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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 annotator.util import annotator_ckpts_path |
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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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| |
|
| | 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] |
| |
|
