xhs-note-crawling/python/py/Lib/site-packages/kornia/geometry/transform/crop3d.py

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# Copyright 2018 Kornia Team
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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from typing import Tuple

import torch

from kornia.geometry.bbox import infer_bbox_shape3d, validate_bbox3d

from .imgwarp import get_perspective_transform3d, warp_affine3d

__all__ = ["center_crop3d", "crop_and_resize3d", "crop_by_boxes3d", "crop_by_transform_mat3d"]


def crop_and_resize3d(
    tensor: torch.Tensor,
    boxes: torch.Tensor,
    size: Tuple[int, int, int],
    interpolation: str = "bilinear",
    align_corners: bool = False,
) -> torch.Tensor:
    r"""Extract crops from 3D volumes (5D tensor) and resize them.

    Args:
        tensor: the 3D volume tensor with shape (B, C, D, H, W).
        boxes: a tensor with shape (B, 8, 3) containing the coordinates of the bounding boxes
            to be extracted. The tensor must have the shape of Bx8x3, where each box is defined in the clockwise
            order: front-top-left, front-top-right, front-bottom-right, front-bottom-left, back-top-left,
            back-top-right, back-bottom-right, back-bottom-left. The coordinates must be in x, y, z order.
        size: a tuple with the height and width that will be
            used to resize the extracted patches.
        interpolation: Interpolation flag.
        align_corners: mode for grid_generation.

    Returns:
        tensor containing the patches with shape (Bx)CxN1xN2xN3.

    Example:
        >>> input = torch.arange(64, dtype=torch.float32).view(1, 1, 4, 4, 4)
        >>> input
        tensor([[[[[ 0.,  1.,  2.,  3.],
                   [ 4.,  5.,  6.,  7.],
                   [ 8.,  9., 10., 11.],
                   [12., 13., 14., 15.]],
        <BLANKLINE>
                  [[16., 17., 18., 19.],
                   [20., 21., 22., 23.],
                   [24., 25., 26., 27.],
                   [28., 29., 30., 31.]],
        <BLANKLINE>
                  [[32., 33., 34., 35.],
                   [36., 37., 38., 39.],
                   [40., 41., 42., 43.],
                   [44., 45., 46., 47.]],
        <BLANKLINE>
                  [[48., 49., 50., 51.],
                   [52., 53., 54., 55.],
                   [56., 57., 58., 59.],
                   [60., 61., 62., 63.]]]]])
        >>> boxes = torch.tensor([[
        ...     [1., 1., 1.],
        ...     [3., 1., 1.],
        ...     [3., 3., 1.],
        ...     [1., 3., 1.],
        ...     [1., 1., 2.],
        ...     [3., 1., 2.],
        ...     [3., 3., 2.],
        ...     [1., 3., 2.],
        ... ]])  # 1x8x3
        >>> crop_and_resize3d(input, boxes, (2, 2, 2), align_corners=True)
        tensor([[[[[21.0000, 23.0000],
                   [29.0000, 31.0000]],
        <BLANKLINE>
                  [[37.0000, 39.0000],
                   [45.0000, 47.0000]]]]])

    """
    if not isinstance(tensor, (torch.Tensor)):
        raise TypeError(f"Input tensor type is not a torch.Tensor. Got {type(tensor)}")
    if not isinstance(boxes, (torch.Tensor)):
        raise TypeError(f"Input boxes type is not a torch.Tensor. Got {type(boxes)}")
    if not isinstance(size, (tuple, list)) and len(size) != 3:
        raise ValueError(f"Input size must be a tuple/list of length 3. Got {size}")
    if len(tensor.shape) != 5:
        raise AssertionError(f"Only tensor with shape (B, C, D, H, W) supported. Got {tensor.shape}.")
    # unpack input data
    dst_d, dst_h, dst_w = size[0], size[1], size[2]

    # [x, y, z] origin
    # from front to back
    # top-left, top-right, bottom-right, bottom-left
    points_src: torch.Tensor = boxes

    # [x, y, z] destination
    # from front to back
    # top-left, top-right, bottom-right, bottom-left
    points_dst: torch.Tensor = torch.tensor(
        [
            [
                [0, 0, 0],
                [dst_w - 1, 0, 0],
                [dst_w - 1, dst_h - 1, 0],
                [0, dst_h - 1, 0],
                [0, 0, dst_d - 1],
                [dst_w - 1, 0, dst_d - 1],
                [dst_w - 1, dst_h - 1, dst_d - 1],
                [0, dst_h - 1, dst_d - 1],
            ]
        ],
        dtype=tensor.dtype,
        device=tensor.device,
    ).expand(points_src.shape[0], -1, -1)

    return crop_by_boxes3d(tensor, points_src, points_dst, interpolation, align_corners)


def center_crop3d(
    tensor: torch.Tensor, size: Tuple[int, int, int], interpolation: str = "bilinear", align_corners: bool = True
) -> torch.Tensor:
    r"""Crop the 3D volumes (5D tensor) at the center.

