|
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 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,) |
