Image decoding and cropping benchmarks

ImageProcessingBenchmark.cs

using BenchmarkDotNet.Attributes;
using BenchmarkDotNet.Configs;
using ImageMagick;
using OpenCvSharp;
using SixLabors.ImageSharp;
using SixLabors.ImageSharp.PixelFormats;
using SixLabors.ImageSharp.Processing;
using SkiaSharp;

namespace ImageProcessingBenchmark;

// use me like: dotnet run -c Release -- --filter * --category Decoding
// BenchmarkRunner.Run<ImageBenchmark>();
// images from https://picsum.photos/1000

[MemoryDiagnoser]
[ThreadingDiagnoser]
//[DisassemblyDiagnoser]
//[NativeMemoryProfiler]
//[InliningDiagnoser(true,true)]
[GroupBenchmarksBy(BenchmarkLogicalGroupRule.ByCategory)]
[CategoriesColumn]
public class ImageBenchmark
{
    //[Params(1, 2, 4, 8)]
    // public int DegreeOfParallelism { get; set; }

    #region Init

    private readonly string[] _imagePaths =
    [
        "images/image1.jpg",
        "images/image2.jpg",
        "images/image3.jpg",
        "images/image4.jpg",
        "images/image5.jpg"
    ];

    private Mat[] _images;
    private MagickImage[] _magickImages;
    private SKBitmap[] _skiaImages;
    private Image<Rgba32>[] _imageSharpImages;

    [GlobalSetup]
    public void Setup()
    {
        _images = _imagePaths.Select(p => new Mat(p)).ToArray();
        _magickImages = _imagePaths.Select(p => new MagickImage(p)).ToArray();
        _skiaImages = _imagePaths.Select(SKBitmap.Decode).ToArray();
        _imageSharpImages = _imagePaths.Select(Image.Load<Rgba32>).ToArray();
    }

    #endregion

    #region Decoding

    [Benchmark]
    [BenchmarkCategory("Decoding")]
    public List<Mat> Decode_OpenCvSharp()
    {
        var decoded = _imagePaths
            .Select(p => new Mat(p))
            .ToList();

        // Decode JPEG file to Mat using something more 
        //byte[] jpegBytes = File.ReadAllBytes("input.jpg");
        //Mat mat = Cv2.ImDecode(jpegBytes, ImreadModes.Color);
        
        // Encode Mat to JPEG bytes
        //byte[] encodedJpeg;
        //Cv2.ImEncode(".jpg", mat, out encodedJpeg);
        return decoded;
    }

    [Benchmark]
    [BenchmarkCategory("Decoding")]
    public List<MagickImage> Decode_ImageMagick()
    {
        var decoded = _imagePaths
            .Select(p => new MagickImage(p))
            .ToList();
        return decoded;
    }

    [Benchmark]
    [BenchmarkCategory("Decoding")]
    public List<SKBitmap> Decode_SkiaSharp()
    {
        var decoded = _imagePaths
            .Select(SKBitmap.Decode)
            .ToList();
        return decoded;
    }

    [Benchmark]
    [BenchmarkCategory("Decoding")]
    public List<Image<Rgba32>> Decode_ImageSharp()
    {
        var decoded = _imagePaths
            .Select(p => Image.Load<Rgba32>(p))
            .ToList();
        return decoded;
    }

    #endregion
    
    #region Encoding
    [Benchmark]
    [BenchmarkCategory("Encoding")]
    public List<byte[]> Encode_OpenCvSharp()
    {
        var encoded = _images
            .Select(p => p.ToBytes())
            .ToList();
        return encoded;
    }

    [Benchmark]
    [BenchmarkCategory("Encoding")]
    public List<byte[]> Encode_ImageMagick()
    {
        var encoded = _magickImages
            .Select(img =>
            {
                using var mem = new MemoryStream();
                img.Write(mem, MagickFormat.Jpeg);
                return mem.ToArray();
            })
            .ToList();
        return encoded;
    }
    
    [Benchmark]
    [BenchmarkCategory("Encoding")]
    public List<byte[]> Encode_SkiaSharp()
    {
        var encoded = _skiaImages
            .Select(img =>
            {
                using var data = img.Encode(SKEncodedImageFormat.Jpeg, 90);
                return data.ToArray();
            })
            .ToList();
        return encoded;
    }

