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import os |
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import cv2 |
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import numpy as np |
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import torch |
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from einops import rearrange |
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from .zoedepth.models.zoedepth.zoedepth_v1 import ZoeDepth |
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from .zoedepth.utils.config import get_config |
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from ...annotator.util import annotator_ckpts_path |
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from huggingface_hub import hf_hub_download |
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class ZoeDetector: |
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def __init__(self): |
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model_path = os.path.join(annotator_ckpts_path, "ZoeD_M12_N.pt") |
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if not os.path.exists(model_path): |
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model_path = hf_hub_download("lllyasviel/Annotators", "ZoeD_M12_N.pt") |
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conf = get_config("zoedepth", "infer") |
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model = ZoeDepth.build_from_config(conf) |
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model.load_state_dict(torch.load(model_path)['model'], strict=False) |
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model = model.cuda() |
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model.device = 'cuda' |
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model.eval() |
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self.model = model |
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def __call__(self, input_image): |
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assert input_image.ndim == 3 |
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image_depth = input_image |
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with torch.no_grad(): |
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image_depth = torch.from_numpy(image_depth).float().cuda() |
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image_depth = image_depth / 255.0 |
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image_depth = rearrange(image_depth, 'h w c -> 1 c h w') |
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depth = self.model.infer(image_depth) |
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depth = depth[0, 0].cpu().numpy() |
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vmin = np.percentile(depth, 2) |
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vmax = np.percentile(depth, 85) |
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depth -= vmin |
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depth /= vmax - vmin |
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depth = 1.0 - depth |
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depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8) |
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return depth_image |
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