59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
import math, cv2, random, torch, torchvision, json, os
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import numpy as np
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from PIL import Image, ImageOps, ImageSequence
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import nodes, folder_paths # 기본노드, 파일로드
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class abyz22_SaveImage:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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self.type = "output"
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self.prefix_append = ""
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self.compress_level = 4
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": ("IMAGE",),
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"folder_path": ("STRING", {"default": ""}),
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},
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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RETURN_TYPES = ()
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FUNCTION = "save_images"
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OUTPUT_NODE = True
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CATEGORY = "abyz22"
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def save_images(self, images, folder_path, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
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filename_prefix += self.prefix_append
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_, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
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filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
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)
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if not os.path.exists(folder_path):
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os.mkdir(folder_path)
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full_output_folder = folder_path
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file_list = os.listdir(folder_path)
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file_list= [int(f.split('.')[0]) for f in file_list]
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file_list.sort(reverse=True)
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counter = 1 if len(file_list)==0 else file_list[0] +1
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results = list()
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for image in images:
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i = 255.0 * image.cpu().numpy()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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file = f"{counter}.png"
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if folder_path != 'no-data':
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img.save(os.path.join(full_output_folder, file), pnginfo=None, compress_level=self.compress_level)
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results.append({"filename": file, "subfolder": subfolder, "type": self.type})
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counter += 1
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return {"ui": {"images": results}}
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