aiforge/vendor/github.com/nfnt/resize

• Resize
• • Installation
  • Usage
  • Caveats
  • Downsizing Samples
  • • Artificial sample
    • Real-Life sample
  • License

Resize

Image resizing for the Go programming language with common interpolation methods.

Installation

$ go get github.com/nfnt/resize

It's that easy!

Usage

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.
  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.

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
• Bilinear: Bilinear interpolation
• Bicubic: Bicubic interpolation
• MitchellNetravali: Mitchell-Netravali interpolation
• Lanczos2: Lanczos resampling with a=2
• Lanczos3: Lanczos resampling with a=3

Which 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)
}

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

Nearest-Neighbor	Bilinear
Bicubic	Mitchell-Netravali
Lanczos2	Lanczos3

Real-Life sample

Original image

Nearest-Neighbor	Bilinear
Bicubic	Mitchell-Netravali
Lanczos2	Lanczos3

License

Copyright (c) 2012 Jan Schlicht janschlicht@gmail.com
Resize is released under a MIT style license.