    [Benchmark]
    [BenchmarkCategory("Encoding")]
    public List<byte[]> Encode_ImageSharp()
    {
        var encoded = _imageSharpImages
            .Select(img =>
            {
                using var mem = new MemoryStream();
                img.SaveAsJpeg(mem);
                return mem.ToArray();
            })
            .ToList();
        return encoded;
    }
    #endregion

    #region Cropping

    [Benchmark]
    [BenchmarkCategory("Cropping")]
    public List<Mat> CropCenter_OpenCvSharp_View()
    {
        var results = new List<Mat>();
        foreach (var img in _images)
        {
            var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
            results.Add(new Mat(img, rect));
        }

        return results;
    }
    
    [Benchmark]
    [BenchmarkCategory("Cropping")]
    public List<Mat> CropCenter_OpenCvSharp_Copy()
    {
        var results = new List<Mat>();
        foreach (var img in _images)
        {
            var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
            results.Add(new Mat(img, rect).Clone());
        }

        return results;
    }
    
    [Benchmark]
    [BenchmarkCategory("Cropping")]
    public List<Mat> CropCenter_OpenCvSharp_SubMatCopy()
    {
        var results = new List<Mat>();
        foreach (var img in _images)
        {
            var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
            using var subMat = img.SubMat(rect);
            results.Add(subMat.Clone());
        }
        return results;
    }

    [Benchmark]
    [BenchmarkCategory("Cropping")]
    public List<MagickImage> CropCenter_ImageMagick()
    {
        var results = new List<MagickImage>();
        foreach (var img in _magickImages)
        {
            var x = (int)img.Width / 4;
            var y = (int)img.Height / 4;
            var width = img.Width / 2;
            var height = img.Height / 2;
            var cropped = img.Clone();
            cropped.Crop(new MagickGeometry(x, y, width, height));
            results.Add(new MagickImage(cropped));
        }

        return results;
    }

    [Benchmark]
    [BenchmarkCategory("Cropping")]
    public List<SKBitmap> CropCenter_SkiaSharp()
    {
        var results = new List<SKBitmap>();
        foreach (var img in _skiaImages)
        {
            var x = img.Width / 4;
            var y = img.Height / 4;
            var width = img.Width / 2;
            var height = img.Height / 2;
            using var cropped = new SKBitmap(width, height);
            using (var canvas = new SKCanvas(cropped))
            {
                var srcRect = new SKRectI(x, y, x + width, y + height);
                var destRect = new SKRectI(0, 0, width, height);
                canvas.DrawBitmap(img, srcRect, destRect);
                results.Add(cropped);
            }
        }

        return results;
    }

    [Benchmark]
    [BenchmarkCategory("Cropping")]
    public List<Image<Rgba32>> CropCenter_ImageSharp()
    {
        var results = new List<Image<Rgba32>>();
        foreach (var img in _imageSharpImages)
        {
            var x = img.Width / 4;
            var y = img.Height / 4;
            var width = img.Width / 2;
            var height = img.Height / 2;
            var cropped = img.Clone(ctx => ctx.Crop(new Rectangle(x, y, width, height)));
            results.Add(cropped);
        }

        return results;
    }

    #endregion

    #region Parallel

    [Benchmark]
    [BenchmarkCategory("Parallel")]
    public List<Mat> CropCenter_OpenCvSharp_ParallelFor()
    {
        var results = new Mat[_images.Length];
        Parallel.For(0, _images.Length, i =>
        {
            var img = _images[i];
            var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
            results[i] = new Mat(img, rect);
        });
        return results.ToList();
    }

    [Benchmark]
    [BenchmarkCategory("Parallel")]
    public int CropCenter_OpenCvSharp_PLINQ()
    {
        var results = _images
            .AsParallel()
            .Select(img =>
            {
                var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
                return new Mat(img, rect);
            })
            .ToList();

        return results.Count;
    }

    [Benchmark]
    [BenchmarkCategory("Parallel")]
    public async Task<int> CropCenter_OpenCvSharp_Async()
    {
        var results = new List<Mat>();
        var tasks = _images.Select(async img =>
        {
            var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
            var cropped = new Mat(img, rect);
            lock (results)
            {
                results.Add(cropped);
            }

            await Task.CompletedTask;
        });
        await Task.WhenAll(tasks);
        return results.Count;
    }

