Source code for kornia.geometry.transform.imgwarp

from typing import List, Optional, Tuple

import torch
import torch.nn.functional as F
from torch.nn.functional import grid_sample

from kornia.core import Tensor, concatenate, stack, tensor, zeros
from kornia.geometry.conversions import (
    angle_axis_to_rotation_matrix,
    angle_to_rotation_matrix,
    convert_affinematrix_to_homography,
    convert_affinematrix_to_homography3d,
    deg2rad,
    normalize_homography,
    normalize_homography3d,
    normalize_pixel_coordinates,
)
from kornia.geometry.linalg import transform_points
from kornia.testing import KORNIA_CHECK, KORNIA_CHECK_SHAPE
from kornia.utils import create_meshgrid, create_meshgrid3d, eye_like
from kornia.utils.helpers import _torch_inverse_cast, _torch_solve_cast

__all__ = [
    "warp_perspective",
    "warp_affine",
    "get_perspective_transform",
    "get_rotation_matrix2d",
    "remap",
    "invert_affine_transform",
    "get_affine_matrix2d",
    "get_affine_matrix3d",
    "get_shear_matrix2d",
    "get_shear_matrix3d",
    "warp_affine3d",
    "get_projective_transform",
    "projection_from_Rt",
    "get_perspective_transform3d",
    "warp_perspective3d",
    "warp_grid",
    "warp_grid3d",
    "homography_warp",
    "homography_warp3d",
]


[docs]def warp_perspective( src: Tensor, M: Tensor, dsize: Tuple[int, int], mode: str = 'bilinear', padding_mode: str = 'zeros', align_corners: bool = True, fill_value: Tensor = zeros(3), # needed for jit ) -> Tensor: r"""Apply a perspective transformation to an image. .. image:: https://kornia-tutorials.readthedocs.io/en/latest/_images/warp_perspective_10_1.png The function warp_perspective transforms the source image using the specified matrix: .. math:: \text{dst} (x, y) = \text{src} \left( \frac{M^{-1}_{11} x + M^{-1}_{12} y + M^{-1}_{13}}{M^{-1}_{31} x + M^{-1}_{32} y + M^{-1}_{33}} , \frac{M^{-1}_{21} x + M^{-1}_{22} y + M^{-1}_{23}}{M^{-1}_{31} x + M^{-1}_{32} y + M^{-1}_{33}} \right ) Args: src: input image with shape :math:`(B, C, H, W)`. M: transformation matrix with shape :math:`(B, 3, 3)`. dsize: size of the output image (height, width). mode: interpolation mode to calculate output values ``'bilinear'`` | ``'nearest'``. padding_mode: padding mode for outside grid values ``'zeros'`` | ``'border'`` | ``'reflection'`` | ``'fill'``. align_corners: interpolation flag. fill_value: tensor of shape :math:`(3)` that fills the padding area. Only supported for RGB. Returns: the warped input image :math:`(B, C, H, W)`. Example: >>> img = torch.rand(1, 4, 5, 6) >>> H = torch.eye(3)[None] >>> out = warp_perspective(img, H, (4, 2), align_corners=True) >>> print(out.shape) torch.Size([1, 4, 4, 2]) .. note:: This function is often used in conjunction with :func:`get_perspective_transform`. .. note:: See a working example `here <https://kornia-tutorials.readthedocs.io/en/ latest/warp_perspective.html>`_. """ if not isinstance(src, Tensor): raise TypeError(f"Input src type is not a Tensor. Got {type(src)}") if not isinstance(M, Tensor): raise TypeError(f"Input M type is not a Tensor. Got {type(M)}") if not len(src.shape) == 4: raise ValueError(f"Input src must be a BxCxHxW tensor. Got {src.shape}") if not (len(M.shape) == 3 and M.shape[-2:] == (3, 3)): raise ValueError(f"Input M must be a Bx3x3 tensor. Got {M.shape}") # fill padding is only supported for 3 channels because we can't set fill_value default # to None as this gives jit issues. if padding_mode == "fill" and fill_value.shape != torch.Size([3]): raise ValueError(f"Padding_tensor only supported for 3 channels. Got {fill_value.shape}") B, _, H, W = src.size() h_out, w_out = dsize # we normalize the 3x3 transformation matrix and convert to 3x4 dst_norm_trans_src_norm: Tensor = normalize_homography(M, (H, W), (h_out, w_out)) # Bx3x3 src_norm_trans_dst_norm = _torch_inverse_cast(dst_norm_trans_src_norm) # Bx3x3 # this piece of code substitutes F.affine_grid since it does not support 3x3 grid = ( create_meshgrid(h_out, w_out, normalized_coordinates=True, device=src.device) .to(src.dtype) .expand(B, h_out, w_out, 2) ) grid = transform_points(src_norm_trans_dst_norm[:, None, None], grid) if padding_mode == "fill": return _fill_and_warp(src, grid, align_corners=align_corners, mode=mode, fill_value=fill_value) return F.grid_sample(src, grid, align_corners=align_corners, mode=mode, padding_mode=padding_mode)
[docs]def warp_affine( src: Tensor, M: Tensor, dsize: Tuple[int, int], mode: str = 'bilinear', padding_mode: str = 'zeros', align_corners: bool = True, fill_value: Tensor = zeros(3), # needed for jit ) -> Tensor: r"""Apply an affine transformation to a tensor. .. image:: _static/img/warp_affine.png The function warp_affine transforms the source tensor using the specified matrix: .. math:: \text{dst}(x, y) = \text{src} \left( M_{11} x + M_{12} y + M_{13} , M_{21} x + M_{22} y + M_{23} \right ) Args: src: input tensor of shape :math:`(B, C, H, W)`. M: affine transformation of shape :math:`(B, 2, 3)`. dsize: size of the output image (height, width). mode: interpolation mode to calculate output values ``'bilinear'`` | ``'nearest'``. padding_mode: padding mode for outside grid values ``'zeros'`` | ``'border'`` | ``'reflection'`` | ``'fill'``. align_corners : mode for grid_generation. fill_value: tensor of shape :math:`(3)` that fills the padding area. Only supported for RGB. Returns: the warped tensor with shape :math:`(B, C, H, W)`. .. note:: This function is often used in conjunction with :func:`get_rotation_matrix2d`, :func:`get_shear_matrix2d`, :func:`get_affine_matrix2d`, :func:`invert_affine_transform`. .. note:: See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/ rotate_affine.html>`__. Example: >>> img = torch.rand(1, 4, 5, 6) >>> A = torch.eye(2, 3)[None] >>> out = warp_affine(img, A, (4, 2), align_corners=True) >>> print(out.shape) torch.Size([1, 4, 4, 2]) """ if not isinstance(src, Tensor): raise TypeError(f"Input src type is not a Tensor. Got {type(src)}") if not isinstance(M, Tensor): raise TypeError(f"Input M type is not a Tensor. Got {type(M)}") if not len(src.shape) == 4: raise ValueError(f"Input src must be a BxCxHxW tensor. Got {src.shape}") if not (len(M.shape) == 3 or M.shape[-2:] == (2, 3)): raise ValueError(f"Input M must be a Bx2x3 tensor. Got {M.shape}") # fill padding is only supported for 3 channels because we can't set fill_value default # to None as this gives jit issues. if padding_mode == "fill" and fill_value.shape != torch.Size([3]): raise ValueError(f"Padding_tensor only supported for 3 channels. Got {fill_value.shape}") B, C, H, W = src.size() # we generate a 3x3 transformation matrix from 2x3 affine M_3x3: Tensor = convert_affinematrix_to_homography(M) dst_norm_trans_src_norm: Tensor = normalize_homography(M_3x3, (H, W), dsize) # src_norm_trans_dst_norm = torch.inverse(dst_norm_trans_src_norm) src_norm_trans_dst_norm = _torch_inverse_cast(dst_norm_trans_src_norm) grid = F.affine_grid(src_norm_trans_dst_norm[:, :2, :], [B, C, dsize[0], dsize[1]], align_corners=align_corners) if padding_mode == "fill": return _fill_and_warp(src, grid, align_corners=align_corners, mode=mode, fill_value=fill_value) return F.grid_sample(src, grid, align_corners=align_corners, mode=mode, padding_mode=padding_mode)
def _fill_and_warp(src: Tensor, grid: Tensor, mode: str, align_corners: bool, fill_value: Tensor) -> Tensor: r"""Warp a mask of ones, then multiple with fill_value and add to default warp. Args: src: input tensor of shape :math:`(B, 3, H, W)`. grid: grid tensor from `transform_points`. mode: interpolation mode to calculate output values ``'bilinear'`` | ``'nearest'``. align_corners: interpolation flag. fill_value: tensor of shape :math:`(3)` that fills the padding area. Only supported for RGB. Returns: the warped and filled tensor with shape :math:`(B, 3, H, W)`. """ ones_mask = torch.ones_like(src) fill_value = fill_value.to(ones_mask)[None, :, None, None] # cast and add dimensions for broadcasting inv_ones_mask = 1 - F.grid_sample(ones_mask, grid, align_corners=align_corners, mode=mode, padding_mode="zeros") inv_color_mask = inv_ones_mask * fill_value return F.grid_sample(src, grid, align_corners=align_corners, mode=mode, padding_mode="zeros") + inv_color_mask def warp_grid(grid: Tensor, src_homo_dst: Tensor) -> Tensor: r"""Compute the grid to warp the coordinates grid by the homography/ies. Args: grid: Unwrapped grid of the shape :math:`(1, N, W, 2)`. src_homo_dst: Homography or homographies (stacked) to transform all points in the grid. Shape of the homography has to be :math:`(1, 3, 3)` or :math:`(N, 1, 3, 3)`. Returns: the transformed grid of shape :math:`(N, H, W, 2)`. """ batch_size: int = src_homo_dst.size(0) _, height, width, _ = grid.size() # expand grid to match the input batch size grid = grid.expand(batch_size, -1, -1, -1) # NxHxWx2 if len(src_homo_dst.shape) == 3: # local homography case src_homo_dst = src_homo_dst.view(batch_size, 1, 3, 3) # Nx1x3x3 # perform the actual grid transformation, # the grid is copied to input device and casted to the same type flow: Tensor = transform_points(src_homo_dst, grid.to(src_homo_dst)) # NxHxWx2 return flow.view(batch_size, height, width, 2) # NxHxWx2 def warp_grid3d(grid: Tensor, src_homo_dst: Tensor) -> Tensor: r"""Compute the grid to warp the coordinates grid by the homography/ies. Args: grid: Unwrapped grid of the shape :math:`(1, D, H, W, 3)`. src_homo_dst: Homography or homographies (stacked) to transform all points in the grid. Shape of the homography has to be :math:`(1, 4, 4)` or :math:`(N, 1, 4, 4)`. Returns: the transformed grid of shape :math:`(N, H, W, 3)`. """ batch_size: int = src_homo_dst.size(0) _, depth, height, width, _ = grid.size() # expand grid to match the input batch size grid = grid.expand(batch_size, -1, -1, -1, -1) # NxDxHxWx3 if len(src_homo_dst.shape) == 3: # local homography case src_homo_dst = src_homo_dst.view(batch_size, 1, 4, 4) # Nx1x3x3 # perform the actual grid transformation, # the grid is copied to input device and casted to the same type flow: Tensor = transform_points(src_homo_dst, grid.to(src_homo_dst)) # NxDxHxWx3 return flow.view(batch_size, depth, height, width, 3) # NxDxHxWx3 # TODO: move to kornia.geometry.projective # TODO: create the nn.Module -- TBD what inputs/outputs etc # class PerspectiveTransform(nn.Module): # def __init__(self) -> None: # super().__init__()
