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jst 494d8de4e5 Speedup calculation by exploiting the separability of the resizing filter. Should be ~5x faster! More optimization will follow. before: > go test -bench . PASS Benchmark_BigResizeLanczos3-4 1 2438137093 ns/op Benchmark_BigResizeLanczos3Lut-4 1 1157612362 ns/op Benchmark_Reduction-4 2 743950618 ns/op after: > go test -bench . PASS Benchmark_BigResizeLanczos3-4 5 403685685 ns/op Benchmark_BigResizeLanczos3Lut-4 10 225539497 ns/op Benchmark_Reduction-4 10 207004759 ns/op		2013-11-18 19:54:31 +01:00
.travis.yml	Add Travis integration	2013-04-03 20:42:17 +02:00
converter.go	Add documentation	2012-12-11 20:35:02 +01:00
filters.go	Speedup calculation by exploiting the separability of the resizing filter.	2013-11-18 19:54:31 +01:00
LICENSE	Documentation updated	2012-08-03 17:39:38 +02:00
README.md	Fixed yet another typo.	2013-07-08 21:06:48 +02:00
resize_test.go	Add benchmarks for Lanczos3Lut to compare it with Lanczos3 performance.	2013-07-08 20:55:46 +02:00
resize.go	Speedup calculation by exploiting the separability of the resizing filter.	2013-11-18 19:54:31 +01:00
sinc_test.go	initial commit	2012-08-02 21:59:40 +02:00
sinc.go	initial commit	2012-08-02 21:59:40 +02:00

README.md

Resize

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

Installation

$ go get github.com/nfnt/resize

It's that easy!

Usage

Import package with

import "github.com/nfnt/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.Resize(width, height 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
Lanczos2Lut: Lanczos resampling with a=2 using a look-up table for fast computation
Lanczos2: Lanczos resampling with a=2
Lanczos3Lut: Lanczos resampling with a=3 using a look-up table for fast computation
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)
}

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.