v1.1.0

masks_nodes.py

@@ -0,0 +1,52 @@
+import numpy as np
+import scipy.ndimage as ndi
+import torch
+
+class LargestMaskOnly:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask": ("MASK",),
+            }
+        }
+    
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "process"
+    CATEGORY = "mask"
+
+    def process(self, mask):
+        # Convert mask to numpy array
+        mask_np = mask.cpu().numpy()
+        
+        # Print debug info about mask
+        print(f"Mask shape: {mask_np.shape}")
+        print(f"Mask dtype: {mask_np.dtype}")
+        print(f"Mask min: {mask_np.min()}, max: {mask_np.max()}")
+        
+        # Ensure binary mask (0 and 1)
+        binary_mask = (mask_np > 0.5).astype(np.uint8)
+        
+        # Use scipy's label function instead of OpenCV
+        labeled_array, num_features = ndi.label(binary_mask)
+        print(f"Found {num_features} connected components")
+        
+        if num_features <= 1:  # No components or just one
+            return (mask,)
+        
+        # Find sizes of all labeled regions
+        sizes = np.bincount(labeled_array.ravel())
+        # Skip background (label 0)
+        if len(sizes) > 1:
+            sizes = sizes[1:]
+            # Find the label of the largest component (add 1 because we skipped background)
+            largest_label = np.argmax(sizes) + 1
+            # Create a mask with only the largest component
+            largest_mask = (labeled_array == largest_label).astype(np.float32)
+        else:
+            # Fallback if something went wrong with the labeling
+            largest_mask = binary_mask.astype(np.float32)
+        
+        # Convert back to tensor and return
+        result = torch.from_numpy(largest_mask)
+        return (result,)