Files
melMass-comfy_mtb/utils.py
T
2023-07-06 17:45:16 +02:00

81 lines
2.2 KiB
Python

from PIL import Image
import numpy as np
import torch
from pathlib import Path
import sys
from typing import Union, List
def add_path(path, prepend=False):
if isinstance(path, list):
for p in path:
add_path(p, prepend)
return
if isinstance(path, Path):
path = path.resolve().as_posix()
if path not in sys.path:
if prepend:
sys.path.insert(0, path)
else:
sys.path.append(path)
# Get the absolute path of the parent directory of the current script
here = Path(__file__).parent.resolve()
# Construct the absolute path to the ComfyUI directory
comfy_dir = here.parent.parent
# Construct the path to the font file
font_path = here / "font.ttf"
# Add extern folder to path
extern_root = here / "extern"
add_path(extern_root)
for pth in extern_root.iterdir():
if pth.is_dir():
add_path(pth)
# Add the ComfyUI directory and custom nodes path to the sys.path list
add_path(comfy_dir)
add_path((comfy_dir / "custom_nodes"))
def tensor2pil(image: torch.Tensor) -> Union[Image.Image, List[Image.Image]]:
batch_count = 1
if len(image.shape) > 3:
batch_count = image.size(0)
if batch_count == 1:
return Image.fromarray(
np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
)
return [tensor2pil(image[i]) for i in range(batch_count)]
def pil2tensor(image: Image.Image | List[Image.Image]) -> torch.Tensor:
if isinstance(image, list):
return torch.cat([pil2tensor(img) for img in image], dim=0)
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
def np2tensor(img_np: np.ndarray | List[np.ndarray]) -> torch.Tensor:
if isinstance(img_np, list):
return torch.cat([np2tensor(img) for img in img_np], dim=0)
return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0)
def tensor2np(tensor: torch.Tensor) -> Union[np.ndarray, List[np.ndarray]]:
batch_count = 1
if len(tensor.shape) > 3:
batch_count = tensor.size(0)
if batch_count > 1:
return [tensor2np(tensor[i]) for i in range(batch_count)]
return np.clip(255.0 * tensor.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)