| |
| |
|
|
| import os |
| import torch |
| import numpy as np |
| from einops import rearrange |
| from annotator.pidinet.model import pidinet |
| |
| from ..util_1_1 import annotator_ckpts_path, safe_step |
|
|
|
|
| class PidiNetDetector: |
| def __init__(self): |
| |
| modelpath = os.path.join("./pretrained_models/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] |
|
|
