98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
import os
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import folder_paths
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import node_helpers
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import numpy as np
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import torch
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import hashlib
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from PIL import Image, ImageOps, ImageSequence, ImageFile
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#code credit: nodes.py comfui
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class DataSet_LoadImage:
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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 = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
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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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CATEGORY = "🔶DATASET🔶"
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RETURN_TYPES = ("IMAGE", "MASK", "STRING", "STRING", "STRING", "STRING")
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RETURN_NAMES = ("image", "image_mask", "image_name", "image_name_without_extension", "image_path", "image_directory_path")
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FUNCTION = "load_image"
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def load_image(self, image):
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image_path = folder_paths.get_annotated_filepath(image)
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img = node_helpers.pillow(Image.open, image_path)
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output_images = []
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output_masks = []
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w, h = None, None
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excluded_formats = ['MPO']
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for i in ImageSequence.Iterator(img):
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i = node_helpers.pillow(ImageOps.exif_transpose, i)
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if i.mode == 'I':
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i = i.point(lambda i: i * (1 / 255))
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image = i.convert("RGB")
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if len(output_images) == 0:
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w = image.size[0]
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h = image.size[1]
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if image.size[0] != w or image.size[1] != h:
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continue
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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. - 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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output_images.append(image)
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output_masks.append(mask.unsqueeze(0))
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if len(output_images) > 1 and img.format not in excluded_formats:
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output_image = torch.cat(output_images, dim=0)
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output_mask = torch.cat(output_masks, dim=0)
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else:
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output_image = output_images[0]
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output_mask = output_masks[0]
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image_name = os.path.basename(image_path)
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image_dir = os.path.dirname(image_path)
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image_name_without_extension = os.path.splitext(image_name)[0]
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return (output_image, output_mask, image_name, image_name_without_extension, image_path, image_dir)
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@classmethod
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def IS_CHANGED(s, 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(s, 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
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N_CLASS_MAPPINGS = {
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"DataSet_LoadImage": DataSet_LoadImage,
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}
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N_DISPLAY_NAME_MAPPINGS = {
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"DataSet_LoadImage": "DataSet_LoadImage",
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}
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