torch-image-interpolation 0.5.2rc1

Linear interpolation and gridding for 2D and 3D images in PyTorch

torch-image-interpolation

1D/2D/3D image interpolation routines in PyTorch.

Why?

This package provides a simple, consistent API for

• sampling values from 1D/2D/3D images (sample_image_1d()/sample_image_2d()/sample_image_3d())
• inserting values into 2D/3D images (insert_into_image_1d()/insert_into_image_2d()/insert_into_image_3d())

Operations are differentiable, multichannel data and complex valued images are supported.

torch.nn.functional.grid_sample is used under the hood for sampling.

Installation

pip install torch-image-interpolation

Usage

Coordinate System

This library uses an array-like coordinate system where coordinate values span from 0 to dim_size - 1 for each dimension. Fractional coordinates are supported and values are interpolated appropriately.

1D Images

For 1D images (vectors) with shape (w, ) or (c, w) coordinates are a (...) array of coordinates in the w dimension.

For example, in a (28, ) image, valid coordinates range from 0 to 27.

2D Images

For 2D images with shape (h, w) or (c, h, w):

Coordinates are ordered as [y, x] where:

• y is the position in the height dimension (first dimension of shape)
• x is the position in the width dimension (second dimension of shape)

For example, in a (28, 28) image, valid coordinates range from [0, 0] to [27, 27]

3D Images

For 3D images with shape (d, h, w) or (c, d, h, w):

Coordinates are ordered as [z, y, x] where:

• z is the position in the depth dimension (first dimension of shape)
• y is the position in the height dimension (second dimension of shape)
• x is the position in the width dimension (third dimension of shape)

For example, in a (28, 28, 28) volume, valid coordinates range from [0, 0, 0] to [27, 27, 27].

Sample from image

import torch
import numpy as np
from torch_image_interpolation import sample_image_2d

# example (h, w) image
image = torch.rand((28, 28))

# make an arbitrary stack (..., 2) of 2d coords
coords = torch.tensor(np.random.uniform(low=0, high=27, size=(6, 7, 8, 2))).float()

# sampling returns a (6, 7, 8) array of samples
# using bilinear interpolation (the default)
sample_bilinear = sample_image_2d(image=image, coordinates=coords, interpolation='bilinear')

# or a different interpolation mode...
samples_nearest = sample_image_2d(image=image, coordinates=coords, interpolation='nearest')
samples_bicubic = sample_image_2d(image=image, coordinates=coords, interpolation='bicubic')

The API is identical for 1D (w) or 3D (d, h, w) images but takes (...) or (..., 3) arrays of coordinates respectively.

Sampling is supported for multichannel images in 1D (c, w), 2D (c, h, w) and 3D (c, d, h, w). Sampling multichannel images returns (..., c) arrays of values.

Insert into image

import torch
import numpy as np
from torch_image_interpolation import insert_into_image_2d

# example (h, w) image
image = torch.zeros((28, 28))

# make an arbitrary stack (..., 2) of 2d coords
coords = torch.tensor(np.random.uniform(low=0, high=27, size=(3, 4, 2)))

# generate random values to place at coords
values = torch.rand(size=(3, 4))

# insert values into the image with bilinear interpolation (the default)
image_bilinear, weights_bilinear = insert_into_image_2d(
    values, image=image, coordinates=coords, interpolation='bilinear'
)

# you can specify a different interpolation mode
image_nearest, weights_nearest = insert_into_image_2d(
    values, image=image, coordinates=coords, interpolation='nearest'
)

The API is identical for 1D (w, ) and 3D (d, h, w) images but requires (...) and (..., 3)` arrays of coordinates respectively.

Insertion is supported for multichannel images in 1D (c, w), 2D (c, h, w) and 3D (c, d, h, w). Inserting into multichannel images requires (..., c) arrays of values.

Similar packages

• https://github.com/balbasty/torch-interpol