Update custom_node_furniture_mask.py

custom_node_furniture_mask.py

@@ -1,61 +1,47 @@
 # custom_node_furniture_mask.py by StyleSpace (and GPT4)
-
-# custom_node_furniture_mask.py
 import torch
-import numpy as np
-from PIL import Image
 import torchvision.transforms as T
 from torchvision.models.segmentation import deeplabv3_resnet50
 
-class FurnitureMask:
+class FurnitureMaskNode:
     def __init__(self):
-        self.segmentation_model = deeplabv3_resnet50(pretrained=True, progress=False, num_classes=150).eval()
+        self.model = deeplabv3_resnet50(pretrained=True).eval()
+        self.transforms = T.Compose([
+            T.Resize(256),
+            T.CenterCrop(224),
+            T.ToTensor(),
+            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ])
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "image": ("IMAGE",),
+                "input_image": ("IMAGE",),
             },
         }
 
-    RETURN_TYPES = {
-        "latent": "LATENT",
-        "mask": "MASK",
-    }
-    FUNCTION = "generate_mask"
-
-    CATEGORY = "masking"
-
-    def generate_mask(self, image):
-        pil_image = self.tensor2pil(image)
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "detect_furniture"
 
-        furniture_classes = [20, 33, 63, 84, 85, 87, 88, 89, 91, 96, 97, 98, 100, 102, 104, 105, 106, 107, 109, 112, 113, 115, 116, 117, 118, 120, 121, 122, 123, 124, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152]
-
-        preprocess = T.Compose([
-            T.Resize(256),
-            T.CenterCrop(224),
-            T.ToTensor(),
-            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        ])
-
-        input_tensor = preprocess(pil_image).unsqueeze(0)
+    CATEGORY = "custom"
 
+    def detect_furniture(self, input_image):
+        input_tensor = self.transforms(input_image).unsqueeze(0)
         with torch.no_grad():
-            output = self.segmentation_model(input_tensor)['out'][0]
-            predicted = output.argmax(0)
-
-            mask = torch.zeros_like(predicted).bool()
-            for cls in furniture_classes:
-                mask |= (predicted == cls)
+            output = self.model(input_tensor)['out'][0]
+        output_predictions = output.argmax(0)
 
-            mask = mask.unsqueeze(0).unsqueeze(0).float()
+        non_furniture_classes = [0, 1, 2, 3, 4, 5, 6, 9, 10, 14, 15, 16, 18, 20]
+        mask = torch.zeros_like(output_predictions, dtype=torch.bool)
+        for cls in non_furniture_classes:
+            mask |= (output_predictions == cls)
 
-        return {"latent": image, "mask": mask}
+        mask = ~mask
+        masked_image = input_image * mask.unsqueeze(-1).float()
 
-    def tensor2pil(self, image):
-        return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+        return masked_image, mask
 
 NODE_CLASS_MAPPINGS = {
-    "Furniture Mask": FurnitureMask
+    "FurnitureMask": FurnitureMaskNode
 }