199 строки
7.6 KiB
Markdown
199 строки
7.6 KiB
Markdown
# Imaging
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[](https://godoc.org/github.com/disintegration/imaging)
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[](https://travis-ci.org/disintegration/imaging)
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[](https://coveralls.io/github/disintegration/imaging?branch=master)
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Package imaging provides basic image manipulation functions (resize, rotate, flip, crop, etc.).
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This package is based on the standard Go image package and works best along with it.
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Image manipulation functions provided by the package take any image type
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that implements `image.Image` interface as an input, and return a new image of
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`*image.NRGBA` type (32bit RGBA colors, not premultiplied by alpha).
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## Installation
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Imaging requires Go version 1.2 or greater.
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go get -u github.com/disintegration/imaging
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## Documentation
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http://godoc.org/github.com/disintegration/imaging
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## Usage examples
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A few usage examples can be found below. See the documentation for the full list of supported functions.
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### Image resizing
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```go
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// resize srcImage to size = 128x128px using the Lanczos filter
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dstImage128 := imaging.Resize(srcImage, 128, 128, imaging.Lanczos)
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// resize srcImage to width = 800px preserving the aspect ratio
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dstImage800 := imaging.Resize(srcImage, 800, 0, imaging.Lanczos)
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// scale down srcImage to fit the 800x600px bounding box
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dstImageFit := imaging.Fit(srcImage, 800, 600, imaging.Lanczos)
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// resize and crop the srcImage to fill the 100x100px area
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dstImageFill := imaging.Fill(srcImage, 100, 100, imaging.Center, imaging.Lanczos)
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```
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Imaging supports image resizing using various resampling filters. The most notable ones:
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- `NearestNeighbor` - Fastest resampling filter, no antialiasing.
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- `Box` - Simple and fast averaging filter appropriate for downscaling. When upscaling it's similar to NearestNeighbor.
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- `Linear` - Bilinear filter, smooth and reasonably fast.
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- `MitchellNetravali` - А smooth bicubic filter.
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- `CatmullRom` - A sharp bicubic filter.
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- `Gaussian` - Blurring filter that uses gaussian function, useful for noise removal.
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- `Lanczos` - High-quality resampling filter for photographic images yielding sharp results, but it's slower than cubic filters.
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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.
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**Resampling filters comparison**
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Original image. Will be resized from 512x512px to 128x128px.
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Filter | Resize result
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---|---
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`imaging.NearestNeighbor` | 
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`imaging.Box` | 
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`imaging.Linear` | 
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`imaging.MitchellNetravali` | 
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`imaging.CatmullRom` | 
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`imaging.Gaussian` | 
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`imaging.Lanczos` | 
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**Resize functions comparison**
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Original image:
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Resize the image to width=100px and height=100px:
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```go
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dstImage := imaging.Resize(srcImage, 100, 100, imaging.Lanczos)
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```
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Resize the image to width=100px preserving the aspect ratio:
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```go
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dstImage := imaging.Resize(srcImage, 100, 0, imaging.Lanczos)
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```
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Resize the image to fit the 100x100px boundng box preserving the aspect ratio:
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```go
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dstImage := imaging.Fit(srcImage, 100, 100, imaging.Lanczos)
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```
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Resize and crop the image with a center anchor point to fill the 100x100px area:
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```go
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dstImage := imaging.Fill(srcImage, 100, 100, imaging.Center, imaging.Lanczos)
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```
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### Gaussian Blur
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```go
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dstImage := imaging.Blur(srcImage, 0.5)
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```
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Sigma parameter allows to control the strength of the blurring effect.
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Original image | Sigma = 0.5 | Sigma = 1.5
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---|---|---
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 |  | 
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### Sharpening
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```go
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dstImage := imaging.Sharpen(srcImage, 0.5)
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```
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Uses gaussian function internally. Sigma parameter allows to control the strength of the sharpening effect.
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Original image | Sigma = 0.5 | Sigma = 1.5
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---|---|---
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 |  | 
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### Gamma correction
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```go
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dstImage := imaging.AdjustGamma(srcImage, 0.75)
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```
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Original image | Gamma = 0.75 | Gamma = 1.25
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---|---|---
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### Contrast adjustment
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```go
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dstImage := imaging.AdjustContrast(srcImage, 20)
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```
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Original image | Contrast = 20 | Contrast = -20
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---|---|---
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 |  | 
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### Brightness adjustment
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```go
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dstImage := imaging.AdjustBrightness(srcImage, 20)
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```
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Original image | Brightness = 20 | Brightness = -20
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---|---|---
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### Complete code example
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Here is the code example that loads several images, makes thumbnails of them
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and combines them together side-by-side.
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```go
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package main
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import (
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"image"
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"image/color"
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"github.com/disintegration/imaging"
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)
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func main() {
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// input files
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files := []string{"01.jpg", "02.jpg", "03.jpg"}
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// load images and make 100x100 thumbnails of them
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var thumbnails []image.Image
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for _, file := range files {
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img, err := imaging.Open(file)
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if err != nil {
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panic(err)
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}
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thumb := imaging.Thumbnail(img, 100, 100, imaging.CatmullRom)
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thumbnails = append(thumbnails, thumb)
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}
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// create a new blank image
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dst := imaging.New(100*len(thumbnails), 100, color.NRGBA{0, 0, 0, 0})
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// paste thumbnails into the new image side by side
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for i, thumb := range thumbnails {
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dst = imaging.Paste(dst, thumb, image.Pt(i*100, 0))
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}
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// save the combined image to file
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err := imaging.Save(dst, "dst.jpg")
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if err != nil {
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panic(err)
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}
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}
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```
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