A tiny PyTorch library for depth map manipulations
Project description
Dungeon Maps
A tiny PyTorch library for depth map manipulations.
Version: 0.0.2a2
Features
Batching | Multi-channels | GPU acceleration | |
---|---|---|---|
Orthographic projection (Top-down map) |
:heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
Egocentric motion flow | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
3D affine transformation (Camera space) |
:heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
2D affine transformation (Top-down view) |
:heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
Map builder | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: |
Demos
Orthographic projection
Depth maps
(Watch this video in high quality)
This example shows how to project depth maps to top-down maps (plan view).
- Top left: RGB
- Top right: Depth map
- Bottom left: top-down maps in local space
- Bottom right: top-down maps in global space
Run this example
python -m dungeon_maps.demos.height_map.run
Control: W
, A
, S
, D
. Q
for exit
Semantic segmentations
(Watch this video in height quality)
This example shows how to project arbitrary value maps, e.g. semantic segmentation, to top-down maps (plan view).
- Top left: RGB
- Top center: Depth map
- Top right: Semantic segmentation
- Bottom left: top-down maps in local space
- Bottom right: top-down maps in global space
Run this example
python -m dungeon_maps.demos.object_map.run
Control: W
, A
, S
, D
. Q
for exit
Egocentric motion flow
(Watch this video in high quality)
This example shows how to calculate the flow fields caused by camera motion.
Run this example
python -m dungeon_maps.demos.ego_flow.run
Installation
Basic requirements:
- Python >= 3.6
- PyTorch >= 1.8.0
- PyTorch Scatter
Install from pip
pip install dungeon_maps
Install from GitHub repo
pip install git+https://github.com.Ending2015a/dungeon_map.git@master
Install demos
pip install dungeon_maps[sim]
Project details
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