famudy-data 0.0.7

<<<ENTER_DESCRIPTION>>>

famudy-data

Light-weight Python access to multi-view data from NeRSemble dataset.
The main data resides on the cluster in /cluster/doriath/tkirschstein/data/famudy/full

1. Installation

pip install git+ssh://[e-mail]/tobias-kirschstein/famudy-data.git

2. Setup

Create a file in your home directory: ~/.config/famudy/.env
with content:

FAMUDY_PROCESSED_CAPTURE_DATA_PATH_NEW="/cluster/doriath/tkirschstein/data/famudy/full"

Additionally, if you mounted rohan locally and want to access the data from your local machine, use another environment variable:

FAMUDY_REMOTE_PROCESSED_CAPTURE_DATA_PATH_NEW="<<<PATH_TO_LOCAL_ROHAN_MOUNT>>>/cluster/doriath/tkirschstein/data/famudy/full"

For example, using WSL2 on Wndows with rohan mounted into /mnt/rohan, <<<PATH_TO_LOCAL_ROHAN_MOUNT>>> would be //wsl.localhost/Ubuntu/mnt/.

Usage

from famudy.data import FamudySequenceDataManager

participant_id = ...
sequence_name = ...

data_manager = FamudySequenceDataManager(participant_id, sequence_name, downscale_factor=2)

Images

timestep = ...
cam_id = ...

image = data_manager.load_image(timestep, cam_id)

Pointclouds, Depth maps or Normal maps

timestep = ...
cam_id = ...

depth_map = data_manager.load_depth_map(timestep, cam_id, n_cameras=16)
normal_map = data_manager.load_normal_map(timestep, cam_id, n_cameras=16)
points, colors, normals = data_manager.load_point_cloud(timestep, n_cameras=16)
# n_cameras=16 indicates that COLMAP was ran on all 16 cameras

Background masks

timestep = ...
cam_id = ...

alpha_map = data_manager.load_robust_matting_alpha_image(timestep, cam_id)

Camera extrinsics/intrinsics

Extrinsics are in OpenCV format and world2cam:

world_2_cam_poses = data_manager.load_calibration_result().params_result.get_poses()
intrinsics = data_manager.load_calibration_result().params_result.get_intrinsics()

Note: the intrinsics is given wrt to the full resolution recordings (2200x3208). However, in the interest of storage, images and depth maps are downscaled by a factor of 2 (yielding 1100x1604). Hence, the intrinsics most likely needs to be rescaled as well by intrinsics.rescale(0.5).

Segmentation masks

timestep = ...
cam_id = ...

segmentation_mask = data_manager.load_bisenet_segmentation_mask(timestep, cam_id)

Landmarks

The most reliable landmarks are those detected from the front camera (serial = 222200037 / cam_id = 8)

cam_id = ...
landmarks = data_manager.load_2d_landmarks(cam_id)

FLAME tracking

flame_params = data_manager.load_3DMM_tracking("FLAME2023_v2")