# This package is no longer being updated! Please look for alternatives if that bothers you.
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)
<table>
<tr>
<th><img src="http://nfnt.github.com/img/rings_300_NearestNeighbor.png" /><br>Nearest-Neighbor</th>
<th><img src="http://nfnt.github.com/img/rings_300_Bilinear.png" /><br>Bilinear</th>
</tr>
<tr>
<th><img src="http://nfnt.github.com/img/rings_300_Bicubic.png" /><br>Bicubic</th>
<th><img src="http://nfnt.github.com/img/rings_300_MitchellNetravali.png" /><br>Mitchell-Netravali</th>
</tr>
<tr>
<th><img src="http://nfnt.github.com/img/rings_300_Lanczos2.png" /><br>Lanczos2</th>
<th><img src="http://nfnt.github.com/img/rings_300_Lanczos3.png" /><br>Lanczos3</th>
</tr>
</table>
### Real-Life sample
Original image
![Original](http://nfnt.github.com/img/IMG_3694_720.jpg)
<table>
<tr>
<th><img src="http://nfnt.github.com/img/IMG_3694_300_NearestNeighbor.png" /><br>Nearest-Neighbor</th>
<th><img src="http://nfnt.github.com/img/IMG_3694_300_Bilinear.png" /><br>Bilinear</th>
</tr>
<tr>
<th><img src="http://nfnt.github.com/img/IMG_3694_300_Bicubic.png" /><br>Bicubic</th>
<th><img src="http://nfnt.github.com/img/IMG_3694_300_MitchellNetravali.png" /><br>Mitchell-Netravali</th>
</tr>
<tr>
<th><img src="http://nfnt.github.com/img/IMG_3694_300_Lanczos2.png" /><br>Lanczos2</th>
<th><img src="http://nfnt.github.com/img/IMG_3694_300_Lanczos3.png" /><br>Lanczos3</th>
</tr>
</table>
License
-------
Copyright (c) 2012 Jan Schlicht <janschlicht@gmail.com>
Resize is released under a MIT style license.
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Golang 轻量级的图片服务器.rar
共40个文件
go:26个
md:3个
gitignore:2个
需积分: 5 0 下载量 55 浏览量
2023-05-24
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习惯于Java或者C#开发的人应该对控制反转与依赖注入应该再熟悉不过了。在Java平台有鼎鼎大名的Spring框架,在C#平台有Autofac,Unity,Windsor等,我当年C#开发时用的最多的就是Windsor。使用IoC容器是面向对象开发中非常方便的解耦模块之间的依赖的方法。各个模块之间不依赖于实现,而是依赖于接口,然后在构造函数或者属性或者方法中注入特定的实现,方便了各个模块的拆分以及模块的独立单元测试。 在[长安链]的设计中,各个模块可以灵活组装,模块之间的依赖基于protocol中定义的接口,每个接口有一个或者多个官方实现,当然第三方也可以提供该接口更多的实现。为了实现更灵活的组装各个模块,管理各个模块的依赖关系,于是我写了iocgo这个轻量级的golang版Ioc容器。
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Golang 轻量级的图片服务器.rar (40个子文件)
Golang 轻量级的图片服务器
goimg-master
uphand
info.go 1KB
test.go 560B
response.go 1KB
controller.go 5KB
error.go 1KB
Gopkg.toml 795B
imghand
init.go 398B
imgtype.go 422B
imgpath.go 2KB
handle.go 2KB
Gopkg.lock 573B
config.ini 35B
route
route.go 400B
vendor
github.com
go-ini
ini
key.go 21KB
.travis.yml 333B
struct.go 14KB
Makefile 245B
LICENSE 10KB
file.go 10KB
ini.go 7KB
section.go 6KB
parser.go 11KB
.gitignore 105B
error.go 883B
README.md 1KB
nfnt
resize
resize.go 20KB
ycc.go 9KB
nearest.go 9KB
.travis.yml 59B
LICENSE 756B
converter.go 12KB
thumbnail.go 2KB
filters.go 4KB
README.md 5KB
.gitignore 21B
server
http.go 859B
README.md 1KB
config
config.go 611B
main.go 236B
新建文本文档.txt 19B
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