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