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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, a=np.pi * 2.0, bg_th=0.1): |
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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_pt = depth.clone() |
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depth_pt -= torch.min(depth_pt) |
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depth_pt /= torch.max(depth_pt) |
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depth_pt = depth_pt.cpu().numpy() |
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depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8) |
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depth_np = depth.cpu().numpy() |
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x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3) |
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y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3) |
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z = np.ones_like(x) * a |
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x[depth_pt < bg_th] = 0 |
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y[depth_pt < bg_th] = 0 |
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normal = np.stack([x, y, z], axis=2) |
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normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5 |
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normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8) |
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return depth_image, normal_image |
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