109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
# Test align depth images
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# Author: Bingxin Ke
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# Last modified: 2023-12-11
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import numpy as np
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import torch
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from scipy.optimize import minimize
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def inter_distances(tensors):
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"""
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To calculate the distance between each two depth maps.
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"""
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distances = []
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for i, j in torch.combinations(torch.arange(tensors.shape[0])):
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arr1 = tensors[i:i+1]
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arr2 = tensors[j:j+1]
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distances.append(arr1 - arr2)
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dist = torch.concatenate(distances, dim=0)
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return dist
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def ensemble_depths(input_images, regularizer_strength=0.02, max_iter=2, tol=1e-3, reduction='median', max_res=None, disp=False, device='cuda'):
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"""
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To ensemble multiple affine-invariant depth images (up to scale and shift),
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by aligning estimating the scale and shift
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"""
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device = input_images.device
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original_input = input_images.clone()
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n_img = input_images.shape[0]
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ori_shape = input_images.shape
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if max_res is not None:
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scale_factor = torch.min(max_res / torch.tensor(ori_shape[-2:]))
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if scale_factor < 1:
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downscaler = torch.nn.Upsample(scale_factor=scale_factor, mode='nearest')
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input_images = downscaler(torch.from_numpy(input_images)).numpy()
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# init guess
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_min = np.min(input_images.reshape((n_img, -1)).cpu().numpy(), axis=1)
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_max = np.max(input_images.reshape((n_img, -1)).cpu().numpy(), axis=1)
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s_init = 1.0 / (_max - _min).reshape((-1, 1, 1))
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t_init = (-1 * s_init.flatten() * _min.flatten()).reshape((-1, 1, 1))
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x = np.concatenate([s_init, t_init]).reshape(-1)
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input_images = input_images.to(device)
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# objective function
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def closure(x):
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x = x.astype(np.float32)
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l = len(x)
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s = x[:int(l/2)]
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t = x[int(l/2):]
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s = torch.from_numpy(s).to(device)
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t = torch.from_numpy(t).to(device)
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transformed_arrays = input_images * s.view((-1, 1, 1)) + t.view((-1, 1, 1))
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dists = inter_distances(transformed_arrays)
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sqrt_dist = torch.sqrt(torch.mean(dists**2))
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if 'mean' == reduction:
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pred = torch.mean(transformed_arrays, dim=0)
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elif 'median' == reduction:
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pred = torch.median(transformed_arrays, dim=0).values
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else:
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raise ValueError
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near_err = torch.sqrt((0 - torch.min(pred))**2)
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far_err = torch.sqrt((1 - torch.max(pred))**2)
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err = sqrt_dist + (near_err + far_err) * regularizer_strength
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err = err.detach().cpu().numpy()
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return err
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res = minimize(closure, x, method='BFGS', tol=tol, options={'maxiter': max_iter, 'disp': disp})
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x = res.x
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l = len(x)
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s = x[:int(l/2)]
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t = x[int(l/2):]
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# Prediction
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try:
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s = torch.from_numpy(s).to(device)
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t = torch.from_numpy(t).to(device)
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except:
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s = torch.from_numpy(s.astype(np.float32)).to(device)
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t = torch.from_numpy(t.astype(np.float32)).to(device)
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transformed_arrays = original_input * s.view(-1, 1, 1) + t.view(-1, 1, 1)
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if 'mean' == reduction:
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aligned_images = torch.mean(transformed_arrays, dim=0)
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std = torch.std(transformed_arrays, dim=0)
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uncertainty = std
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elif 'median' == reduction:
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aligned_images = torch.median(transformed_arrays, dim=0).values
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# MAD (median absolute deviation) as uncertainty indicator
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abs_dev = torch.abs(transformed_arrays - aligned_images)
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mad = torch.median(abs_dev, dim=0).values
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uncertainty = mad
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else:
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raise ValueError
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# Scale and shift to [0, 1]
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_min = torch.min(aligned_images)
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_max = torch.max(aligned_images)
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aligned_images = (aligned_images - _min) / (_max - _min)
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uncertainty /= (_max - _min)
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return aligned_images, uncertainty
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