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| # TRI-VIDAR - Copyright 2022 Toyota Research Institute. All rights reserved. | |
| import torch | |
| import torch.nn.functional as tf | |
| def construct_K(fx, fy, cx, cy, dtype=torch.float, device=None): | |
| """Construct a [3,3] camera intrinsics from pinhole parameters""" | |
| return torch.tensor([[fx, 0, cx], | |
| [ 0, fy, cy], | |
| [ 0, 0, 1]], dtype=dtype, device=device) | |
| def scale_intrinsics(K, x_scale, y_scale): | |
| """Scale intrinsics given x_scale and y_scale factors""" | |
| K = K.clone() | |
| K[..., 0, 0] *= x_scale | |
| K[..., 1, 1] *= y_scale | |
| # K[..., 0, 2] = (K[..., 0, 2] + 0.5) * x_scale - 0.5 | |
| # K[..., 1, 2] = (K[..., 1, 2] + 0.5) * y_scale - 0.5 | |
| K[..., 0, 2] = K[..., 0, 2] * x_scale | |
| K[..., 1, 2] = K[..., 1, 2] * y_scale | |
| return K | |
| def invert_intrinsics(K): | |
| """Invert camera intrinsics""" | |
| Kinv = K.clone() | |
| Kinv[:, 0, 0] = 1. / K[:, 0, 0] | |
| Kinv[:, 1, 1] = 1. / K[:, 1, 1] | |
| Kinv[:, 0, 2] = -1. * K[:, 0, 2] / K[:, 0, 0] | |
| Kinv[:, 1, 2] = -1. * K[:, 1, 2] / K[:, 1, 1] | |
| return Kinv | |
| def view_synthesis(ref_image, depth, ref_cam, cam, scene_flow=None, | |
| mode='bilinear', padding_mode='zeros', align_corners=True): | |
| """ | |
| Synthesize an image from another plus a depth map. | |
| Parameters | |
| ---------- | |
| ref_image : torch.Tensor | |
| Reference image to be warped [B,3,H,W] | |
| depth : torch.Tensor | |
| Depth map from the original image [B,1,H,W] | |
| ref_cam : Camera | |
| Camera class for the reference image | |
| cam : Camera | |
| Camera class for the original image | |
| scene_flow : torch.Tensor | |
| Scene flow use for warping [B,3,H,W] | |
| mode : str | |
| Mode for grid sampling | |
| padding_mode : str | |
| Padding mode for grid sampling | |
| align_corners : bool | |
| Corner alignment for grid sampling | |
| Returns | |
| ------- | |
| ref_warped : torch.Tensor | |
| Warped reference image in the original frame of reference [B,3,H,W] | |
| """ | |
| assert depth.shape[1] == 1, 'Depth map should have C=1' | |
| # Reconstruct world points from target_camera | |
| world_points = cam.reconstruct(depth, frame='w', scene_flow=scene_flow) | |
| # Project world points onto reference camera | |
| ref_coords = ref_cam.project(world_points, frame='w') | |
| # View-synthesis given the projected reference points | |
| return tf.grid_sample(ref_image, ref_coords, mode=mode, | |
| padding_mode=padding_mode, align_corners=align_corners) | |
| def view_synthesis_generic(ref_image, depth, ref_cam, cam, | |
| mode='bilinear', padding_mode='zeros', align_corners=True, | |
| progress=0.0): | |
| """ | |
| Synthesize an image from another plus a depth map. | |
| Parameters | |
| ---------- | |
| ref_image : torch.Tensor | |
| Reference image to be warped [B,3,H,W] | |
| depth : torch.Tensor | |
| Depth map from the original image [B,1,H,W] | |
| ref_cam : Camera | |
| Camera class for the reference image | |
| cam : Camera | |
| Camera class for the original image | |
| mode : str | |
| Interpolation mode | |
| padding_mode : str | |
| Padding mode for interpolation | |
| align_corners : bool | |
| Corner alignment for grid sampling | |
| progress : float | |
| Training process (percentage) | |
| Returns | |
| ------- | |
| ref_warped : torch.Tensor | |
| Warped reference image in the original frame of reference [B,3,H,W] | |
| """ | |
| assert depth.shape[1] == 1, 'Depth map should have C=1' | |
| # Reconstruct world points from target_camera | |
| world_points = cam.reconstruct(depth, frame='w') | |
| # Project world points onto reference camera | |
| ref_coords = ref_cam.project(world_points, progress=progress, frame='w') | |
| # View-synthesis given the projected reference points | |
| return tf.grid_sample(ref_image, ref_coords, mode=mode, | |
| padding_mode=padding_mode, align_corners=align_corners) | |