peize-liu / mono_dataset_code

Code for Monocular Visual Odometry Dataset - https://vision.cs.tum.edu/data/datasets/mono-dataset

How to use Docker image

download from sample_data_set

Step 1

Build local image first with command docker build -t photometric_calibration:latest -f ./photomatric_calibration.dockerfile . in current dir.

Step 2

Modify ./start_docker_container.sh with your local dir path (defualt setting is for my workstation!!!)

Run with paramter ./start_docker_container.sh 0 to run for application, everything you modify will not be saved ./start_docker_container.sh 1 run for development, you can modify code for your own application all files in this dir will be saved

Step 3 Response function calibration

1. prepare your dataset like

   

2. camera.txt:

3. run ./responseCalib get calibration result (pcalib.txt)

Step 4 Vig calibration

1. prepare your dataset like (./data/vig_calibration)

2. run calibration

refer to ./data/response_calibration for more clear file tree

Install

1. Install Eigen & OpenCV (if you don't have it):

sudo apt-get install libeigen3-dev libopencv-dev

2. install ziplib:

sudo apt-get install zlib1g-dev
cd thirdparty
tar -zxvf libzip-1.1.1.tar.gz
cd libzip-1.1.1/
./configure
make
sudo make install
sudo cp lib/zipconf.h /usr/local/include/zipconf.h   # (no idea why that is needed).

3. install aruco marker detection (optional - only required for vignette calibration):

see eg here:

http://maztories.blogspot.de/2013/07/installing-aruco-augmented-reality.html 

tested with version 1.3.0. which is included in /thirdparty.

4. Build

cmake . && make

Usage: C++ code

playbackDataset: read images, photometric undistortion & rectification.

Shows images of a dataset. Meant as example code regarding how to read the dataset. Run with (and replace X with the location of the dataset. Mind the trailing slash):

./playDataset X/sequence_01/  

optionally, the calibration is used for

• rectification ( r )
• response function inversion ( g )
• vignette removal ( v )
• removal of over-exposed (white) images. ( o ).

Pressing the respective key toggles the option. See code for details.

responseCalib: calibrate response function.

Performs photometric calibration from a set of images, showing the exact same scene at different exposures. Run with (and replace X with the location of the dataset. Mind the trailing slash):

./responseCalib X/CalibrationDatasets/narrow_sweep1/"

outputs some intermediate results, and pcalib.txt containing the calibrated inverse response function to ./photoCalibResult. See code for details.

vignetteCalib: calibrate vignette.

Performs photometric calibration from a set of images, showing a flat surface with an ARMarker. Run with (and replace X with the location of the dataset. Mind the trailing slash):

./vignetteCalib X/CalibrationDatasets/narrow_vignette/"

outputs some intermediate results, and vignette.png (16-bit png) containing the calibrated vignette function. vignetteSmoothed.png is a slightly smoothed version, mainly to remove the black borders (pixels at the border are never observed). See code for details. WARNING: requires a lot of Memory (16GB ram for 1000 input images)! Can easily be changed at the cost of slightly slower runtime... you'll have to do that yourself though.

Usage: Matlab evaluation code

Implements Sim(3) alignment of a tracked trajectory to the ground-truth segments, and subsequent computation of the different error values. See MatlabEvaluationCode/Example.m for an example, and some documentation regarding the computed values. Further, we include example results computed with DSO for all 50 sequences.

ArucoTag

Original Tag No 213

License

The code provided in this repository is licensed under the BSD license.