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| # -*- coding: utf-8 -*- | |
| # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is | |
| # holder of all proprietary rights on this computer program. | |
| # You can only use this computer program if you have closed | |
| # a license agreement with MPG or you get the right to use the computer | |
| # program from someone who is authorized to grant you that right. | |
| # Any use of the computer program without a valid license is prohibited and | |
| # liable to prosecution. | |
| # | |
| # Copyright©2019 Max-Planck-Gesellschaft zur Förderung | |
| # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute | |
| # for Intelligent Systems. All rights reserved. | |
| # | |
| # Contact: ps-license@tuebingen.mpg.de | |
| import torch | |
| def index(feat, uv): | |
| ''' | |
| :param feat: [B, C, H, W] image features | |
| :param uv: [B, 2, N] uv coordinates in the image plane, range [0, 1] | |
| :return: [B, C, N] image features at the uv coordinates | |
| ''' | |
| uv = uv.transpose(1, 2) # [B, N, 2] | |
| (B, N, _) = uv.shape | |
| C = feat.shape[1] | |
| if uv.shape[-1] == 3: | |
| # uv = uv[:,:,[2,1,0]] | |
| # uv = uv * torch.tensor([1.0,-1.0,1.0]).type_as(uv)[None,None,...] | |
| uv = uv.unsqueeze(2).unsqueeze(3) # [B, N, 1, 1, 3] | |
| else: | |
| uv = uv.unsqueeze(2) # [B, N, 1, 2] | |
| # NOTE: for newer PyTorch, it seems that training results are degraded due to implementation diff in F.grid_sample | |
| # for old versions, simply remove the aligned_corners argument. | |
| samples = torch.nn.functional.grid_sample( | |
| feat, uv, align_corners=True) # [B, C, N, 1] | |
| return samples.view(B, C, N) # [B, C, N] | |
| def orthogonal(points, calibrations, transforms=None): | |
| ''' | |
| Compute the orthogonal projections of 3D points into the image plane by given projection matrix | |
| :param points: [B, 3, N] Tensor of 3D points | |
| :param calibrations: [B, 3, 4] Tensor of projection matrix | |
| :param transforms: [B, 2, 3] Tensor of image transform matrix | |
| :return: xyz: [B, 3, N] Tensor of xyz coordinates in the image plane | |
| ''' | |
| rot = calibrations[:, :3, :3] | |
| trans = calibrations[:, :3, 3:4] | |
| pts = torch.baddbmm(trans, rot, points) # [B, 3, N] | |
| if transforms is not None: | |
| scale = transforms[:2, :2] | |
| shift = transforms[:2, 2:3] | |
| pts[:, :2, :] = torch.baddbmm(shift, scale, pts[:, :2, :]) | |
| return pts | |
| def perspective(points, calibrations, transforms=None): | |
| ''' | |
| Compute the perspective projections of 3D points into the image plane by given projection matrix | |
| :param points: [Bx3xN] Tensor of 3D points | |
| :param calibrations: [Bx3x4] Tensor of projection matrix | |
| :param transforms: [Bx2x3] Tensor of image transform matrix | |
| :return: xy: [Bx2xN] Tensor of xy coordinates in the image plane | |
| ''' | |
| rot = calibrations[:, :3, :3] | |
| trans = calibrations[:, :3, 3:4] | |
| homo = torch.baddbmm(trans, rot, points) # [B, 3, N] | |
| xy = homo[:, :2, :] / homo[:, 2:3, :] | |
| if transforms is not None: | |
| scale = transforms[:2, :2] | |
| shift = transforms[:2, 2:3] | |
| xy = torch.baddbmm(shift, scale, xy) | |
| xyz = torch.cat([xy, homo[:, 2:3, :]], 1) | |
| return xyz | |