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import cv2
import numpy as np
import torch
import os
from einops import rearrange
from .models.mbv2_mlsd_tiny import MobileV2_MLSD_Tiny
from .models.mbv2_mlsd_large import MobileV2_MLSD_Large
from .utils import pred_lines
from modules import devices
from annotator.annotator_path import models_path
mlsdmodel = None
remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/mlsd_large_512_fp32.pth"
old_modeldir = os.path.dirname(os.path.realpath(__file__))
modeldir = os.path.join(models_path, "mlsd")
def unload_mlsd_model():
global mlsdmodel
if mlsdmodel is not None:
mlsdmodel = mlsdmodel.cpu()
def apply_mlsd(input_image, thr_v, thr_d):
global modelpath, mlsdmodel
if mlsdmodel is None:
modelpath = os.path.join(modeldir, "mlsd_large_512_fp32.pth")
old_modelpath = os.path.join(old_modeldir, "mlsd_large_512_fp32.pth")
if os.path.exists(old_modelpath):
modelpath = old_modelpath
elif not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=modeldir)
mlsdmodel = MobileV2_MLSD_Large()
mlsdmodel.load_state_dict(torch.load(modelpath), strict=True)
mlsdmodel = mlsdmodel.to(devices.get_device_for("controlnet")).eval()
model = mlsdmodel
assert input_image.ndim == 3
img = input_image
img_output = np.zeros_like(img)
try:
with torch.no_grad():
lines = pred_lines(img, model, [img.shape[0], img.shape[1]], thr_v, thr_d)
for line in lines:
x_start, y_start, x_end, y_end = [int(val) for val in line]
cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1)
except Exception as e:
pass
return img_output[:, :, 0]
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