nodes.py

import os
import json

import numpy as np
import random
import torch
from PIL import Image, ImageSequence, ImageOps
from PIL.PngImagePlugin import PngInfo

import folder_paths
import node_helpers
from comfy.cli_args import args

class LoadImageICC:
    @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 = "image"

    RETURN_TYPES = ("IMAGE", "MASK", "ICC_PROFILE")
    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]

        return (output_image, output_mask, img.info.get('icc_profile', None))
    
    
class SaveImageICC:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()
        self.type = "output"
        self.prefix_append = ""
        self.compress_level = 4

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "images": ("IMAGE", {"tooltip": "The images to save."}),
                "icc_profile": ("ICC_PROFILE", {"tooltip": "The ICC profile to save."}),
                "filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."})
            },
            "hidden": {
                "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
            },
        }

    RETURN_TYPES = ()
    FUNCTION = "save_images"

    OUTPUT_NODE = True

    CATEGORY = "image"
    DESCRIPTION = "Saves the input images to your ComfyUI output directory."

    def save_images(self, images, icc_profile=None, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
        filename_prefix += self.prefix_append
        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
        results = list()
        extra_save_args = {}
        if icc_profile is not None:
            extra_save_args["icc_profile"] = icc_profile
        for (batch_number, image) in enumerate(images):
            i = 255. * image.cpu().numpy()
            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
            metadata = None
            if not args.disable_metadata:
                metadata = PngInfo()
                if prompt is not None:
                    metadata.add_text("prompt", json.dumps(prompt))
                if extra_pnginfo is not None:
                    for x in extra_pnginfo:
                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))
    
            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
            file = f"{filename_with_batch_num}_{counter:05}_.png"
            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level, **extra_save_args)
            results.append({
                "filename": file,
                "subfolder": subfolder,
                "type": self.type
            })
            counter += 1

        return { "ui": { "images": results } }
    
    
class PreviewImageICC(SaveImageICC):
    def __init__(self):
        self.output_dir = folder_paths.get_temp_directory()
        self.type = "temp"
        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
        self.compress_level = 1

    @classmethod
    def INPUT_TYPES(s):
        return {
                "required":{
                    "images": ("IMAGE", ),
                    "icc_profile": ("ICC_PROFILE", {"tooltip": "The ICC profile to save."}),
                },
                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
            }
        
        
NODE_CLASS_MAPPINGS = {
    "LoadImageICC": LoadImageICC,
    "SaveImageICC": SaveImageICC,
    "PreviewImageICC": PreviewImageICC,
}

# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
    "LoadImageICC": "Load Image ICC",
    "SaveImageICC": "Save Image ICC",
    "PreviewImageICC": "Preview Image ICC",
}