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