2D/3D bounding box library for Computer Vision
bbox a Python library that is intended to ease the use of 2D and 3D bounding boxes in areas such as Object Detection by providing a set of flexible primitives and functions that are intuitive and easy to use out of the box.
2D Bounding Box
Easily work with bounding boxes using a simple class that abstracts and maintains various attributes.
from bbox import BBox2D, XYXY # x, y, w, h box = BBox2D([0, 0, 32, 32]) # equivalently, in (x1, y1, x2, y2) (aka two point format), we can use box = BBox2D([0, 0, 31, 31], mode=XYXY) print(box.x1, box.y1) # -> 0 0 print(box.x2, box.y2) # -> 31 31 print(box.height, box.width) # -> 32 32 # Syntatic sugar for height and width print(box.h, box.w) # -> 32 32
Sequence of 2D bounding boxes
Most tasks involve dealing with multiple bounding boxes. This can also be handled conveniently with the
bbl = BBox2DList(np.random.randint(10, 4), mode=XYWH)
The above snippet creates a list of 10 bounding boxes neatly abstracted into a convenient object.
Need to perform non-maximum suppression? It is as easy as a single function call.
from bbox.utils import nms # bbl -> BBox2DList # scores -> list/ndarray of confidence scores new_boxes = nms(bbl, scores)
Intersection over Union (Jaccard Index)
The Jaccard Index or IoU is a very useful metric for finding similarities between bounding boxes.
bbox provides native support for this.
from bbox.metrics import jaccard_index_2d box1 = BBox2D([0, 0, 32, 32]) box2 = BBox2D([10, 12, 32, 46]) iou = jaccard_index_2d(box1, box2)
We can even use the Jaccard Index to compute a distance metric between boxes as a distance matrix:
from bbox.metrics import multi_jaccard_index_2d dist = 1 - multi_jaccard_index_2d(bbl, bbl)
3D Bounding Box
bbox also support 3D bounding boxes, providing convenience methods and attributes for working with them.
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