| import os |
| import json |
| import hashlib |
| import numpy as np |
| import torch |
| from PIL import Image, ImageOps |
| import folder_paths |
|
|
|
|
| class TZ_LoadPNGWithMetadata: |
| @classmethod |
| def INPUT_TYPES(cls): |
| input_dir = folder_paths.get_input_directory() |
| files = [] |
|
|
| if os.path.isdir(input_dir): |
| for f in os.listdir(input_dir): |
| if f.lower().endswith(".png"): |
| files.append(f) |
|
|
| files.sort() |
|
|
| if not files: |
| files = ["example.png"] |
|
|
| return { |
| "required": { |
| "image": (files, {"image_upload": True}), |
| } |
| } |
|
|
| RETURN_TYPES = ("IMAGE", "MASK", "STRING", "STRING") |
| RETURN_NAMES = ("image", "mask", "image_path", "raw_metadata") |
| FUNCTION = "load_image" |
| CATEGORY = "Herve/metadata" |
|
|
| @classmethod |
| def IS_CHANGED(cls, image): |
| image_path = folder_paths.get_annotated_filepath(image) |
| m = hashlib.sha256() |
| m.update(image.encode("utf-8")) |
| if os.path.exists(image_path): |
| stat = os.stat(image_path) |
| m.update(str(stat.st_mtime_ns).encode("utf-8")) |
| m.update(str(stat.st_size).encode("utf-8")) |
| return m.hexdigest() |
|
|
| @classmethod |
| def VALIDATE_INPUTS(cls, image): |
| if not image.lower().endswith(".png"): |
| return "Este nodo solo acepta archivos PNG" |
| image_path = folder_paths.get_annotated_filepath(image) |
| if not os.path.isfile(image_path): |
| return f"No se encontró el archivo: {image}" |
| return True |
|
|
| def load_image(self, image): |
| image_path = folder_paths.get_annotated_filepath(image) |
|
|
| img = Image.open(image_path) |
| img = ImageOps.exif_transpose(img) |
|
|
| info = getattr(img, "info", {}) or {} |
| raw_metadata = json.dumps(info, ensure_ascii=False, indent=2) |
|
|
| image_rgb = img.convert("RGB") |
| image_np = np.array(image_rgb).astype(np.float32) / 255.0 |
| image_tensor = torch.from_numpy(image_np)[None,] |
|
|
| if "A" in img.getbands(): |
| mask_np = np.array(img.getchannel("A")).astype(np.float32) / 255.0 |
| mask = 1.0 - torch.from_numpy(mask_np) |
| else: |
| mask = torch.zeros((64, 64), dtype=torch.float32) |
|
|
| return (image_tensor, mask, image_path, raw_metadata) |
|
|
|
|
| NODE_CLASS_MAPPINGS = { |
| "TZ_LoadPNGWithMetadata": TZ_LoadPNGWithMetadata |
| } |
|
|
| NODE_DISPLAY_NAME_MAPPINGS = { |
| "TZ_LoadPNGWithMetadata": "TZ Load PNG With Metadata" |
| } |
