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# Imaging
[](https://godoc.org/github.com/disintegration/imaging)
[](https://travis-ci.org/disintegration/imaging)
[](https://coveralls.io/github/disintegration/imaging?branch=master)
[](https://goreportcard.com/report/github.com/disintegration/imaging)
Package imaging provides basic image processing functions (resize, rotate, crop, brightness/contrast adjustments, etc.).
All the image processing functions provided by the package accept any image type that implements `image.Image` interface
as an input, and return a new image of `*image.NRGBA` type (32bit RGBA colors, not premultiplied by alpha).
## Installation
go get -u github.com/disintegration/imaging
## Documentation
http://godoc.org/github.com/disintegration/imaging
## Usage examples
A few usage examples can be found below. See the documentation for the full list of supported functions.
### Image resizing
```go
// Resize srcImage to size = 128x128px using the Lanczos filter.
dstImage128 := imaging.Resize(srcImage, 128, 128, imaging.Lanczos)
// Resize srcImage to width = 800px preserving the aspect ratio.
dstImage800 := imaging.Resize(srcImage, 800, 0, imaging.Lanczos)
// Scale down srcImage to fit the 800x600px bounding box.
dstImageFit := imaging.Fit(srcImage, 800, 600, imaging.Lanczos)
// Resize and crop the srcImage to fill the 100x100px area.
dstImageFill := imaging.Fill(srcImage, 100, 100, imaging.Center, imaging.Lanczos)
```
Imaging supports image resizing using various resampling filters. The most notable ones:
- `NearestNeighbor` - Fastest resampling filter, no antialiasing.
- `Box` - Simple and fast averaging filter appropriate for downscaling. When upscaling it's similar to NearestNeighbor.
- `Linear` - Bilinear filter, smooth and reasonably fast.
- `MitchellNetravali` - А smooth bicubic filter.
- `CatmullRom` - A sharp bicubic filter.
- `Gaussian` - Blurring filter that uses gaussian function, useful for noise removal.
- `Lanczos` - High-quality resampling filter for photographic images yielding sharp results, slower than cubic filters.
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.
**Resampling filters comparison**
Original image:
The same image resized from 600x400px to 150x100px using different resampling filters.
From faster (lower quality) to slower (higher quality):
Filter | Resize result
--------------------------|---------------------------------------------
`imaging.NearestNeighbor` | 
`imaging.Linear` | 
`imaging.CatmullRom` | 
`imaging.Lanczos` | 
### Gaussian Blur
```go
dstImage := imaging.Blur(srcImage, 0.5)
```
Sigma parameter allows to control the strength of the blurring effect.
Original image | Sigma = 0.5 | Sigma = 1.5
-----------------------------------|----------------------------------------|---------------------------------------
 |  | 
### Sharpening
```go
dstImage := imaging.Sharpen(srcImage, 0.5)
```
`Sharpen` uses gaussian function internally. Sigma parameter allows to control the strength of the sharpening effect.
Original image | Sigma = 0.5 | Sigma = 1.5
-----------------------------------|-------------------------------------------|------------------------------------------
 |  | 
### Gamma correction
```go
dstImage := imaging.AdjustGamma(srcImage, 0.75)
```
Original image | Gamma = 0.75 | Gamma = 1.25
-----------------------------------|------------------------------------------|-----------------------------------------
 |  | 
### Contrast adjustment
```go
dstImage := imaging.AdjustContrast(srcImage, 20)
```
Original image | Contrast = 15 | Contrast = -15
-----------------------------------|--------------------------------------------|-------------------------------------------
 |  | 
### Brightness adjustment
```go
dstImage := imaging.AdjustBrightness(srcImage, 20)
```
Original image | Brightness = 10 | Brightness = -10
-----------------------------------|----------------------------------------------|---------------------------------------------
 |  | 
## Example code
```go
package main
import (
"image"
"image/color"
"log"
"github.com/disintegration/imaging"
)
func main() {
// Open a test image.
src, err := imaging.Open("testdata/flowers.png")
if err != nil {
log.Fatalf("failed to open image: %v", err)
}
// Crop the original image to 300x300px size using the center anchor.
src = imaging.CropAnchor(src, 300, 300, imaging.Center)
// Resize the cropped image to width = 200px preserving the aspect ratio.
src = imaging.Resize(src, 200, 0, imaging.Lanczos)
// Create a blurred version of the image.
img1 := imaging.Blur(src, 5)
// Create a grayscale version of the image with higher contrast and sharpness.
img2 := imaging.Grayscale(src)
img2 = imaging.AdjustContrast(img2, 20)
img2 = imaging.Sharpen(img2, 2)
// Create an inverted version of the image.
img3 := imaging.Invert(src)
// Create an embossed version of the image using a convolution filter.
img4 := imaging.Convolve3x3(
src,
[9]float64{
-1, -1, 0,
-1, 1, 1,
0, 1, 1,
},
nil,
)
// Create a new image and paste the four produced images into it.
dst := imaging.New(400, 400, color.NRGBA{0, 0, 0, 0})
dst = imaging.Paste(dst, img1, image.Pt(0, 0))
dst = imaging.Paste(dst, img2, image.Pt(0, 200))
dst = imaging.Paste(dst, img3, image.Pt(200, 0))
dst = imaging.Paste(dst, img4, image.Pt(200, 200))
// Save the resulting image as JPEG.
err = imaging.Save(dst, "testdata/out_example.jpg")
if err != nil {
log.Fatalf("failed to save image: %v", err)
}
}
```
Output:
|
