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https://github.com/anthonynsimon/bild

Image processing algorithms in pure Go
https://github.com/anthonynsimon/bild

algorithm concurrency effects go histogram image-editing image-processing parallelism resize signal-processing

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Image processing algorithms in pure Go

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README 

        
# bild



![bild logo](assets/img/logo.png)  



[![MIT License](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/anthonynsimon/bild/blob/master/LICENSE)

[![GoDoc](https://godoc.org/github.com/anthonynsimon/bild?status.svg)](https://godoc.org/github.com/anthonynsimon/bild)

[![Go Report Card](https://goreportcard.com/badge/github.com/anthonynsimon/bild)](https://goreportcard.com/report/github.com/anthonynsimon/bild)



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.

 

## Documentation

The documentation for the various packages is available [here](https://pkg.go.dev/mod/github.com/anthonynsimon/bild?tab=packages).



## CLI usage



Download and compile from sources:

```

go get github.com/anthonynsimon/bild

```



Or get the pre-compiled binaries for your platform on the [releases page](https://github.com/anthonynsimon/bild/releases)



```

bild



A collection of parallel image processing algorithms in pure Go



Usage:

  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 method



Flags:

  -h, --help      help for bild

      --version   version for bild



Use "bild [command] --help" for more information about a command.

```



For example, to apply a median effect with a radius of 1.5 on the image `input.png`, writing the result into a new file called `output.png`:

```

bild effect median --radius 1.5 input.png output.png

```



## Install package



bild requires Go version 1.11 or greater.



```bash

go get github.com/anthonynsimon/bild/...

```



## Basic package usage example:

```go

package main



import (

    "github.com/anthonynsimon/bild/effect"

    "github.com/anthonynsimon/bild/imgio"

    "github.com/anthonynsimon/bild/transform"

)



func main() {

    img, err := imgio.Open("input.jpg")

    if err != nil {

        fmt.Println(err)

        return

    }



    inverted := effect.Invert(img)

    resized := transform.Resize(inverted, 800, 800, transform.Linear)

    rotated := transform.Rotate(resized, 45, nil)



    if err := imgio.Save("output.png", rotated, imgio.PNGEncoder()); err != nil {

        fmt.Println(err)

        return

    }

}

```



# Output examples

## Adjustment

    import "github.com/anthonynsimon/bild/adjust"



### Brightness

    result := adjust.Brightness(img, 0.25)



![example](assets/img/brightness.jpg)  



### Contrast

    result := adjust.Contrast(img, -0.5)



![example](assets/img/contrast.jpg)  



### Gamma

    result := adjust.Gamma(img, 2.2)



![example](assets/img/gamma.jpg)  



### Hue

    result := adjust.Hue(img, -42)



![example](assets/img/hue.jpg)  



### Saturation

    result := adjust.Saturation(img, 0.5)



![example](assets/img/saturation.jpg)  



## Blend modes

    import "github.com/anthonynsimon/bild/blend"



    result := blend.Multiply(bg, fg)



| Add | Color Burn | Color Dodge |

| :----------: | :---------: | :------: |

| ![](assets/img/add.jpg) | ![](assets/img/colorburn.jpg) | ![](assets/img/colordodge.jpg) |

| **Darken** | **Difference** | **Divide** |

| ![](assets/img/darken.jpg) | ![](assets/img/difference.jpg) | ![](assets/img/divide.jpg) |

| **Exclusion** | **Lighten** | **Linear Burn** |

| ![](assets/img/exclusion.jpg) | ![](assets/img/lighten.jpg) | ![](assets/img/linearburn.jpg) |

| **Linear Light** | **Multiply** | **Normal** |

| ![](assets/img/linearlight.jpg) | ![](assets/img/multiply.jpg) | ![](assets/img/normal.jpg) |

| **Opacity** | **Overlay** | **Screen** |

| ![](assets/img/opacity.jpg) | ![](assets/img/overlay.jpg) | ![](assets/img/screen.jpg) |

| **Soft Light** | **Subtract** | |

| ![](assets/img/softlight.jpg) | ![](assets/img/subtract.jpg) | |



## Blur

    import "github.com/anthonynsimon/bild/blur"



### Box Blur

    result := blur.Box(img, 3.0)



![example](assets/img/boxblur.jpg)  



### Gaussian Blur

    result := blur.Gaussian(img, 3.0)



![example](assets/img/gaussianblur.jpg)  



## Channel

    import "github.com/anthonynsimon/bild/channel"



### Extract Channels

    result := channel.Extract(img, channel.Alpha)



![example](assets/img/extractchannel.jpg)



