import folder_paths
import os
import node_helpers
import numpy as np
import torch
import hashlib
from PIL import Image, ImageOps, ImageSequence, ImageFile

#code credit: nodes.py comfui 
class DataSet_LoadImage:
    @classmethod
    def INPUT_TYPES(s):
        input_dir = folder_paths.get_input_directory()
        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
        return {
            "required": {
                "image": (sorted(files), {"image_upload": True})
            },
        }

    CATEGORY = "🔶DATASET🔶"

    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "STRING", "STRING", "STRING")
    RETURN_NAMES = ("image", "image_mask", "image_name", "image_name_without_extension", "image_path", "image_directory_path")
    FUNCTION = "load_image"

    def load_image(self, image):

        image_path = folder_paths.get_annotated_filepath(image)
        img = node_helpers.pillow(Image.open, image_path)

        output_images = []
        output_masks = []
        w, h = None, None

        excluded_formats = ['MPO']

        for i in ImageSequence.Iterator(img):
            i = node_helpers.pillow(ImageOps.exif_transpose, i)

            if i.mode == 'I':
                i = i.point(lambda i: i * (1 / 255))
            image = i.convert("RGB")

            if len(output_images) == 0:
                w = image.size[0]
                h = image.size[1]

            if image.size[0] != w or image.size[1] != h:
                continue

            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")
            output_images.append(image)
            output_masks.append(mask.unsqueeze(0))

        if len(output_images) > 1 and img.format not in excluded_formats:
            output_image = torch.cat(output_images, dim=0)
            output_mask = torch.cat(output_masks, dim=0)
        else:
            output_image = output_images[0]
            output_mask = output_masks[0]
        
        image_name = os.path.basename(image_path)
        image_dir = os.path.dirname(image_path)
        image_name_without_extension = os.path.splitext(image_name)[0]

        return (output_image, output_mask, image_name, image_name_without_extension, image_path, image_dir)

    @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


N_CLASS_MAPPINGS = {
    "DataSet_LoadImage": DataSet_LoadImage,
}

N_DISPLAY_NAME_MAPPINGS = {
    "DataSet_LoadImage": "DataSet_LoadImage",
}