tianmoucv 0.3.1

Algorithms library for Tianmouc sensor

   

# TianMouCV-preview version


The official version will be available at [tianmoucv/tianmocv](https://github.com/Tianmouc/tianmoucv)

This is the Python tool for the first complementary vision sensor (CVS), TianMouC.

More details about the project can be found on our project page. [Tianmouc Project](https://www.cbicr.tsinghua.edu.cn/?page_id=971)

## Installation

0. Prepare pytorch environment

Python version should be larger than 3.8 and less than 3.12, recommend 3.10

`bash conda create -n [YOUR ENV NAME] --python=3.10 conda activate [YOUR ENV NAME] conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia `

1. from PyPI

`bash pip install tianmoucv `

2. Install from source codes (using pip):

`bash git clone [e-mail]:Tianmouc/Tianmoucv_preview.git cd Tianmoucv_preview sh install.sh `

## Data

You can download a TianMouC data clip in [THU-sharelink](https://cloud.tsinghua.edu.cn/f/dc0d394efcb44af3b9b3/?dl=1), and refer to tianmoucv/exmaple/data/test_data_read.ipynb for a trial

a standard TianMouC dataset structure:

` ├── dataset │ ├── matchkey │ │ ├── cone │ │ ├── info.txt │ │ ├── xxx.tmdat │ │ ├── rod │ │ ├── info.txt │ │ ├── xxx.tmdat `

where matchkey is the sample name used for the TianMouC data reader

## Examples

For some of the algorithms we’ve provided the example in tianmoucv/example

Including:

1. calculating optical flow

2. reconstruct gray/hdr images

3. key point matching/tracking

4. camera calibration

5. data reeader

…

These samples can be directly run on jupyter notebook

`bash conda activate [your environment] pip install jupyter notebook jupyter notebook `