box_ops.py

# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#

"""Utilities for bounding box manipulation and GIoU."""
from __future__ import annotations

import torch
from torch import Tensor
from torchvision.ops.boxes import box_area


def box_cxcywh_to_xyxy(x: Tensor) -> Tensor:
    """Convert boxes from (cx, cy, w, h) format to (x0, y0, x1, y1) format."""
    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: Tensor) -> Tensor:
    """Convert boxes from (x0, y0, x1, y1) format to (cx, cy, w, h) format."""
    x0, y0, x1, y1 = x.unbind(-1)
    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
    return torch.stack(b, dim=-1)


def box_iou(boxes1: Tensor, boxes2: Tensor) -> tuple[Tensor, Tensor]:
    """Compute the IoU between two sets of boxes of shape (N, 4) and (M, 4)."""
    area1 = box_area(boxes1)
    area2 = box_area(boxes2)

    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]

    wh = (rb - lt).clamp(min=0)  # [N,M,2]
    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]

    union = area1[:, None] + area2 - inter

    iou = inter / (union + 1e-6)
    return iou, union


def generalized_box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
    """Generalized IoU from https://giou.stanford.edu/.

    The boxes should be in [x0, y0, x1, y1] format

    Returns a [N, M] pairwise matrix, where N = len(boxes1)
    and M = len(boxes2)
    """
    # degenerate boxes gives inf / nan results
    # so do an early check
    if not (boxes1[:, 2:] >= boxes1[:, :2]).all():
        msg = "All boxes1 should be in (x0, y0, x1, y1) format"
        raise ValueError(msg)
    if not (boxes2[:, 2:] >= boxes2[:, :2]).all():
        msg = "All boxes2 should be in (x0, y0, x1, y1) format"
        raise ValueError(msg)

    iou, union = box_iou(boxes1, boxes2)

    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])

    wh = (rb - lt).clamp(min=0)  # [N,M,2]
    area = wh[:, :, 0] * wh[:, :, 1]

    return iou - (area - union) / (area + 1e-6)


def masks_to_boxes(masks: Tensor) -> Tensor:
    """Compute the bounding boxes around the provided masks.

    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.

    Returns a [N, 4] tensors, with the boxes in xyxy format
    """
    if masks.numel() == 0:
        return torch.zeros((0, 4), device=masks.device)

    h, w = masks.shape[-2:]

    y = torch.arange(0, h, dtype=torch.float, device=masks.device)
    x = torch.arange(0, w, dtype=torch.float, device=masks.device)
    y, x = torch.meshgrid(y, x)

    x_mask = masks * x.unsqueeze(0)
    x_max = x_mask.flatten(1).max(-1)[0]
    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]

    y_mask = masks * y.unsqueeze(0)
    y_max = y_mask.flatten(1).max(-1)[0]
    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]

    return torch.stack([x_min, y_min, x_max, y_max], 1)