mmpretrain/models/utils/box_utils.py

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torchvision.ops.boxes as boxes


def box_cxcywh_to_xyxy(x):
    x_c, y_c, w, h = x.unbind(-1)
    b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
    return torch.stack(b, dim=-1)


def box_xyxy_to_cxcywh(x):
    x0, y0, x1, y1 = x.unbind(-1)
    b = [(x0 + x1) / 2.0, (y0 + y1) / 2.0, (x1 - x0), (y1 - y0)]
    return torch.stack(b, dim=-1)


def box_iou(boxes1, boxes2):
    """Return intersection-over-union (Jaccard index) between two sets of
    boxes.

    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
    ``0 <= x1 < x2`` and ``0 <= y1 < y2``.

    Args:
        boxes1 (Tensor[N, 4]): first set of boxes
        boxes2 (Tensor[M, 4]): second set of boxes

    Returns:
        Tensor[N, M]: the NxM matrix containing the pairwise IoU values for
        every element in boxes1 and boxes2
    """
    return boxes.box_iou(boxes1, boxes2)


def generalized_box_iou(boxes1, boxes2):
    """Return generalized intersection-over-union (Jaccard index) between two
    sets of boxes.

    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
    ``0 <= x1 < x2`` and ``0 <= y1 < y2``.

    Args:
        boxes1 (Tensor[N, 4]): first set of boxes
        boxes2 (Tensor[M, 4]): second set of boxes

    Returns:
        Tensor[N, M]: the NxM matrix containing the pairwise generalized IoU
        values for every element in boxes1 and boxes2
    """
    # degenerate boxes gives inf / nan results
    # so do an early check
    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()

    return boxes.generalized_box_iou(boxes1, boxes2)