Update app.py

app.py

@@ -277,11 +277,13 @@ def get_detection_data(image_data):
             # get model size and confidence threshold
             model_size = image_data.get("model_size", "Small - Better performance and less accuracy")
             confidence_threshold = image_data.get("confidence_threshold", 0.1)  # Default from Lens Studio
+            distance_threshold = image_data.get("distance_threshold", 10.0)  # Default to 10 meters
         else:
             full_data_url = image_data
             
             model_size = "Small - Better performance and less accuracy"  # Fallback default
             confidence_threshold = 0.6  # Fallback default
+            distance_threshold = 10.0  # Default to 10 meters
 
         if not full_data_url:
             return {"error": "No base64 data found in input."}
@@ -335,18 +337,31 @@ def get_detection_data(image_data):
             # Convert BGR to RGB
             color_rgb = (int(color_bgr[2]), int(color_bgr[1]), int(color_bgr[0]))
 
-            detections.append({
-                "class_id": cls_id,
-                "class_name": cls_name,
-                "bounding_box": {
-                    "vertices": get_box_vertices([x1, y1, x2, y2])
-                },
-                "center_2d": center, 
-                "distance": float(real_distance),
-                "color": color_rgb,
-                "confidence": float(confidence)
+    #       detections.append({
+    #           "class_id": cls_id,
+    #           "class_name": cls_name,
+    #           "bounding_box": {
+    #                "vertices": get_box_vertices([x1, y1, x2, y2])
+    #            },
+    #            "center_2d": center, 
+    #            "distance": float(real_distance),
+    #            "color": color_rgb,
+    #            "confidence": float(confidence)
             
-    })
+    #})
+            # Filter based on distance threshold
+            if real_distance <= distance_threshold:
+                detections.append({
+                    "class_id": cls_id,
+                    "class_name": cls_name,
+                    "bounding_box": {"vertices": get_box_vertices([x1, y1, x2, y2])},
+                    "center_2d": center, 
+                    "distance": float(real_distance),
+                    "color": color_rgb,
+                    "confidence": float(confidence)
+                })
+            else:
+                print(f"Debug - Object {idx} filtered out: Distance {real_distance} exceeds threshold {distance_threshold}")
 
         response = {
             "detections": detections,