experimenting.utils package¶
Submodules¶
experimenting.utils.cv_helpers module¶
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experimenting.utils.cv_helpers.get_heatmaps_steps(xyz, p_mat, width, height)¶ - Args
- xyz :
xyz coordinates as 3XNUM_JOINTS wrt world coord system
- p_mat :
projection matrix from world to image plane
- width :
width of the resulting frame
- height :
height of the resulting frame
- Returns
xyz wrf image coord system, uv image points of skeleton’s joints, uv mask
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experimenting.utils.cv_helpers.decompose_projection_matrix(P)¶ QR decomposition of world2imageplane projection matrix
- Args
- P :
Projection matrix word 2 image plane
- Returns
M matrix, camera matrix
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experimenting.utils.cv_helpers.reproject_xyz_onto_world_coord(xyz, M, invert_z_axis=True)¶ - Parameters
M – World to camera projection matrix
xyz – Skeleton joints as NUM_JOINTSx3
- Returns
Skeleton joints reprojected in world coord system with shape NUM_JOINTSx3
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experimenting.utils.cv_helpers.project_xyz_onto_camera_coord(xyz: torch.Tensor, M: torch.Tensor, invert_z_axis=True) → torch.Tensor¶ - Args
- xyz :
xyz coordinates as NUM_JOINTSx3 wrt world coord
- M :
word2cam EXTRINSIC matrix
- Returns
xyz coordinates projected onto cam coordinates system
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experimenting.utils.cv_helpers.compose_projection_matrix(K, M)¶ Compose intrinsics (K) and extrinsics (M) parameters to get a projection matrix
experimenting.utils.dsntnn module¶
Differentiable DSNT operations for use in PyTorch computation graphs. Author: A. Nibali Url: https://github.com/anibali/margipose License: Apache
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experimenting.utils.dsntnn.js_reg_losses(heatmaps, mu_t, sigma_t)¶ Calculate Jensen-Shannon divergences between heatmaps and target Gaussians.
- Parameters
heatmaps (torch.Tensor) – Heatmaps generated by the model
mu_t (torch.Tensor) – Centers of the target Gaussians (in normalized units)
sigma_t (float) – Standard deviation of the target Gaussians (in pixels)
- Returns
Per-location JS divergences.
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experimenting.utils.dsntnn.dsnt(heatmaps)¶ Differentiable spatial to numerical transform.
- Parameters
heatmaps (torch.Tensor) – Spatial representation of locations
- Returns
Numerical coordinates corresponding to the locations in the heatmaps.
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experimenting.utils.dsntnn.average_loss(losses, mask=None)¶ Calculate the average of per-location losses.
- Parameters
losses (Tensor) – Predictions (B x L)
mask (Tensor, optional) – Mask of points to include in the loss calculation (B x L), defaults to including everything
experimenting.utils.nn_helpers module¶
Integration toolbox for pytorch nn package
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class
experimenting.utils.nn_helpers.FlatSoftmax¶
experimenting.utils.skeleton_helpers module¶
Skeleton wrapper. It provides a toolbox for plotting, projection, normalization, and denormalization of skeletons joints