|
|
""" |
|
|
Hello, welcome on board, |
|
|
""" |
|
|
from __future__ import print_function |
|
|
|
|
|
import os |
|
|
import cv2 |
|
|
import numpy as np |
|
|
|
|
|
import torch |
|
|
|
|
|
from annotator.teed.ted import TED |
|
|
from einops import rearrange |
|
|
from modules import devices |
|
|
from annotator.util import load_model,safe_step |
|
|
from annotator.annotator_path import models_path |
|
|
|
|
|
class TEEDDector: |
|
|
"""https://github.com/xavysp/TEED""" |
|
|
|
|
|
model_dir = os.path.join(models_path, "TEED") |
|
|
|
|
|
def __init__(self): |
|
|
self.device = devices.get_device_for("controlnet") |
|
|
self.model = TED().to(self.device).eval() |
|
|
remote_url = os.environ.get( |
|
|
"CONTROLNET_TEED_MODEL_URL", |
|
|
"https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/resolve/main/Annotators/7_model.pth", |
|
|
) |
|
|
model_path = load_model( |
|
|
"7_model.pth", remote_url=remote_url, model_dir=self.model_dir |
|
|
) |
|
|
self.model.load_state_dict(torch.load(model_path)) |
|
|
|
|
|
def unload_model(self): |
|
|
if self.model is not None: |
|
|
self.model.cpu() |
|
|
|
|
|
def __call__(self, image: np.ndarray, safe_steps: int = 2) -> np.ndarray: |
|
|
|
|
|
self.model.to(self.device) |
|
|
|
|
|
H, W, _ = image.shape |
|
|
with torch.no_grad(): |
|
|
image_teed = torch.from_numpy(image.copy()).float().to(self.device) |
|
|
image_teed = rearrange(image_teed, 'h w c -> 1 c h w') |
|
|
edges = self.model(image_teed) |
|
|
edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges] |
|
|
edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges] |
|
|
edges = np.stack(edges, axis=2) |
|
|
edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64))) |
|
|
if safe_steps != 0: |
|
|
edge = safe_step(edge, safe_steps) |
|
|
edge = (edge * 255.0).clip(0, 255).astype(np.uint8) |
|
|
return edge |
