Omnidirectional camera calibration in python

Last update: Nov 22, 2022

Related tags

Deep Learning py-omnicalib

Overview

Omnidirectional Camera Calibration

Key features

pure python
initial solution based on A Toolbox for Easily Calibrating Omnidirectional Cameras (Davide Scaramuzza, Agostino Martinelli, Roland Siegwart)
Optimized solution via Levenberg–Marquardt algorithm on reprojection error
Lie algebra based extrinsics optimization

(undistort example)

Usage

Corner detection (see python -m omnicalib.detect --help for detailed argument description)

python -m omnical.detect --chessboard <rows> <columns> <square-size> --max-dim <max-dim> --threads <threads> <image-folder>

The file detections.pickle is written, which contains a pickled dictionary with the following format

{
    'detections': {
        <image_path_0>: {'image_points': <image_points>, 'object_points': <object_points>},
        ...
}

Image paths (pathlib.Path) are absolute
image_points are torch.Tensor with shape N x 2 and dtype torch.float64
object_points are torch.Tensor with shape N x 3 and dtype torch.float64 and third colum, the z coordinate, all zeros

If an external corner detection method is used, this file can simply be written manually.

Calibration (see python -m omnicalib --help for detailed argument description)

python -m omnical --degree <degree> detections.pickle

Result

A calibration.yml file (see example_calibration.yml) containing

extrinsics as list of 3 x 4 matrices
poly_incident_angle_to_radius, a polynom that converts incident angle to image radius
poly_radius_to_z, a polynom that converts radius to z component of view vector
principal_point in pixel

Method

For a detailed description see method.md.

Simulation

Play with the calibration on simulated data with the provided jupyter lab notebook. It generates randomized data for the following ideal projection models

equidistant
stereographic
orthographic
equisolid

, see Large Area 3D Human Pose Detection Via Stereo Reconstruction in Panoramic Cameras for a detailed description.

Example

Jupyter lab notebook, example results

You might also like...

Improving Calibration for Long-Tailed Recognition (CVPR2021)

19 Apr 28, 2021

Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021

SNN_Calibration Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021 Feature Comparison of SNN calibration: Features SNN Direct Tr

60 Dec 27, 2022

S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)

S2-BNN (Self-supervised Binary Neural Networks Using Distillation Loss) This is the official pytorch implementation of our paper: "S2-BNN: Bridging th

52 Dec 24, 2022

[CVPR 2022] Official code for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration"

MDCA Calibration This is the official PyTorch implementation for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved

21 Dec 22, 2022

Omnidirectional camera calibration in python

Related tags

Overview

Omnidirectional Camera Calibration

Key features

Usage

Result

Method

Simulation

Example

You might also like...

Improving Calibration for Long-Tailed Recognition (CVPR2021)

Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021

S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)

the code for paper "Energy-Based Open-World Uncertainty Modeling for Confidence Calibration"

We utilize deep reinforcement learning to obtain favorable trajectories for visual-inertial system calibration.

Improving Calibration for Long-Tailed Recognition (CVPR2021)

Source code of NeurIPS 2021 Paper ''Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration''

CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

[CVPR 2022] Official code for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration"

Releases(1.0.1)

1.0.1(Mar 1, 2022)

1.0.0(Dec 21, 2021)

Owner

Thomas Pönitz

Official pytorch implementation of paper "Inception Convolution with Efficient Dilation Search" (CVPR 2021 Oral).

Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation.

Quickly and easily create / train a custom DeepDream model

PyContinual (An Easy and Extendible Framework for Continual Learning)

A tool to estimate time varying instantaneous reproduction number during epidemics

This project provides an unsupervised framework for mining and tagging quality phrases on text corpora with pretrained language models (KDD'21).

Code for Learning Manifold Patch-Based Representations of Man-Made Shapes, in ICLR 2021.

(ICONIP 2020) MobileHand: Real-time 3D Hand Shape and Pose Estimation from Color Image

CUda Matrix Multiply library.

Video Corpus Moment Retrieval with Contrastive Learning (SIGIR 2021)

Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021)

Signals-backend - A suite of card games written in Python

Low Complexity Channel estimation with Neural Network Solutions

BraTs-VNet - BraTS(Brain Tumour Segmentation) using V-Net

Training Cifar-10 Classifier Using VGG16

FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation

Pytorch implementation of PTNet for high-resolution and longitudinal infant MRI synthesis

MazeRL is an application oriented Deep Reinforcement Learning (RL) framework

[ICCV 2021] Our work presents a novel neural rendering approach that can efficiently reconstruct geometric and neural radiance fields for view synthesis.

Multi-Task Deep Neural Networks for Natural Language Understanding