61 lines
2.2 KiB
Python
61 lines
2.2 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
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class AD_BatchImageLoadFromDir:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"Directory": ("STRING", {"default": ""}),
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"Load_Cap": ("INT", {"default": 100, "min": 1, "max": 1000}),
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"Skip_Frame": ("INT", {"default": 0, "min": 0, "max": 100}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING", "STRING", "STRING", "INT")
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RETURN_NAMES = ("Images", "Image_Paths", "Image_Names_suffix", "Image_Names", "Count")
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FUNCTION = "load_images"
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OUTPUT_NODE = True
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OUTPUT_IS_LIST = (True, True, True, True, False)
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CATEGORY = "🌻 Addoor/Batch Operations"
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def load_images(self, Directory, Load_Cap, Skip_Frame):
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image_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.gif', '.webp']
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file_paths = []
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for root, dirs, files in os.walk(Directory):
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for file in files:
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if any(file.lower().endswith(ext) for ext in image_extensions):
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file_paths.append(os.path.join(root, file))
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file_paths = sorted(file_paths)[Skip_Frame:Skip_Frame + Load_Cap]
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images = []
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image_paths = []
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image_names_suffix = []
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image_names = []
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for file_path in file_paths:
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try:
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img = Image.open(file_path).convert("RGB")
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image = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
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images.append(image)
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image_paths.append(file_path)
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image_names_suffix.append(os.path.basename(file_path))
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image_names.append(os.path.splitext(os.path.basename(file_path))[0])
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except Exception as e:
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print(f"Error loading image '{file_path}': {e}")
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count = len(images)
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return (images, image_paths, image_names_suffix, image_names, count)
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N_CLASS_MAPPINGS = {
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"AD_BatchImageLoadFromDir": AD_BatchImageLoadFromDir,
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}
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N_DISPLAY_NAME_MAPPINGS = {
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"AD_BatchImageLoadFromDir": "🌻 Batch Image Load From Directory",
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}
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