Update custom_node_furniture_mask.py

custom_node_furniture_mask.py

@@ -2,6 +2,7 @@
 import torch
 import torchvision.transforms as T
 from torchvision.models.segmentation import deeplabv3_resnet50
+from PIL import Image
 
 class FurnitureMaskNode:
     def __init__(self):
@@ -27,12 +28,16 @@ class FurnitureMaskNode:
     CATEGORY = "custom"
 
     def detect_furniture(self, input_image):
+        input_image = Image.fromarray((input_image * 255).astype('uint8'))
         input_tensor = self.transforms(input_image).unsqueeze(0)
         with torch.no_grad():
             output = self.model(input_tensor)['out'][0]
         output_predictions = output.argmax(0)
 
-        non_furniture_classes = [0, 1, 2, 3, 4, 5, 6, 9, 10, 14, 15, 16, 18, 20]
+        non_furniture_classes = list(range(1, 151))  # Adjust the range based on ADE20K classes
+        furniture_classes = [5, 10, 20, 25, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]  # Based on ADE20K
+        non_furniture_classes = [cls for cls in non_furniture_classes if cls not in furniture_classes]
+
         mask = torch.zeros_like(output_predictions, dtype=torch.bool)
         for cls in non_furniture_classes:
             mask |= (output_predictions == cls)