict) -> bool