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https://github.com/nfnt/resize

Pure golang image resizing
https://github.com/nfnt/resize

go image-processing resize

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Pure golang image resizing

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Resize

======



Image resizing for the [Go programming language](http://golang.org) with common interpolation methods.



[![Build Status](https://travis-ci.org/nfnt/resize.svg)](https://travis-ci.org/nfnt/resize)



Installation

------------



```bash

$ go get github.com/nfnt/resize

```



It's that easy!



Usage

-----



This package needs at least Go 1.1. Import package with



```go

import "github.com/nfnt/resize"

```



The resize package provides 2 functions:



* `resize.Resize` creates a scaled image with new dimensions (`width`, `height`) using the interpolation function `interp`.

  If either `width` or `height` is set to 0, it will be set to an aspect ratio preserving value.

* `resize.Thumbnail` downscales an image preserving its aspect ratio to the maximum dimensions (`maxWidth`, `maxHeight`).

  It will return the original image if original sizes are smaller than the provided dimensions.



```go

resize.Resize(width, height uint, img image.Image, interp resize.InterpolationFunction) image.Image

resize.Thumbnail(maxWidth, maxHeight uint, img image.Image, interp resize.InterpolationFunction) image.Image

```



The provided interpolation functions are (from fast to slow execution time)



- `NearestNeighbor`: [Nearest-neighbor interpolation](http://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)

- `Bilinear`: [Bilinear interpolation](http://en.wikipedia.org/wiki/Bilinear_interpolation)

- `Bicubic`: [Bicubic interpolation](http://en.wikipedia.org/wiki/Bicubic_interpolation)

- `MitchellNetravali`: [Mitchell-Netravali interpolation](http://dl.acm.org/citation.cfm?id=378514)

- `Lanczos2`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=2

- `Lanczos3`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=3



Which of these methods gives the best results depends on your use case.



Sample usage:



```go

package main



import (

	"github.com/nfnt/resize"

	"image/jpeg"

	"log"

	"os"

)



func main() {

	// open "test.jpg"

	file, err := os.Open("test.jpg")

	if err != nil {

		log.Fatal(err)

	}



	// decode jpeg into image.Image

	img, err := jpeg.Decode(file)

	if err != nil {

		log.Fatal(err)

	}

	file.Close()



	// resize to width 1000 using Lanczos resampling

	// and preserve aspect ratio

	m := resize.Resize(1000, 0, img, resize.Lanczos3)



	out, err := os.Create("test_resized.jpg")

	if err != nil {

		log.Fatal(err)

	}

	defer out.Close()



	// write new image to file

	jpeg.Encode(out, m, nil)

}

```



Caveats

-------



* Optimized access routines are used for `image.RGBA`, `image.NRGBA`, `image.RGBA64`, `image.NRGBA64`, `image.YCbCr`, `image.Gray`, and `image.Gray16` types. All other image types are accessed in a generic way that will result in slow processing speed.

* JPEG images are stored in `image.YCbCr`. This image format stores data in a way that will decrease processing speed. A resize may be up to 2 times slower than with `image.RGBA`. 



Downsizing Samples

-------



Downsizing is not as simple as it might look like. Images have to be filtered before they are scaled down, otherwise aliasing might occur.

Filtering is highly subjective: Applying too much will blur the whole image, too little will make aliasing become apparent.

Resize tries to provide sane defaults that should suffice in most cases.



### Artificial sample



Original image

![Rings](http://nfnt.github.com/img/rings_lg_orig.png)




Nearest-Neighbor


Bilinear




Bicubic


Mitchell-Netravali




Lanczos2


Lanczos3



### Real-Life sample



Original image  

![Original](http://nfnt.github.com/img/IMG_3694_720.jpg)




Nearest-Neighbor


Bilinear




Bicubic


Mitchell-Netravali




Lanczos2


Lanczos3



License

-------



Copyright (c) 2012 Jan Schlicht 

Resize is released under a MIT style license.