[docs]def get_perspective_transform(points_src: Tensor, points_dst: Tensor) -> Tensor: r"""Calculate a perspective transform from four pairs of the corresponding points. The algorithm is a vanilla implementation of the Direct Linear transform (DLT). See more: https://www.cs.cmu.edu/~16385/s17/Slides/10.2_2D_Alignment__DLT.pdf The function calculates the matrix of a perspective transform that maps from the source to destination points: .. math :: \begin{bmatrix} x^{'} \\ y^{'} \\ 1 \\ \end{bmatrix} = \begin{bmatrix} h_1 & h_2 & h_3 \\ h_4 & h_5 & h_6 \\ h_7 & h_8 & h_9 \\ \end{bmatrix} \cdot \begin{bmatrix} x \\ y \\ 1 \\ \end{bmatrix} Args: points_src: coordinates of quadrangle vertices in the source image with shape :math:`(B, 4, 2)`. points_dst: coordinates of the corresponding quadrangle vertices in the destination image with shape :math:`(B, 4, 2)`. Returns: the perspective transformation with shape :math:`(B, 3, 3)`. .. note:: This function is often used in conjunction with :func:`warp_perspective`. Example: >>> x1 = torch.tensor([[[0., 0.], [1., 0.], [1., 1.], [0., 1.]]]) >>> x2 = torch.tensor([[[1., 0.], [0., 0.], [0., 1.], [1., 1.]]]) >>> x2_trans_x1 = get_perspective_transform(x1, x2) """ KORNIA_CHECK_SHAPE(points_src, ["B", "4", "2"]) KORNIA_CHECK_SHAPE(points_dst, ["B", "4", "2"]) KORNIA_CHECK(points_src.shape == points_dst.shape, "Source data shape must match Destination data shape.") KORNIA_CHECK(points_src.dtype == points_dst.dtype, "Source data type must match Destination data type.") # we build matrix A by using only 4 point correspondence. The linear # system is solved with the least square method, so here # we could even pass more correspondence # create the lhs tensor with shape # Bx8x8 B: int = points_src.shape[0] # batch_size A = torch.empty(B, 8, 8, device=points_src.device, dtype=points_src.dtype) # we need to perform in batch _zeros = zeros(B, device=points_src.device, dtype=points_src.dtype) _ones = torch.ones(B, device=points_src.device, dtype=points_src.dtype) for i in range(4): x1, y1 = points_src[..., i, 0], points_src[..., i, 1] # Bx4 x2, y2 = points_dst[..., i, 0], points_dst[..., i, 1] # Bx4 A[:, 2 * i] = stack([x1, y1, _ones, _zeros, _zeros, _zeros, -x1 * x2, -y1 * x2], -1) A[:, 2 * i + 1] = stack([_zeros, _zeros, _zeros, x1, y1, _ones, -x1 * y2, -y1 * y2], -1) # the rhs tensor b = points_dst.view(-1, 8, 1) # solve the system Ax = b X: Tensor = _torch_solve_cast(A, b) # create variable to return the Bx3x3 transform M = torch.empty(B, 9, device=points_src.device, dtype=points_src.dtype) M[..., :8] = X[..., 0] # Bx8 M[..., -1].fill_(1) return M.view(-1, 3, 3) # Bx3x3
# TODO: move to kornia.geometry.affine
[docs]def get_rotation_matrix2d(center: Tensor, angle: Tensor, scale: Tensor) -> Tensor: r"""Calculate an affine matrix of 2D rotation. The function calculates the following matrix: .. math:: \begin{bmatrix} \alpha & \beta & (1 - \alpha) \cdot \text{x} - \beta \cdot \text{y} \\ -\beta & \alpha & \beta \cdot \text{x} + (1 - \alpha) \cdot \text{y} \end{bmatrix} where .. math:: \alpha = \text{scale} \cdot cos(\text{angle}) \\ \beta = \text{scale} \cdot sin(\text{angle}) The transformation maps the rotation center to itself If this is not the target, adjust the shift. Args: center: center of the rotation in the source image with shape :math:`(B, 2)`. angle: rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner) with shape :math:`(B)`. scale: scale factor for x, y scaling with shape :math:`(B, 2)`. Returns: the affine matrix of 2D rotation with shape :math:`(B, 2, 3)`. Example: >>> center = zeros(1, 2) >>> scale = torch.ones((1, 2)) >>> angle = 45. * torch.ones(1) >>> get_rotation_matrix2d(center, angle, scale) tensor([[[ 0.7071, 0.7071, 0.0000], [-0.7071, 0.7071, 0.0000]]]) .. note:: This function is often used in conjunction with :func:`warp_affine`. """ if not isinstance(center, Tensor): raise TypeError(f"Input center type is not a Tensor. Got {type(center)}") if not isinstance(angle, Tensor): raise TypeError(f"Input angle type is not a Tensor. Got {type(angle)}") if not isinstance(scale, Tensor): raise TypeError(f"Input scale type is not a Tensor. Got {type(scale)}") if not (len(center.shape) == 2 and center.shape[1] == 2): raise ValueError(f"Input center must be a Bx2 tensor. Got {center.shape}") if not len(angle.shape) == 1: raise ValueError(f"Input angle must be a B tensor. Got {angle.shape}") if not (len(scale.shape) == 2 and scale.shape[1] == 2): raise ValueError(f"Input scale must be a Bx2 tensor. Got {scale.shape}") if not (center.shape[0] == angle.shape[0] == scale.shape[0]): raise ValueError( "Inputs must have same batch size dimension. Got center {}, angle {} and scale {}".format( center.shape, angle.shape, scale.shape ) ) if not (center.device == angle.device == scale.device) or not (center.dtype == angle.dtype == scale.dtype): raise ValueError( "Inputs must have same device Got center ({}, {}), angle ({}, {}) and scale ({}, {})".format( center.device, center.dtype, angle.device, angle.dtype, scale.device, scale.dtype ) ) shift_m = eye_like(3, center) shift_m[:, :2, 2] = center shift_m_inv = eye_like(3, center) shift_m_inv[:, :2, 2] = -center scale_m = eye_like(3, center) scale_m[:, 0, 0] *= scale[:, 0] scale_m[:, 1, 1] *= scale[:, 1] rotat_m = eye_like(3, center) rotat_m[:, :2, :2] = angle_to_rotation_matrix(angle) affine_m = shift_m @ rotat_m @ scale_m @ shift_m_inv return affine_m[:, :2, :] # Bx2x3
