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Image processing algorithms in pure Go
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anthonynsimon/bild
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A collection of parallel image processing algorithms in pure Go.
The aim of this project is simplicity in use and development over absolute high performance, but most algorithms are designed to be efficient and make use of parallelism when available.
It uses packages from the standard library whenever possible to reduce dependency use and development abstractions.
All operations return image types from the standard library.
The documentation for the various packages is availablehere.
Download and compile from sources:
go get github.com/anthonynsimon/bildOr get the pre-compiled binaries for your platform on thereleases page
bildA collection of parallel image processing algorithms in pure GoUsage: bild [command]Available Commands: adjust adjust basic image features like brightness or contrast blend blend two images together blur blur an image using the specified method channel channel operations on images effect apply effects on images help Help about any command histogram histogram operations on images imgio i/o operations on images noise noise generators segment segment an image using the specified methodFlags: -h, --help help for bild --version version for bildUse "bild [command] --help" for more information about a command.For example, to apply a median effect with a radius of 1.5 on the imageinput.png, writing the result into a new file calledoutput.png:
bild effect median --radius 1.5 input.png output.pngbild requires Go version 1.11 or greater.
go get github.com/anthonynsimon/bild/...
package mainimport ("github.com/anthonynsimon/bild/effect""github.com/anthonynsimon/bild/imgio""github.com/anthonynsimon/bild/transform")funcmain() {img,err:=imgio.Open("input.jpg")iferr!=nil {fmt.Println(err)return }inverted:=effect.Invert(img)resized:=transform.Resize(inverted,800,800,transform.Linear)rotated:=transform.Rotate(resized,45,nil)iferr:=imgio.Save("output.png",rotated,imgio.PNGEncoder());err!=nil {fmt.Println(err)return }}
import "github.com/anthonynsimon/bild/adjust"result := adjust.Brightness(img, 0.25)
result := adjust.Contrast(img, -0.5)
result := adjust.Gamma(img, 2.2)
result := adjust.Hue(img, -42)
result := adjust.Saturation(img, 0.5)
import "github.com/anthonynsimon/bild/blend"result := blend.Multiply(bg, fg)| Add | Color Burn | Color Dodge |
|---|---|---|
 |  |  |
| Darken | Difference | Divide |
 |  |  |
| Exclusion | Lighten | Linear Burn |
 |  |  |
| Linear Light | Multiply | Normal |
 |  |  |
| Opacity | Overlay | Screen |
 |  |  |
| Soft Light | Subtract | |
 |  |
import "github.com/anthonynsimon/bild/blur"result := blur.Box(img, 3.0)
result := blur.Gaussian(img, 3.0)
import "github.com/anthonynsimon/bild/channel"result := channel.Extract(img, channel.Alpha)
result := channel.ExtractMultiple(img, channel.Red, channel.Alpha)import "github.com/anthonynsimon/bild/effect"result := effect.Dilate(img, 3)
result := effect.EdgeDetection(img, 1.0)
result := effect.Emboss(img)
result := effect.Erode(img, 3)
result := effect.Grayscale(img)
result := effect.Invert(img)
result := effect.Median(img, 10.0)
result := effect.Sepia(img)
result := effect.Sharpen(img)
result := effect.Sobel(img)
result := effect.UnsharpMask(img, 0.6, 1.2)
import "github.com/anthonynsimon/bild/histogram"hist := histogram.NewRGBAHistogram(img)result := hist.Image()
import "github.com/anthonynsimon/bild/noise"result := noise.Generate(280, 280, &noise.Options{Monochrome: false, NoiseFn: noise.Uniform})
result := noise.Generate(280, 280, &noise.Options{Monochrome: true, NoiseFn: noise.Binary})
result := noise.Generate(280, 280, &noise.Options{Monochrome: true, NoiseFn: noise.Gaussian})
result := noise.GeneratePerlin(280, 280, 0.25)
import "github.com/anthonynsimon/bild/paint"// Fuzz is the percentage of maximum color distance that is toleratedresult := paint.FloodFill(img, image.Point{240, 0}, color.RGBA{255, 0, 0, 255}, 15)
import "github.com/anthonynsimon/bild/segment"result := segment.Threshold(img, 128)
import "github.com/anthonynsimon/bild/transform"// Source image is 280x280result := transform.Crop(img, image.Rect(70,70,210,210))
result := transform.FlipH(img)
result := transform.FlipV(img)
result := transform.Resize(img, 280, 280, transform.Linear)| Nearest Neighbor | Linear | Gaussian |
|---|---|---|
 |  |  |
| Mitchell Netravali | Catmull Rom | Lanczos |
 |  |  |
// Options set to nil will use defaults (ResizeBounds set to false, Pivot at center)result := transform.Rotate(img, -45.0, nil)
// If ResizeBounds is set to true, the full rotation bounding area is usedresult := transform.Rotate(img, -45.0, &transform.RotationOptions{ResizeBounds: true})
// Pivot coordinates are set from the top-left corner// Notice ResizeBounds being set to default (false)result := transform.Rotate(img, -45.0, &transform.RotationOptions{Pivot: &image.Point{0, 0}})
result := transform.ShearH(img, 30)
result := transform.ShearV(img, 30)
result := transform.Translate(img, 80, 0)
Want to hack on the project? Any kind of contribution is welcome!
Simply follow the next steps:
- Fork the project.
- Create a new branch.
- Make your changes and write tests when practical.
- Commit your changes to the new branch.
- Send a pull request, it will be reviewed shortly.
In case you want to add a feature, please create a new issue and briefly explain what the feature would consist of.For bugs or requests, before creating an issue please check if one has already been created for it.
Please see thechangelog for more details.
This project is licensed under the MIT license. Please read the LICENSE file.
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Image processing algorithms in pure Go