| 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 annotator.util import annotator_ckpts_path |
|
|
|
|
| remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/mlsd_large_512_fp32.pth" |
|
|
|
|
| class MLSDdetector: |
| def __init__(self): |
| model_path = os.path.join(annotator_ckpts_path, "mlsd_large_512_fp32.pth") |
| 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=annotator_ckpts_path) |
| model = MobileV2_MLSD_Large() |
| model.load_state_dict(torch.load(model_path), strict=True) |
| self.model = model.cuda().eval() |
|
|
| def __call__(self, input_image, thr_v, thr_d): |
| 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) |
| 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] |
|
|
