# Image Transformations¶

The functions in this section perform various geometrical transformations of 2D images.

warp_perspective(src, M, dsize, flags='bilinear', border_mode=None, border_value=0)[source]

Applies a perspective transformation to an image.

The function warp_perspective transforms the source image using the specified matrix:

$\text{dst} (x, y) = \text{src} \left( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )$
Parameters: src (torch.Tensor) – input image. M (Tensor) – transformation matrix. dsize (tuple) – size of the output image (height, width). the warped input image. Tensor
Shape:
• Input: $$(B, C, H, W)$$ and $$(B, 3, 3)$$
• Output: $$(B, C, H, W)$$

Note

See a working example here.

warp_affine(src: torch.Tensor, M: torch.Tensor, dsize: Tuple[int, int], flags: Optional[str] = 'bilinear', padding_mode: Optional[str] = 'zeros') → torch.Tensor[source]

Applies an affine transformation to a tensor.

The function warp_affine transforms the source tensor using the specified matrix:

$\text{dst}(x, y) = \text{src} \left( M_{11} x + M_{12} y + M_{13} , M_{21} x + M_{22} y + M_{23} \right )$
Parameters: src (torch.Tensor) – input tensor of shape $$(B, C, H, W)$$. M (torch.Tensor) – affine transformation of shape $$(B, 2, 3)$$. dsize (Tuple[int, int]) – size of the output image (height, width). mode (Optional[str]) – interpolation mode to calculate output values ‘bilinear’ | ‘nearest’. Default: ‘bilinear’. padding_mode (Optional[str]) – padding mode for outside grid values ‘zeros’ | ‘border’ | ‘reflection’. Default: ‘zeros’. the warped tensor. torch.Tensor
Shape:
• Output: $$(B, C, H, W)$$

Note

See a working example here.

get_perspective_transform(src, dst)[source]

Calculates a perspective transform from four pairs of the corresponding points.

The function calculates the matrix of a perspective transform so that:

$\begin{split}\begin{bmatrix} t_{i}x_{i}^{'} \\ t_{i}y_{i}^{'} \\ t_{i} \\ \end{bmatrix} = \textbf{map_matrix} \cdot \begin{bmatrix} x_{i} \\ y_{i} \\ 1 \\ \end{bmatrix}\end{split}$

where

$dst(i) = (x_{i}^{'},y_{i}^{'}), src(i) = (x_{i}, y_{i}), i = 0,1,2,3$
Parameters: src (Tensor) – coordinates of quadrangle vertices in the source image. dst (Tensor) – coordinates of the corresponding quadrangle vertices in the destination image. the perspective transformation. Tensor
Shape:
• Input: $$(B, 4, 2)$$ and $$(B, 4, 2)$$
• Output: $$(B, 3, 3)$$
get_rotation_matrix2d(center, angle, scale)[source]

Calculates an affine matrix of 2D rotation.

The function calculates the following matrix:

$\begin{split}\begin{bmatrix} \alpha & \beta & (1 - \alpha) \cdot \text{x} - \beta \cdot \text{y} \\ -\beta & \alpha & \beta \cdot \text{x} + (1 - \alpha) \cdot \text{y} \end{bmatrix}\end{split}$

where

$\begin{split}\alpha = \text{scale} \cdot cos(\text{angle}) \\ \beta = \text{scale} \cdot sin(\text{angle})\end{split}$

The transformation maps the rotation center to itself If this is not the target, adjust the shift.

Parameters: center (Tensor) – center of the rotation in the source image. angle (Tensor) – rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner). scale (Tensor) – isotropic scale factor. the affine matrix of 2D rotation. Tensor
Shape:
• Input: $$(B, 2)$$, $$(B)$$ and $$(B)$$
• Output: $$(B, 2, 3)$$

Example

>>> center = torch.zeros(1, 2)
>>> scale = torch.ones(1)
>>> angle = 45. * torch.ones(1)
>>> M = tgm.get_rotation_matrix2d(center, angle, scale)
tensor([[[ 0.7071,  0.7071,  0.0000],
[-0.7071,  0.7071,  0.0000]]])