Files
LlamaCasty/ytLive.Tests/BackgroundEffectEngineTests.cs
T
gramps 9f3c154d84 TASK 51 slice 1: background-effects engine — bundled MediaPipe Selfie Segmenter (ONNX) + Remove (Unit: bg-effects engine)
- Model: Google's official MediaPipe Selfie Segmenter (landscape, Apache-2.0 per model card),
  converted to ONNX by tools/selfie-seg/convert_tflite_to_onnx.py (custom op
  Convolution2DTransposeBias -> ConvTranspose) and verified against MediaPipe's own runtime
  (verify_against_mediapipe.py: max |diff| 5e-5, 100% binary agreement). Embedded resource, 430 KB.
- Services/BackgroundEffects: IPersonSegmenter seam, OnnxPersonSegmenter (onnxruntime 1.31.0 CPU,
  never throws), BackgroundEffectProcessor (None passthrough, Remove = alpha mask). Not wired yet.
- Tests: Remove integration test (fake segmenter) + bundled-model contract test vs MediaPipe
  reference mask. Full suite 407/408 — the one failure is the documented interactive-desktop flake
  RealMouseDrag_OnTheLayerList_PersistsTheReorder (passed in the run before; code path untouched).
- Docs: ai.md reversal + section + licensing guard, THIRD-PARTY-NOTICES 11-12, TASK 51, indexes,
  MyMistakes recipe, HANDOFF.

References: obs-backgroundremoval (approach only — ONNX Runtime on CPU, 2 threads; GPL-3.0, no code
used) https://github.com/royshil/obs-backgroundremoval ; MediaPipe image segmenter
https://ai.google.dev/edge/mediapipe/solutions/vision/image_segmenter
2026-10-09 09:24:13 -07:00

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using System.IO;
using System.Windows.Media;
using System.Windows.Media.Imaging;
using Xunit;
using ytLive.Models;
using ytLive.Services;
using ytLive.Services.BackgroundEffects;
namespace ytLive.Tests;
/// <summary>TASK 51 slice 1 — the background-effects engine.</summary>
public class BackgroundEffectEngineTests
{
private sealed class FakeSegmenter : IPersonSegmenter
{
private readonly SegmentationMask? _mask;
public int Calls;
public FakeSegmenter(SegmentationMask? mask) => _mask = mask;
public SegmentationMask? Segment(VideoFrame frame)
{
Calls++;
return _mask;
}
}
/// <summary>The integration test for this unit: a webcam frame run through the
/// processor with Remove comes back with the person kept and the background
/// transparent, colors untouched, flagged non-opaque so the compositor blends it.
/// None is a true passthrough (same instance, segmenter never called).</summary>
[Fact]
public void Remove_keeps_the_person_and_makes_the_background_transparent()
{
const int w = 64, h = 32;
var pixels = new byte[w * h * 4];
for (var i = 0; i < pixels.Length; i += 4)
{
pixels[i] = 10; pixels[i + 1] = 20; pixels[i + 2] = 30; pixels[i + 3] = 255;
}
var frame = new VideoFrame(w, h, pixels) { IsOpaque = true };
// 4×2 mask: left half person, right half background.
var mask = new SegmentationMask(4, 2, new[] { 1f, 1f, 0f, 0f, 1f, 1f, 0f, 0f });
var segmenter = new FakeSegmenter(mask);
var processor = new BackgroundEffectProcessor(segmenter);
Assert.Same(frame, processor.Apply(frame, BackgroundEffect.None));
Assert.Equal(0, segmenter.Calls);
var result = processor.Apply(frame, BackgroundEffect.Remove);
Assert.NotSame(frame, result);
Assert.False(result.IsOpaque);
Assert.Equal(w, result.Width);
Assert.Equal(h, result.Height);
for (var y = 0; y < h; y++)
{
Assert.Equal(255, Alpha(result, 4, y)); // well inside the person half
Assert.Equal(0, Alpha(result, w - 5, y)); // well inside the background half
}
for (var i = 0; i < pixels.Length; i += 4)
{
Assert.Equal(pixels[i], result.BgraPixels[i]);
Assert.Equal(pixels[i + 1], result.BgraPixels[i + 1]);
Assert.Equal(pixels[i + 2], result.BgraPixels[i + 2]);
}
Assert.Equal(255, frame.BgraPixels[(w - 5) * 4 + 3]); // source frame never mutated
}
/// <summary>Model contract: the BUNDLED (embedded) ONNX model, driven through the C#
/// preprocessing, reproduces Google's own MediaPipe runtime on a fixed fixture.
/// The expected mask was produced by tools/selfie-seg/ running the original .tflite in
/// MediaPipe (fixture: MediaPipe's public test image mozart_square.jpg, a public-domain
/// painting, cropped to 256×144). Guards the embedding, the input layout (RGB, NHWC,
/// /255) and the conversion itself.</summary>
[Fact]
public void Bundled_model_matches_the_MediaPipe_reference_mask()
{
var frame = LoadBgra(Fixture("selfie-fixture-256x144.png"));
var expected = LoadBgra(Fixture("selfie-fixture-expected-mask.png"));
using var segmenter = new OnnxPersonSegmenter();
var mask = segmenter.Segment(frame);
Assert.NotNull(mask);
Assert.Equal(OnnxPersonSegmenter.ModelWidth, mask!.Width);
Assert.Equal(OnnxPersonSegmenter.ModelHeight, mask.Height);
var agree = 0;
var person = 0;
for (var i = 0; i < mask.Width * mask.Height; i++)
{
var expectPerson = expected.BgraPixels[i * 4] > 127;
var gotPerson = mask.Values[i] > 0.5f;
if (expectPerson == gotPerson) agree++;
if (gotPerson) person++;
}
var total = mask.Width * mask.Height;
Assert.True(agree >= total * 0.995, $"binary agreement {100.0 * agree / total:0.00}% < 99.5%");
Assert.InRange(100.0 * person / total, 45, 60); // reference: 52.5% person
}
private static int Alpha(VideoFrame f, int x, int y) => f.BgraPixels[(y * f.Width + x) * 4 + 3];
private static string Fixture(string name) => Path.Combine(AppContext.BaseDirectory, "Fixtures", name);
private static VideoFrame LoadBgra(string path)
{
using var stream = File.OpenRead(path);
var decoded = BitmapDecoder.Create(stream, BitmapCreateOptions.PreservePixelFormat, BitmapCacheOption.OnLoad).Frames[0];
var bgra = new FormatConvertedBitmap(decoded, PixelFormats.Bgra32, null, 0);
var pixels = new byte[bgra.PixelWidth * bgra.PixelHeight * 4];
bgra.CopyPixels(pixels, bgra.PixelWidth * 4, 0);
return new VideoFrame(bgra.PixelWidth, bgra.PixelHeight, pixels);
}
}