Create furniture_mask_node

furniture_mask_node

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+# 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:
+    def __init__(self):
+        self.segmentation_model = deeplabv3_resnet50(pretrained=True, progress=False, num_classes=150).eval()
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = {
+        "latent": "LATENT",
+        "mask": "MASK",
+    }
+    FUNCTION = "generate_mask"
+
+    CATEGORY = "masking"
+
+    def generate_mask(self, image):
+        pil_image = self.tensor2pil(image)
+
+        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)
+
+        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)
+
+            mask = mask.unsqueeze(0).unsqueeze(0).float()
+
+        return {"latent": image, "mask": mask}
+
+    def tensor2pil(self, image):
+        return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+NODE_CLASS_MAPPINGS = {
+    "Furniture Mask": FurnitureMask
+}