    [Benchmark]
    [BenchmarkCategory("Parallel")]
    public async Task<List<Mat>> CropCenter_OpenCvSharp_Async_NoLock()
    {
        var tasks = _images.Select(img =>
            Task.Run(() =>
            {
                var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
                return new Mat(img, rect);
            })
        );
        var results = await Task.WhenAll(tasks);
        return results.ToList();
    }
    
    
    [Benchmark]
    [BenchmarkCategory("Parallel")]
    public List<Mat> CropCenter_OpenCvSharp_ParallelFor_COPY()
    {
        var results = new Mat[_images.Length];
        Parallel.For(0, _images.Length, i =>
        {
            var img = _images[i];
            var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
            results[i] = (new Mat(img, rect)).Clone();
        });
        return results.ToList();
    }

    [Benchmark]
    [BenchmarkCategory("Parallel")]
    public int CropCenter_OpenCvSharp_PLINQ_COPY()
    {
        var results = _images
            .AsParallel()
            .Select(img =>
            {
                var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
                return (new Mat(img, rect)).Clone();
            })
            .ToList();

        return results.Count;
    }

    [Benchmark]
    [BenchmarkCategory("Parallel")]
    public async Task<int> CropCenter_OpenCvSharp_Async_COPY()
    {
        var results = new List<Mat>();
        var tasks = _images.Select(async img =>
        {
            var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
            var cropped = new Mat(img, rect);
            lock (results)
            {
                results.Add(cropped.Clone());
            }

            await Task.CompletedTask;
        });
        await Task.WhenAll(tasks);
        return results.Count;
    }

    [Benchmark]
    [BenchmarkCategory("Parallel")]
    public async Task<List<Mat>> CropCenter_OpenCvSharp_Async_NoLock_COPY()
    {
        var tasks = _images.Select(img =>
            Task.Run(() =>
            {
                var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
                return (new Mat(img, rect)).Clone();
            })
        );
        var results = await Task.WhenAll(tasks);
        return results.ToList();
    }

    #endregion
    
    #region FullProcess
    // OpenCvSharp full process
[Benchmark]
[BenchmarkCategory("FullProcess")]
public List<byte[]> FullProcess_OpenCvSharp()
{
    var result = new List<byte[]>();
    foreach (var path in _imagePaths)
    {
        using var img = new Mat(path);
        var rect = new Rect(img.Width / 4, img.Height / 4, img.Width / 2, img.Height / 2);
        using var cropped = new Mat(img, rect);
        result.Add(cropped.ToBytes());
    }
    return result;
}

// ImageMagick full process
[Benchmark]
[BenchmarkCategory("FullProcess")]
public List<byte[]> FullProcess_ImageMagick()
{
    var result = new List<byte[]>();
    foreach (var path in _imagePaths)
    {
        using var img = new MagickImage(path);
        var x = (int)img.Width / 4;
        var y = (int)img.Height / 4;
        var width = img.Width / 2;
        var height = img.Height / 2;
        using var cropped = img.Clone();
        cropped.Crop(new MagickGeometry(x, y, width, height));
        using var mem = new MemoryStream();
        cropped.Write(mem, MagickFormat.Jpeg);
        result.Add(mem.ToArray());
    }
    return result;
}

// SkiaSharp full process
[Benchmark]
[BenchmarkCategory("FullProcess")]
public List<byte[]> FullProcess_SkiaSharp()
{
    var result = new List<byte[]>();
    foreach (var path in _imagePaths)
    {
        using var img = SKBitmap.Decode(path);
        
        var x = img.Width / 4;
        var y = img.Height / 4;
        var width = img.Width / 2;
        var height = img.Height / 2;
        using var cropped = new SKBitmap(width, height);
        using (var canvas = new SKCanvas(cropped))
        {
            var srcRect = new SKRectI(x, y, x + width, y + height);
            var destRect = new SKRectI(0, 0, width, height);
            canvas.DrawBitmap(img, srcRect, destRect);
        }
        using var data = cropped.Encode(SKEncodedImageFormat.Jpeg, 90);
        result.Add(data.ToArray());
    }
    return result;
}

