README.md

[# Moving Least Squares (MLS) (Numpy & PyTorch)

Introduction

Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested.

In computer graphics, the moving least squares method is useful for reconstructing a surface from a set of points. Often it is used to create a 3D surface from a point cloud through either downsampling or upsampling.

Methods

• Affine deformation
• Similarity deformation
• Rigid deformation

Usage

1. Install Packages

pip install -r requirements.txt

The accelerated algorithms requires PyTorch. PyTorch Installation Guide

2. Try the demo

Please check the demo.py for usage. We provide four demos:

demo()          # Toy
demo2()         # Monalisa
demo3()         # Cells
demo_torch()    # Toy in PyTorch

NEW 2023-04-28: @spedr provides an interactive demo. (See interactive_demo.py)

You can run the demo with

python interactive_demo.py images/monalisa.jpg

Hotkeys:
q or ESC - Quit
d - Delete the selected control point
c - Clear all control points
a - Create an affine deformation and display it in a separate window
s - Create a similarity deformation and display it in a separate window
r - Create a rigid deformation and display it in a separate window
w - Write the last deformation to the images folder

Here's an usage example of performing a rigid deformation on Monalisa's smile.

https://user-images.githubusercontent.com/22013744/231604569-c747ce8b-e074-4765-88ea-942fc3c60e8b.mp4

Results

• Toy

• Monalisa (Rigid)

• Cells (Download data)

The original label is overlapped on the deformed labels for better comparison.

Code list

• img_utils.py: Numpy implementation of the algorithms
• img_utils_pytorch.py: PyTorch implementation of the algorithms
• interp_torch.py: Interpolation 1D in PyTorch
• demo.py: Demo programs

Metrics

Optimize memory usage

• Here lists some examples of memory usage and running time of the numpy implementation

Image Size	Control Points	Affine	Similarity	Rigid
500 x 500	16	0.57s / 0.15GB	0.99s / 0.16GB	0.89s / 0.13GB
500 x 500	64	1.6s / 0.34GB	3.7s / 0.3GB	3.6s / 0.2GB
1000 x 1000	64	7.7s / 1.1GB	17s / 0.98GB	15s / 0.82GB
2000 x 2000	64	30s / 4.2GB	65s / 3.6GB	69s / 3.1GB

• Estimate memory usage for large image: (h x w x N x 4 x 2) x 2~2.5
  • h, w: image size
  • N: number of control points
  • 4: float32
  • 2: coordinates (x, y)
  • 2~2.5: intermediate results

Accelerated by PyTorch

The algorithm is also implemented with PyTorch and has faster speed benefiting from the CUDA acceleration.

• Rigid deformation

Image Size	Control Points	Numpy	PyTorch with CUDA
100 x 100	16	0.025s	0.128s
500 x 500	16	0.753s	0.187s
500 x 500	32	1.934s	0.205s
500 x 500	64	3.384s	0.483s
1000 x 1000	64	13.089s	0.663s
2000 x 2000	64	61.874s	1.784s

(* Tested on pytorch=1.6.0 with cudatoolkit=10.1)

Update

• 2023-04-28 Add an interactive demo. (Thanks to @spedr)

• 2022-01-12 Implement three algorithms with PyTorch

• 2021-12-24: Fix a bug of nan values in mls_rigid_deformation(). (see issue #13)

• 2021-07-14: Optimize memory usage. Now a 2000x2000 image with 64 control points spend about 4.2GB memory. (20GB in the previous version)

• 2020-09-25: No need for so-called inverse transformation. Just transform target pixels to the corresponding source pixels.

Reference

[1] Schaefer S, Mcphail T, Warren J. Image deformation using moving least squares[C]// ACM SIGGRAPH. ACM, 2006:533-540.

[2] interp implementation in interp_torch.py. Github: aliutkus/torchinterp1d ](https://github.com/spedr)