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wandb.data_types.BoundingBoxes2D
Format images with 2D bounding box overlays for logging to W&B.
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BoundingBoxes2D(
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val: dict,
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key: str
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) -> None
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Arguments
Text
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 bounding boxes for a single image
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import numpy as np
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import wandb
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​
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wandb.init()
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image = np.random.randint(low=0, high=256, size=(200, 300, 3))
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​
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class_labels = {
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0: "person",
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1: "car",
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2: "road",
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3: "building"
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}
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​
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img = wandb.Image(image, boxes={
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"predictions": {
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"box_data": [
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{
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# one box expressed in the default relative/fractional domain
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"position": {
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"minX": 0.1,
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"maxX": 0.2,
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"minY": 0.3,
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"maxY": 0.4
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},
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"class_id" : 1,
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"box_caption": class_labels[1],
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"scores" : {
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"acc": 0.2,
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"loss": 1.2
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}
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},
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{
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# another box expressed in the pixel domain
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"position": {
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"middle": [150, 20],
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"width": 68,
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"height": 112
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},
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"domain" : "pixel",
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"class_id" : 3,
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"box_caption": "a building",
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"scores" : {
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"acc": 0.5,
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"loss": 0.7
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}
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},
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# Log as many boxes an as needed
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],
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"class_labels": class_labels
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}
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})
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​
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wandb.log({"driving_scene": img})
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Log a bounding box overlay to a Table
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import numpy as np
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import wandb
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​
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wandb.init()
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image = np.random.randint(low=0, high=256, size=(200, 300, 3))
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​
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class_labels = {
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0: "person",
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1: "car",
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2: "road",
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3: "building"
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}
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​
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class_set = wandb.Classes([
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{"name" : "person", "id" : 0},
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{"name" : "car", "id" : 1},
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{"name" : "road", "id" : 2},
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{"name" : "building", "id" : 3}
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])
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​
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img = wandb.Image(image, boxes={
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"predictions": {
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"box_data": [
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{
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# one box expressed in the default relative/fractional domain
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"position": {
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"minX": 0.1,
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"maxX": 0.2,
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"minY": 0.3,
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"maxY": 0.4
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},
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"class_id" : 1,
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"box_caption": class_labels[1],
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"scores" : {
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"acc": 0.2,
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"loss": 1.2
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}
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},
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{
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# another box expressed in the pixel domain
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"position": {
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"middle": [150, 20],
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"width": 68,
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"height": 112
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},
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"domain" : "pixel",
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"class_id" : 3,
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"box_caption": "a building",
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"scores" : {
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"acc": 0.5,
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"loss": 0.7
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}
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},
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# Log as many boxes an as needed
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],
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"class_labels": class_labels
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}
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}, classes=class_set)
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​
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table = wandb.Table(columns=["image"])
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table.add_data(img)
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wandb.log({"driving_scene": table})
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Methods
type_name
type_name1
@classmethod
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type_name() -> str
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validate
validate1
validate(
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val: dict
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) -> bool
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Last modified 3mo ago