    Args:
        tensor: the 3D volume tensor with shape (B, C, D, H, W).
        size: a tuple with the expected depth, height and width
            of the output patch.
        interpolation: Interpolation flag.
        align_corners : mode for grid_generation.

    Returns:
        the output tensor with patches.

    Examples:
        >>> input = torch.arange(64, dtype=torch.float32).view(1, 1, 4, 4, 4)
        >>> input
        tensor([[[[[ 0.,  1.,  2.,  3.],
                   [ 4.,  5.,  6.,  7.],
                   [ 8.,  9., 10., 11.],
                   [12., 13., 14., 15.]],
        <BLANKLINE>
                  [[16., 17., 18., 19.],
                   [20., 21., 22., 23.],
                   [24., 25., 26., 27.],
                   [28., 29., 30., 31.]],
        <BLANKLINE>
                  [[32., 33., 34., 35.],
                   [36., 37., 38., 39.],
                   [40., 41., 42., 43.],
                   [44., 45., 46., 47.]],
        <BLANKLINE>
                  [[48., 49., 50., 51.],
                   [52., 53., 54., 55.],
                   [56., 57., 58., 59.],
                   [60., 61., 62., 63.]]]]])
        >>> center_crop3d(input, (2, 2, 2), align_corners=True)
        tensor([[[[[21.0000, 22.0000],
                   [25.0000, 26.0000]],
        <BLANKLINE>
                  [[37.0000, 38.0000],
                   [41.0000, 42.0000]]]]])

    """
    if not isinstance(tensor, torch.Tensor):
        raise TypeError(f"Input tensor type is not a torch.Tensor. Got {type(tensor)}")

    if len(tensor.shape) != 5:
        raise AssertionError(f"Only tensor with shape (B, C, D, H, W) supported. Got {tensor.shape}.")

    if not isinstance(size, (tuple, list)) and len(size) == 3:
        raise ValueError(f"Input size must be a tuple/list of length 3. Got {size}")

    # unpack input sizes
    dst_d, dst_h, dst_w = size
    src_d, src_h, src_w = tensor.shape[-3:]

    # compute start/end offsets
    dst_d_half = dst_d / 2
    dst_h_half = dst_h / 2
    dst_w_half = dst_w / 2
    src_d_half = src_d / 2
    src_h_half = src_h / 2
    src_w_half = src_w / 2

    start_x = src_w_half - dst_w_half
    start_y = src_h_half - dst_h_half
    start_z = src_d_half - dst_d_half

    end_x = start_x + dst_w - 1
    end_y = start_y + dst_h - 1
    end_z = start_z + dst_d - 1
    # [x, y, z] origin
    # top-left-front, top-right-front, bottom-right-front, bottom-left-front
    # top-left-back, top-right-back, bottom-right-back, bottom-left-back
    points_src: torch.Tensor = torch.tensor(
        [
            [
                [start_x, start_y, start_z],
                [end_x, start_y, start_z],
                [end_x, end_y, start_z],
                [start_x, end_y, start_z],
                [start_x, start_y, end_z],
                [end_x, start_y, end_z],
                [end_x, end_y, end_z],
                [start_x, end_y, end_z],
            ]
        ],
        device=tensor.device,
    )

    # [x, y, z] destination
    # top-left-front, top-right-front, bottom-right-front, bottom-left-front
    # top-left-back, top-right-back, bottom-right-back, bottom-left-back
    points_dst: torch.Tensor = torch.tensor(
        [
            [
                [0, 0, 0],
                [dst_w - 1, 0, 0],
                [dst_w - 1, dst_h - 1, 0],
                [0, dst_h - 1, 0],
                [0, 0, dst_d - 1],
                [dst_w - 1, 0, dst_d - 1],
                [dst_w - 1, dst_h - 1, dst_d - 1],
                [0, dst_h - 1, dst_d - 1],
            ]
        ],
        device=tensor.device,
    ).expand(points_src.shape[0], -1, -1)

    return crop_by_boxes3d(
        tensor, points_src.to(tensor.dtype), points_dst.to(tensor.dtype), interpolation, align_corners
    )


def crop_by_boxes3d(
    tensor: torch.Tensor,
    src_box: torch.Tensor,
    dst_box: torch.Tensor,
    interpolation: str = "bilinear",
    align_corners: bool = False,
) -> torch.Tensor:
    """Perform crop transform on 3D volumes (5D tensor) by bounding boxes.

    Given an input tensor, this function selected the interested areas by the provided bounding boxes (src_box).
    Then the selected areas would be fitted into the targeted bounding boxes (dst_box) by a perspective transformation.
    So far, the ragged tensor is not supported by PyTorch right now. This function hereby requires the bounding boxes
    in a batch must be rectangles with same width, height and depth.

