98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
import hashlib
|
|
import os
|
|
|
|
import folder_paths
|
|
import numpy as np
|
|
import torch
|
|
from PIL import Image, ImageOps, ImageSequence
|
|
|
|
from .node_helpers import pillow
|
|
|
|
|
|
class LoadImagePath:
|
|
|
|
@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))]
|
|
files = folder_paths.filter_files_content_types(files, ["image"])
|
|
return {
|
|
"required": {
|
|
"image": (sorted(files), {
|
|
"image_upload": True
|
|
})
|
|
},
|
|
}
|
|
|
|
CATEGORY = "fastvideo"
|
|
|
|
RETURN_TYPES = ("STRING", "IMAGE", "MASK")
|
|
RETURN_NAMES = ("image_path", "IMAGE", "MASK")
|
|
FUNCTION = "load_image"
|
|
|
|
def load_image(self, image):
|
|
image_path = folder_paths.get_annotated_filepath(image)
|
|
|
|
img = pillow(Image.open, image_path)
|
|
|
|
output_images: list[torch.Tensor] = []
|
|
output_masks: list[torch.Tensor] = []
|
|
w, h = None, None
|
|
|
|
excluded_formats = ['MPO']
|
|
|
|
for i in ImageSequence.Iterator(img):
|
|
processed_image = pillow(ImageOps.exif_transpose, i)
|
|
if processed_image is None:
|
|
continue
|
|
|
|
if processed_image.mode == 'I':
|
|
processed_image = processed_image.point(lambda i: i * (1 / 255))
|
|
image = processed_image.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 processed_image.getbands():
|
|
mask = np.array(processed_image.getchannel('A')).astype(np.float32) / 255.0
|
|
mask = 1. - torch.from_numpy(mask)
|
|
elif processed_image.mode == 'P' and 'transparency' in processed_image.info:
|
|
mask = np.array(processed_image.convert('RGBA').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 (image_path, 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())
|
|
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
|