Depth
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packnet_sfm.utils.depth.calc_smoothness(inv_depths, images, num_scales)[source] Calculate smoothness values for inverse depths
- Parameters
inv_depths (list of torch.Tensor [B,1,H,W]) – Inverse depth maps
images (list of torch.Tensor [B,3,H,W]) – Inverse depth maps
num_scales (int) – Number of scales considered
- Returns
smoothness_x (list of torch.Tensor [B,1,H,W]) – Smoothness values in direction x
smoothness_y (list of torch.Tensor [B,1,H,W]) – Smoothness values in direction y
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packnet_sfm.utils.depth.compute_depth_metrics(config, gt, pred, use_gt_scale=True)[source] Compute depth metrics from predicted and ground-truth depth maps
- Parameters
config (CfgNode) – Metrics parameters
gt (torch.Tensor [B,1,H,W]) – Ground-truth depth map
pred (torch.Tensor [B,1,H,W]) – Predicted depth map
use_gt_scale (bool) – True if ground-truth median-scaling is to be used
- Returns
metrics – Depth metrics (abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3)
- Return type
torch.Tensor [7]
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packnet_sfm.utils.depth.depth2inv(depth)[source] Invert a depth map to produce an inverse depth map
- Parameters
depth (torch.Tensor or list of torch.Tensor [B,1,H,W]) – Depth map
- Returns
inv_depth – Inverse depth map
- Return type
torch.Tensor or list of torch.Tensor [B,1,H,W]
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packnet_sfm.utils.depth.fuse_inv_depth(inv_depth, inv_depth_hat, method='mean')[source] Fuse inverse depth and flipped inverse depth maps
- Parameters
inv_depth (torch.Tensor [B,1,H,W]) – Inverse depth map
inv_depth_hat (torch.Tensor [B,1,H,W]) – Flipped inverse depth map produced from a flipped image
method (str) – Method that will be used to fuse the inverse depth maps
- Returns
fused_inv_depth – Fused inverse depth map
- Return type
torch.Tensor [B,1,H,W]
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packnet_sfm.utils.depth.inv2depth(inv_depth)[source] Invert an inverse depth map to produce a depth map
- Parameters
inv_depth (torch.Tensor or list of torch.Tensor [B,1,H,W]) – Inverse depth map
- Returns
depth – Depth map
- Return type
torch.Tensor or list of torch.Tensor [B,1,H,W]
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packnet_sfm.utils.depth.inv_depths_normalize(inv_depths)[source] Inverse depth normalization
- Parameters
inv_depths (list of torch.Tensor [B,1,H,W]) – Inverse depth maps
- Returns
norm_inv_depths – Normalized inverse depth maps
- Return type
list of torch.Tensor [B,1,H,W]
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packnet_sfm.utils.depth.post_process_inv_depth(inv_depth, inv_depth_flipped, method='mean')[source] Post-process an inverse and flipped inverse depth map
- Parameters
inv_depth (torch.Tensor [B,1,H,W]) – Inverse depth map
inv_depth_flipped (torch.Tensor [B,1,H,W]) – Inverse depth map produced from a flipped image
method (str) – Method that will be used to fuse the inverse depth maps
- Returns
inv_depth_pp – Post-processed inverse depth map
- Return type
torch.Tensor [B,1,H,W]
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packnet_sfm.utils.depth.viz_inv_depth(inv_depth, normalizer=None, percentile=95, colormap='plasma', filter_zeros=False)[source] Converts an inverse depth map to a colormap for visualization.
- Parameters
inv_depth (torch.Tensor [B,1,H,W]) – Inverse depth map to be converted
normalizer (float) – Value for inverse depth map normalization
percentile (float) – Percentile value for automatic normalization
colormap (str) – Colormap to be used
filter_zeros (bool) – If True, do not consider zero values during normalization
- Returns
colormap – Colormap generated from the inverse depth map
- Return type
np.array [H,W,3]