| | |
| | |
| |
|
| | import os |
| | import torch |
| | import numpy as np |
| | from einops import rearrange |
| | from annotator.pidinet.model import pidinet |
| | from annotator.util import annotator_ckpts_path, safe_step |
| |
|
| |
|
| | class PidiNetDetector: |
| | def __init__(self): |
| | remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/table5_pidinet.pth" |
| | modelpath = os.path.join(annotator_ckpts_path, "table5_pidinet.pth") |
| | if not os.path.exists(modelpath): |
| | from basicsr.utils.download_util import load_file_from_url |
| | load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path) |
| | self.netNetwork = pidinet() |
| | self.netNetwork.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(modelpath)['state_dict'].items()}) |
| | self.netNetwork = self.netNetwork.cuda() |
| | self.netNetwork.eval() |
| |
|
| | def __call__(self, input_image, safe=False): |
| | assert input_image.ndim == 3 |
| | input_image = input_image[:, :, ::-1].copy() |
| | with torch.no_grad(): |
| | image_pidi = torch.from_numpy(input_image).float().cuda() |
| | image_pidi = image_pidi / 255.0 |
| | image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w') |
| | edge = self.netNetwork(image_pidi)[-1] |
| | edge = edge.cpu().numpy() |
| | if safe: |
| | edge = safe_step(edge) |
| | edge = (edge * 255.0).clip(0, 255).astype(np.uint8) |
| | return edge[0][0] |
| |
|
