https://github.com/dfdx/piecewiseaffinetransforms.jl

Smooth image transformations for complex shapes
https://github.com/dfdx/piecewiseaffinetransforms.jl

Last synced: 7 months ago
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Smooth image transformations for complex shapes

• Host: GitHub
• URL: https://github.com/dfdx/piecewiseaffinetransforms.jl
• Owner: dfdx
• License: other
• Created: 2015-04-21T21:17:34.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2020-02-08T16:09:04.000Z (over 6 years ago)
• Last Synced: 2025-01-22T07:16:49.193Z (over 1 year ago)
• Language: Julia
• Size: 1.61 MB
• Stars: 12
• Watchers: 4
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
# Piecewise Affine Transformations

[![Build Status](https://travis-ci.org/dfdx/PiecewiseAffineTransforms.jl.svg)](https://travis-ci.org/dfdx/PiecewiseAffineTransforms.jl)

Package for smooth deformation of complex shapes.

## Installation

    Pkg.add("PiecewiseAffineTransforms")

## Usage Overview

Piecewise affine transformation resembles ordinary affine transformation, but instead of warping single region linearly, it splits down area under the question into a set of triangles and warps each such triangle separately.

Let's say, we have an image of a face and want to warp it to have different expression (destination image is here only for demonstration, we will not use it):

    using PiecewiseAffineTransforms

    src_img = ...

    dst_img = ...

(full version of code is available in `examples/ex.jl`)

  

    Source imageDestination image

  

  

    

      

        

      

      

        

      

    

  

We will also assume that both faces are annotated with corresponding shape landmarks:

    src_shape = ... # should be a Nx2 matrix of Float64,

                    #  where N is a number of landmarks

    dst_shape = ...

First of all, we need to split the shapes into triangles, i.e. triangulate them:

    trigs = delaunayindexes(src_shape)  # Tx3 matrix of Int, where T is

                                        #  a number of resuling triangles

    # needs ImageView installed (Pkg.add("ImageView"))

    triplot(src_img, src_shape, trigs)

    triplot(dst_img, dst_shape, trigs)

**WARNING:** Triangulation is based on [VoronoiDelaunay.jl](https://github.com/JuliaGeometry/VoronoiDelaunay.jl/), which currently has a [bug](https://github.com/JuliaGeometry/VoronoiDelaunay.jl/issues/6) resulting in one lost triangle from time to time. To overcome this, just get good sample of triangulation and save it for future use.

  

    Source shapeDestination shape

  

  

    

      

        

      

      

        

      

    

  

Warping `src_image` from `src_shape` to `dst_shape` may be as simple as calling this:

    @time warped = pa_warp(src_img, src_shape, dst_shape, trigs)

    # 1.44 seconds

But if you are going to repeat warping to `dst_shape` for many source images or just many times, it's worth to prepare warp by creating `PAWarpParams` object and using it for all future transformation to `dst_shape`:

    @time pa_params = pa_warp_params(dst_shape, trigs, (480, 640))

    # 5.92 seconds

    @time warped = pa_warp(pa_params, src_img, src_shape)

    # 0.075 seconds

But anyway, they both give (almost) the same result:

  

    Original imageWarped image

  

  

    

      

        

      

      

        

      

    

  

## Acknowledgement

Code for prepared warp was mostly extracted from ICAAM project by Luca Vezzaro.