wandb.data_types.BoundingBoxes2D

​​View source on GitHub​
Wandb class for logging 2D bounding boxes on images, useful for tasks like object detection
BoundingBoxes2D(
    val: dict,
    key: str
) -> None
Arguments
​
val
(dictionary) A dictionary of the following form: box_data: (list of dictionaries) One dictionary for each bounding box, containing: position: (dictionary) the position and size of the bounding box, in one of two formats Note that boxes need not all use the same format. {"minX", "minY", "maxX", "maxY"}: (dictionary) A set of coordinates defining the upper and lower bounds of the box (the bottom left and top right corners) {"middle", "width", "height"}: (dictionary) A set of coordinates defining the center and dimensions of the box, with "middle" as a list [x, y] for the center point and "width" and "height" as numbers domain: (string) One of two options for the bounding box coordinate domain null: By default, or if no argument is passed, the coordinate domain is assumed to be relative to the original image, expressing this box as a fraction or percentage of the original image. This means all coordinates and dimensions passed into the "position" argument are floating point numbers between 0 and 1. "pixel": (string literal) The coordinate domain is set to the pixel space. This means all coordinates and dimensions passed into "position" are integers within the bounds of the image dimensions. class_id: (integer) The class label id for this box scores: (dictionary of string to number, optional) A mapping of named fields to numerical values (float or int), can be used for filtering boxes in the UI based on a range of values for the corresponding field box_caption: (string, optional) A string to be displayed as the label text above this box in the UI, often composed of the class label, class name, and/or scores class_labels: (dictionary, optional) A map of integer class labels to their readable class names
key
(string) The readable name or id for this set of bounding boxes (e.g. predictions, ground_truth)
Examples:
Log a set of predicted and ground truth bounding boxes for a given image
class_labels = {
    0: "person",
    1: "car",
    2: "road",
    3: "building"
}
img = wandb.Image(image, boxes={
    "predictions": {
        "box_data": [
            {
                # one box expressed in the default relative/fractional domain
                "position": {
                    "minX": 0.1,
                    "maxX": 0.2,
                    "minY": 0.3,
                    "maxY": 0.4
                },
                "class_id" : 1,
                "box_caption": class_labels[1],
                "scores" : {
                    "acc": 0.2,
                    "loss": 1.2
                }
            },
            {
                # another box expressed in the pixel domain
                "position": {
                    "middle": [150, 20],
                    "width": 68,
                    "height": 112
                },
                "domain" : "pixel",
                "class_id" : 3,
                "box_caption": "a building",
                "scores" : {
                    "acc": 0.5,
                    "loss": 0.7
                }
            },
            ...
            # Log as many boxes an as needed
        ],
        "class_labels": class_labels
    },
    # Log each meaningful group of boxes with a unique key name
    "ground_truth": {
    ...
    }
})
​
wandb.log({"driving_scene": img})
Prepare an image with bounding boxes to be added to a wandb.Table
raw_image_path = "sample_image.png"
​
class_set = wandb.Classes([
    {"name" : "person", "id" : 0},
    {"name" : "car", "id" : 1},
    {"name" : "road", "id" : 2},
    {"name" : "building", "id" : 3}
])
​
image_with_boxes = wandb.Image(raw_image_path, classes=class_set,
    boxes=[...identical to previous example...])
Methods
type_name
​View source​
@classmethod
type_name() -> str
validate
​View source​
validate(
    val: dict
) -> bool

wandb.data_types.Audio

wandb.data_types.Graph

Last updated 18 hours ago

wandb.data_types.BoundingBoxes2D

Examples:

Methods

`type_name`

`validate`

Arguments
`val`	(dictionary) A dictionary of the following form: box_data: (list of dictionaries) One dictionary for each bounding box, containing: position: (dictionary) the position and size of the bounding box, in one of two formats Note that boxes need not all use the same format. {"minX", "minY", "maxX", "maxY"}: (dictionary) A set of coordinates defining the upper and lower bounds of the box (the bottom left and top right corners) {"middle", "width", "height"}: (dictionary) A set of coordinates defining the center and dimensions of the box, with "middle" as a list [x, y] for the center point and "width" and "height" as numbers domain: (string) One of two options for the bounding box coordinate domain null: By default, or if no argument is passed, the coordinate domain is assumed to be relative to the original image, expressing this box as a fraction or percentage of the original image. This means all coordinates and dimensions passed into the "position" argument are floating point numbers between 0 and 1. "pixel": (string literal) The coordinate domain is set to the pixel space. This means all coordinates and dimensions passed into "position" are integers within the bounds of the image dimensions. class_id: (integer) The class label id for this box scores: (dictionary of string to number, optional) A mapping of named fields to numerical values (float or int), can be used for filtering boxes in the UI based on a range of values for the corresponding field box_caption: (string, optional) A string to be displayed as the label text above this box in the UI, often composed of the class label, class name, and/or scores class_labels: (dictionary, optional) A map of integer class labels to their readable class names
`key`	(string) The readable name or id for this set of bounding boxes (e.g. predictions, ground_truth)