A collection of parallel image processing algorithms in pure Go.
The aim of this project is simplicity in use and development over high performance, but most algorithms are designed to be efficient and make use of parallelism when available. It is based on standard Go packages to reduce dependency use and development abstractions.
Notice: This package is under heavy development and the API might change at any time until a 1.0 version is reached.
http://godoc.org/github.com/anthonynsimon/bild
bild requires Go version 1.4 or greater.
go get -u github.com/anthonynsimon/bild/...
Notice the '...' at the end, this is to signify that you want all the packages on the repo. In your code, simply import the specific package that you want to use (see example below).
package main
import (
"github.com/anthonynsimon/bild/effect"
"github.com/anthonynsimon/bild/imgio"
"github.com/anthonynsimon/bild/transform"
)
func main() {
img, err := imgio.Open("filename.jpg")
if err != nil {
panic(err)
}
inverted := effect.Invert(img)
resized := transform.Resize(inverted, 800, 800, transform.Linear)
rotated := transform.Rotate(resized, 45, nil)
if err := imgio.Save("filename", rotated, imgio.PNG); err != nil {
panic(err)
}
}
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)
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})
import "github.com/anthonynsimon/bild/paint"
// Fuzz is the percentage of maximum color distance that is tolerated
result := 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 280x280
result := 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 used
result := 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)
This project is licensed under the MIT license. Please read the LICENSE file.
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.