Refactor process_batch to return detections in list format for improved consistency

detect-objects.py

@@ -251,20 +251,16 @@ def process_batch(
 
     except Exception as e:
         logger.warning(f"⚠️  Failed to process batch: {e}")
-        # Return empty detections for all images in batch (dict-of-lists format)
-        num_images = len(pil_images)
+        # Return empty detections for all images in batch
         return {
-            "objects": {
-                "bbox": [[] for _ in range(num_images)],
-                "category": [[] for _ in range(num_images)],
-                "score": [[] for _ in range(num_images)],
-            }
+            "objects": [
+                {"bbox": [], "category": [], "score": []}
+                for _ in range(len(pil_images))
+            ]
         }
 
-    # Convert to HuggingFace object detection format (dict-of-lists)
-    batch_bboxes = []
-    batch_categories = []
-    batch_scores = []
+    # Convert to HuggingFace object detection format (dict-of-lists per image)
+    batch_objects = []
 
     for result in results:
         boxes = result.get("boxes", torch.tensor([]))
@@ -272,9 +268,7 @@ def process_batch(
 
         # Handle empty results
         if len(boxes) == 0:
-            batch_bboxes.append([])
-            batch_categories.append([])
-            batch_scores.append([])
+            batch_objects.append({"bbox": [], "category": [], "score": []})
             continue
 
         # Build lists for this image
@@ -291,17 +285,13 @@ def process_batch(
             image_categories.append(0)  # Single class, always index 0
             image_scores.append(float(score))
 
-        batch_bboxes.append(image_bboxes)
-        batch_categories.append(image_categories)
-        batch_scores.append(image_scores)
+        batch_objects.append({
+            "bbox": image_bboxes,
+            "category": image_categories,
+            "score": image_scores,
+        })
 
-    return {
-        "objects": {
-            "bbox": batch_bboxes,
-            "category": batch_categories,
-            "score": batch_scores,
-        }
-    }
+    return {"objects": batch_objects}
 
 
 def main():