Files

75 lines
2.1 KiB
Python

import os
from PIL import Image, ImageOps, ImageSequence
import numpy as np
import torch
import hashlib
import folder_paths
class CachingImageLoader:
@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": ("STRING", {"default": 'enter path to file', "multiline": False}),
}
}
CATEGORY = "image"
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "load_image"
@classmethod
def load_image(self, image):
img = Image.open(image)
output_images = []
output_masks = []
for i in ImageSequence.Iterator(img):
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")
output_images.append(image)
output_masks.append(mask.unsqueeze(0))
if len(output_images) > 1:
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
@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())
print(f'############### Digest {image} {image_path} {m.digest().hex()}')
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
@staticmethod
def status():
return "OK"