solidlime
1f3bfe02ef
- Single image and folder random selection modes - Subfolder scanning support - Resolution output (width/height) - Seed control for reproducible random selection - Support for PNG, JPEG, WebP, BMP, GIF formats
142 lines
4.6 KiB
Python
142 lines
4.6 KiB
Python
"""
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Random Image Picker Node Implementation
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"""
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import os
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import random
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from pathlib import Path
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from typing import Tuple, List
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import folder_paths
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from PIL import Image
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import numpy as np
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import torch
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class RandomImagePicker:
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"""
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ComfyUI node that loads either a single image or a random image from a folder.
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Supports subfolder scanning and outputs image with resolution information.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"path": ("STRING", {"default": "", "multiline": False}),
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"folder_mode": ("BOOLEAN", {"default": False, "label_on": "Folder", "label_off": "Single"}),
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"include_subfolders": ("BOOLEAN", {"default": False, "label_on": "ON", "label_off": "OFF"}),
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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", "INT", "INT")
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RETURN_NAMES = ("IMAGE", "width", "height")
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FUNCTION = "load_image"
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CATEGORY = "image"
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@staticmethod
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def get_image_files(directory: str, include_subfolders: bool = False) -> List[str]:
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"""
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Get all image files from a directory.
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Args:
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directory: Directory path to scan
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include_subfolders: Whether to include subfolders in the scan
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Returns:
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List of image file paths
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"""
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supported_formats = {'.png', '.jpg', '.jpeg', '.webp', '.bmp', '.gif'}
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image_files = []
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if include_subfolders:
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for root, _, files in os.walk(directory):
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for file in files:
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if Path(file).suffix.lower() in supported_formats:
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image_files.append(os.path.join(root, file))
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else:
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for file in os.listdir(directory):
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file_path = os.path.join(directory, file)
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if os.path.isfile(file_path) and Path(file).suffix.lower() in supported_formats:
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image_files.append(file_path)
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return image_files
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@staticmethod
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def load_image_file(image_path: str) -> Tuple[torch.Tensor, int, int]:
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"""
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Load an image file and convert to ComfyUI format.
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Args:
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image_path: Path to the image file
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Returns:
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Tuple of (image_tensor, width, height)
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"""
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img = Image.open(image_path)
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# Convert to RGB if needed
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if img.mode != 'RGB':
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img = img.convert('RGB')
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# Get dimensions
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width, height = img.size
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# Convert to numpy array
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img_array = np.array(img).astype(np.float32) / 255.0
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# Convert to torch tensor and add batch dimension
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# ComfyUI expects [batch, height, width, channels]
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img_tensor = torch.from_numpy(img_array)[None,]
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return img_tensor, width, height
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def load_image(self, path: str, folder_mode: bool, include_subfolders: bool, seed: int):
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"""
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Load image based on mode settings.
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Args:
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path: File or folder path
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folder_mode: True for folder mode, False for single file mode
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include_subfolders: Whether to scan subfolders
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seed: Random seed for folder mode
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Returns:
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Tuple of (image_tensor, width, height)
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"""
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if not path or not os.path.exists(path):
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raise ValueError(f"Path does not exist: {path}")
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if folder_mode:
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# Folder mode: pick random image
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if not os.path.isdir(path):
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raise ValueError(f"Folder mode enabled but path is not a directory: {path}")
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image_files = self.get_image_files(path, include_subfolders)
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if not image_files:
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raise ValueError(f"No image files found in directory: {path}")
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# Use seed for reproducible random selection
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random.seed(seed)
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selected_image = random.choice(image_files)
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print(f"[Random Image Picker] Selected: {selected_image}")
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return self.load_image_file(selected_image)
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else:
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# Single file mode
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if not os.path.isfile(path):
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raise ValueError(f"Single file mode enabled but path is not a file: {path}")
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return self.load_image_file(path)
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# Node registration
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NODE_CLASS_MAPPINGS = {
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"RandomImagePicker": RandomImagePicker
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"RandomImagePicker": "Random Image Picker"
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}
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