VISAM/models/structures/boxes.py

# ------------------------------------------------------------------------
# Modified from Detectron2 (https://github.com/facebookresearch/detectron2)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------

import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device

_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.ndarray]
def _maybe_jit_unused(x):
    return x


@unique
class BoxMode(IntEnum):
    """
    Enum of different ways to represent a box.
    """

    XYXY_ABS = 0
    """
    (x0, y0, x1, y1) in absolute floating points coordinates.
    The coordinates in range [0, width or height].
    """
    XYWH_ABS = 1
    """
    (x0, y0, w, h) in absolute floating points coordinates.
    """
    XYXY_REL = 2
    """
    Not yet supported!
    (x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image.
    """
    XYWH_REL = 3
    """
    Not yet supported!
    (x0, y0, w, h) in range [0, 1]. They are relative to the size of the image.
    """
    XYWHA_ABS = 4
    """
    (xc, yc, w, h, a) in absolute floating points coordinates.
    (xc, yc) is the center of the rotated box, and the angle a is in degrees ccw.
    """

    @staticmethod
    def convert(box: _RawBoxType, from_mode: "BoxMode", to_mode: "BoxMode") -> _RawBoxType:
        """
        Args:
            box: can be a k-tuple, k-list or an Nxk array/tensor, where k = 4 or 5
            from_mode, to_mode (BoxMode)

        Returns:
            The converted box of the same type.
        """
        if from_mode == to_mode:
            return box

        original_type = type(box)
        is_numpy = isinstance(box, np.ndarray)
        single_box = isinstance(box, (list, tuple))
        if single_box:
            assert len(box) == 4 or len(box) == 5, (
                "BoxMode.convert takes either a k-tuple/list or an Nxk array/tensor,"
                " where k == 4 or 5"
            )
            arr = torch.tensor(box)[None, :]
        else:
            # avoid modifying the input box
            if is_numpy:
                arr = torch.from_numpy(np.asarray(box)).clone()
            else:
                arr = box.clone()

        assert to_mode not in [BoxMode.XYXY_REL, BoxMode.XYWH_REL] and from_mode not in [
            BoxMode.XYXY_REL,
            BoxMode.XYWH_REL,
        ], "Relative mode not yet supported!"

        if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS:
            assert (
                arr.shape[-1] == 5
            ), "The last dimension of input shape must be 5 for XYWHA format"
            original_dtype = arr.dtype
            arr = arr.double()

            w = arr[:, 2]
            h = arr[:, 3]
            a = arr[:, 4]
            c = torch.abs(torch.cos(a * math.pi / 180.0))
            s = torch.abs(torch.sin(a * math.pi / 180.0))
            # This basically computes the horizontal bounding rectangle of the rotated box
            new_w = c * w + s * h
            new_h = c * h + s * w

            # convert center to top-left corner
            arr[:, 0] -= new_w / 2.0
            arr[:, 1] -= new_h / 2.0
            # bottom-right corner
            arr[:, 2] = arr[:, 0] + new_w
            arr[:, 3] = arr[:, 1] + new_h

            arr = arr[:, :4].to(dtype=original_dtype)
        elif from_mode == BoxMode.XYWH_ABS and to_mode == BoxMode.XYWHA_ABS:
            original_dtype = arr.dtype
            arr = arr.double()
            arr[:, 0] += arr[:, 2] / 2.0
            arr[:, 1] += arr[:, 3] / 2.0
            angles = torch.zeros((arr.shape[0], 1), dtype=arr.dtype)
            arr = torch.cat((arr, angles), axis=1).to(dtype=original_dtype)
        else:
            if to_mode == BoxMode.XYXY_ABS and from_mode == BoxMode.XYWH_ABS:
                arr[:, 2] += arr[:, 0]
                arr[:, 3] += arr[:, 1]
            elif from_mode == BoxMode.XYXY_ABS and to_mode == BoxMode.XYWH_ABS:
                arr[:, 2] -= arr[:, 0]
                arr[:, 3] -= arr[:, 1]
            else:
                raise NotImplementedError(
                    "Conversion from BoxMode {} to {} is not supported yet".format(
                        from_mode, to_mode
                    )
                )

        if single_box:
            return original_type(arr.flatten().tolist())
        if is_numpy:
            return arr.numpy()
        else:
            return arr


class Boxes:
    """
    This structure stores a list of boxes as a Nx4 torch.Tensor.
    It supports some common methods about boxes
    (`area`, `clip`, `nonempty`, etc),
    and also behaves like a Tensor
    (support indexing, `to(device)`, `.device`, and iteration over all boxes)

