Branches

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, non-premultiplied alpha).

Installation

go get -u github.com/disintegration/imaging

Documentation

https://pkg.go.dev/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:

Lanczos - A high-quality resampling filter for photographic images yielding sharp results.
CatmullRom - A sharp cubic filter that is faster than Lanczos filter while providing similar results.
MitchellNetravali - A cubic filter that produces smoother results with less ringing artifacts than CatmullRom.
Linear - Bilinear resampling filter, produces smooth output. Faster than cubic filters.
Box - Simple and fast averaging filter appropriate for downscaling. When upscaling it's similar to NearestNeighbor.
NearestNeighbor - Fastest resampling filter, no antialiasing.

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:

srcImage

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