fix: ASRX duplication, TKG edges, trace ingest, and add pipeline progress publishing

- ASRX handler no longer stores duplicate 'asr' pre_chunks
- Pre_chunks storage made idempotent (delete-before-insert)
- Rule 1 + trace_ingest changed to query 'asrx' not 'asr'
- Trace chunks removed (dynamic from TKG/Qdrant)
- TKG scroll_face_points fixed: trace_id >= 1 (not == 1)
- TKG AsrxSegmentEntry: start/end -> start_time/end_time (match ASRX JSON)
- Unregister error handling: log instead of silent discard
- Add publish_pipeline_progress calls at each pipeline stage
  (processors, rule1, face_trace, identity_agent, TKG, rule2, completion)

scripts/face_processor.py

@@ -35,7 +35,7 @@ from redis_publisher import RedisPublisher
 from qdrant_faces import push_face_embeddings_batch
 
 SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
-SWIFT_BIN = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "debug", "swift_face_pose")
+SWIFT_BIN = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "release", "swift_face_pose")
 FACENET_PATH = os.path.join(SCRIPT_DIR, "..", "models", "facenet512.mlpackage")
 
 # Pose angle classification from roll/yaw
@@ -84,7 +84,12 @@ class FaceProcessorVision:
         self.total_frames = int(self.video.get(cv2.CAP_PROP_FRAME_COUNT))
         self.width = int(self.video.get(cv2.CAP_PROP_FRAME_WIDTH))
         self.height = int(self.video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        
+        # Calculate 8Hz sample interval based on FPS
+        self.sample_interval = max(1, round(self.fps / 8))
+        
         print(f"[FACE_V2] Video: {self.width}x{self.height}, {self.fps:.1f}fps, {self.total_frames}f")
+        print(f"[FACE_V2] 8Hz sample interval: {self.fps:.1f}/8 = {self.sample_interval}")
 
     def extract_face_embedding(self, face_img: np.ndarray) -> Optional[list]:
         """Run CoreML FaceNet on cropped face"""
@@ -126,11 +131,15 @@ class FaceProcessorVision:
         output_basename = os.path.basename(self.output_path)
         pose_basename = output_basename.replace("face", "pose")
         swift_pose_out = os.path.join(output_dir, pose_basename)
+        # Appearance output: same directory, but replace "face" with "appearance" in filename
+        appearance_basename = output_basename.replace("face", "appearance")
+        swift_appearance_out = os.path.join(output_dir, appearance_basename)
         cmd = [
             SWIFT_BIN,
             self.video_path,
             swift_face_out,
             swift_pose_out,
+            swift_appearance_out,
             "--sample-interval", str(self.sample_interval),
         ]
         if self.uuid:
@@ -286,17 +295,28 @@ class FaceProcessorVision:
 
         # Convert dict frames to list for Rust FaceResult format
         frames_list = []
+        total_faces = 0
         for fnum_str, fdata in sorted(face_data["frames"].items(), key=lambda x: int(x[0])):
+            faces = fdata["faces"]
+            total_faces += len(faces)
             frames_list.append({
                 "frame": int(fnum_str),
                 "timestamp": fdata["time_seconds"],
-                "faces": fdata["faces"],
+                "faces": faces,
             })
 
+        # Determine status based on face count
+        if total_faces > 0:
+            status = "has_faces"
+        else:
+            status = "no_faces"
+
         output = {
+            "status": status,
             "frame_count": len(frames_list),
             "fps": self.fps,
             "frames": frames_list,
+            "total_faces": total_faces,
         }
 
         with open(self.output_path, "w") as f:
@@ -339,6 +359,9 @@ def main():
         args.uuid, args.sample_interval, publisher
     )
 
+    # Open video to get FPS and calculate sample_interval
+    processor.open_video()
+
     # Step 1: Vision detection (bbox + pose via ANE)
     try:
         detection = processor.process_with_swift()