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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 .api import MiDaSInference |
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class MidasDetector: |
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def __init__(self): |
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self.model = MiDaSInference(model_type="dpt_hybrid").cuda() |
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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 / 127.5 - 1.0 |
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image_depth = rearrange(image_depth, 'h w c -> 1 c h w') |
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depth = self.model(image_depth)[0] |
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depth -= torch.min(depth) |
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depth /= torch.max(depth) |
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depth = depth.cpu().numpy() |
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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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