62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
import hashlib
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import os
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from PIL import Image, ImageOps
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import torch
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import numpy as np
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import folder_paths
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class PoseNode(object):
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@classmethod
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def INPUT_TYPES(self):
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input_dir = folder_paths.get_input_directory()
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if not os.path.isdir(input_dir):
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os.makedirs(input_dir)
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input_dir = folder_paths.get_input_directory()
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imgs = [img
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for img in os.listdir(input_dir)
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if os.path.isfile(os.path.join(input_dir, img))]
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return {
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"required": {"image": (sorted(imgs),)},
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}
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RETURN_TYPES = ("IMAGE", "MASK")
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FUNCTION = "output_pose"
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DESCRIPTION = "PoseNode allows you to set a pose for subsequent use in ControlNet."
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CATEGORY = "AlekPet Nodes/image"
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def output_pose(self, image):
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image_path = folder_paths.get_annotated_filepath(image)
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i = Image.open(image_path)
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i = ImageOps.exif_transpose(i)
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image = i.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if "A" in i.getbands():
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mask = np.array(i.getchannel("A")).astype(np.float32) / 255.0
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mask = 1.0 - torch.from_numpy(mask)
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else:
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mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
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return (image, mask.unsqueeze(0))
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@classmethod
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def IS_CHANGED(self, image):
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image_path = folder_paths.get_annotated_filepath(image)
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m = hashlib.sha256()
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with open(image_path, "rb") as f:
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m.update(f.read())
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return m.digest().hex()
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@classmethod
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def VALIDATE_INPUTS(self, image):
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if not folder_paths.exists_annotated_filepath(image):
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return "Invalid image file: {}".format(image)
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return True |