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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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