Spaces:
Running
Running
| import cv2 | |
| import numpy as np | |
| import torch | |
| import os | |
| from einops import rearrange | |
| from annotator.base_annotator import BaseProcessor | |
| from .models.mbv2_mlsd_tiny import MobileV2_MLSD_Tiny | |
| from .models.mbv2_mlsd_large import MobileV2_MLSD_Large | |
| from .utils import pred_lines | |
| 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__)) | |
| class MLSDProcessor(BaseProcessor): | |
| def __init__(self, **kwargs): | |
| super().__init__(**kwargs) | |
| self.model = None | |
| self.model_dir = os.path.join(self.models_path, "mlsd") | |
| def unload_model(self): | |
| if self.model is not None: | |
| self.model = self.model.cpu() | |
| def load_model(self): | |
| model_path = os.path.join(self.model_dir, "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 | |
| if not os.path.exists(model_path): | |
| from basicsr.utils.download_util import load_file_from_url | |
| load_file_from_url(remote_model_path, model_dir=self.model_dir) | |
| mlsdmodel = MobileV2_MLSD_Large() | |
| mlsdmodel.load_state_dict(torch.load(model_path), strict=True) | |
| mlsdmodel = mlsdmodel.to(self.device).eval() | |
| self.model = mlsdmodel | |
| def __call__(self, input_image, thr_v= 0.1, thr_d= 0.1, **kwargs): | |
| # global modelpath, mlsdmodel | |
| if self.model is None: | |
| self.load_model() | |
| assert input_image.ndim == 3 | |
| img = input_image | |
| img_output = np.zeros_like(img) | |
| try: | |
| with torch.no_grad(): | |
| lines = pred_lines(img, self.model, [img.shape[0], img.shape[1]], thr_v, thr_d, self.device) | |
| 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] | |