[docs]def remap( image: Tensor, map_x: Tensor, map_y: Tensor, mode: str = 'bilinear', padding_mode: str = 'zeros', align_corners: Optional[bool] = None, normalized_coordinates: bool = False, ) -> Tensor: r"""Apply a generic geometrical transformation to an image tensor. .. image:: _static/img/remap.png The function remap transforms the source tensor using the specified map: .. math:: \text{dst}(x, y) = \text{src}(map_x(x, y), map_y(x, y)) Args: image: the tensor to remap with shape (B, C, H, W). Where C is the number of channels. map_x: the flow in the x-direction in pixel coordinates. The tensor must be in the shape of (B, H, W). map_y: the flow in the y-direction in pixel coordinates. The tensor must be in the shape of (B, H, W). 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. normalized_coordinates: whether the input coordinates are normalized in the range of [-1, 1]. Returns: the warped tensor with same shape as the input grid maps. Example: >>> import torch >>> from kornia.utils import create_meshgrid >>> grid = create_meshgrid(2, 2, False) # 1x2x2x2 >>> grid += 1 # apply offset in both directions >>> input = torch.ones(1, 1, 2, 2) >>> remap(input, grid[..., 0], grid[..., 1], align_corners=True) # 1x1x2x2 tensor([[[[1., 0.], [0., 0.]]]]) .. note:: This function is often used in conjunction with :func:`kornia.utils.create_meshgrid`. """ KORNIA_CHECK_SHAPE(image, ["B", "C", "H", "W"]) KORNIA_CHECK_SHAPE(map_x, ["B", "H", "W"]) KORNIA_CHECK_SHAPE(map_y, ["B", "H", "W"]) batch_size, _, height, width = image.shape # grid_sample need the grid between -1/1 map_xy: Tensor = stack([map_x, map_y], -1) # normalize coordinates if not already normalized if not normalized_coordinates: map_xy = normalize_pixel_coordinates(map_xy, height, width) # simulate broadcasting since grid_sample does not support it map_xy = map_xy.expand(batch_size, -1, -1, -1) # warp the image tensor and return return grid_sample(image, map_xy, mode=mode, padding_mode=padding_mode, align_corners=align_corners)
[docs]def invert_affine_transform(matrix: Tensor) -> Tensor: r"""Invert an affine transformation. The function computes an inverse affine transformation represented by 2×3 matrix: .. math:: \begin{bmatrix} a_{11} & a_{12} & b_{1} \\ a_{21} & a_{22} & b_{2} \\ \end{bmatrix} The result is also a 2×3 matrix of the same type as M. Args: matrix: original affine transform. The tensor must be in the shape of :math:`(B, 2, 3)`. Return: the reverse affine transform with shape :math:`(B, 2, 3)`. .. note:: This function is often used in conjunction with :func:`warp_affine`. """ if not isinstance(matrix, Tensor): raise TypeError(f"Input matrix type is not a Tensor. Got {type(matrix)}") if not (len(matrix.shape) == 3 and matrix.shape[-2:] == (2, 3)): raise ValueError(f"Input matrix must be a Bx2x3 tensor. Got {matrix.shape}") matrix_tmp: Tensor = convert_affinematrix_to_homography(matrix) matrix_inv: Tensor = _torch_inverse_cast(matrix_tmp) return matrix_inv[..., :2, :3]
[docs]def get_affine_matrix2d( translations: Tensor, center: Tensor, scale: Tensor, angle: Tensor, sx: Optional[Tensor] = None, sy: Optional[Tensor] = None, ) -> Tensor: r"""Compose affine matrix from the components. Args: translations: tensor containing the translation vector with shape :math:`(B, 2)`. center: tensor containing the center vector with shape :math:`(B, 2)`. scale: tensor containing the scale factor with shape :math:`(B, 2)`. angle: tensor of angles in degrees :math:`(B)`. sx: tensor containing the shear factor in the x-direction with shape :math:`(B)`. sy: tensor containing the shear factor in the y-direction with shape :math:`(B)`. Returns: the affine transformation matrix :math:`(B, 3, 3)`. .. note:: This function is often used in conjunction with :func:`warp_affine`, :func:`warp_perspective`. """ transform: Tensor = get_rotation_matrix2d(center, -angle, scale) transform[..., 2] += translations # tx/ty # pad transform to get Bx3x3 transform_h = convert_affinematrix_to_homography(transform) if any(s is not None for s in [sx, sy]): shear_mat = get_shear_matrix2d(center, sx, sy) transform_h = transform_h @ shear_mat return transform_h
[docs]def get_shear_matrix2d(center: Tensor, sx: Optional[Tensor] = None, sy: Optional[Tensor] = None): r"""Compose shear matrix Bx4x4 from the components. Note: Ordered shearing, shear x-axis then y-axis. .. math:: \begin{bmatrix} 1 & b \\ a & ab + 1 \\ \end{bmatrix} Args: center: shearing center coordinates of (x, y). sx: shearing degree along x axis. sy: shearing degree along y axis. Returns: params to be passed to the affine transformation with shape :math:`(B, 3, 3)`. Examples: >>> rng = torch.manual_seed(0) >>> sx = torch.randn(1) >>> sx tensor([1.5410]) >>> center = torch.tensor([[0., 0.]]) # Bx2 >>> get_shear_matrix2d(center, sx=sx) tensor([[[ 1.0000, -33.5468, 0.0000], [ -0.0000, 1.0000, 0.0000], [ 0.0000, 0.0000, 1.0000]]]) .. note:: This function is often used in conjunction with :func:`warp_affine`, :func:`warp_perspective`. """ sx = tensor([0.0]).repeat(center.size(0)) if sx is None else sx sy = tensor([0.0]).repeat(center.size(0)) if sy is None else sy x, y = torch.split(center, 1, dim=-1) x, y = x.view(-1), y.view(-1) sx_tan = torch.tan(sx) sy_tan = torch.tan(sy) ones = torch.ones_like(sx) shear_mat = stack([ones, -sx_tan, sx_tan * y, -sy_tan, ones + sx_tan * sy_tan, sy_tan * (sx_tan * y + x)], -1).view( -1, 2, 3 ) shear_mat = convert_affinematrix_to_homography(shear_mat) return shear_mat
