Spaces:
Runtime error
Runtime error
File size: 1,887 Bytes
c19ca42 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
"""
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 # TEED architecture
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 |