BoundingBoxes2D
3 minute read
Format images with 2D bounding box overlays for logging to W&B.
BoundingBoxes2D(
val: dict,
key: str
) -> None
| Args | |
|---|---|
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
import numpy as np
import wandb
run = wandb.init()
image = np.random.randint(low=0, high=256, size=(200, 300, 3))
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,
}
},
)
run.log({"driving_scene": img})
Log a bounding box overlay to a Table
import numpy as np
import wandb
run = wandb.init()
image = np.random.randint(low=0, high=256, size=(200, 300, 3))
class_labels = {0: "person", 1: "car", 2: "road", 3: "building"}
class_set = wandb.Classes(
[
{"name": "person", "id": 0},
{"name": "car", "id": 1},
{"name": "road", "id": 2},
{"name": "building", "id": 3},
]
)
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,
}
},
classes=class_set,
)
table = wandb.Table(columns=["image"])
table.add_data(img)
run.log({"driving_scene": table})
Methods
type_name
@classmethod
type_name() -> str
validate
validate(
val: dict
) -> bool
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