refactor(demo): simplify visualizer wiring and typing

Apply Oracle-guided cleanup to make the demo pipeline contract explicit and remove defensive runtime indirection while preserving existing visualization behavior.

opengait/demo/__main__.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import argparse
-import inspect
 import logging
 import sys
 
@@ -90,7 +89,6 @@ if __name__ == "__main__":
         )
 
         # Build kwargs based on what ScoliosisPipeline accepts
-        sig = inspect.signature(ScoliosisPipeline.__init__)
         pipeline_kwargs = {
             "source": args.source,
             "checkpoint": args.checkpoint,
@@ -108,10 +106,8 @@ if __name__ == "__main__":
             "silhouette_visualize_dir": args.silhouette_visualize_dir,
             "result_export_path": args.result_export_path,
             "result_export_format": args.result_export_format,
+            "visualize": args.visualize,
         }
-        if "visualize" in sig.parameters:
-            pipeline_kwargs["visualize"] = args.visualize
-
         pipeline = ScoliosisPipeline(**pipeline_kwargs)
         raise SystemExit(pipeline.run())
     except ValueError as err:

opengait/demo/pipeline.py

@@ -5,7 +5,7 @@ from contextlib import suppress
 import logging
 from pathlib import Path
 import time
-from typing import Protocol, cast
+from typing import TYPE_CHECKING, Protocol, cast
 
 from beartype import beartype
 import click
@@ -22,6 +22,9 @@ from .preprocess import frame_to_person_mask, mask_to_silhouette
 from .sconet_demo import ScoNetDemo
 from .window import SilhouetteWindow, select_person
 
+if TYPE_CHECKING:
+    from .visualizer import OpenCVVisualizer
+
 logger = logging.getLogger(__name__)
 
 JaxtypedDecorator = Callable[[Callable[..., object]], Callable[..., object]]
@@ -78,7 +81,7 @@ class ScoliosisPipeline:
     _result_export_path: Path | None
     _result_export_format: str
     _result_buffer: list[dict[str, object]]
-    _visualizer: object | None
+    _visualizer: OpenCVVisualizer | None
 
     def __init__(
         self,
@@ -342,19 +345,22 @@ class ScoliosisPipeline:
                 if self._visualizer is not None and viz_payload is not None:
                     # Cast viz_payload to dict for type checking
                     viz_dict = cast(dict[str, object], viz_payload)
-                    mask_raw = viz_dict.get("mask_raw")
-                    bbox = viz_dict.get("bbox")
-                    silhouette = viz_dict.get("silhouette")
+                    mask_raw_obj = viz_dict.get("mask_raw")
+                    bbox_obj = viz_dict.get("bbox")
+                    silhouette_obj = viz_dict.get("silhouette")
                     track_id_val = viz_dict.get("track_id", 0)
                     track_id = track_id_val if isinstance(track_id_val, int) else 0
-                    label = viz_dict.get("label")
-                    confidence = viz_dict.get("confidence")
+                    label_obj = viz_dict.get("label")
+                    confidence_obj = viz_dict.get("confidence")
 
-                    # Cast _visualizer to object with update method
-                    visualizer = cast(object, self._visualizer)
-                    update_fn = getattr(visualizer, "update", None)
-                    if callable(update_fn):
-                        keep_running = update_fn(
+                    # Cast extracted values to expected types
+                    mask_raw = cast(NDArray[np.uint8] | None, mask_raw_obj)
+                    bbox = cast(tuple[int, int, int, int] | None, bbox_obj)
+                    silhouette = cast(NDArray[np.float32] | None, silhouette_obj)
+                    label = cast(str | None, label_obj)
+                    confidence = cast(float | None, confidence_obj)
+
+                    keep_running = self._visualizer.update(
                         frame_u8,
                         bbox,
                         track_id,
@@ -385,11 +391,8 @@ class ScoliosisPipeline:
 
         # Close visualizer if enabled
         if self._visualizer is not None:
-            visualizer = cast(object, self._visualizer)
-            close_viz = getattr(visualizer, "close", None)
-            if callable(close_viz):
             with suppress(Exception):
-                    _ = close_viz()
+                self._visualizer.close()
 
         # Export silhouettes if requested
         if self._silhouette_export_path is not None and self._silhouette_buffer:

opengait/demo/visualizer.py

@@ -315,20 +315,30 @@ class OpenCVVisualizer:
         Returns:
             Displayable side-by-side image
         """
-        # Prepare individual views
-        raw_view = self._prepare_raw_view(mask_raw)
-        norm_view = self._prepare_normalized_view(silhouette)
-
-        # Convert to grayscale for side-by-side composition
-        if len(raw_view.shape) == 3:
-            raw_gray = cast(ImageArray, cv2.cvtColor(raw_view, cv2.COLOR_BGR2GRAY))
+        # Prepare individual views without mode indicators (will be drawn on combined)
+        # Raw view preparation (without indicator)
+        if mask_raw is None:
+            raw_gray = np.zeros((DISPLAY_HEIGHT, DISPLAY_WIDTH), dtype=np.uint8)
         else:
-            raw_gray = raw_view
-
-        if len(norm_view.shape) == 3:
-            norm_gray = cast(ImageArray, cv2.cvtColor(norm_view, cv2.COLOR_BGR2GRAY))
+            if len(mask_raw.shape) == 3:
+                mask_gray = cast(ImageArray, cv2.cvtColor(mask_raw, cv2.COLOR_BGR2GRAY))
             else:
-            norm_gray = norm_view
+                mask_gray = mask_raw
+            raw_gray = cast(
+                ImageArray,
+                cv2.resize(
+                    mask_gray,
+                    (DISPLAY_WIDTH, DISPLAY_HEIGHT),
+                    interpolation=cv2.INTER_NEAREST,
+                ),
+            )
+
+        # Normalized view preparation (without indicator)
+        if silhouette is None:
+            norm_gray = np.zeros((DISPLAY_HEIGHT, DISPLAY_WIDTH), dtype=np.uint8)
+        else:
+            upscaled = self._upscale_silhouette(silhouette)
+            norm_gray = upscaled
 
         # Stack horizontally
         combined = np.hstack([raw_gray, norm_gray])