Lowered threshold

app.py

@@ -59,11 +59,11 @@ print("✓ SamPredictor initialized for interactive segmentation")
 mask_generator = SamAutomaticMaskGenerator(
     model=sam,
     points_per_side=32,            # Grid density (32x32 = 1024 points)
-    pred_iou_thresh=0.88,          # IoU threshold for filtering
-    stability_score_thresh=0.95,   # Stability threshold
+    pred_iou_thresh=0.7,           # IoU threshold for filtering (lowered from 0.88)
+    stability_score_thresh=0.7,   # Stability threshold (lowered from 0.95 - was too strict)
     crop_n_layers=0,               # Disable multi-scale crops to avoid IndexError
     crop_n_points_downscale_factor=2,
-    min_mask_region_area=100       # Minimum mask area in pixels
+    min_mask_region_area=0         # Minimum mask area (lowered from 100 to allow small masks)
 )
 print("✓ SamAutomaticMaskGenerator initialized for automatic segmentation")
 print("✓ MedSAM model loaded successfully!")
@@ -414,6 +414,19 @@ def generate_auto_masks(image, request_json):
         masks = mask_generator.generate(image_array)
         
         print(f"Generated {len(masks)} masks")
+        if len(masks) > 0:
+            # Log some stats about the masks
+            areas = [m['area'] for m in masks]
+            ious = [m['predicted_iou'] for m in masks]
+            stabilities = [m['stability_score'] for m in masks]
+            print(f"  Area range: {min(areas)} - {max(areas)} pixels")
+            print(f"  IoU range: {min(ious):.3f} - {max(ious):.3f}")
+            print(f"  Stability range: {min(stabilities):.3f} - {max(stabilities):.3f}")
+        else:
+            print("  WARNING: No masks generated! This could mean:")
+            print("    - Image is too uniform/simple")
+            print("    - Thresholds are still too strict")
+            print("    - Image size is too small or too large")
         
         # Convert masks to JSON-serializable format
         masks_output = []