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import torch
import hashlib
from pathlib import Path
from typing import Iterable
from PIL import Image, ImageOps
import numpy as np
import folder_paths
class LoadImageFromPath:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"image": ("STRING", {"default": r"ComfyUI_00001_-assets\ComfyUI_00001_.png [output]"})},
}
CATEGORY = "image"
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "load_image"
def load_image(self, image):
image_path = LoadImageFromPath._resolve_path(image)
i = Image.open(image_path)
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")
return (image, mask)
def _resolve_path(image) -> Path:
image_path = Path(folder_paths.get_annotated_filepath(image))
return image_path
@classmethod
def IS_CHANGED(s, image):
image_path = LoadImageFromPath._resolve_path(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 image is an output of another node, it will be None during validation
if image is None:
return True
image_path = LoadImageFromPath._resolve_path(image)
if not image_path.exists():
return "Invalid image path: {}".format(image_path)
return True
class PILToImage:
@classmethod
def INPUT_TYPES(s):
return {'required':
{'images': ('PIL_IMAGE', )},
}
RETURN_TYPES = ('IMAGE',)
FUNCTION = 'pil_images_to_images'
CATEGORY = 'image/PIL'
def pil_images_to_images(images: Iterable[Image.Image]) -> torch.Tensor:
pil_images = images
images = []
for pil_image in pil_images:
i = pil_image
i = ImageOps.exif_transpose(i)
if i.mode == 'I':
i = i.point(lambda i: i * (1 / 255))
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
images.append(image)
if len(images) > 1:
images = torch.cat(images, dim=0)
else:
images = images[0]
return (images,)
class PILToMask:
@classmethod
def INPUT_TYPES(s):
return {'required':
{'images': ('PIL_IMAGE', )},
}
RETURN_TYPES = ('IMAGE',)
FUNCTION = 'pil_images_to_masks'
CATEGORY = 'image/PIL'
def pil_images_to_masks(images: Iterable[Image.Image]) -> torch.Tensor:
pil_images = images
masks = []
for pil_image in pil_images:
i = pil_image
i = ImageOps.exif_transpose(i)
if i.mode == 'I':
i = i.point(lambda i: i * (1 / 255))
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")
masks.append(mask)
if len(masks) > 1:
masks = torch.cat(masks, dim=0)
else:
masks = masks[0]
return (masks,)
class ImageToPIL:
@classmethod
def INPUT_TYPES(s):
return {'required':
{'images': ('IMAGE', )},
}
RETURN_TYPES = ('PIL_IMAGE',)
FUNCTION = 'images_to_pil_images'
CATEGORY = 'image/PIL'
def images_to_pil_images(self, images: torch.Tensor) -> list[Image.Image]:
pil_images = []
for image in images:
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
pil_images.append(img)
return (pil_images,) |