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nfnt 891127d8d1 Fix alpha pre-multiplication. The alpha pre-multiplication was applied to an aggregate value an not to the color value. This could cause wrong colors for images with an alpha channel. Fixes #47.		пре 8 година
.travis.yml	Go >=1.1 is needed for this package.	пре 10 година
LICENSE	Documentation updated	пре 12 година
README.md	Mention optimized access for image.NRGBA, image.NRGBA64 in README.	пре 9 година
converter.go	Fix alpha pre-multiplication.	пре 8 година
converter_test.go	Add unit tests for converter.go	пре 10 година
filters.go	Dst pixel coordinate fix	пре 9 година
nearest.go	Add functions for nearest-neighbor resize of NRGBA, NRGBA16.	пре 9 година
nearest_test.go	Optimize YCbCr image resize	пре 10 година
resize.go	Use RGBA, RGBA64 image types as output.	пре 9 година
resize_test.go	Use RGBA, RGBA64 image types as output.	пре 9 година
thumbnail.go	Add: thumbnail helper function	пре 11 година
thumbnail_test.go	Add: thumbnail helper function	пре 11 година
ycc.go	Fix YCC SubImage.	пре 10 година
ycc_test.go	Fix error in YCC PixOffset and SubImage.	пре 10 година

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.