49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
import os
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import torch
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import numpy as np
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from PIL import Image, ImageOps
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import folder_paths
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class EmAySee_LoadImageExtended:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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input_dir = folder_paths.get_input_directory()
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files = folder_paths.get_filename_list("input")
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return {
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"required": {
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"image": (sorted(files), {"image_upload": True})
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT", "STRING")
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RETURN_NAMES = ("image", "mask", "width", "height", "filename")
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FUNCTION = "load"
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CATEGORY = "EmAySee/Image"
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def load(self, image):
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image_path = folder_paths.get_annotated_filepath(image)
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img = Image.open(image_path)
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img = ImageOps.exif_transpose(img)
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image_rgb = img.convert("RGB")
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image_np = np.array(image_rgb).astype(np.float32) / 255.0
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image_tensor = torch.from_numpy(image_np)[None,]
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if 'A' in img.getbands():
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mask = np.array(img.getchannel('A')).astype(np.float32) / 255.0
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mask_tensor = 1.0 - torch.from_numpy(mask)[None,]
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else:
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mask_tensor = torch.zeros((1, img.height, img.width), dtype=torch.float32)
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return (image_tensor, mask_tensor, img.width, img.height, os.path.basename(image_path))
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NODE_CLASS_MAPPINGS = {
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"EmAySee_LoadImageExtended": EmAySee_LoadImageExtended
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"EmAySee_LoadImageExtended": "EmAySee Load Image Extended"
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} |