    // ImageSharp full process
    [Benchmark]
    [BenchmarkCategory("FullProcess")]
    public List<byte[]> FullProcess_ImageSharp()
    {
        var result = new List<byte[]>();
        foreach (var path in _imagePaths)
        {
            using var img = Image.Load<Rgba32>(path);
            var x = img.Width / 4;
            var y = img.Height / 4;
            var width = img.Width / 2;
            var height = img.Height / 2;
            using var cropped = img.Clone(ctx => ctx.Crop(new Rectangle(x, y, width, height)));
            using var mem = new MemoryStream();
            cropped.SaveAsJpeg(mem);
            result.Add(mem.ToArray());
        }
        return result;
    }

    #endregion
    
    
    //TODO: Add more benchmarks for other operations like resizing, rotating, etc.
}

// * Summary *

BenchmarkDotNet v0.15.2, Windows 11 (10.0.26100.4351/24H2/2024Update/HudsonValley)
AMD Ryzen 9 6900HS with Radeon Graphics 3.30GHz, 1 CPU, 16 logical and 8 physical cores
.NET SDK 9.0.300
[Host] : .NET 9.0.5 (9.0.525.21509), X64 RyuJIT AVX2
DefaultJob : .NET 9.0.5 (9.0.525.21509), X64 RyuJIT AVX2

Method	Categories	Mean	Error	StdDev	Completed Work Items	Lock Contentions	Gen0	Gen1	Gen2	Allocated
CropCenter_OpenCvSharp_View	Cropping	1.016 us	0.0086 us	0.0080 us	-	-	0.0591	0.0572	-	496 B
CropCenter_OpenCvSharp_Copy	Cropping	718.691 us	14.3064 us	18.0930 us	-	-	-	-	-	816 B
CropCenter_OpenCvSharp_SubMatCopy	Cropping	716.046 us	14.2492 us	18.5280 us	-	-	-	-	-	816 B
CropCenter_ImageMagick	Cropping	9,704.698 us	24.5037 us	20.4617 us	-	-	-	-	-	19056 B
CropCenter_SkiaSharp	Cropping	4,279.567 us	58.3512 us	51.7268 us	-	-	-	-	-	1616 B
CropCenter_ImageSharp	Cropping	571.734 us	6.7460 us	6.3102 us	28.6748	0.0010	5.8594	1.9531	1.9531	2556920 B
Decode_OpenCvSharp	Decoding	33,739.379 us	518.3452 us	459.4995 us	-	-	-	-	-	464 B
Decode_ImageMagick	Decoding	65,520.181 us	760.2030 us	711.0944 us	-	-	-	-	-	15224 B
Decode_SkiaSharp	Decoding	50,356.675 us	546.5347 us	511.2289 us	-	-	-	-	-	10264 B
Decode_ImageSharp	Decoding	36,822.421 us	533.5865 us	499.1172 us	-	-	-	-	-	117541 B
Encode_OpenCvSharp	Encoding	122,052.356 us	893.0460 us	791.6620 us	-	-	200.0000	200.0000	200.0000	6534259 B
Encode_ImageMagick	Encoding	35,818.595 us	195.1123 us	182.5082 us	-	-	428.5714	357.1429	357.1429	2045993 B
Encode_SkiaSharp	Encoding	58,997.266 us	254.9138 us	238.4465 us	-	-	111.1111	111.1111	111.1111	726095 B
Encode_ImageSharp	Encoding	19,658.088 us	84.3782 us	78.9274 us	-	-	468.7500	375.0000	375.0000	2163233 B
FullProcess_OpenCvSharp	FullProcess	64,981.480 us	1,138.3206 us	1,217.9903 us	-	-	375.0000	375.0000	375.0000	1622850 B
FullProcess_ImageMagick	FullProcess	86,796.775 us	1,708.5734 us	1,754.5791 us	-	-	-	-	-	614747 B
FullProcess_SkiaSharp	FullProcess	73,240.871 us	1,419.7512 us	1,457.9799 us	-	-	-	-	-	262648 B
FullProcess_ImageSharp	FullProcess	42,235.261 us	349.4506 us	309.7788 us	13.7500	-	83.3333	-	-	845981 B
CropCenter_OpenCvSharp_ParallelFor	Parallel	2.414 us	0.0163 us	0.0144 us	3.0231	0.0000	0.3166	0.3128	-	2637 B
CropCenter_OpenCvSharp_PLINQ	Parallel	4.592 us	0.0447 us	0.0374 us	15.0000	0.0000	0.9460	0.9308	-	7928 B
CropCenter_OpenCvSharp_Async	Parallel	1.239 us	0.0231 us	0.0193 us	-	-	0.1049	0.1030	-	880 B
CropCenter_OpenCvSharp_Async_NoLock	Parallel	2.254 us	0.0319 us	0.0298 us	5.0011	0.0000	0.2098	0.2060	-	1760 B
CropCenter_OpenCvSharp_ParallelFor_COPY	Parallel	349.659 us	6.8084 us	8.8529 us	6.0000	0.0015	-	-	-	3526 B
CropCenter_OpenCvSharp_PLINQ_COPY	Parallel	469.242 us	9.2610 us	21.8294 us	15.0000	-	0.9766	0.4883	-	8312 B
CropCenter_OpenCvSharp_Async_COPY	Parallel	718.345 us	14.0336 us	18.2476 us	-	-	-	-	-	1200 B
CropCenter_OpenCvSharp_Async_NoLock_COPY	Parallel	333.870 us	6.5263 us	8.7124 us	5.0000	0.0005	-	-	-	2144 B

