| import hashlib
|
| import os
|
| from PIL import Image, ImageOps, ImageSequence
|
| import torch
|
| import numpy as np
|
|
|
| import folder_paths
|
| import node_helpers
|
|
|
| class PoseNode(object):
|
| @classmethod
|
| def INPUT_TYPES(self):
|
| input_dir = folder_paths.get_input_directory()
|
|
|
| if not os.path.isdir(input_dir):
|
| os.makedirs(input_dir)
|
|
|
| input_dir = folder_paths.get_input_directory()
|
|
|
| imgs = [img
|
| for img in os.listdir(input_dir)
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| if os.path.isfile(os.path.join(input_dir, img))]
|
|
|
| return {
|
| "required": {"image": (sorted(imgs),)},
|
| }
|
|
|
| RETURN_TYPES = ("IMAGE", "MASK")
|
| FUNCTION = "output_pose"
|
| DESCRIPTION = "PoseNode allows you to set a pose for subsequent use in ControlNet."
|
| CATEGORY = "AlekPet Nodes/image"
|
|
|
| def output_pose(self, image):
|
| image_path = folder_paths.get_annotated_filepath(image)
|
|
|
| img = node_helpers.pillow(Image.open, image_path)
|
|
|
| output_images = []
|
| output_masks = []
|
| w, h = None, None
|
|
|
| excluded_formats = ['MPO']
|
|
|
| for i in ImageSequence.Iterator(img):
|
| i = node_helpers.pillow(ImageOps.exif_transpose, i)
|
|
|
| if i.mode == 'I':
|
| i = i.point(lambda i: i * (1 / 255))
|
| image = i.convert("RGB")
|
|
|
| if len(output_images) == 0:
|
| w = image.size[0]
|
| h = image.size[1]
|
|
|
| if image.size[0] != w or image.size[1] != h:
|
| continue
|
|
|
| image = np.array(image).astype(np.float32) / 255.0
|
| image = torch.from_numpy(image)[None,]
|
| if 'A' in i.getbands():
|
| mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
|
| mask = 1. - torch.from_numpy(mask)
|
| elif i.mode == 'P' and 'transparency' in i.info:
|
| mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
|
| mask = 1. - torch.from_numpy(mask)
|
| else:
|
| mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
|
| output_images.append(image)
|
| output_masks.append(mask.unsqueeze(0))
|
|
|
| if len(output_images) > 1 and img.format not in excluded_formats:
|
| output_image = torch.cat(output_images, dim=0)
|
| output_mask = torch.cat(output_masks, dim=0)
|
| else:
|
| output_image = output_images[0]
|
| output_mask = output_masks[0]
|
|
|
| return (output_image, output_mask)
|
|
|
| @classmethod
|
| def IS_CHANGED(self, image):
|
| image_path = folder_paths.get_annotated_filepath(image)
|
|
|
| m = hashlib.sha256()
|
| with open(image_path, "rb") as f:
|
| m.update(f.read())
|
| return m.digest().hex()
|
|
|
| @classmethod
|
| def VALIDATE_INPUTS(self, image):
|
| if not folder_paths.exists_annotated_filepath(image):
|
| return "Invalid image file: {}".format(image)
|
|
|
| return True |