[docs]def get_affine_matrix3d( translations: Tensor, center: Tensor, scale: Tensor, angles: Tensor, sxy: Optional[Tensor] = None, sxz: Optional[Tensor] = None, syx: Optional[Tensor] = None, syz: Optional[Tensor] = None, szx: Optional[Tensor] = None, szy: Optional[Tensor] = None, ) -> Tensor: r"""Compose 3d affine matrix from the components. Args: translations: tensor containing the translation vector (dx,dy,dz) with shape :math:`(B, 3)`. center: tensor containing the center vector (x,y,z) with shape :math:`(B, 3)`. scale: tensor containing the scale factor with shape :math:`(B)`. angle: angle axis vector containing the rotation angles in degrees in the form of (rx, ry, rz) with shape :math:`(B, 3)`. Internally it calls Rodrigues to compute the rotation matrix from axis-angle. sxy: tensor containing the shear factor in the xy-direction with shape :math:`(B)`. sxz: tensor containing the shear factor in the xz-direction with shape :math:`(B)`. syx: tensor containing the shear factor in the yx-direction with shape :math:`(B)`. syz: tensor containing the shear factor in the yz-direction with shape :math:`(B)`. szx: tensor containing the shear factor in the zx-direction with shape :math:`(B)`. szy: tensor containing the shear factor in the zy-direction with shape :math:`(B)`. Returns: the 3d affine transformation matrix :math:`(B, 3, 3)`. .. note:: This function is often used in conjunction with :func:`warp_perspective`. """ transform: Tensor = get_projective_transform(center, -angles, scale) transform[..., 3] += translations # tx/ty/tz # pad transform to get Bx3x3 transform_h = convert_affinematrix_to_homography3d(transform) if any(s is not None for s in [sxy, sxz, syx, syz, szx, szy]): shear_mat = get_shear_matrix3d(center, sxy, sxz, syx, syz, szx, szy) transform_h = transform_h @ shear_mat return transform_h
[docs]def get_shear_matrix3d( center: Tensor, sxy: Optional[Tensor] = None, sxz: Optional[Tensor] = None, syx: Optional[Tensor] = None, syz: Optional[Tensor] = None, szx: Optional[Tensor] = None, szy: Optional[Tensor] = None, ): r"""Compose shear matrix Bx4x4 from the components. Note: Ordered shearing, shear x-axis then y-axis then z-axis. .. math:: \begin{bmatrix} 1 & o & r & oy + rz \\ m & p & s & mx + py + sz -y \\ n & q & t & nx + qy + tz -z \\ 0 & 0 & 0 & 1 \\ \end{bmatrix} Where: m = S_{xy} n = S_{xz} o = S_{yx} p = S_{xy}S_{yx} + 1 q = S_{xz}S_{yx} + S_{yz} r = S_{zx} + S_{yx}S_{zy} s = S_{xy}S_{zx} + (S_{xy}S_{yx} + 1)S_{zy} t = S_{xz}S_{zx} + (S_{xz}S_{yx} + S_{yz})S_{zy} + 1 Params: center: shearing center coordinates of (x, y, z). sxy: shearing degree along x axis, towards y plane. sxz: shearing degree along x axis, towards z plane. syx: shearing degree along y axis, towards x plane. syz: shearing degree along y axis, towards z plane. szx: shearing degree along z axis, towards x plane. szy: shearing degree along z axis, towards y plane. Returns: params to be passed to the affine transformation. Examples: >>> rng = torch.manual_seed(0) >>> sxy, sxz, syx, syz = torch.randn(4, 1) >>> sxy, sxz, syx, syz (tensor([1.5410]), tensor([-0.2934]), tensor([-2.1788]), tensor([0.5684])) >>> center = torch.tensor([[0., 0., 0.]]) # Bx3 >>> get_shear_matrix3d(center, sxy=sxy, sxz=sxz, syx=syx, syz=syz) tensor([[[ 1.0000, -1.4369, 0.0000, 0.0000], [-33.5468, 49.2039, 0.0000, 0.0000], [ 0.3022, -1.0729, 1.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, 1.0000]]]) .. note:: This function is often used in conjunction with :func:`warp_perspective3d`. """ sxy = tensor([0.0]).repeat(center.size(0)) if sxy is None else sxy sxz = tensor([0.0]).repeat(center.size(0)) if sxz is None else sxz syx = tensor([0.0]).repeat(center.size(0)) if syx is None else syx syz = tensor([0.0]).repeat(center.size(0)) if syz is None else syz szx = tensor([0.0]).repeat(center.size(0)) if szx is None else szx szy = tensor([0.0]).repeat(center.size(0)) if szy is None else szy x, y, z = torch.split(center, 1, dim=-1) x, y, z = x.view(-1), y.view(-1), z.view(-1) # Prepare parameters sxy_tan = torch.tan(sxy) sxz_tan = torch.tan(sxz) syx_tan = torch.tan(syx) syz_tan = torch.tan(syz) szx_tan = torch.tan(szx) szy_tan = torch.tan(szy) # compute translation matrix m00, m10, m20, m01, m11, m21, m02, m12, m22 = _compute_shear_matrix_3d( sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan ) m03 = m01 * y + m02 * z m13 = m10 * x + m11 * y + m12 * z - y m23 = m20 * x + m21 * y + m22 * z - z # shear matrix is implemented with negative values sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan = -sxy_tan, -sxz_tan, -syx_tan, -syz_tan, -szx_tan, -szy_tan m00, m10, m20, m01, m11, m21, m02, m12, m22 = _compute_shear_matrix_3d( sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan ) shear_mat = stack([m00, m01, m02, m03, m10, m11, m12, m13, m20, m21, m22, m23], -1).view(-1, 3, 4) shear_mat = convert_affinematrix_to_homography3d(shear_mat) return shear_mat
