Delete maskgen.py

maskgen.py

@@ -1,63 +0,0 @@
-import albumentations as albu
-import numpy as np
-from iglovikov_helper_functions.utils.image_utils import pad, unpad
-from iglovikov_helper_functions.dl.pytorch.utils import tensor_from_rgb_image
-import cv2
-import torch
-from PIL import Image
-
-def pil2tensor(image):
-    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
-
-def tensor2np(image):
-    return np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
-
-class GenImageMask:
-
-    def __init__(self):
-        pass
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        """
-            Return a dictionary which contains config for all input fields.
-            Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
-            Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
-            The type can be a list for selection.
-
-            Returns: `dict`:
-                - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
-                - Value input_fields (`dict`): Contains input fields config:
-                    * Key field_name (`string`): Name of a entry-point method's argument
-                    * Value field_config (`tuple`):
-                        + First value is a string indicate the type of field or a list for selection.
-                        + Secound value is a config for type "INT", "STRING" or "FLOAT".
-        """
-        return {"required": {"image": ("IMAGE",), "segm_model": ("SEGM_MODEL",)}}
-
-    CATEGORY = "image"
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("BW Mask",)
-
-    FUNCTION = "mask_image"
-    def mask_image(self, image, segm_model):
-
-        transform = albu.Compose([albu.Normalize(p=1)], p=1)
-        image = tensor2np(image)
-        padded_image, pads = pad(image, factor=32, border=cv2.BORDER_CONSTANT)
-        x = transform(image=padded_image)["image"]
-        x = torch.unsqueeze(tensor_from_rgb_image(x), 0)
-        with torch.no_grad():
-            prediction = segm_model(x)[0][0]
-
-        mask = (prediction > 0.01).cpu().numpy().astype(np.uint8)
-
-        mask = unpad(mask, pads)
-
-        imag = pil2tensor(Image.fromarray(cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB) * 255))
-        return (imag,)
-
-
-NODE_CLASS_MAPPINGS = {
-    "GenImageMask": GenImageMask,
-}