feat: hide class labels on non-mission-relevant objects in rendered video

Non-relevant objects now show only a gray bbox/mask with no label text.
Mission-relevant objects keep their red color and visible class label.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

inference.py

@@ -144,20 +144,27 @@ _COLOR_NOT_RELEVANT = (128, 128, 128)      # gray
 def _mission_colors_for_dets(
     dets: list,
     verdicts: Dict[str, bool],
-) -> Optional[List[Tuple[int, int, int]]]:
-    """Return per-detection BGR colors based on mission verdicts, or None if no verdicts."""
+) -> Tuple[Optional[List[Tuple[int, int, int]]], Optional[List[bool]]]:
+    """Return per-detection BGR colors and hide_labels flags based on mission verdicts.
+
+    Returns (None, None) if no verdicts available yet.
+    """
     if not verdicts:
-        return None
+        return None, None
     colors = []
+    hide = []
     for d in dets:
         tid = d.get("track_id")
         if tid and tid in verdicts:
-            colors.append(_COLOR_MISSION_RELEVANT if verdicts[tid] else _COLOR_NOT_RELEVANT)
+            relevant = verdicts[tid]
+            colors.append(_COLOR_MISSION_RELEVANT if relevant else _COLOR_NOT_RELEVANT)
+            hide.append(not relevant)
         else:
-            # Unassessed — use default hash color
+            # Unassessed — use default hash color, show label
             label = d.get("label", "obj")
             colors.append(_color_for_label(label))
-    return colors
+            hide.append(False)
+    return colors, hide
 
 
 def draw_boxes(
@@ -167,6 +174,7 @@ def draw_boxes(
     queries: Optional[Sequence[str]] = None,
     label_names: Optional[Sequence[str]] = None,
     colors: Optional[Sequence[Tuple[int, int, int]]] = None,
+    hide_labels: Optional[Sequence[bool]] = None,
 ) -> np.ndarray:
     output = frame.copy()
     if boxes is None:
@@ -188,7 +196,8 @@ def draw_boxes(
         else:
             color = (128, 128, 128) if not label else _color_for_label(label)
         cv2.rectangle(output, (x1, y1), (x2, y2), color, thickness=2)
-        if label:
+        should_hide = hide_labels is not None and idx < len(hide_labels) and hide_labels[idx]
+        if label and not should_hide:
             font = cv2.FONT_HERSHEY_SIMPLEX
             font_scale = 0.6
             thickness = 2
@@ -219,6 +228,7 @@ def draw_masks(
     alpha: float = 0.65,
     labels: Optional[Sequence[str]] = None,
     colors: Optional[Sequence[Tuple[int, int, int]]] = None,
+    hide_labels: Optional[Sequence[bool]] = None,
 ) -> np.ndarray:
     output = frame.copy()
     if masks is None or len(masks) == 0:
@@ -248,8 +258,9 @@ def draw_masks(
         )
         if contours:
             cv2.drawContours(output, contours, -1, color, thickness=2)
-        # Only draw label text when explicit labels were provided
-        if label:
+        # Only draw label text when explicit labels were provided and not hidden
+        should_hide = hide_labels is not None and idx < len(hide_labels) and hide_labels[idx]
+        if label and not should_hide:
             coords = np.column_stack(np.where(mask_bool))
             if coords.size:
                 y, x = coords[0]
@@ -962,10 +973,11 @@ def run_inference(
                             display_labels = [d.get('label', 'obj') for d in dets]
                             # Apply mission-relevance colors (red/gray) if verdicts available
                             det_colors = None
+                            det_hide = None
                             if job_id:
                                 verdicts = get_job_storage().get_mission_verdicts(job_id)
-                                det_colors = _mission_colors_for_dets(dets, verdicts)
-                            p_frame = draw_boxes(p_frame, display_boxes, label_names=display_labels, colors=det_colors)
+                                det_colors, det_hide = _mission_colors_for_dets(dets, verdicts)
+                            p_frame = draw_boxes(p_frame, display_boxes, label_names=display_labels, colors=det_colors, hide_labels=det_hide)
 
                         writer.write(p_frame)
 
@@ -1108,6 +1120,7 @@ def _gsam2_render_frame(
     masks_list: List[np.ndarray] = []
     mask_labels: List[str] = []
     mask_colors: Optional[List[Tuple[int, int, int]]] = None if not mission_verdicts else []
+    mask_hide: Optional[List[bool]] = None if not mission_verdicts else []
 
     for obj_id, obj_info in frame_objects.items():
         mask = obj_info.mask
@@ -1128,13 +1141,16 @@ def _gsam2_render_frame(
             if mask_colors is not None:
                 tid = str(obj_id)
                 if tid in mission_verdicts:
-                    mask_colors.append(_COLOR_MISSION_RELEVANT if mission_verdicts[tid] else _COLOR_NOT_RELEVANT)
+                    relevant = mission_verdicts[tid]
+                    mask_colors.append(_COLOR_MISSION_RELEVANT if relevant else _COLOR_NOT_RELEVANT)
+                    mask_hide.append(not relevant)
                 else:
                     mask_colors.append(_color_for_label(label or f"object_{obj_id}"))
+                    mask_hide.append(False)
 
     if masks_list:
         # Draw masks with labels — no bboxes for segmentation mode
-        frame = draw_masks(frame, np.stack(masks_list), labels=mask_labels, colors=mask_colors)
+        frame = draw_masks(frame, np.stack(masks_list), labels=mask_labels, colors=mask_colors, hide_labels=mask_hide)
 
     return frame