112 lines
4.2 KiB
Python
112 lines
4.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Copyright 2025-2026 Raykosan (RaykoStudio)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import glob
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import json
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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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from PIL import Image
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from typing import List, Dict, Any
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class RSLoadImagesFromDir:
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@classmethod
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def INPUT_TYPES(cls) -> Dict[str, Dict[str, Any]]:
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return {
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"required": {
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"node_data": ("STRING", {"default": "{}", "hidden": True}),
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"folder_path": ("STRING", {"default": "", "multiline": False}),
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"filter_type": (["*.*", "*.png", "*.jpg", "*.jpeg", "*.webp", "*.bmp", "*.gif", "custom"], {"default": "*.png"}),
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"start_index": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1}),
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"end_index": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1}),
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},
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"optional": {
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"custom_filter": ("STRING", {"default": "*.png", "multiline": False}),
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING", "STRING", "INT")
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RETURN_NAMES = ("image", "filename", "folder_path", "number_of_files")
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OUTPUT_IS_LIST = (True, True, False, False)
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FUNCTION = "load_images"
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CATEGORY = "🦊 RaykoStudio"
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DESCRIPTION = "Load images from directory. Preserves original size and alpha channel."
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def load_images(
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self,
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node_data: str = "{}",
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folder_path: str = "",
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filter_type: str = "*.png",
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start_index: int = 1,
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end_index: int = 1,
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custom_filter: str = "*.png"
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):
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try:
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data = json.loads(node_data) if node_data else {}
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if data:
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folder_path = data.get("folder_path", folder_path)
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filter_type = data.get("filter_type", filter_type)
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start_index = data.get("start_index", start_index)
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end_index = data.get("end_index", end_index)
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custom_filter = data.get("custom_filter", custom_filter)
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except Exception:
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pass
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if not folder_path or not os.path.isdir(folder_path):
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raise ValueError(f"Invalid folder path: {folder_path}")
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pattern = custom_filter if filter_type == "custom" else filter_type
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search_pattern = os.path.join(folder_path, pattern)
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file_list = sorted(
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[f for f in glob.glob(search_pattern) if os.path.isfile(f)],
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key=lambda x: os.path.basename(x).lower()
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)
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total_files = len(file_list)
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images_list: List[torch.Tensor] = []
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filenames_list: List[str] = []
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if total_files == 0:
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return (images_list, filenames_list, folder_path, total_files)
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start_idx = max(0, min(start_index - 1, total_files - 1))
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end_idx = max(start_idx, min(end_index - 1, total_files - 1))
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for idx in range(start_idx, end_idx + 1):
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filepath = file_list[idx]
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filename = os.path.basename(filepath)
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try:
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img = Image.open(filepath)
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img_np = np.array(img).astype(np.float32) / 255.0
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if img_np.ndim == 2:
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img_np = np.stack([img_np] * 3, axis=-1)
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img_tensor = torch.from_numpy(img_np).unsqueeze(0)
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images_list.append(img_tensor)
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filenames_list.append(filename)
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except Exception as e:
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print(f"[RaykoStudio] Error loading {filename}: {e}")
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return (images_list, filenames_list, folder_path, total_files)
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NODE_CLASS_MAPPINGS = {"RSLoadImagesFromDir": RSLoadImagesFromDir}
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NODE_DISPLAY_NAME_MAPPINGS = {"RSLoadImagesFromDir": "🦊 RS Load Images From Dir"} |