# -*- 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