Common Go image processing libraries include standard library image packages and third-party libraries, such as imaging, bimg, and imagick. 1. The image package is suitable for basic operations; 2. Imaging has complete functions and a simple API, which is suitable for most needs; 3. Bimg is based on libvips, has strong performance, which is suitable for large images or high concurrency; 4. Imagick binds ImageMagick, which is powerful but has heavy dependencies. Quickly implement image scaling and cropping. You can use the imaging library to complete it with a few lines of code from the Resize and CropAnchor functions, and support multiple parameter configurations. Adding filters or adjusting tones can be implemented through the color transformation functions provided by imaging, such as Grayscale, AdjustContrast, Blur, etc. Complex filters require custom pixel processing logic. In terms of performance, it is recommended to select bimg or imagick for small-scale processing, and pay attention to controlling concurrency and memory usage.

Image processing is becoming more and more common in modern development. Whether it is web pages, apps or data analysis, image operations cannot be avoided. If you develop in Go, there are several libraries that can help you solve common tasks such as cropping, scaling, and filtering.

What are the common Go image processing libraries?

The Go standard library comes with an image package, which can do some basic operations, such as reading and writing image formats and drawing simple graphics. But if you want to do something advanced, such as adding filters, adjusting colors or batch processing, you have to look at the third-party library.

• github.com/disintegration/imaging : It has a relatively complete function and a simple API, which is suitable for most needs.
• github.com/h2non/bimg : Based on libvips, it has strong performance and is suitable for handling large images or high concurrency scenarios.
• github.com/gographics/imagick : Bind ImageMagick, which is extremely powerful, but the dependencies are also more heavy.

These libraries each have their own focus. Which one is chosen depends on your project's requirements and performance requirements.

How to quickly achieve image scaling and cropping?

These two are the most common operations, and can be done with a few lines of code in the imaging library:

 srcImg, err := imaging.Open("input.jpg")
if err != nil {
    log.Fatal(err)
}

// Scale to a scale with a width of 800 resizedImg := imaging.Resize(srcImg, 800, 0, imaging.Lanczos)

// Cropped the middle area croppedImg := imaging.CropAnchor(resizedImg, 600, 400, imaging.Center)

imaging.Save(croppedImg, "output.jpg")

This code basically covers two core steps in daily use. Resize and Crop both support multiple parameters, such as anchor position, interpolation algorithm, etc. You can change different options as needed.

If you use bimg, it supports automatic rotation and EXIF processing by default, which is more friendly to the pictures uploaded by users.

How to add filters or adjust the tone of the picture?

This one is a little more complicated, but not difficult. imaging provides some simple color transformation functions, such as grayscale conversion, contrast adjustment, saturation control, etc.

For example, turn a picture into a black and white wind:

 grayImg := imaging.Grayscale(srcImg)

You can also combine multiple operations, such as adjusting the brightness first and then adding blur:

 adjusted := imaging.AdjustContrast(srcImg, 30)
blurred := imaging.Blur(adjusted, 2)

If you want more complex filter effects, such as retro style, cool tones, warm tones, etc., you have to write some pixel-level processing logic yourself. Although it's a bit troublesome, the idea is to iterate through each pixel point and apply the color conversion formula you want.

What should I pay attention to in terms of performance?

If you just process a few small pictures occasionally, any library can handle it. However, if you are doing real-time image processing in a web service, especially when facing a large number of requests, the performance difference will be obvious.

• bimg is much faster than imaging because it uses libvips at the bottom, which is a high-performance image processing library written in C.
• If your service needs to process dozens or even hundreds of images at the same time, it is recommended to give priority to bimg or call ImageMagick directly (via imageck).
• In addition, remember to control the number of concurrency to avoid memory bursting. Especially for large images processing, a picture may take up dozens of MB of memory.

Basically that's it. Choose a good tool and be familiar with a few commonly used functions. Image processing is actually not that difficult.

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