    Attributes:
        tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).
    """

    def __init__(self, tensor: torch.Tensor):
        """
        Args:
            tensor (Tensor[float]): a Nx4 matrix.  Each row is (x1, y1, x2, y2).
        """
        device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu")
        tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
        if tensor.numel() == 0:
            # Use reshape, so we don't end up creating a new tensor that does not depend on
            # the inputs (and consequently confuses jit)
            tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32, device=device)
        assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size()

        self.tensor = tensor

    def clone(self) -> "Boxes":
        """
        Clone the Boxes.

        Returns:
            Boxes
        """
        return Boxes(self.tensor.clone())

    @_maybe_jit_unused
    def to(self, device: torch.device):
        # Boxes are assumed float32 and does not support to(dtype)
        return Boxes(self.tensor.to(device=device))

    def area(self) -> torch.Tensor:
        """
        Computes the area of all the boxes.

        Returns:
            torch.Tensor: a vector with areas of each box.
        """
        box = self.tensor
        area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])
        return area

    def clip(self, box_size: Tuple[int, int]) -> None:
        """
        Clip (in place) the boxes by limiting x coordinates to the range [0, width]
        and y coordinates to the range [0, height].

        Args:
            box_size (height, width): The clipping box's size.
        """
        assert torch.isfinite(self.tensor).all(), "Box tensor contains infinite or NaN!"
        h, w = box_size
        x1 = self.tensor[:, 0].clamp(min=0, max=w)
        y1 = self.tensor[:, 1].clamp(min=0, max=h)
        x2 = self.tensor[:, 2].clamp(min=0, max=w)
        y2 = self.tensor[:, 3].clamp(min=0, max=h)
        self.tensor = torch.stack((x1, y1, x2, y2), dim=-1)

    def nonempty(self, threshold: float = 0.0) -> torch.Tensor:
        """
        Find boxes that are non-empty.
        A box is considered empty, if either of its side is no larger than threshold.

        Returns:
            Tensor:
                a binary vector which represents whether each box is empty
                (False) or non-empty (True).
        """
        box = self.tensor
        widths = box[:, 2] - box[:, 0]
        heights = box[:, 3] - box[:, 1]
        keep = (widths > threshold) & (heights > threshold)
        return keep

    def __getitem__(self, item) -> "Boxes":
        """
        Args:
            item: int, slice, or a BoolTensor

        Returns:
            Boxes: Create a new :class:`Boxes` by indexing.

        The following usage are allowed:

        1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.
        2. `new_boxes = boxes[2:10]`: return a slice of boxes.
        3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor
           with `length = len(boxes)`. Nonzero elements in the vector will be selected.

        Note that the returned Boxes might share storage with this Boxes,
        subject to Pytorch's indexing semantics.
        """
        if isinstance(item, int):
            return Boxes(self.tensor[item].view(1, -1))
        b = self.tensor[item]
        assert b.dim() == 2, "Indexing on Boxes with {} failed to return a matrix!".format(item)
        return Boxes(b)

    def __len__(self) -> int:
        return self.tensor.shape[0]

    def __repr__(self) -> str:
        return "Boxes(" + str(self.tensor) + ")"

    def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:
        """
        Args:
            box_size (height, width): Size of the reference box.
            boundary_threshold (int): Boxes that extend beyond the reference box
                boundary by more than boundary_threshold are considered "outside".

