# Copyright 2020 Toyota Research Institute. All rights reserved.
import torch
import torch.nn.functional as funct
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[docs]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)
[docs]def scale_intrinsics(K, x_scale, y_scale):
"""Scale intrinsics given x_scale and y_scale factors"""
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
return K
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[docs]def view_synthesis(ref_image, depth, ref_cam, cam,
mode='bilinear', padding_mode='zeros'):
"""
Synthesize an image from another plus a depth map.
Parameters
----------
ref_image : torch.Tensor [B,3,H,W]
Reference image to be warped
depth : torch.Tensor [B,1,H,W]
Depth map from the original image
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
Returns
-------
ref_warped : torch.Tensor [B,3,H,W]
Warped reference image in the original frame of reference
"""
assert depth.size(1) == 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, frame='w')
# View-synthesis given the projected reference points
return funct.grid_sample(ref_image, ref_coords, mode=mode,
padding_mode=padding_mode, align_corners=True)
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