    Args:
        tensor : the 3D volume tensor with shape (B, C, D, H, W).
        src_box : a tensor with shape (B, 8, 3) containing the coordinates of the bounding boxes
            to be extracted. The tensor must have the shape of Bx8x3, where each box is defined in the clockwise
            order: front-top-left, front-top-right, front-bottom-right, front-bottom-left, back-top-left,
            back-top-right, back-bottom-right, back-bottom-left. The coordinates must be in x, y, z order.
        dst_box: a tensor with shape (B, 8, 3) containing the coordinates of the bounding boxes
            to be placed. The tensor must have the shape of Bx8x3, where each box is defined in the clockwise
            order: front-top-left, front-top-right, front-bottom-right, front-bottom-left, back-top-left,
            back-top-right, back-bottom-right, back-bottom-left. The coordinates must be in x, y, z order.
        interpolation: Interpolation flag.
        align_corners: mode for grid_generation.

    Returns:
        the output tensor with patches.

    Examples:
        >>> input = torch.tensor([[[
        ...         [[ 0.,  1.,  2.,  3.],
        ...          [ 4.,  5.,  6.,  7.],
        ...          [ 8.,  9., 10., 11.],
        ...          [12., 13., 14., 15.]],
        ...         [[16., 17., 18., 19.],
        ...          [20., 21., 22., 23.],
        ...          [24., 25., 26., 27.],
        ...          [28., 29., 30., 31.]],
        ...         [[32., 33., 34., 35.],
        ...          [36., 37., 38., 39.],
        ...          [40., 41., 42., 43.],
        ...          [44., 45., 46., 47.]]]]])
        >>> src_box = torch.tensor([[
        ...     [1., 1., 1.],
        ...     [3., 1., 1.],
        ...     [3., 3., 1.],
        ...     [1., 3., 1.],
        ...     [1., 1., 2.],
        ...     [3., 1., 2.],
        ...     [3., 3., 2.],
        ...     [1., 3., 2.],
        ... ]])  # 1x8x3
        >>> dst_box = torch.tensor([[
        ...     [0., 0., 0.],
        ...     [2., 0., 0.],
        ...     [2., 2., 0.],
        ...     [0., 2., 0.],
        ...     [0., 0., 1.],
        ...     [2., 0., 1.],
        ...     [2., 2., 1.],
        ...     [0., 2., 1.],
        ... ]])  # 1x8x3
        >>> crop_by_boxes3d(input, src_box, dst_box, interpolation='nearest', align_corners=True)
        tensor([[[[[21., 22., 23.],
                   [25., 26., 27.],
                   [29., 30., 31.]],
        <BLANKLINE>
                  [[37., 38., 39.],
                   [41., 42., 43.],
                   [45., 46., 47.]]]]])

    """
    validate_bbox3d(src_box)
    validate_bbox3d(dst_box)

    if len(tensor.shape) != 5:
        raise AssertionError(f"Only tensor with shape (B, C, D, H, W) supported. Got {tensor.shape}.")

    # compute transformation between points and warp
    # Note: Tensor.dtype must be float. "solve_cpu" not implemented for 'Long'
    dst_trans_src: torch.Tensor = get_perspective_transform3d(src_box.to(tensor.dtype), dst_box.to(tensor.dtype))
    # simulate broadcasting
    dst_trans_src = dst_trans_src.expand(tensor.shape[0], -1, -1).type_as(tensor)

    bbox = infer_bbox_shape3d(dst_box)
    if not ((bbox[0] == bbox[0][0]).all() and (bbox[1] == bbox[1][0]).all() and (bbox[2] == bbox[2][0]).all()):
        raise AssertionError(
            "Cropping height, width and depth must be exact same in a batch."
            f"Got height {bbox[0]}, width {bbox[1]} and depth {bbox[2]}."
        )

    patches: torch.Tensor = crop_by_transform_mat3d(
        tensor,
        dst_trans_src,
        (int(bbox[0][0].item()), int(bbox[1][0].item()), int(bbox[2][0].item())),
        mode=interpolation,
        align_corners=align_corners,
    )

    return patches


def crop_by_transform_mat3d(
    tensor: torch.Tensor,
    transform: torch.Tensor,
    out_size: Tuple[int, int, int],
    mode: str = "bilinear",
    padding_mode: str = "zeros",
    align_corners: bool = True,
) -> torch.Tensor:
    """Perform crop transform on 3D volumes (5D tensor) given a perspective transformation matrix.

    Args:
        tensor: the 2D image tensor with shape (B, C, H, W).
        transform: a perspective transformation matrix with shape (B, 4, 4).
        out_size: size of the output image (depth, height, width).
        mode: interpolation mode to calculate output values
          ``'bilinear'`` | ``'nearest'``.
        padding_mode: padding mode for outside grid values
          ``'zeros'`` | ``'border'`` | ``'reflection'``.
        align_corners: mode for grid_generation.

    Returns:
        the output tensor with patches.

    """
    # simulate broadcasting
    dst_trans_src = transform.expand(tensor.shape[0], -1, -1)

    patches: torch.Tensor = warp_affine3d(
        tensor, dst_trans_src[:, :3, :], out_size, flags=mode, padding_mode=padding_mode, align_corners=align_corners
    )

    return patches