Imaging

Package imaging provides basic image processing functions (resize, rotate, crop, brightness/contrast adjustments, etc.).

All the image processing functions provided by the package accept any image type that implements image.Image interface as an input, and return a new image of *image.NRGBA type (32bit RGBA colors, not premultiplied by alpha).

Installation

go get -u github.com/disintegration/imaging

Documentation

http://godoc.org/github.com/disintegration/imaging

Usage examples

A few usage examples can be found below. See the documentation for the full list of supported functions.

Image resizing

// Resize srcImage to size = 128x128px using the Lanczos filter.
dstImage128 := imaging.Resize(srcImage, 128, 128, imaging.Lanczos)

// Resize srcImage to width = 800px preserving the aspect ratio.
dstImage800 := imaging.Resize(srcImage, 800, 0, imaging.Lanczos)

// Scale down srcImage to fit the 800x600px bounding box.
dstImageFit := imaging.Fit(srcImage, 800, 600, imaging.Lanczos)

// Resize and crop the srcImage to fill the 100x100px area.
dstImageFill := imaging.Fill(srcImage, 100, 100, imaging.Center, imaging.Lanczos)

Imaging supports image resizing using various resampling filters. The most notable ones:

• NearestNeighbor - Fastest resampling filter, no antialiasing.
• Box - Simple and fast averaging filter appropriate for downscaling. When upscaling it's similar to NearestNeighbor.
• Linear - Bilinear filter, smooth and reasonably fast.
• MitchellNetravali - А smooth bicubic filter.
• CatmullRom - A sharp bicubic filter.
• Gaussian - Blurring filter that uses gaussian function, useful for noise removal.
• Lanczos - High-quality resampling filter for photographic images yielding sharp results, slower than cubic filters.

The full list of supported filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali, CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine. Custom filters can be created using ResampleFilter struct.

Resampling filters comparison

Original image:

The same image resized from 600x400px to 150x100px using different resampling filters. From faster (lower quality) to slower (higher quality):

Filter	Resize result
imaging.NearestNeighbor
imaging.Linear
imaging.CatmullRom
imaging.Lanczos

Gaussian Blur

dstImage := imaging.Blur(srcImage, 0.5)

Sigma parameter allows to control the strength of the blurring effect.

Original image	Sigma = 0.5	Sigma = 1.5

Sharpening

dstImage := imaging.Sharpen(srcImage, 0.5)

Sharpen uses gaussian function internally. Sigma parameter allows to control the strength of the sharpening effect.

Original image	Sigma = 0.5	Sigma = 1.5

Gamma correction

dstImage := imaging.AdjustGamma(srcImage, 0.75)

Original image	Gamma = 0.75	Gamma = 1.25

Contrast adjustment

dstImage := imaging.AdjustContrast(srcImage, 20)

Original image	Contrast = 15	Contrast = -15

Brightness adjustment

dstImage := imaging.AdjustBrightness(srcImage, 20)

Original image	Brightness = 10	Brightness = -10

Example code

package main

import (
	"image"
	"image/color"
	"log"

	"github.com/disintegration/imaging"
)

func main() {
	// Open a test image.
	src, err := imaging.Open("testdata/flowers.png")
	if err != nil {
		log.Fatalf("failed to open image: %v", err)
	}

	// Crop the original image to 300x300px size using the center anchor.
	src = imaging.CropAnchor(src, 300, 300, imaging.Center)

	// Resize the cropped image to width = 200px preserving the aspect ratio.
	src = imaging.Resize(src, 200, 0, imaging.Lanczos)

	// Create a blurred version of the image.
	img1 := imaging.Blur(src, 5)

	// Create a grayscale version of the image with higher contrast and sharpness.
	img2 := imaging.Grayscale(src)
	img2 = imaging.AdjustContrast(img2, 20)
	img2 = imaging.Sharpen(img2, 2)

	// Create an inverted version of the image.
	img3 := imaging.Invert(src)

	// Create an embossed version of the image using a convolution filter.
	img4 := imaging.Convolve3x3(
		src,
		[9]float64{
			-1, -1, 0,
			-1, 1, 1,
			0, 1, 1,
		},
		nil,
	)

	// Create a new image and paste the four produced images into it.
	dst := imaging.New(400, 400, color.NRGBA{0, 0, 0, 0})
	dst = imaging.Paste(dst, img1, image.Pt(0, 0))
	dst = imaging.Paste(dst, img2, image.Pt(0, 200))
	dst = imaging.Paste(dst, img3, image.Pt(200, 0))
	dst = imaging.Paste(dst, img4, image.Pt(200, 200))

	// Save the resulting image as JPEG.
	err = imaging.Save(dst, "testdata/out_example.jpg")
	if err != nil {
		log.Fatalf("failed to save image: %v", err)
	}
}

Output: