A set of tools to pre-calibrate and calibrate (multi-focus) plenoptic cameras (e.g., a Raytrix R12) based on the libpleno.

Overview

banner-logo


COMPOTE: Calibration Of Multi-focus PlenOpTic camEra.

COMPOTE is a set of tools to pre-calibrate and calibrate (multifocus) plenoptic cameras (e.g., a Raytrix R12) based on the libpleno.

Quick Start

Pre-requisites

The COMPOTE applications have a light dependency list:

  • boost version 1.54 and up, portable C++ source libraries,
  • libpleno, an open-souce C++ library for plenoptic camera,

and was compiled and tested on:

  • Ubuntu 18.04.4 LTS, GCC 7.5.0, with Eigen 3.3.4, Boost 1.65.1, and OpenCV 3.2.0.

Compilation & Test

If you are comfortable with Linux and CMake and have already installed the prerequisites above, the following commands should compile the applications on your system.

mkdir build && cd build
cmake ..
make -j6

To test the calibrate application you can use the example script from the build directory:

./../example/run_calibration.sh

Applications

Configuration

All applications use .js (json) configuration file. The path to this configuration files are given in the command line using boost program options interface.

Options:

short long default description
-h --help Print help messages
-g --gui true Enable GUI (image viewers, etc.)
-v --verbose true Enable output with extra information
-l --level ALL (15) Select level of output to print (can be combined): NONE=0, ERR=1, WARN=2, INFO=4, DEBUG=8, ALL=15
-i --pimages Path to images configuration file
-c --pcamera Path to camera configuration file
-p --pparams "internals.js" Path to camera internal parameters configuration file
-s --pscene Path to scene configuration file
-f --features "observations.bin.gz" Path to observations file
-e --extrinsics "extrinsics.js" Path to save extrinsics parameters file
-o --output "intrinsics.js" Path to save intrinsics parameters file

For instance to run calibration:

./calibrate -i images.js -c camera.js -p params.js -f observations.bin.gz -s scene.js -g true -l 7

Configuration file examples are given for the dataset R12-A in the folder examples/.

Pre-calibration

precalibrate uses whites raw images taken at different aperture to calibrate the Micro-Images Array (MIA) and computes the internal parameters used to initialize the camera and to detect the Blur Aware Plenoptic (BAP) features.

Requirements: minimal camera configuration, white images. Output: radii statistics (.csv), internal parameters, initial camera parameters.

Features Detection

detect extracts the newly introduced Blur Aware Plenoptic (BAP) features in checkerboard images.

Requirements: calibrated MIA, internal parameters, checkerboard images, and scene configuration. Output: micro-image centers and BAP features.

Camera Calibration

calibrate runs the calibration of the plenoptic camera (set I=0 to act as pinholes array, or I>0 for multifocus case). It generates the intrinsics and extrinsics parameters.

Requirements: calibrated MIA, internal parameters, features and scene configuration. If none are given all steps are re-done. Output: error statistics, calibrated camera parameters, camera poses.

Extrinsics Estimation & Calibration Evaluation

extrinsics runs the optimization of extrinsics parameters given a calibrated camera and generates the poses.

Requirements: internal parameters, features, calibrated camera and scene configuration. Output: error statistics, estimated poses.

COMPOTE also provides two applications to run stats evaluation on the optimized poses optained with a constant step linear translation along the z-axis:

  • linear_evaluation gives the absolute errors (mean + std) and the relative errors (mean + std) of translation of the optimized poses,
  • linear_raytrix_evaluation takes .xyz pointcloud obtained by Raytrix calibration software and gives the absolute errors (mean + std) and the relative errors (mean + std) of translation.

Note: those apps are legacy and have been moved and generalized in the [BLADE] app's evaluate.

Blur Proportionality Coefficient Calibration

blurcalib runs the calibration of the blur proportionality coefficient kappa linking the spread parameter of the PSF with the blur radius. It updates the internal parameters with the optimized value of kappa.

Requirements: internal parameters, features and images. Output: internal parameters.

Datasets

Datasets R12-A, R12-B and R12-C can be downloaded from here. The dataset R12-D, and the simulated unfocused plenoptic camera dataset UPC-S are also available from here.

Citing

If you use COMPOTE or libpleno in an academic context, please cite the following publication:

@inproceedings{labussiere2020blur,
  title 	=	{Blur Aware Calibration of Multi-Focus Plenoptic Camera},
  author	=	{Labussi{\`e}re, Mathieu and Teuli{\`e}re, C{\'e}line and Bernardin, Fr{\'e}d{\'e}ric and Ait-Aider, Omar},
  booktitle	=	{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages		=	{2545--2554},
  year		=	{2020}
}

License

COMPOTE is licensed under the GNU General Public License v3.0. Enjoy!


