wandb.data_types.BoundingBoxes2D
Format images with 2D bounding box overlays for logging to W&B.
BoundingBoxes2D(
val: dict,
key: str
) -> None
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) |
import numpy as np
import wandb
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
}
})
wandb.log({"driving_scene": img})
import numpy as np
import wandb
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)
wandb.log({"driving_scene": table})
@classmethod
type_name() -> str
validate(
val: dict
) -> bool
Last modified 3mo ago