191 строка
7.1 KiB
Markdown
191 строка
7.1 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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The original image.
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The same image resized from 512x512px to 128x128px using different resampling filters.
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From faster (lower quality) to slower (higher quality):
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Filter | Resize result
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--------------------------|---------------------------------------------
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`imaging.NearestNeighbor` | 
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`imaging.Linear` | 
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`imaging.CatmullRom` | 
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`imaging.Lanczos` | 
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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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`Sharpen` 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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### 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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 |  | 
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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 = 10 | Contrast = -10
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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 = 10 | Brightness = -10
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-----------------------------------|----------------------------------------------|---------------------------------------------
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## Example code
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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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"log"
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"github.com/disintegration/imaging"
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)
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func main() {
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// Open the test image.
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src, err := imaging.Open("testdata/lena_512.png")
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if err != nil {
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log.Fatalf("Open failed: %v", err)
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}
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// Crop the original image to 350x350px size using the center anchor.
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src = imaging.CropAnchor(src, 350, 350, imaging.Center)
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// Resize the cropped image to width = 256px preserving the aspect ratio.
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src = imaging.Resize(src, 256, 0, imaging.Lanczos)
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// Create a blurred version of the image.
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img1 := imaging.Blur(src, 2)
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// Create a grayscale version of the image with higher contrast and sharpness.
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img2 := imaging.Grayscale(src)
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img2 = imaging.AdjustContrast(img2, 20)
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img2 = imaging.Sharpen(img2, 2)
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// Create an inverted version of the image.
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img3 := imaging.Invert(src)
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// Create an embossed version of the image using a convolution filter.
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img4 := imaging.Convolve3x3(
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src,
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[9]float64{
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-1, -1, 0,
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-1, 1, 1,
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0, 1, 1,
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},
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nil,
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)
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// Create a new image and paste the four produced images into it.
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dst := imaging.New(512, 512, color.NRGBA{0, 0, 0, 0})
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dst = imaging.Paste(dst, img1, image.Pt(0, 0))
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dst = imaging.Paste(dst, img2, image.Pt(0, 256))
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dst = imaging.Paste(dst, img3, image.Pt(256, 0))
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dst = imaging.Paste(dst, img4, image.Pt(256, 256))
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// Save the resulting image using JPEG format.
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err = imaging.Save(dst, "testdata/out_example.jpg")
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if err != nil {
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log.Fatalf("Save failed: %v", err)
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}
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}
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```
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Output:
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