texture_packer/rectpack2D/README.md

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If you are looking for the old version of rectpack2D, it can still be found in [a legacy branch](https://github.com/TeamHypersomnia/rectpack2D/tree/legacy).
# rectpack2D
[![Build Status](https://travis-ci.org/TeamHypersomnia/rectpack2D.svg?branch=master)](https://travis-ci.org/TeamHypersomnia/rectpack2D)
[![Appveyor Build Status](https://ci.appveyor.com/api/projects/status/aojyt3r6ysvadkgl?svg=true)](https://ci.appveyor.com/project/geneotech/rectpack2D)
Table of contents:
- [Benchmarks](#benchmarks)
- [Usage](#usage)
- [Building the example](#building-the-example)
* [Windows](#windows)
* [Linux](#linux)
- [Algorithm](#algorithm)
* [Insertion algorithm](#insertion-algorithm)
* [Additional heuristics](#additional-heuristics)
Rectangle packing library (no longer tiny!).
This is a refactored and **highly optimized** branch of the original library which is easier to use and customize.
![7](https://user-images.githubusercontent.com/3588717/42707552-d8b1c65e-86da-11e8-9412-54c580bd2696.jpg)
## Benchmarks
Tests were conducted on a ``Intel(R) Core(TM) i7-4770K CPU @ 3.50GHz``.
The binary was built with ``clang 6.0.0``, using an -03 switch.
### Arbitrary game sprites: 582 subjects.
**Runtime: 0.8 ms**
**Wasted pixels: 10982 (0.24% - equivalent of a 105 x 105 square)**
Output (1896 x 2382):
![1](images/atlas_small.png)
In color:
(black is wasted space)
![2](images/atlas_small_color.png)
### Arbitrary game sprites + Japanese glyphs: 3264 subjects.
**Runtime: 4 ms**
**Wasted pixels: 15538 (0.31% - equivalent of a 125 x 125 square)**
Output (2116 x 2382):
![3](images/atlas_big.png)
In color:
(black is wasted space)
![4](images/atlas_big_color.png)
### Japanese glyphs + some GUI sprites: 3122 subjects.
**Runtime: 3.5 - 7 ms**
**Wasted pixels: 9288 (1.23% - equivalent of a 96 x 96 square)**
Output (866 x 871):
![5](images/atlas_tiny.png)
In color:
(black is wasted space)
![6](images/atlas_tiny_color.png)
## Usage
This is a header-only library.
Just include the ``src/finders_interface.h`` and you should be good to go.
For an example use, see ``example/main.cpp``.
## Building the example
### Windows
From the repository's folder, run:
```bash
mkdir build
cd build
cmake -G "Visual Studio 15 2017 Win64" ..
````
Then just build the generated ``.sln`` file using the newest Visual Studio Preview.
### Linux
From the repository's folder, run:
```bash
cmake/build_example.sh Release gcc g++
cd build/current
ninja run
````
Or, if you want to use clang, run:
```bash
cmake/build_example.sh Release clang clang++
cd build/current
ninja run
````
## Algorithm
### Insertion algorithm
The library started as an implementation of this algorithm:
http://blackpawn.com/texts/lightmaps/default.html
The current version somewhat derives from the concept described there -
however, it uses just a **vector of empty spaces, instead of a tree** - this turned out to be a performance breakthrough.
Given
```cpp
struct rect_xywh {
int x;
int y;
int w;
int h;
};
````
Let us create a vector and call it empty_spaces.
```cpp
std::vector<rect_xywh> empty_spaces;
````
Given a user-specified initial bin, which is a square of some size S, we initialize the first empty space.
```cpp
empty_spaces.push_back(rect_xywh(0, 0, S, S));
````
Now, we'd like to insert the first image rectangle.
To do this, we iterate the vector of empty spaces **backwards** and look for an empty space into which the image can fit.
For now, we only have the S x S square: let's save the index of this candidate empty space,
which is ``candidate_space_index = 0;``
If our image is strictly smaller than the candidate space, we have something like this:
![diag01](images/diag01.png)
The blue is our image rectangle.
We now calculate the gray rectangles labeled as "bigger split" and "smaller split",
and save them like this:
```cpp
// Erase the space that we've just inserted to, by swapping and popping.
empty_spaces[candidate_space_index] = empty_spaces.back();
empty_spaces.pop_back();
// Save the resultant splits
empty_spaces.push_back(bigger_split);
empty_spaces.push_back(smaller_split);
````
Notice that we push the smaller split *after* the bigger one.
This is fairly important, because later the candidate images will encounter the smaller splits first,
which will make better use of empty spaces overall.
#### Corner cases:
- If the image dimensions equal the dimensions of the candidate empty space (image fits exactly),
- we just delete the space and create no splits.
- If the image fits into the candidate empty space, but exactly one of the image dimensions equals the respective dimension of the candidate empty space (e.g. image = 20x40, candidate space = 30x40)
- we delete the space and create a single split. In this case a 10x40 space.
To see the complete, modular procedure for calculating the splits (along with the corner cases),
[see this source](src/insert_and_split.h).
If the insertion fails, we also try the same procedure for a flipped image.
### Additional heuristics
Now we know how to insert individual images into a bin of a given initial size S.
1. However, what S should be passed to the algorithm so that the rectangles end up wasting the least amount of space?
- We perform a binary search.
- We start with the size specified by the library user. Typically, it would be the maximum texture size allowed on a particular GPU.
- If the packing was successful on the given bin size, decrease the size and try to pack again.
- If the packing has failed on the given bin size - because some rectangles could not be further inserted - increase the size and try to pack again.
- The search is aborted if we've successfully inserted into a bin and the dimensions of the next candidate would differ from the previous by less than ``discard_step``.
- This variable exists so that we may easily trade accuracy for a speedup. ``discard_step = 1`` yields the highest accuracy. ``discard_step = 128`` will yield worse packings, but will be a lot faster, etc.
- The search is performed first by decreasing the bin size by both width and height, keeping it in square shape.
- Then we do the same, but only decreasing width.
- Then we do the same, but only decreasing height.
- The last two were a breakthrough in packing tightness. It turns out important to consider non-square bins.
2. In what order should the rectangles be inserted so that they pack the tightest?
- By default, the library tries 6 decreasing orders:
- By area.
- By perimeter.
- By the bigger side.
- By width.
- By height.
- By 'a pathological multiplier": ``max(w, h) / min(w, h) * w * h``
- This makes some giant, irregular mutants always go first, which is good, because it doesn't shred our empty spaces to a lot of useless pieces.