Fast (linear time) implementation of the Gaussian Blur algorithm in Go.
Original algorithm taken from http://blog.ivank.net/fastest-gaussian-blur.html, and use goroutine.
Download and compile from this repository.
go get -u github.com/matsuyoshi30/song2
And import as a package, call the API song2.GaussianBlur(src, blurRadius)
.
package main
import (
"fmt"
"image"
"image/png"
"os"
"github.com/matsuyoshi30/song2"
)
func main() {
img, err := os.Open("./input.png")
if err != nil {
fmt.Println(err)
return
}
defer img.Close()
img, _, err := image.Decode(file)
if err != nil {
fmt.Println(err)
return
}
blurred := song2.GaussianBlur(img, 3.0)
out, err := os.Create("./output.png")
if err != nil {
fmt.Println(err)
return
}
defer out.Close()
if err := png.Encode(out, blurred); err != nil {
fmt.Println(err)
return
}
}
Clone this repository, and go install
.
git clone https://github.com/matsuyoshi30/song2
cd song2/cmd/song2 && go install
You can use song2
as a cli tool.
Usage:
song2 [FLAGS] [FILE]
FLAGS:
-o Write output image to specifig filepath [default: blurred.png]
-r Radius [default: 3.0]
Author:
matsuyoshi30 <sfbgwm30@gmail.com>
song2 -o assets/blurred.png assets/sample.png
Original | Blured |
---|---|
This image is from http://sipi.usc.edu/database/database.php?volume=misc&image=10#top
I also implemented another algorithms of this post in test package (AnotherAlgorithm1-3).
% go test -bench . -benchmem
goos: darwin
goarch: amd64
pkg: github.com/matsuyoshi30/song2
cpu: Intel(R) Core(TM) i5-5287U CPU @ 2.90GHz
BenchmarkGaussianBlur1-4 1 12656993105 ns/op 2097280 B/op 4 allocs/op
BenchmarkGaussianBlur2-4 1 1649545490 ns/op 2097304 B/op 5 allocs/op
BenchmarkGaussianBlur3-4 4 263524278 ns/op 2097310 B/op 5 allocs/op
BenchmarkGaussianBlur-4 18 56368998 ns/op 2097309 B/op 5 allocs/op
BenchmarkStackblur-4 40 28339048 ns/op 3146083 B/op 524301 allocs/op
BenchmarkBildBlur-4 24 48113312 ns/op 4245167 B/op 22 allocs/op
BenchmarkGaussianBlurUsingGoroutine-4 39 29797842 ns/op 2097535 B/op 11 allocs/op
reference stackblur-go and bild.