// * Warnings *
MultimodalDistribution
ImageBenchmark.CropCenter_OpenCvSharp_Copy: Default -> It seems that the distribution is bimodal (mValue = 3.83)
ImageBenchmark.CropCenter_OpenCvSharp_SubMatCopy: Default -> It seems that the distribution is bimodal (mValue = 3.67)
ImageBenchmark.CropCenter_OpenCvSharp_Async_NoLock_COPY: Default -> It seems that the distribution can have several modes (mValue = 3)

// * Hints *
Outliers
ImageBenchmark.CropCenter_OpenCvSharp_SubMatCopy: Default -> 1 outlier was removed (805.82 us)
ImageBenchmark.CropCenter_ImageMagick: Default -> 2 outliers were removed (9.77 ms, 9.78 ms)
ImageBenchmark.CropCenter_SkiaSharp: Default -> 1 outlier was removed (5.03 ms)
ImageBenchmark.Decode_OpenCvSharp: Default -> 1 outlier was removed (35.46 ms)
ImageBenchmark.Decode_ImageMagick: Default -> 1 outlier was detected (63.81 ms)
ImageBenchmark.Encode_OpenCvSharp: Default -> 1 outlier was removed (124.08 ms)
ImageBenchmark.Encode_ImageMagick: Default -> 1 outlier was detected (35.42 ms)
ImageBenchmark.FullProcess_OpenCvSharp: Default -> 1 outlier was removed (68.39 ms)
ImageBenchmark.FullProcess_ImageSharp: Default -> 1 outlier was removed (43.57 ms)
ImageBenchmark.CropCenter_OpenCvSharp_ParallelFor: Default -> 1 outlier was removed, 2 outliers were detected (2.39 us, 2.50 us)
ImageBenchmark.CropCenter_OpenCvSharp_PLINQ: Default -> 2 outliers were removed (5.16 us, 7.79 us)
ImageBenchmark.CropCenter_OpenCvSharp_Async: Default -> 2 outliers were removed (1.32 us, 1.32 us)

// * Legends *
Categories : All categories of the corresponded method, class, and assembly
Mean : Arithmetic mean of all measurements
Error : Half of 99.9% confidence interval
StdDev : Standard deviation of all measurements
Completed Work Items : The number of work items that have been processed in ThreadPool (per single operation)
Lock Contentions : The number of times there was contention upon trying to take a Monitor's lock (per single operation)
Gen0 : GC Generation 0 collects per 1000 operations
Gen1 : GC Generation 1 collects per 1000 operations
Gen2 : GC Generation 2 collects per 1000 operations
Allocated : Allocated memory per single operation (managed only, inclusive, 1KB = 1024B)
1 us : 1 Microsecond (0.000001 sec)

// * Diagnostic Output - ThreadingDiagnoser *

// * Diagnostic Output - MemoryDiagnoser *

// ***** BenchmarkRunner: End *****
Run time: 00:07:52 (472.17 sec), executed benchmarks: 26

Global total time: 00:07:57 (477.97 sec), executed benchmarks: 26
// * Artifacts cleanup *
Artifacts cleanup is finished