        Returns:
            a binary vector, indicating whether each box is inside the reference box.
        """
        height, width = box_size
        inds_inside = (
            (self.tensor[..., 0] >= -boundary_threshold)
            & (self.tensor[..., 1] >= -boundary_threshold)
            & (self.tensor[..., 2] < width + boundary_threshold)
            & (self.tensor[..., 3] < height + boundary_threshold)
        )
        return inds_inside

    def get_centers(self) -> torch.Tensor:
        """
        Returns:
            The box centers in a Nx2 array of (x, y).
        """
        return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2

    def scale(self, scale_x: float, scale_y: float) -> None:
        """
        Scale the box with horizontal and vertical scaling factors
        """
        self.tensor[:, 0::2] *= scale_x
        self.tensor[:, 1::2] *= scale_y

    @classmethod
    @_maybe_jit_unused
    def cat(cls, boxes_list: List["Boxes"]) -> "Boxes":
        """
        Concatenates a list of Boxes into a single Boxes

        Arguments:
            boxes_list (list[Boxes])

        Returns:
            Boxes: the concatenated Boxes
        """
        assert isinstance(boxes_list, (list, tuple))
        if len(boxes_list) == 0:
            return cls(torch.empty(0))
        assert all([isinstance(box, Boxes) for box in boxes_list])

        # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input
        cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))
        return cat_boxes

    @property
    def device(self) -> device:
        return self.tensor.device

    # type "Iterator[torch.Tensor]", yield, and iter() not supported by torchscript
    # https://github.com/pytorch/pytorch/issues/18627
    @torch.jit.unused
    def __iter__(self):
        """
        Yield a box as a Tensor of shape (4,) at a time.
        """
        yield from self.tensor


def pairwise_intersection(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
    """
    Given two lists of boxes of size N and M,
    compute the intersection area between __all__ N x M pairs of boxes.
    The box order must be (xmin, ymin, xmax, ymax)

    Args:
        boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.

    Returns:
        Tensor: intersection, sized [N,M].
    """
    boxes1, boxes2 = boxes1.tensor, boxes2.tensor
    width_height = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max(
        boxes1[:, None, :2], boxes2[:, :2]
    )  # [N,M,2]

    width_height.clamp_(min=0)  # [N,M,2]
    intersection = width_height.prod(dim=2)  # [N,M]
    return intersection


# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
# with slight modifications
def pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
    """
    Given two lists of boxes of size N and M, compute the IoU
    (intersection over union) between **all** N x M pairs of boxes.
    The box order must be (xmin, ymin, xmax, ymax).

    Args:
        boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.

    Returns:
        Tensor: IoU, sized [N,M].
    """
    area1 = boxes1.area()  # [N]
    area2 = boxes2.area()  # [M]
    inter = pairwise_intersection(boxes1, boxes2)

    # handle empty boxes
    iou = torch.where(
        inter > 0,
        inter / (area1[:, None] + area2 - inter),
        torch.zeros(1, dtype=inter.dtype, device=inter.device),
    )
    return iou


def pairwise_ioa(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
    """
    Similar to :func:`pariwise_iou` but compute the IoA (intersection over boxes2 area).

    Args:
        boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.

    Returns:
        Tensor: IoA, sized [N,M].
    """
    area2 = boxes2.area()  # [M]
    inter = pairwise_intersection(boxes1, boxes2)

    # handle empty boxes
    ioa = torch.where(
        inter > 0, inter / area2, torch.zeros(1, dtype=inter.dtype, device=inter.device)
    )
    return ioa


def matched_boxlist_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
    """
    Compute pairwise intersection over union (IOU) of two sets of matched
    boxes. The box order must be (xmin, ymin, xmax, ymax).
    Similar to boxlist_iou, but computes only diagonal elements of the matrix

    Args:
        boxes1: (Boxes) bounding boxes, sized [N,4].
        boxes2: (Boxes) bounding boxes, sized [N,4].
    Returns:
        Tensor: iou, sized [N].
    """
    assert len(boxes1) == len(
        boxes2
    ), "boxlists should have the same" "number of entries, got {}, {}".format(
        len(boxes1), len(boxes2)
    )
    area1 = boxes1.area()  # [N]
    area2 = boxes2.area()  # [N]
    box1, box2 = boxes1.tensor, boxes2.tensor
    lt = torch.max(box1[:, :2], box2[:, :2])  # [N,2]
    rb = torch.min(box1[:, 2:], box2[:, 2:])  # [N,2]
    wh = (rb - lt).clamp(min=0)  # [N,2]
    inter = wh[:, 0] * wh[:, 1]  # [N]
    iou = inter / (area1 + area2 - inter)  # [N]
    return iou