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https://github.com/disintegration/imaging
Imaging is a simple image processing package for Go
https://github.com/disintegration/imaging
blur brightness contrast convolution crop gamma image resize rotate sharpness
Last synced: 5 days ago
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Imaging is a simple image processing package for Go
- Host: GitHub
- URL: https://github.com/disintegration/imaging
- Owner: disintegration
- License: mit
- Created: 2012-12-06T20:21:21.000Z (almost 12 years ago)
- Default Branch: master
- Last Pushed: 2023-09-21T02:16:10.000Z (about 1 year ago)
- Last Synced: 2024-10-14T15:24:46.377Z (20 days ago)
- Topics: blur, brightness, contrast, convolution, crop, gamma, image, resize, rotate, sharpness
- Language: Go
- Homepage:
- Size: 3.51 MB
- Stars: 5,265
- Watchers: 80
- Forks: 436
- Open Issues: 32
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Imaging
[![PkgGoDev](https://pkg.go.dev/badge/github.com/disintegration/imaging)](https://pkg.go.dev/github.com/disintegration/imaging)
[![Build Status](https://travis-ci.org/disintegration/imaging.svg?branch=master)](https://travis-ci.org/disintegration/imaging)
[![Coverage Status](https://coveralls.io/repos/github/disintegration/imaging/badge.svg?branch=master&service=github)](https://coveralls.io/github/disintegration/imaging?branch=master)
[![Go Report Card](https://goreportcard.com/badge/github.com/disintegration/imaging)](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, non-premultiplied alpha).## Installation
go get -u github.com/disintegration/imaging
## Documentation
https://pkg.go.dev/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:
- `Lanczos` - A high-quality resampling filter for photographic images yielding sharp results.
- `CatmullRom` - A sharp cubic filter that is faster than Lanczos filter while providing similar results.
- `MitchellNetravali` - A cubic filter that produces smoother results with less ringing artifacts than CatmullRom.
- `Linear` - Bilinear resampling filter, produces smooth output. Faster than cubic filters.
- `Box` - Simple and fast averaging filter appropriate for downscaling. When upscaling it's similar to NearestNeighbor.
- `NearestNeighbor` - Fastest resampling filter, no antialiasing.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:
![srcImage](testdata/branches.png)
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` | ![dstImage](testdata/out_resize_nearest.png)
`imaging.Linear` | ![dstImage](testdata/out_resize_linear.png)
`imaging.CatmullRom` | ![dstImage](testdata/out_resize_catrom.png)
`imaging.Lanczos` | ![dstImage](testdata/out_resize_lanczos.png)### 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
-----------------------------------|----------------------------------------|---------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_blur_0.5.png) | ![dstImage](testdata/out_blur_1.5.png)### 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
-----------------------------------|-------------------------------------------|------------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_sharpen_0.5.png) | ![dstImage](testdata/out_sharpen_1.5.png)### Gamma correction
```go
dstImage := imaging.AdjustGamma(srcImage, 0.75)
```Original image | Gamma = 0.75 | Gamma = 1.25
-----------------------------------|------------------------------------------|-----------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_gamma_0.75.png) | ![dstImage](testdata/out_gamma_1.25.png)### Contrast adjustment
```go
dstImage := imaging.AdjustContrast(srcImage, 20)
```Original image | Contrast = 15 | Contrast = -15
-----------------------------------|--------------------------------------------|-------------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_contrast_p15.png) | ![dstImage](testdata/out_contrast_m15.png)### Brightness adjustment
```go
dstImage := imaging.AdjustBrightness(srcImage, 20)
```Original image | Brightness = 10 | Brightness = -10
-----------------------------------|----------------------------------------------|---------------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_brightness_p10.png) | ![dstImage](testdata/out_brightness_m10.png)### Saturation adjustment
```go
dstImage := imaging.AdjustSaturation(srcImage, 20)
```Original image | Saturation = 30 | Saturation = -30
-----------------------------------|----------------------------------------------|---------------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_saturation_p30.png) | ![dstImage](testdata/out_saturation_m30.png)### Hue adjustment
```go
dstImage := imaging.AdjustHue(srcImage, 20)
```Original image | Hue = 60 | Hue = -60
-----------------------------------|----------------------------------------------|---------------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_hue_p60.png) | ![dstImage](testdata/out_hue_m60.png)## FAQ
### Incorrect image orientation after processing (e.g. an image appears rotated after resizing)
Most probably, the given image contains the EXIF orientation tag.
The standard `image/*` packages do not support loading and saving
this kind of information. To fix the issue, try opening images with
the `AutoOrientation` decode option. If this option is set to `true`,
the image orientation is changed after decoding, according to the
orientation tag (if present). Here's the example:```go
img, err := imaging.Open("test.jpg", imaging.AutoOrientation(true))
```### What's the difference between `imaging` and `gift` packages?
[imaging](https://github.com/disintegration/imaging)
is designed to be a lightweight and simple image manipulation package.
It provides basic image processing functions and a few helper functions
such as `Open` and `Save`. It consistently returns *image.NRGBA image
type (8 bits per channel, RGBA).[gift](https://github.com/disintegration/gift)
supports more advanced image processing, for example, sRGB/Linear color
space conversions. It also supports different output image types
(e.g. 16 bits per channel) and provides easy-to-use API for chaining
multiple processing steps together.## Example code
```go
package mainimport (
"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:
![dstImage](testdata/out_example.jpg)