446eea2f7b
Caching kernel weights makes using the LUT based approach obsolete. Now they seem to be even slower than their counterparts.
138 lines
3.8 KiB
Markdown
138 lines
3.8 KiB
Markdown
Resize
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======
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Image resizing for the [Go programming language](http://golang.org) with common interpolation methods.
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[](https://travis-ci.org/nfnt/resize)
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Installation
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------------
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```bash
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$ go get github.com/nfnt/resize
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```
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It's that easy!
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Usage
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-----
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Import package with
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```go
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import "github.com/nfnt/resize"
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```
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Resize creates a scaled image with new dimensions (`width`, `height`) using the interpolation function `interp`.
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If either `width` or `height` is set to 0, it will be set to an aspect ratio preserving value.
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```go
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resize.Resize(width, height uint, img image.Image, interp resize.InterpolationFunction) image.Image
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```
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The provided interpolation functions are (from fast to slow execution time)
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- `NearestNeighbor`: [Nearest-neighbor interpolation](http://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
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- `Bilinear`: [Bilinear interpolation](http://en.wikipedia.org/wiki/Bilinear_interpolation)
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- `Bicubic`: [Bicubic interpolation](http://en.wikipedia.org/wiki/Bicubic_interpolation)
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- `MitchellNetravali`: [Mitchell-Netravali interpolation](http://dl.acm.org/citation.cfm?id=378514)
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- `Lanczos2`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=2
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- `Lanczos3`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=3
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Which of these methods gives the best results depends on your use case.
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Sample usage:
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```go
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package main
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import (
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"github.com/nfnt/resize"
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"image/jpeg"
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"log"
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"os"
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)
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func main() {
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// open "test.jpg"
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file, err := os.Open("test.jpg")
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if err != nil {
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log.Fatal(err)
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}
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// decode jpeg into image.Image
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img, err := jpeg.Decode(file)
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if err != nil {
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log.Fatal(err)
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}
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file.Close()
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// resize to width 1000 using Lanczos resampling
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// and preserve aspect ratio
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m := resize.Resize(1000, 0, img, resize.Lanczos3)
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out, err := os.Create("test_resized.jpg")
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if err != nil {
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log.Fatal(err)
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}
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defer out.Close()
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// write new image to file
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jpeg.Encode(out, m, nil)
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}
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```
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Downsizing Samples
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-------
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Downsizing is not as simple as it might look like. Images have to be filtered before they are scaled down, otherwise aliasing might occur.
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Filtering is highly subjective: Applying too much will blur the whole image, too little will make aliasing become apparent.
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Resize tries to provide sane defaults that should suffice in most cases.
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### Artificial sample
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Original image
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<table>
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<tr>
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<th><img src="http://nfnt.github.com/img/rings_300_NearestNeighbor.png" /><br>Nearest-Neighbor</th>
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<th><img src="http://nfnt.github.com/img/rings_300_Bilinear.png" /><br>Bilinear</th>
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</tr>
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<tr>
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<th><img src="http://nfnt.github.com/img/rings_300_Bicubic.png" /><br>Bicubic</th>
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<th><img src="http://nfnt.github.com/img/rings_300_MitchellNetravali.png" /><br>Mitchell-Netravali</th>
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</tr>
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<tr>
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<th><img src="http://nfnt.github.com/img/rings_300_Lanczos2.png" /><br>Lanczos2</th>
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<th><img src="http://nfnt.github.com/img/rings_300_Lanczos3.png" /><br>Lanczos3</th>
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</tr>
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</table>
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### Real-Life sample
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Original image
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<table>
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<tr>
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<th><img src="http://nfnt.github.com/img/IMG_3694_300_NearestNeighbor.png" /><br>Nearest-Neighbor</th>
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<th><img src="http://nfnt.github.com/img/IMG_3694_300_Bilinear.png" /><br>Bilinear</th>
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</tr>
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<tr>
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<th><img src="http://nfnt.github.com/img/IMG_3694_300_Bicubic.png" /><br>Bicubic</th>
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<th><img src="http://nfnt.github.com/img/IMG_3694_300_MitchellNetravali.png" /><br>Mitchell-Netravali</th>
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</tr>
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<tr>
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<th><img src="http://nfnt.github.com/img/IMG_3694_300_Lanczos2.png" /><br>Lanczos2</th>
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<th><img src="http://nfnt.github.com/img/IMG_3694_300_Lanczos3.png" /><br>Lanczos3</th>
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</tr>
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</table>
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License
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-------
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Copyright (c) 2012 Jan Schlicht <janschlicht@gmail.com>
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Resize is released under a MIT style license.
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