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chromium / external / github.com / disintegration / imaging / refs/tags/v1.2.4
tagfc05e1fd57c3f33090f2fae2648fc3a11f9da7da
taggerGrigory Dryapak <grigory.dryapak@gmail.com>Thu Oct 26 21:13:29 2017
objectdd50a3ee9985ccd313a2f03c398fcaedc96dc707 
v1.2.4
commitdd50a3ee9985ccd313a2f03c398fcaedc96dc707[log] [tgz]
authorGrigory Dryapak <grigory.dryapak@gmail.com>Thu Oct 26 19:06:16 2017
committerGrigory Dryapak <grigory.dryapak@gmail.com>Thu Oct 26 19:06:16 2017
treef07ace5e420a9cfe17300b9b23771c3732def566
parent0df405384fbb536c584ff141b08f30fca67a0b70 [diff]
transform: omit unnecessary conversion
1 file changed
tree: f07ace5e420a9cfe17300b9b23771c3732def566
  1. testdata/
  2. .travis.yml
  3. adjust.go
  4. adjust_test.go
  5. clone.go
  6. clone_test.go
  7. convolution.go
  8. convolution_test.go
  9. effects.go
  10. effects_test.go
  11. example_test.go
  12. helpers.go
  13. helpers_test.go
  14. histogram.go
  15. histogram_test.go
  16. LICENSE
  17. README.md
  18. resize.go
  19. resize_test.go
  20. tools.go
  21. tools_test.go
  22. transform.go
  23. transform_test.go
  24. utils.go
  25. utils_test.go
README.md

Imaging

GoDoc Build Status Coverage Status

Package imaging provides basic image manipulation functions (resize, rotate, flip, crop, etc.). This package is based on the standard Go image package and works best along with it.

Image manipulation functions provided by the package take 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

Imaging requires Go version 1.2 or greater.

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, but it's 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

The original image.

srcImage

The same image resized from 512x512px to 128x128px 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 = 10Contrast = -10
srcImagedstImagedstImage

Brightness adjustment

dstImage := imaging.AdjustBrightness(srcImage, 20)
Original imageBrightness = 10Brightness = -10
srcImagedstImagedstImage

Example code

package main

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

	"github.com/disintegration/imaging"
)

func main() {
	// Open the test image.
	src, err := imaging.Open("testdata/lena_512.png")
	if err != nil {
		log.Fatalf("Open failed: %v", err)
	}

	// Crop the original image to 350x350px size using the center anchor.
	src = imaging.CropAnchor(src, 350, 350, imaging.Center)

	// Resize the cropped image to width = 256px preserving the aspect ratio.
	src = imaging.Resize(src, 256, 0, imaging.Lanczos)

	// Create a blurred version of the image.
	img1 := imaging.Blur(src, 2)

	// 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(512, 512, color.NRGBA{0, 0, 0, 0})
	dst = imaging.Paste(dst, img1, image.Pt(0, 0))
	dst = imaging.Paste(dst, img2, image.Pt(0, 256))
	dst = imaging.Paste(dst, img3, image.Pt(256, 0))
	dst = imaging.Paste(dst, img4, image.Pt(256, 256))

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

Output:

dstImage