kornia.geometry.depth#

kornia.geometry.depth.depth_from_disparity(disparity, baseline, focal)[source]#

Computes depth from disparity.

Parameters:

  • disparity (Tensor) – Disparity tensor of shape \((*, H, W)\).

  • baseline (Union[float, Tensor]) – float/tensor containing the distance between the two lenses.

  • focal (Union[float, Tensor]) – float/tensor containing the focal length.

Return type:

Tensor

Returns:

Depth map of the shape \((*, H, W)\).

Example

>>> disparity = torch.rand(4, 1, 4, 4)
>>> baseline = torch.rand(1)
>>> focal = torch.rand(1)
>>> depth_from_disparity(disparity, baseline, focal).shape
torch.Size([4, 1, 4, 4])
kornia.geometry.depth.depth_to_3d(depth, camera_matrix, normalize_points=False)[source]#

Compute a 3d point per pixel given its depth value and the camera intrinsics.

Parameters:

  • depth (Tensor) – image tensor containing a depth value per pixel with shape \((B, 1, H, W)\).

  • camera_matrix (Tensor) – tensor containing the camera intrinsics with shape \((B, 3, 3)\).

  • normalize_points (bool, optional) – whether to normalise the pointcloud. This must be set to True when the depth is represented as the Euclidean ray length from the camera position. Default: False

Return type:

Tensor

Returns:

tensor with a 3d point per pixel of the same resolution as the input \((B, 3, H, W)\).

Example

>>> depth = torch.rand(1, 1, 4, 4)
>>> K = torch.eye(3)[None]
>>> depth_to_3d(depth, K).shape
torch.Size([1, 3, 4, 4])
kornia.geometry.depth.depth_to_normals(depth, camera_matrix, normalize_points=False)[source]#

Compute the normal surface per pixel.

Parameters:

  • depth (Tensor) – image tensor containing a depth value per pixel with shape \((B, 1, H, W)\).

  • camera_matrix (Tensor) – tensor containing the camera intrinsics with shape \((B, 3, 3)\).

  • normalize_points (bool, optional) – whether to normalise the pointcloud. This must be set to True when the depth is Default: False

  • position. (represented as the Euclidean ray length from the camera) –

Return type:

Tensor

Returns:

tensor with a normal surface vector per pixel of the same resolution as the input \((B, 3, H, W)\).

Example

>>> depth = torch.rand(1, 1, 4, 4)
>>> K = torch.eye(3)[None]
>>> depth_to_normals(depth, K).shape
torch.Size([1, 3, 4, 4])
kornia.geometry.depth.warp_frame_depth(image_src, depth_dst, src_trans_dst, camera_matrix, normalize_points=False)[source]#

Warp a tensor from a source to destination frame by the depth in the destination.

Compute 3d points from the depth, transform them using given transformation, then project the point cloud to an image plane.

Parameters:

  • image_src (Tensor) – image tensor in the source frame with shape \((B,D,H,W)\).

  • depth_dst (Tensor) – depth tensor in the destination frame with shape \((B,1,H,W)\).

  • src_trans_dst (Tensor) – transformation matrix from destination to source with shape \((B,4,4)\).

  • camera_matrix (Tensor) – tensor containing the camera intrinsics with shape \((B,3,3)\).

  • normalize_points (bool, optional) – whether to normalise the pointcloud. This must be set to True when the depth is represented as the Euclidean ray length from the camera position. Default: False

Return type:

Tensor

Returns:

the warped tensor in the source frame with shape \((B,3,H,W)\).