def _compute_shear_matrix_3d(sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan): ones = torch.ones_like(sxy_tan) m00, m10, m20 = ones, sxy_tan, sxz_tan m01, m11, m21 = syx_tan, sxy_tan * syx_tan + ones, sxz_tan * syx_tan + syz_tan m02 = syx_tan * szy_tan + szx_tan m12 = sxy_tan * szx_tan + szy_tan * m11 m22 = sxz_tan * szx_tan + szy_tan * m21 + ones return m00, m10, m20, m01, m11, m21, m02, m12, m22
[docs]def warp_affine3d( src: Tensor, M: Tensor, dsize: Tuple[int, int, int], flags: str = 'bilinear', padding_mode: str = 'zeros', align_corners: bool = True, ) -> Tensor: r"""Apply a projective transformation a to 3d tensor. .. warning:: This API signature it is experimental and might suffer some changes in the future. Args: src : input tensor of shape :math:`(B, C, D, H, W)`. M: projective transformation matrix of shape :math:`(B, 3, 4)`. dsize: 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: Tensor: the warped 3d tensor with shape :math:`(B, C, D, H, W)`. .. note:: This function is often used in conjunction with :func:`get_perspective_transform3d`. """ if len(src.shape) != 5: raise AssertionError(src.shape) if not (len(M.shape) == 3 and M.shape[-2:] == (3, 4)): raise AssertionError(M.shape) if len(dsize) != 3: raise AssertionError(dsize) B, C, D, H, W = src.size() size_src: Tuple[int, int, int] = (D, H, W) size_out: Tuple[int, int, int] = dsize M_4x4 = convert_affinematrix_to_homography3d(M) # Bx4x4 # we need to normalize the transformation since grid sample needs -1/1 coordinates dst_norm_trans_src_norm: Tensor = normalize_homography3d(M_4x4, size_src, size_out) # Bx4x4 src_norm_trans_dst_norm = _torch_inverse_cast(dst_norm_trans_src_norm) P_norm: Tensor = src_norm_trans_dst_norm[:, :3] # Bx3x4 # compute meshgrid and apply to input dsize_out: List[int] = [B, C] + list(size_out) grid = F.affine_grid(P_norm, dsize_out, align_corners=align_corners) return grid_sample(src, grid, align_corners=align_corners, mode=flags, padding_mode=padding_mode)
[docs]def projection_from_Rt(rmat: Tensor, tvec: Tensor) -> Tensor: r"""Compute the projection matrix from Rotation and translation. .. warning:: This API signature it is experimental and might suffer some changes in the future. Concatenates the batch of rotations and translations such that :math:`P = [R | t]`. Args: rmat: the rotation matrix with shape :math:`(*, 3, 3)`. tvec: the translation vector with shape :math:`(*, 3, 1)`. Returns: the projection matrix with shape :math:`(*, 3, 4)`. """ if not (len(rmat.shape) >= 2 and rmat.shape[-2:] == (3, 3)): raise AssertionError(rmat.shape) if not (len(tvec.shape) >= 2 and tvec.shape[-2:] == (3, 1)): raise AssertionError(tvec.shape) return concatenate([rmat, tvec], -1) # Bx3x4
[docs]def get_projective_transform(center: Tensor, angles: Tensor, scales: Tensor) -> Tensor: r"""Calculate the projection matrix for a 3D rotation. .. warning:: This API signature it is experimental and might suffer some changes in the future. The function computes the projection matrix given the center and angles per axis. Args: center: center of the rotation (x,y,z) in the source with shape :math:`(B, 3)`. angles: angle axis vector containing the rotation angles in degrees in the form of (rx, ry, rz) with shape :math:`(B, 3)`. Internally it calls Rodrigues to compute the rotation matrix from axis-angle. scales: scale factor for x-y-z-directions with shape :math:`(B, 3)`. Returns: the projection matrix of 3D rotation with shape :math:`(B, 3, 4)`. .. note:: This function is often used in conjunction with :func:`warp_affine3d`. """ if not (len(center.shape) == 2 and center.shape[-1] == 3): raise AssertionError(center.shape) if not (len(angles.shape) == 2 and angles.shape[-1] == 3): raise AssertionError(angles.shape) if center.device != angles.device: raise AssertionError(center.device, angles.device) if center.dtype != angles.dtype: raise AssertionError(center.dtype, angles.dtype) # create rotation matrix angle_axis_rad: Tensor = deg2rad(angles) rmat: Tensor = angle_axis_to_rotation_matrix(angle_axis_rad) # Bx3x3 scaling_matrix: Tensor = eye_like(3, rmat) scaling_matrix = scaling_matrix * scales.unsqueeze(dim=1) rmat = rmat @ scaling_matrix.to(rmat) # define matrix to move forth and back to origin from_origin_mat = eye_like(4, rmat, shared_memory=False) # Bx4x4 from_origin_mat[..., :3, -1] += center to_origin_mat = from_origin_mat.clone() to_origin_mat = _torch_inverse_cast(from_origin_mat) # append translation with zeros proj_mat = projection_from_Rt(rmat, torch.zeros_like(center)[..., None]) # Bx3x4 # chain 4x4 transforms proj_mat = convert_affinematrix_to_homography3d(proj_mat) # Bx4x4 proj_mat = from_origin_mat @ proj_mat @ to_origin_mat return proj_mat[..., :3, :] # Bx3x4
