fix(demo): pace gait windows before buffering

Make the OpenGait-studio demo drop unpaced frames before they grow the silhouette window. Separate source-frame gap tracking from paced-frame stride tracking so runtime scheduling matches the documented demo-window-and-stride behavior. Add regressions for paced window growth and schedule-frame stride semantics.
2026-03-14 11:31:44 +08:00
parent ede9690318
commit d4e2a59ad2
4 changed files with 131 additions and 14 deletions
@@ -927,6 +927,88 @@ def test_frame_pacer_emission_count_24_to_15() -> None:
    assert 60 <= emitted <= 65


+def test_pipeline_pacing_skips_window_growth_until_emitted() -> None:
+    from opengait_studio.pipeline import ScoliosisPipeline
+
+    with (
+        mock.patch("opengait_studio.pipeline.YOLO") as mock_yolo,
+        mock.patch("opengait_studio.pipeline.create_source") as mock_source,
+        mock.patch("opengait_studio.pipeline.create_publisher") as mock_publisher,
+        mock.patch("opengait_studio.pipeline.ScoNetDemo") as mock_classifier,
+        mock.patch("opengait_studio.pipeline.select_person") as mock_select_person,
+        mock.patch("opengait_studio.pipeline.mask_to_silhouette") as mock_mask_to_sil,
+    ):
+        mock_detector = mock.MagicMock()
+        mock_box = mock.MagicMock()
+        mock_box.xyxy = np.array([[100, 100, 200, 300]], dtype=np.float32)
+        mock_box.id = np.array([1], dtype=np.int64)
+        mock_mask = mock.MagicMock()
+        mock_mask.data = np.random.rand(1, 480, 640).astype(np.float32)
+        mock_result = mock.MagicMock()
+        mock_result.boxes = mock_box
+        mock_result.masks = mock_mask
+        mock_detector.track.return_value = [mock_result]
+        mock_yolo.return_value = mock_detector
+        mock_source.return_value = []
+        mock_publisher.return_value = mock.MagicMock()
+
+        mock_model = mock.MagicMock()
+        mock_model.predict.return_value = ("neutral", 0.7)
+        mock_classifier.return_value = mock_model
+
+        dummy_mask = np.random.randint(0, 256, (480, 640), dtype=np.uint8)
+        dummy_bbox_mask = (100, 100, 200, 300)
+        dummy_bbox_frame = (100, 100, 200, 300)
+        dummy_silhouette = np.random.rand(64, 44).astype(np.float32)
+        mock_select_person.return_value = (
+            dummy_mask,
+            dummy_bbox_mask,
+            dummy_bbox_frame,
+            1,
+        )
+        mock_mask_to_sil.return_value = dummy_silhouette
+
+        pipeline = ScoliosisPipeline(
+            source="dummy.mp4",
+            checkpoint="dummy.pt",
+            config=str(CONFIG_PATH) if CONFIG_PATH.exists() else "dummy.yaml",
+            device="cpu",
+            yolo_model="dummy.pt",
+            window=2,
+            stride=1,
+            nats_url=None,
+            nats_subject="test",
+            max_frames=None,
+            target_fps=15.0,
+        )
+        frame = np.zeros((480, 640, 3), dtype=np.uint8)
+
+        first = pipeline.process_frame(
+            frame,
+            {"frame_count": 0, "timestamp_ns": 1_000_000_000},
+        )
+        second = pipeline.process_frame(
+            frame,
+            {"frame_count": 1, "timestamp_ns": 1_033_000_000},
+        )
+        third = pipeline.process_frame(
+            frame,
+            {"frame_count": 2, "timestamp_ns": 1_067_000_000},
+        )
+
+        assert first is not None
+        assert second is not None
+        assert third is not None
+        assert first["segmentation_input"] is not None
+        assert second["segmentation_input"] is not None
+        assert third["segmentation_input"] is not None
+        assert first["segmentation_input"].shape[0] == 1
+        assert second["segmentation_input"].shape[0] == 1
+        assert second["label"] is None
+        assert third["segmentation_input"].shape[0] == 2
+        assert third["label"] == "neutral"
+
+
 def test_frame_pacer_requires_positive_target_fps() -> None:
    from opengait_studio.pipeline import _FramePacer

@@ -144,6 +144,21 @@ class TestSilhouetteWindow:
        window.push(sil, frame_idx=7, track_id=1)
        assert window.should_classify()

+    def test_should_classify_uses_schedule_frame_idx(self) -> None:
+        """Stride should be measured in paced/scheduled frames, not source frames."""
+        window = SilhouetteWindow(window_size=2, stride=2, gap_threshold=50)
+        sil = np.ones((64, 44), dtype=np.float32)
+
+        window.push(sil, frame_idx=0, track_id=1, schedule_frame_idx=0)
+        window.push(sil, frame_idx=10, track_id=1, schedule_frame_idx=1)
+
+        assert window.should_classify()
+
+        window.mark_classified()
+        window.push(sil, frame_idx=20, track_id=1, schedule_frame_idx=2)
+
+        assert not window.should_classify()
+
    def test_should_classify_not_ready(self) -> None:
        """should_classify should return False when window not ready."""
        window = SilhouetteWindow(window_size=5, stride=1, gap_threshold=10)