# png_to_klg

**Repository Path**: LiAker/png_to_klg

## Basic Information

- **Project Name**: png_to_klg
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-07-01
- **Last Updated**: 2021-03-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![DOI](https://zenodo.org/badge/72743088.svg)](https://zenodo.org/badge/latestdoi/72743088)

You can cite by
```
@misc{jacky_liu_2018_1419222,
  author    = {Tse-An (Jacky) Liu},
  title     = {png to klg}
  version   = {1.0},
  publisher = {Zenodo},
  month     = Aug,
  year      = 2018,
  doi       = {10.5281/zenodo.1419222},
  url       = {https://github.com/HTLife/png_to_klg}
}
```
> docker image is provided here https://github.com/HTLife/png_to_klg-docker 

> converted klg could be download from [here](https://drive.google.com/drive/folders/1nen4wcKzB1PKhz9MiA13rPCS0QzOMwBU?usp=sharing)

# png to klg format convertor for ElasticFusion

Convert [TUM RGB-D png dataset](http://vision.in.tum.de/data/datasets/rgbd-dataset/download#freiburg1_desk) to .klg format for [Kintinuous](https://github.com/mp3guy/Kintinuous) and [ElasticFusion](https://github.com/mp3guy/ElasticFusion)

Also work with [ICL-NUIM dataset](https://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html) (TUM RGB-D Compatible PNGs)

# What do I need to build it?

  - Ubuntu 14.04, 15.04 or 16.04 (Though many other linux distros will work fine)
  - CMake
  - Boost
  - zlib
  - libjpeg
  - [OpenCV](http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/2.4.9/opencv-2.4.9.zip)

```bash
sudo apt-get install g++ cmake cmake-gui libboost-all-dev build-essential

wget http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/2.4.9/opencv-2.4.9.zip
unzip opencv-2.4.9.zip
cd opencv-2.4.9.zip
mkdir build
cd build
cmake -D BUILD_NEW_PYTHON_SUPPORT=OFF -D WITH_OPENCL=OFF -D WITH_OPENMP=ON -D INSTALL_C_EXAMPLES=OFF -D BUILD_DOCS=OFF -D BUILD_EXAMPLES=OFF -D WITH_QT=OFF -D WITH_OPENGL=OFF -D WITH_VTK=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_TESTS=OFF -D WITH_CUDA=OFF -D BUILD_opencv_gpu=OFF ..
make -j8
sudo make install
```
  
## Python package
  - numpy

```bash
sudo apt-get install pip
pip install numpy
```


# Build #
```bash
cd ./pngtoklg
mkdir build
cd build
cmake ..
make
```


# Usage #
## Parameters ##
All parameters are required.
- -w working directory
- -o output file name (the output file will be place under working directory)
- -r associations.txt is in reverse order (rgb)(depth)
- -t TUM format / defualt format is ICL-NUIM
- -s Scale factor in floating point.  default=5000

>For more **scale factor** detail, please reference: 
>http://vision.in.tum.de/data/datasets/rgbd-dataset/file_formats#intrinsic_camera_calibration_of_the_kinect

## Prerequirement##
Should place **associations.txt** under working directory.
About how to generate associations.txt please read "Related files" section.

## Download ##
### ICL-NUIM ###
Download the file provided by [ICL-NUIM](https://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html)
(Living Room 'lr kt0') => (TUM RGB-D Compatible PNGs)

### TUM ###
 [TUM RGB-D png dataset](http://vision.in.tum.de/data/datasets/rgbd-dataset/download#freiburg1_desk)
 
## Convert TUM dataset ##
```bash
./pngtoklg -w '/TUM/rgbd_dataset_freiburg2_desk/' -o '/TUM/rgbd_dataset_freiburg2_desk/fr2desk.klg' -t
```
After execute the command above, "/TUM/rgbd_dataset_freiburg2_desk" folder should contain fr2desk.klg file (about 4.4GB).

Run with ElasticFusion
```bash
./ElasticFusion -l (path to fr2desk.klg) -d 12 -c 3 -f
```

## Convert ICL-NUIM dataset
remove -t option which is stand for tum
```bash
./pngtoklg -w '/iclnuim/living_room_traj0_frei_png/' -o '/iclnuim/living_room_traj0_frei_png/liv.klg'
```
http://www.doc.ic.ac.uk/~ahanda/living_room_traj0_frei_png.tar.gz
# Related files #


### rgb.txt format ###
One row contain two informations.
First is time sequence.
Actually the time is not important.  We only need increasing number sequence.
```
timeSequence filePath
```
Sample file content
```
0.033333 ./rgb/scene_00_0000_rs.png
0.066666 ./rgb/scene_00_0001_rs.png
0.099999 ./rgb/scene_00_0002_rs.png
0.133332 ./rgb/scene_00_0003_rs.png
0.166665 ./rgb/scene_00_0004_rs.png
0.199998 ./rgb/scene_00_0005_rs.png
...
```
## associate.py ##
> This code is developed by TUM, which use to associate rgb.txt and depth.txt

Type the following command

**(Be aware you should remain the order of parameters, feed the depth file first and then rgb file.)**
```bash
python associate.py PATH_TO_SEQUENCE/depth.txt PATH_TO_SEQUENCE/rgb.txt > associations.txt
```

Sample file content (TUM RGB-D dataset format)
If you are using ICL-NUIM, the timestamp will be integer number
```
0.033333 ./depth/scene_00_0000_rs.png 0.033333 ./rgb/scene_00_0000_rs.png
0.066666 ./depth/scene_00_0001_rs.png 0.066666 ./rgb/scene_00_0001_rs.png
0.099999 ./depth/scene_00_0002_rs.png 0.099999 ./rgb/scene_00_0002_rs.png
0.133332 ./depth/scene_00_0003_rs.png 0.133332 ./rgb/scene_00_0003_rs.png
0.166665 ./depth/scene_00_0004_rs.png 0.166665 ./rgb/scene_00_0004_rs.png
0.199998 ./depth/scene_00_0005_rs.png 0.199998 ./rgb/scene_00_0005_rs.png
```