[docs]def get_perspective_transform3d(src: Tensor, dst: Tensor) -> Tensor: r"""Calculate a 3d perspective transform from four pairs of the corresponding points. The function calculates the matrix of a perspective transform so that: .. math :: \begin{bmatrix} t_{i}x_{i}^{'} \\ t_{i}y_{i}^{'} \\ t_{i}z_{i}^{'} \\ t_{i} \\ \end{bmatrix} = \textbf{map_matrix} \cdot \begin{bmatrix} x_{i} \\ y_{i} \\ z_{i} \\ 1 \\ \end{bmatrix} where .. math :: dst(i) = (x_{i}^{'},y_{i}^{'},z_{i}^{'}), src(i) = (x_{i}, y_{i}, z_{i}), i = 0,1,2,5,7 Concrete math is as below: .. math :: \[ u_i =\frac{c_{00} * x_i + c_{01} * y_i + c_{02} * z_i + c_{03}} {c_{30} * x_i + c_{31} * y_i + c_{32} * z_i + c_{33}} \] \[ v_i =\frac{c_{10} * x_i + c_{11} * y_i + c_{12} * z_i + c_{13}} {c_{30} * x_i + c_{31} * y_i + c_{32} * z_i + c_{33}} \] \[ w_i =\frac{c_{20} * x_i + c_{21} * y_i + c_{22} * z_i + c_{23}} {c_{30} * x_i + c_{31} * y_i + c_{32} * z_i + c_{33}} \] .. math :: \begin{pmatrix} x_0 & y_0 & z_0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -x_0*u_0 & -y_0*u_0 & -z_0 * u_0 \\ x_1 & y_1 & z_1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -x_1*u_1 & -y_1*u_1 & -z_1 * u_1 \\ x_2 & y_2 & z_2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -x_2*u_2 & -y_2*u_2 & -z_2 * u_2 \\ x_5 & y_5 & z_5 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -x_5*u_5 & -y_5*u_5 & -z_5 * u_5 \\ x_7 & y_7 & z_7 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -x_7*u_7 & -y_7*u_7 & -z_7 * u_7 \\ 0 & 0 & 0 & 0 & x_0 & y_0 & z_0 & 1 & 0 & 0 & 0 & 0 & -x_0*v_0 & -y_0*v_0 & -z_0 * v_0 \\ 0 & 0 & 0 & 0 & x_1 & y_1 & z_1 & 1 & 0 & 0 & 0 & 0 & -x_1*v_1 & -y_1*v_1 & -z_1 * v_1 \\ 0 & 0 & 0 & 0 & x_2 & y_2 & z_2 & 1 & 0 & 0 & 0 & 0 & -x_2*v_2 & -y_2*v_2 & -z_2 * v_2 \\ 0 & 0 & 0 & 0 & x_5 & y_5 & z_5 & 1 & 0 & 0 & 0 & 0 & -x_5*v_5 & -y_5*v_5 & -z_5 * v_5 \\ 0 & 0 & 0 & 0 & x_7 & y_7 & z_7 & 1 & 0 & 0 & 0 & 0 & -x_7*v_7 & -y_7*v_7 & -z_7 * v_7 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & x_0 & y_0 & z_0 & 1 & -x_0*w_0 & -y_0*w_0 & -z_0 * w_0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & x_1 & y_1 & z_1 & 1 & -x_1*w_1 & -y_1*w_1 & -z_1 * w_1 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & x_2 & y_2 & z_2 & 1 & -x_2*w_2 & -y_2*w_2 & -z_2 * w_2 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & x_5 & y_5 & z_5 & 1 & -x_5*w_5 & -y_5*w_5 & -z_5 * w_5 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & x_7 & y_7 & z_7 & 1 & -x_7*w_7 & -y_7*w_7 & -z_7 * w_7 \\ \end{pmatrix} Args: src: coordinates of quadrangle vertices in the source image with shape :math:`(B, 8, 3)`. dst: coordinates of the corresponding quadrangle vertices in the destination image with shape :math:`(B, 8, 3)`. Returns: the perspective transformation with shape :math:`(B, 4, 4)`. .. note:: This function is often used in conjunction with :func:`warp_perspective3d`. """ if not isinstance(src, (Tensor)): raise TypeError(f"Input type is not a Tensor. Got {type(src)}") if not isinstance(dst, (Tensor)): raise TypeError(f"Input type is not a Tensor. Got {type(dst)}") if not src.shape[-2:] == (8, 3): raise ValueError(f"Inputs must be a Bx8x3 tensor. Got {src.shape}") if not src.shape == dst.shape: raise ValueError(f"Inputs must have the same shape. Got {dst.shape}") if not (src.shape[0] == dst.shape[0]): raise ValueError(f"Inputs must have same batch size dimension. Expect {src.shape} but got {dst.shape}") if not (src.device == dst.device and src.dtype == dst.dtype): raise AssertionError( f"Expect `src` and `dst` to be in the same device (Got {src.dtype}, {dst.dtype}) " f"with the same dtype (Got {src.dtype}, {dst.dtype})." ) # we build matrix A by using only 4 point correspondence. The linear # system is solved with the least square method, so here # we could even pass more correspondence p = [] # 000, 100, 110, 101, 011 for i in [0, 1, 2, 5, 7]: p.append(_build_perspective_param3d(src[:, i], dst[:, i], 'x')) p.append(_build_perspective_param3d(src[:, i], dst[:, i], 'y')) p.append(_build_perspective_param3d(src[:, i], dst[:, i], 'z')) # A is Bx15x15 A = stack(p, 1) # b is a Bx15x1 b = stack( [ dst[:, 0:1, 0], dst[:, 0:1, 1], dst[:, 0:1, 2], dst[:, 1:2, 0], dst[:, 1:2, 1], dst[:, 1:2, 2], dst[:, 2:3, 0], dst[:, 2:3, 1], dst[:, 2:3, 2], # dst[:, 3:4, 0], dst[:, 3:4, 1], dst[:, 3:4, 2], # dst[:, 4:5, 0], dst[:, 4:5, 1], dst[:, 4:5, 2], dst[:, 5:6, 0], dst[:, 5:6, 1], dst[:, 5:6, 2], # dst[:, 6:7, 0], dst[:, 6:7, 1], dst[:, 6:7, 2], dst[:, 7:8, 0], dst[:, 7:8, 1], dst[:, 7:8, 2], ], 1, ) # solve the system Ax = b X: Tensor = _torch_solve_cast(A, b) # create variable to return batch_size: int = src.shape[0] M = torch.empty(batch_size, 16, device=src.device, dtype=src.dtype) M[..., :15] = X[..., 0] M[..., -1].fill_(1) return M.view(-1, 4, 4) # Bx4x4
def _build_perspective_param3d(p: Tensor, q: Tensor, axis: str) -> Tensor: ones = torch.ones_like(p)[..., 0:1] zeros = torch.zeros_like(p)[..., 0:1] if axis == 'x': return concatenate( [ p[:, 0:1], p[:, 1:2], p[:, 2:3], ones, zeros, zeros, zeros, zeros, zeros, zeros, zeros, zeros, -p[:, 0:1] * q[:, 0:1], -p[:, 1:2] * q[:, 0:1], -p[:, 2:3] * q[:, 0:1], ], 1, ) if axis == 'y': return concatenate( [ zeros, zeros, zeros, zeros, p[:, 0:1], p[:, 1:2], p[:, 2:3], ones, zeros, zeros, zeros, zeros, -p[:, 0:1] * q[:, 1:2], -p[:, 1:2] * q[:, 1:2], -p[:, 2:3] * q[:, 1:2], ], 1, ) if axis == 'z': return concatenate( [ zeros, zeros, zeros, zeros, zeros, zeros, zeros, zeros, p[:, 0:1], p[:, 1:2], p[:, 2:3], ones, -p[:, 0:1] * q[:, 2:3], -p[:, 1:2] * q[:, 2:3], -p[:, 2:3] * q[:, 2:3], ], 1, ) raise NotImplementedError(f"perspective params for axis `{axis}` is not implemented.")
