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.travis.yml | 5 years ago | |
LICENSE | 7 years ago | |
README.md | 5 years ago | |
converter.go | 7 years ago | |
filters.go | 7 years ago | |
nearest.go | 7 years ago | |
resize.go | 7 years ago | |
thumbnail.go | 7 years ago | |
ycc.go | 7 years ago |
Image resizing for the Go programming language with common interpolation methods.
$ go get github.com/nfnt/resize
It's that easy!
This package needs at least Go 1.1. Import package with
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
.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
).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 interpolationBilinear
: Bilinear interpolationBicubic
: Bicubic interpolationMitchellNetravali
: Mitchell-Netravali interpolationLanczos2
: Lanczos resampling with a=2Lanczos3
: Lanczos resampling with a=3Which of these methods gives the best results depends on your use case.
Sample usage:
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)
}
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.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 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.
Nearest-Neighbor |
Bilinear |
---|---|
Bicubic |
Mitchell-Netravali |
Lanczos2 |
Lanczos3 |
Nearest-Neighbor |
Bilinear |
---|---|
Bicubic |
Mitchell-Netravali |
Lanczos2 |
Lanczos3 |
Copyright (c) 2012 Jan Schlicht janschlicht@gmail.com
Resize is released under a MIT style license.
本项目是群体化方法与技术的开源实现案例,在基于Gitea的基础上,进一步支持社交化的协同开发、协同学习、协同研究等群体创新实践服务,特别是针对新一代人工智能技术特点,重点支持项目管理、git代码管理、大数据集存储管理与智能计算平台接入。
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