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A tiny PyTorch library for depth map manipulations

Project description

Dungeon Maps

A tiny PyTorch library for depth map manipulations.

Version: 0.0.3a1

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_check_mark:

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

Use case

One of the real case is to use this package along with the Habitat Lab to render the semantic topdown maps.

Installation

Basic requirements:

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]

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