Bindings for the Imageflow image processing library. Also remember to install Imageflow.NativeRuntime.[your platform] or use Imageflow.AllPlatforms instead.
Imageflow.NET is a .NET API for Imageflow, the fast image optimization and processing library for web servers. Imageflow focuses on security, quality, and performance - in that order. Imageflow.NET is a .NET 8.0 & .NET Standard 2.0 library, and as such is compatible with .NET 4.6.2+, .NET Core 2.0+, and .NET 5/6/7/8.
Note: We recently switched from Newtonsoft to System.Text.Json to support AOT and trimming; see CHANGES.md for details and some breaking changes.
On .NET Core 3.x and .NET 5/6/7/8 (or if using PackageReference on .NET 4.x)
dotnet add package Imageflow.AllPlatforms
If you're still using packages.config on .NET 4.x (such as for ASP.NET projects), you have to install Imageflow directly
Note: On .NET 4.x you must install the appropriate NativeRuntime(s) in the project you are deploying - they have to copy imageflow.dll to the output folder. They are not copied transitively.
Also note: Older versions of Windows may not have the C Runtime
installed (Install 32-bit or 64-bit).
License
Imageflow is dual licensed under a commercial license and the AGPLv3.
Imageflow.NET is tri-licensed under a commercial license, the AGPLv3, and the Apache 2 license.
Imageflow.NET Server is dual licensed under a commercial license and the AGPLv3.
Imageflow.NET's Apache 2 license allows for integration with non-copyleft products, as long as jobs are not actually executed (since the AGPLv3/commercial license is needed when libimageflow is linked at runtime). This can allow end-users to benefit from optional imageflow integration in products.
Examples
Getting image dimensions and format
using Imageflow.Fluent;
public async void TestGetImageInfo()
{
var imageBytes = Convert.FromBase64String(
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABAQMAAAAl21bKAAAAA1BMVEX/TQBcNTh/AAAAAXRSTlPM0jRW/QAAAApJREFUeJxjYgAAAAYAAzY3fKgAAAAASUVORK5CYII=");
var info = await ImageJob.GetImageInfo(new BytesSource(imageBytes));
Assert.Equal(info.ImageWidth, 1);
Assert.Equal(info.ImageHeight, 1);
Assert.Equal(info.PreferredExtension, "png");
Assert.Equal(info.PreferredMimeType, "image/png");
}
Edit images with the fluent API
using Imageflow.Fluent;
public async Task TestAllJob()
{
var imageBytes = Convert.FromBase64String(
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABAQMAAAAl21bKAAAAA1BMVEX/TQBcNTh/AAAAAXRSTlPM0jRW/QAAAApJREFUeJxjYgAAAAYAAzY3fKgAAAAASUVORK5CYII=");
using (var b = new ImageJob())
{
var r = await b.Decode(imageBytes)
.FlipVertical()
.FlipHorizontal()
.Rotate90()
.Rotate180()
.Rotate270()
.Transpose()
.CropWhitespace(80, 0.5f)
.Distort(30, 20)
.Crop(0,0,10,10)
.Region(-5,-5,10,10, AnyColor.Black)
.RegionPercent(-10f, -10f, 110f, 110f, AnyColor.Transparent)
.BrightnessSrgb(-1f)
.ContrastSrgb(1f)
.SaturationSrgb(1f)
.WhiteBalanceSrgb(80)
.ColorFilterSrgb(ColorFilterSrgb.Invert)
.ColorFilterSrgb(ColorFilterSrgb.Sepia)
.ColorFilterSrgb(ColorFilterSrgb.Grayscale_Bt709)
.ColorFilterSrgb(ColorFilterSrgb.Grayscale_Flat)
.ColorFilterSrgb(ColorFilterSrgb.Grayscale_Ntsc)
.ColorFilterSrgb(ColorFilterSrgb.Grayscale_Ry)
.ExpandCanvas(5,5,5,5,AnyColor.FromHexSrgb("FFEECCFF"))
.FillRectangle(2,2,8,8, AnyColor.Black)
.ResizerCommands("width=10&height=10&mode=crop")
.ConstrainWithin(5, 5)
.Watermark(new BytesSource(imageBytes),
new WatermarkOptions()
.SetMarginsLayout(
new WatermarkMargins(WatermarkAlign.Image, 1,1,1,1),
WatermarkConstraintMode.Within,
new ConstraintGravity(90,90))
.SetOpacity(0.5f)
.SetHints(new ResampleHints().SetSharpen(15f, SharpenWhen.Always))
.SetMinCanvasSize(1,1))
.EncodeToBytes(new MozJpegEncoder(80,true))
.Finish().InProcessAsync();
Assert.Equal(5, r.First.Width);
Assert.True(r.First.TryGetBytes().HasValue);
}
}
Generate multiple versions of one image
using Imageflow.Fluent;
public async Task TestMultipleOutputs()
{
var imageBytes = Convert.FromBase64String("iVBORw0KGgoAAAANSUhEUgAAAAEAAAABAQMAAAAl21bKAAAAA1BMVEX/TQBcNTh/AAAAAXRSTlPM0jRW/QAAAApJREFUeJxjYgAAAAYAAzY3fKgAAAAASUVORK5CYII=");
using (var b = new ImageJob())
{
var r = await b.Decode(imageBytes).
Constrain(new Constraint(ConstraintMode.Fit, 160, 120))
.Branch(f => f.ConstrainWithin(80, 60).EncodeToBytes(new WebPLosslessEncoder()))
.Branch(f => f.ConstrainWithin(40, 30).EncodeToBytes(new WebPLossyEncoder(50)))
.EncodeToBytes(new LodePngEncoder())
.Finish().InProcessAsync();
Assert.Equal(60, r.TryGet(1).Width);
Assert.Equal(30, r.TryGet(2).Width);
Assert.Equal(120, r.TryGet(3).Width);
Assert.True(r.First.TryGetBytes().HasValue);
}
}
using Imageflow.Fluent;
public async Task TestBuildCommandString()
{
var imageBytes = Convert.FromBase64String(
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABAQMAAAAl21bKAAAAA1BMVEX/TQBcNTh/AAAAAXRSTlPM0jRW/QAAAApJREFUeJxjYgAAAAYAAzY3fKgAAAAASUVORK5CYII=");
// We wrap the job in a using() statement to free memory faster
using (var b = new ImageJob())
{
var r = await b.BuildCommandString(
new BytesSource(imageBytes), // or new StreamSource(Stream stream, bool disposeStream)
new BytesDestination(), // or new StreamDestination
"width=3&height=2&mode=stretch&scale=both&format=webp&webp.quality=80")
.Finish().InProcessAsync();
Assert.Equal(3, r.First.Width);
Assert.Equal("webp", r.First.PreferredExtension);
Assert.True(r.First.TryGetBytes().HasValue);
}
}