[docs]def warp_perspective3d( src: Tensor, M: Tensor, dsize: Tuple[int, int, int], flags: str = 'bilinear', border_mode: str = 'zeros', align_corners: bool = False, ) -> Tensor: r"""Apply a perspective transformation to an image. The function warp_perspective transforms the source image using the specified matrix: .. math:: \text{dst} (x, y) = \text{src} \left( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right ) Args: src: input image with shape :math:`(B, C, D, H, W)`. M: transformation matrix with shape :math:`(B, 4, 4)`. dsize: size of the output image (height, width). flags: interpolation mode to calculate output values ``'bilinear'`` | ``'nearest'``. border_mode: padding mode for outside grid values ``'zeros'`` | ``'border'`` | ``'reflection'``. align_corners: interpolation flag. Returns: the warped input image :math:`(B, C, D, H, W)`. .. note:: This function is often used in conjunction with :func:`get_perspective_transform3d`. """ if not isinstance(src, Tensor): raise TypeError(f"Input src type is not a Tensor. Got {type(src)}") if not isinstance(M, Tensor): raise TypeError(f"Input M type is not a Tensor. Got {type(M)}") if not len(src.shape) == 5: raise ValueError(f"Input src must be a BxCxDxHxW tensor. Got {src.shape}") if not (len(M.shape) == 3 or M.shape[-2:] == (4, 4)): raise ValueError(f"Input M must be a Bx4x4 tensor. Got {M.shape}") # launches the warper d, h, w = src.shape[-3:] return _transform_warp_impl3d(src, M, (d, h, w), dsize, flags, border_mode, align_corners)
def homography_warp( patch_src: Tensor, src_homo_dst: Tensor, dsize: Tuple[int, int], mode: str = 'bilinear', padding_mode: str = 'zeros', align_corners: bool = False, normalized_coordinates: bool = True, normalized_homography: bool = True, ) -> Tensor: r"""Warp image patches or tensors by normalized 2D homographies. See :class:`~kornia.geometry.warp.HomographyWarper` for details. Args: patch_src: The image or tensor to warp. Should be from source of shape :math:`(N, C, H, W)`. src_homo_dst: The homography or stack of homographies from destination to source of shape :math:`(N, 3, 3)`. dsize: if homography normalized: The height and width of the image to warp. if homography not normalized: size of the output image (height, width). mode: interpolation mode to calculate output values ``'bilinear'`` | ``'nearest'``. padding_mode: padding mode for outside grid values ``'zeros'`` | ``'border'`` | ``'reflection'``. align_corners: interpolation flag. normalized_coordinates: Whether the homography assumes [-1, 1] normalized coordinates or not. normalized_homography: show is homography normalized. Return: Patch sampled at locations from source to destination. Example: >>> input = torch.rand(1, 3, 32, 32) >>> homography = torch.eye(3).view(1, 3, 3) >>> output = homography_warp(input, homography, (32, 32)) Example >>> img = torch.rand(1, 4, 5, 6) >>> H = torch.eye(3)[None] >>> out = homography_warp(img, H, (4, 2), align_corners=True, normalized_homography=False) >>> print(out.shape) torch.Size([1, 4, 4, 2]) """ if not src_homo_dst.device == patch_src.device: raise TypeError( "Patch and homography must be on the same device. \ Got patch.device: {} src_H_dst.device: {}.".format( patch_src.device, src_homo_dst.device ) ) if normalized_homography: height, width = dsize grid = create_meshgrid( height, width, normalized_coordinates=normalized_coordinates, device=patch_src.device, dtype=patch_src.dtype ) warped_grid = warp_grid(grid, src_homo_dst) return F.grid_sample(patch_src, warped_grid, mode=mode, padding_mode=padding_mode, align_corners=align_corners) return warp_perspective( patch_src, src_homo_dst, dsize, mode='bilinear', padding_mode=padding_mode, align_corners=True ) def _transform_warp_impl3d( src: Tensor, dst_pix_trans_src_pix: Tensor, dsize_src: Tuple[int, int, int], dsize_dst: Tuple[int, int, int], grid_mode: str, padding_mode: str, align_corners: bool, ) -> Tensor: """Compute the transform in normalized coordinates and perform the warping.""" dst_norm_trans_src_norm: Tensor = normalize_homography3d(dst_pix_trans_src_pix, dsize_src, dsize_dst) src_norm_trans_dst_norm = torch.inverse(dst_norm_trans_src_norm) return homography_warp3d(src, src_norm_trans_dst_norm, dsize_dst, grid_mode, padding_mode, align_corners, True) def homography_warp3d( patch_src: Tensor, src_homo_dst: Tensor, dsize: Tuple[int, int, int], mode: str = 'bilinear', padding_mode: str = 'zeros', align_corners: bool = False, normalized_coordinates: bool = True, ) -> Tensor: r"""Warp image patches or tensors by normalized 3D homographies. Args: patch_src: The image or tensor to warp. Should be from source of shape :math:`(N, C, D, H, W)`. src_homo_dst: The homography or stack of homographies from destination to source of shape :math:`(N, 4, 4)`. dsize: The height and width of the image to warp. mode: interpolation mode to calculate output values ``'bilinear'`` | ``'nearest'``. padding_mode: padding mode for outside grid values ``'zeros'`` | ``'border'`` | ``'reflection'``. align_corners: interpolation flag. normalized_coordinates: Whether the homography assumes [-1, 1] normalized coordinates or not. Return: Patch sampled at locations from source to destination. Example: >>> input = torch.rand(1, 3, 32, 32) >>> homography = torch.eye(3).view(1, 3, 3) >>> output = homography_warp(input, homography, (32, 32)) """ if not src_homo_dst.device == patch_src.device: raise TypeError( "Patch and homography must be on the same device. \ Got patch.device: {} src_H_dst.device: {}.".format( patch_src.device, src_homo_dst.device ) ) depth, height, width = dsize grid = create_meshgrid3d( depth, height, width, normalized_coordinates=normalized_coordinates, device=patch_src.device ) warped_grid = warp_grid3d(grid, src_homo_dst) return F.grid_sample(patch_src, warped_grid, mode=mode, padding_mode=padding_mode, align_corners=align_corners)