Source code for torchgeometry.utils

from typing import Optional

import torch
import torch.nn as nn
import torchgeometry as tgm

import numpy as np

__all__ = [

[docs]def create_meshgrid( height: int, width: int, normalized_coordinates: Optional[bool] = True): """Generates a coordinate grid for an image. When the flag `normalized_coordinates` is set to True, the grid is normalized to be in the range [-1,1] to be consistent with the pytorch function grid_sample. Args: height (int): the image height (rows). width (int): the image width (cols). normalized_coordinates (Optional[bool]): wether to normalize coordinates in the range [-1, 1] in order to be consistent with the PyTorch function grid_sample. Return: torch.Tensor: returns a grid tensor with shape :math:`(1, H, W, 2)`. """ # generate coordinates xs: Optional[torch.Tensor] = None ys: Optional[torch.Tensor] = None if normalized_coordinates: xs = torch.linspace(-1, 1, width) ys = torch.linspace(-1, 1, height) else: xs = torch.linspace(0, width - 1, width) ys = torch.linspace(0, height - 1, height) # generate grid by stacking coordinates base_grid: torch.Tensor = torch.stack( torch.meshgrid([xs, ys])).transpose(1, 2) # 2xHxW return torch.unsqueeze(base_grid, dim=0).permute(0, 2, 3, 1) # 1xHxWx2
[docs]def image_to_tensor(image): """Converts a numpy image to a PyTorch tensor image. Args: image (numpy.ndarray): image of the form :math:`(H, W, C)`. Returns: torch.Tensor: tensor of the form :math:`(C, H, W)`. """ if not type(image) == np.ndarray: raise TypeError("Input type is not a numpy.ndarray. Got {}".format( type(image))) if len(image.shape) > 3 or len(image.shape) < 2: raise ValueError("Input size must be a two or three dimensional array") tensor = torch.from_numpy(image) if len(tensor.shape) == 2: tensor = torch.unsqueeze(tensor, dim=-1) return tensor.permute(2, 0, 1).squeeze_() # CxHxW
[docs]def tensor_to_image(tensor): """Converts a PyTorch tensor image to a numpy image. In case the tensor is in the GPU, it will be copied back to CPU. Args: tensor (torch.Tensor): image of the form :math:`(C, H, W)`. Returns: numpy.ndarray: image of the form :math:`(H, W, C)`. """ if not torch.is_tensor(tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(tensor))) if len(tensor.shape) > 3 or len(tensor.shape) < 2: raise ValueError( "Input size must be a two or three dimensional tensor") input_shape = tensor.shape if len(input_shape) == 2: tensor = torch.unsqueeze(tensor, dim=0) tensor = tensor.permute(1, 2, 0) if len(input_shape) == 2: tensor = torch.squeeze(tensor, dim=-1) return tensor.contiguous().cpu().detach().numpy()
# TODO: evaluate wether to include it to the main API. ''' def create_pinhole(intrinsic, extrinsic, height, width): pinhole = torch.zeros(12) pinhole[0] = intrinsic[0, 0] # fx pinhole[1] = intrinsic[1, 1] # fy pinhole[2] = intrinsic[0, 2] # cx pinhole[3] = intrinsic[1, 2] # cy pinhole[4] = height pinhole[5] = width pinhole[6:9] = tgm.rotation_matrix_to_angle_axis( torch.tensor(extrinsic)) pinhole[9:12] = torch.tensor(extrinsic[:, 3]) return pinhole.view(1, -1)'''