### Extract Multiple Channels

    result := channel.ExtractMultiple(img, channel.Red, channel.Alpha)



## Effect

    import "github.com/anthonynsimon/bild/effect"



### Dilate

    result := effect.Dilate(img, 3)



![example](assets/img/dilate.jpg)     



### Edge Detection

    result := effect.EdgeDetection(img, 1.0)



![example](assets/img/edgedetection.jpg)  



### Emboss

    result := effect.Emboss(img)



![example](assets/img/emboss.jpg)  



### Erode

    result := effect.Erode(img, 3)



![example](assets/img/erode.jpg)   



### Grayscale

    result := effect.Grayscale(img)



![example](assets/img/grayscale.jpg)  



### Invert

    result := effect.Invert(img)



![example](assets/img/invert.jpg)  



### Median

    result := effect.Median(img, 10.0)



![example](assets/img/median.jpg)  



### Sepia

    result := effect.Sepia(img)



![example](assets/img/sepia.jpg)  



### Sharpen

    result := effect.Sharpen(img)



![example](assets/img/sharpen.jpg)  



### Sobel

    result := effect.Sobel(img)



![example](assets/img/sobel.jpg)  



### Unsharp Mask

    result := effect.UnsharpMask(img, 0.6, 1.2)



![example](assets/img/unsharpmask.jpg)  



## Histogram

    import "github.com/anthonynsimon/bild/histogram"



### RGBA Histogram

    hist := histogram.NewRGBAHistogram(img)

    result := hist.Image()



![example](assets/img/histogram.png)  



## Noise

    import "github.com/anthonynsimon/bild/noise"



### Uniform colored

    result := noise.Generate(280, 280, &noise.Options{Monochrome: false, NoiseFn: noise.Uniform})



![example](assets/img/noiseuniform.jpg)  



### Binary monochrome

    result := noise.Generate(280, 280, &noise.Options{Monochrome: true, NoiseFn: noise.Binary})



![example](assets/img/noisebinary.jpg)  



### Gaussian monochrome

    result := noise.Generate(280, 280, &noise.Options{Monochrome: true, NoiseFn: noise.Gaussian})



![example](assets/img/noisegaussian.jpg)  



### Perlin Noise 

    result := noise.GeneratePerlin(280, 280, 0.25)

![example](assets/img/perlin.jpg)  



## Paint

    import "github.com/anthonynsimon/bild/paint"



### Flood Fill

    // 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)



![example](assets/img/floodfill.jpg) 



## Segmentation

    import "github.com/anthonynsimon/bild/segment"



### Threshold

    result := segment.Threshold(img, 128)



![example](assets/img/threshold.jpg)



## Transform

    import "github.com/anthonynsimon/bild/transform"



### Crop

    // Source image is 280x280

    result := transform.Crop(img, image.Rect(70,70,210,210))



![example](assets/img/crop.jpg)



### FlipH

    result := transform.FlipH(img)



![example](assets/img/fliph.jpg)  



### FlipV

    result := transform.FlipV(img)



![example](assets/img/flipv.jpg) 



### Resize Resampling Filters

    result := transform.Resize(img, 280, 280, transform.Linear)



| Nearest Neighbor | Linear | Gaussian |

|:----------: | :---------: | :------: |

| ![](assets/img/resizenearestneighbor.jpg) | ![](assets/img/resizelinear.jpg) | ![](assets/img/resizegaussian.jpg) |

| **Mitchell Netravali** | **Catmull Rom** | **Lanczos** |

| ![](assets/img/resizemitchell.jpg) | ![](assets/img/resizecatmullrom.jpg) | ![](assets/img/resizelanczos.jpg) |



### Rotate

    // Options set to nil will use defaults (ResizeBounds set to false, Pivot at center)

    result := transform.Rotate(img, -45.0, nil)



![example](assets/img/rotation03.gif)



    // If ResizeBounds is set to true, the full rotation bounding area is used

    result := transform.Rotate(img, -45.0, &transform.RotationOptions{ResizeBounds: true})



![example](assets/img/rotation01.gif)



    // 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}})



![example](assets/img/rotation02.gif)



### Shear Horizontal

    result := transform.ShearH(img, 30)



![example](assets/img/shearh.jpg)  



### Shear Vertical

    result := transform.ShearV(img, 30)



![example](assets/img/shearv.jpg) 



### Translate

    result := transform.Translate(img, 80, 0)



![example](assets/img/translate.jpg) 



## Contribute



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.



## Changelog



Please see the [changelog](CHANGELOG.md) for more details.



## License



This project is licensed under the MIT license. Please read the LICENSE file.