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Imaging

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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:

srcImage

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

FilterResize result
imaging.NearestNeighbordstImage
imaging.LineardstImage
imaging.CatmullRomdstImage
imaging.LanczosdstImage

Gaussian Blur

dstImage := imaging.Blur(srcImage, 0.5)

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

Original imageSigma = 0.5Sigma = 1.5
srcImagedstImagedstImage

Sharpening

dstImage := imaging.Sharpen(srcImage, 0.5)

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

Original imageSigma = 0.5Sigma = 1.5
srcImagedstImagedstImage

Gamma correction

dstImage := imaging.AdjustGamma(srcImage, 0.75)
Original imageGamma = 0.75Gamma = 1.25
srcImagedstImagedstImage

Contrast adjustment

dstImage := imaging.AdjustContrast(srcImage, 20)
Original imageContrast = 15Contrast = -15
srcImage