ITEP-90240: Reid vector length is configurable

Author: saratpoluri

controller/config/reid-config.json

@@ -3,5 +3,6 @@
   "stale_feature_check_interval_secs": 1.0,
   "feature_accumulation_threshold": 12,
   "feature_slice_size": 10,
-  "similarity_threshold": 60
+  "similarity_threshold": 60,
+  "vector_dimensions": 256
 }

controller/src/controller/uuid_manager.py

@@ -8,7 +8,7 @@
 import time
 from unittest import result
 
-from controller.vdms_adapter import VDMSDatabase
+from controller.vdms_adapter import VDMSDatabase, DIMENSIONS
 from scene_common import log
 from scene_common.timestamp import get_epoch_time
 
@@ -35,15 +35,17 @@ def __init__(self, database=DEFAULT_DATABASE, reid_config_data=None):
     self.features_for_database_timestamps = {}  # Track when features were added
     self.quality_features = {}
     self.unique_id_count = 0
-    self.reid_database = available_databases[database]()
+    # Extract vector dimensions from reid config, pass to database
+    if reid_config_data is None:
+      reid_config_data = {}
+    vector_dimensions = reid_config_data.get('vector_dimensions', DIMENSIONS)
+    self.reid_database = available_databases[database](dimensions=vector_dimensions)
     self.pool = concurrent.futures.ThreadPoolExecutor()
     self.similarity_query_times = collections.deque(
       maxlen=DEFAULT_MAX_SIMILARITY_QUERIES_TRACKED)
     self.similarity_query_times_lock = threading.Lock()
     self.reid_enabled = True
     # Extract stale feature timeout from reid config, use default if not provided
-    if reid_config_data is None:
-      reid_config_data = {}
     self.stale_feature_timeout_secs = reid_config_data.get('stale_feature_timeout_secs', DEFAULT_STALE_FEATURE_TIMEOUT_SECS)
     self.stale_feature_check_interval_secs = reid_config_data.get('stale_feature_check_interval_secs', DEFAULT_STALE_FEATURE_CHECK_INTERVAL_SECS)
     self.stale_feature_timer = None

controller/src/controller/vdms_adapter.py

@@ -145,10 +145,10 @@ def addEntry(self, uuid, rvid, object_type, reid_vectors, set_name=SCHEMA_NAME,
     add_query = []
     for reid_vector in reid_vectors:
       # Ensure vector is properly formatted as 1D array of float32
-      # reid_vector might be shape (1, 256) from moving_object, need to flatten to (256,)
+      # reid_vector might be shape (N,) from moving_object, need to flatten to (N,)
       vec_array = np.asarray(reid_vector, dtype="float32").flatten()
-      if vec_array.shape[0] != 256:
-        log.warning(f"addEntry: Expected vector shape (256,) but got {vec_array.shape}, skipping this vector")
+      if vec_array.shape[0] != self.dimensions:
+        log.warning(f"addEntry: Expected vector shape ({self.dimensions},) but got {vec_array.shape}, skipping this vector")
         continue
       descriptor_blobs.append(vec_array.tobytes())
       # Create query dict for each vector

dlstreamer-pipeline-server/user_scripts/gvapython/sscape/sscape_policies.py

@@ -37,7 +37,18 @@ def reidPolicy(pobj, item, fw, fh):
     name = tensor.get('name','')
     if name and ('reid' in name or 'embedding' in name):
       reid_vector = tensor.get('data', [])
-      v = struct.pack("256f",*reid_vector)
+      # Handle variable-length re-id vectors from different models
+      if not reid_vector:
+        continue
+      vector_len = len(reid_vector)
+      # Pack vector with its actual dimensions
+      format_string = f"{vector_len}f"
+      try:
+        v = struct.pack(format_string, *reid_vector)
+      except struct.error as e:
+        import sys
+        print(f"Failed to pack reid vector of length {vector_len}: {e}", file=sys.stderr)
+        continue
       # Move reid under metadata key
       if 'metadata' not in pobj:
         pobj['metadata'] = {}