loadImageWithSubfolders.py

import os

import folder_paths
import torch
import hashlib
from PIL import Image, ImageOps
from PIL.PngImagePlugin import PngInfo
import numpy as np

class LoadImage:
    @classmethod
    def INPUT_TYPES(s):
        input_dir = folder_paths.get_input_directory()
        exclude_folders = ["clipspace", "folder_to_exclude2"]
        file_list = []

        for root, dirs, files in os.walk(input_dir):
            # Exclude specific folders
            dirs[:] = [d for d in dirs if d not in exclude_folders]
            
            for file in files:
                file_path = os.path.relpath(os.path.join(root, file), start=input_dir)
                file_path = file_path.replace("\\", "/")  # so the filename is processed correctly in widgets.js
                file_list.append(file_path)

        return {"required":
                    {"image": (sorted(file_list), {"image_upload": True})},
                }

    CATEGORY = "image"

    RETURN_TYPES = ("IMAGE", "MASK")
    FUNCTION = "load_image"
    def load_image(self, image):
        image_path = folder_paths.get_annotated_filepath(image)
        i = Image.open(image_path)
        i = ImageOps.exif_transpose(i)
        image = i.convert("RGB")
        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)
        else:
            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
        return (image, mask.unsqueeze(0))

    @classmethod
    def IS_CHANGED(s, 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(s, image):
        if not folder_paths.exists_annotated_filepath(image):
            return "Invalid image file: {}".format(image)

        return True
    
# A dictionary that contains all nodes you want to export with their names
# NOTE: names should be globally unique
NODE_CLASS_MAPPINGS = {
    "LoadImagewithSubfolders": LoadImage
}

# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
    "LoadImagewithSubfolders": "Load Image with Subfolders"
}