boxes.py

import torch
from torchvision import _C


def nms(boxes, scores, iou_threshold):
    """
    Performs non-maximum suppression (NMS) on the boxes according
    to their intersection-over-union (IoU).

    NMS iteratively removes lower scoring boxes which have an
    IoU greater than iou_threshold with another (higher scoring)
    box.

    Arguments:
        boxes (Tensor[N, 4]): boxes to perform NMS on
        scores (Tensor[N]): scores for each one of the boxes
        iou_threshold (float): discards all overlapping
            boxes with IoU < iou_threshold

    Returns:
        keep (Tensor): int64 tensor with the indices
            of the elements that have been kept
            by NMS, sorted in decreasing order of scores
    """
    return _C.nms(boxes, scores, iou_threshold)


def batched_nms(boxes, scores, idxs, iou_threshold):
    """
    Performs non-maximum suppression in a batched fashion.

    Each index value correspond to a category, and NMS
    will not be applied between elements of different categories.

    Arguments:
        boxes (Tensor[N, 4]): boxes where NMS will be performed
        scores (Tensor[N]): scores for each one of the boxes
        idxs (Tensor[N]): indices of the categories for each
            one of the boxes.
        iou_threshold (float): discards all overlapping boxes
            with IoU < iou_threshold

    Returns:
        keep (Tensor): int64 tensor with the indices of
            the elements that have been kept by NMS, sorted
            in decreasing order of scores
    """
    if boxes.numel() == 0:
        return torch.empty((0,), dtype=torch.int64, device=boxes.device)
    # strategy: in order to perform NMS independently per class.
    # we add an offset to all the boxes. The offset is dependent
    # only on the class idx, and is large enough so that boxes
    # from different classes do not overlap
    max_coordinate = boxes.max()
    offsets = idxs.to(boxes) * (max_coordinate + 1)
    boxes_for_nms = boxes + offsets[:, None]
    keep = nms(boxes_for_nms, scores, iou_threshold)
    return keep


def remove_small_boxes(boxes, min_size):
    ws, hs = boxes[:, 2] - boxes[:, 0], boxes[:, 3] - boxes[:, 1]
    keep = (ws >= min_size) & (hs >= min_size)
    keep = keep.nonzero().squeeze(1)
    return keep


def clip_boxes_to_image(boxes, size):
    """
    Arguments:
        boxes (Tensor[N, 4])
        size (Tuple[height, width])

    Returns:
        clipped_boxes (Tensor[N, 4])
    """
    dim = boxes.dim()
    boxes_x = boxes[..., 0::2]
    boxes_y = boxes[..., 1::2]
    height, width = size
    boxes_x = boxes_x.clamp(min=0, max=width)
    boxes_y = boxes_y.clamp(min=0, max=height)
    clipped_boxes = torch.stack((boxes_x, boxes_y), dim=dim)
    return clipped_boxes.reshape(boxes.shape)


def box_area(boxes):
    """
    Computes the area of a set of bounding boxes, which are specified by its
    (x0, y0, x1, y1) coordinates.

    Arguments:
        boxes (Tensor[N, 4]): boxes for which the area will be computed. They
            are expected to be in (x0, y0, x1, y1) format

    Returns:
        area (Tensor[N]): area for each box
    """
    return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])


# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
# with slight modifications
def box_iou(boxes1, boxes2):
    """
    Return intersection-over-union (Jaccard index) of boxes.

    Arguments:
        boxes1 (Tensor[N, 4])
        boxes2 (Tensor[M, 4])

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

    iou = inter / (area1[:, None] + area2 - inter)
    return iou