38 lines
1.5 KiB
Python
38 lines
1.5 KiB
Python
# Pidinet
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# https://github.com/hellozhuo/pidinet
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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 .model import pidinet
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from .util import annotator_ckpts_path, safe_step
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class PidiNetDetector:
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def __init__(self, device):
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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(
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{k.replace('module.', ''): v for k, v in torch.load(modelpath)['state_dict'].items()})
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self.netNetwork.to(device).eval().requires_grad_(False)
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def __call__(self, input_image): # , safe=False):
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return self.netNetwork(input_image)[-1]
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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().cuda()
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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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# 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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