Owner
ComSEE - Computers that SEE
Computer Vision research team of the Image, Systems of Perception and Robotics (ISPR) department of the Institut Pascal.
ComSEE - Computers that SEE
Code for paper Adaptively Aligned Image Captioning via Adaptive Attention Time

Adaptively Aligned Image Captioning via Adaptive Attention Time This repository includes the implementation for Adaptively Aligned Image Captioning vi

Lun Huang 45 Aug 27, 2022
Variational autoencoder for anime face reconstruction

VAE animeface Variational autoencoder for anime face reconstruction Introduction This repository is an exploratory example to train a variational auto

Minzhe Zhang 2 Dec 11, 2021
MediaPipeのPythonパッケージのサンプルです。2020/12/11時点でPython実装のある4機能(Hands、Pose、Face Mesh、Holistic)について用意しています。

mediapipe-python-sample MediaPipeのPythonパッケージのサンプルです。 2020/12/11時点でPython実装のある以下4機能について用意しています。 Hands Pose Face Mesh Holistic Requirement mediapipe 0.

KazuhitoTakahashi 217 Dec 12, 2022
Unofficial pytorch implementation of paper "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing"

One-Shot Free-View Neural Talking Head Synthesis Unofficial pytorch implementation of paper "One-Shot Free-View Neural Talking-Head Synthesis for Vide

ZLH 406 Dec 23, 2022
PyTorch Implementation of PIXOR: Real-time 3D Object Detection from Point Clouds

PIXOR: Real-time 3D Object Detection from Point Clouds This is a custom implementation of the paper from Uber ATG using PyTorch 1.0. It represents the

Philip Huang 270 Dec 14, 2022
[ICCV21] Code for RetrievalFuse: Neural 3D Scene Reconstruction with a Database

RetrievalFuse Paper | Project Page | Video RetrievalFuse: Neural 3D Scene Reconstruction with a Database Yawar Siddiqui, Justus Thies, Fangchang Ma, Q

Yawar Nihal Siddiqui 75 Dec 22, 2022
Pairwise model for commonlit competition

Pairwise model for commonlit competition To run: - install requirements - create input directory with train_folds.csv and other competition data - cd

abhishek thakur 45 Aug 31, 2022
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

Hannes Stärk 355 Jan 03, 2023
SPT_LSA_ViT - Implementation for Visual Transformer for Small-size Datasets

Vision Transformer for Small-Size Datasets Seung Hoon Lee and Seunghyun Lee and Byung Cheol Song | Paper Inha University Abstract Recently, the Vision

Lee SeungHoon 87 Jan 01, 2023
ICCV2021: Code for 'Spatial Uncertainty-Aware Semi-Supervised Crowd Counting'

ICCV2021: Code for 'Spatial Uncertainty-Aware Semi-Supervised Crowd Counting'

Yanda Meng 14 May 13, 2022
Official PyTorch Implementation of Hypercorrelation Squeeze for Few-Shot Segmentation, arXiv 2021

Hypercorrelation Squeeze for Few-Shot Segmentation This is the implementation of the paper "Hypercorrelation Squeeze for Few-Shot Segmentation" by Juh

Juhong Min 165 Dec 28, 2022
code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"

Code for our ECCV (2020) paper A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation. Prerequisites: python == 3.6.8 pytorch ==1.1.0

32 Nov 27, 2022
GDSC-ML Team Interview Task

GDSC-ML-Team---Interview-Task Task 1 : Clean or Messy room In this task we have to classify the given test images as clean or messy. - Link for datase

Aayush. 1 Jan 19, 2022
[CVPR2021] De-rendering the World's Revolutionary Artefacts

De-rendering the World's Revolutionary Artefacts Project Page | Video | Paper In CVPR 2021 Shangzhe Wu1,4, Ameesh Makadia4, Jiajun Wu2, Noah Snavely4,

49 Nov 06, 2022
The FIRST GANs-based omics-to-omics translation framework

OmiTrans Please also have a look at our multi-omics multi-task DL freamwork 👀 : OmiEmbed The FIRST GANs-based omics-to-omics translation framework Xi

Xiaoyu Zhang 6 Dec 14, 2022
Pca-on-genotypes - Mini bioinformatics project - PCA on genotypes

Mini bioinformatics project: PCA on genotypes This repo contains the code from t

Maria Nattestad 8 Dec 04, 2022
A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥

Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥 Click on Use this template to initialize new re

Hyunsoo Cho 1 Dec 20, 2021
Image Segmentation using U-Net, U-Net with skip connections and M-Net architectures

Brain-Image-Segmentation Segmentation of brain tissues in MRI image has a number of applications in diagnosis, surgical planning, and treatment of bra

Angad Bajwa 8 Oct 27, 2022
Pytorch implementation for DFN: Distributed Feedback Network for Single-Image Deraining.

DFN:Distributed Feedback Network for Single-Image Deraining Abstract Recently, deep convolutional neural networks have achieved great success for sing

6 Nov 05, 2022
A curated list of resources for Image and Video Deblurring

A curated list of resources for Image and Video Deblurring

Subeesh Vasu 1.7k Jan 01, 2023