perception

KO | EN

gitlite — search

#javascript #python #hacktoberfest #ai #typescript #machine-learning #docker #java #nodejs #awesome #cli #awesome-list #mcp #open-source

(★ 8)

Autonomous Underwater Vehicles at Berkeley's perception stack

perception 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Perception Code Overview

Code Quality [![CodeFactor](https://www.codefactor.io/repository/github/berkeleyauv/perception/badge)](https://www.codefactor.io/repository/github/berkeleyauv/perception)

## Installation

We will use Conda for managing environments. We recommend installing Miniconda for Python 3.11 [here](https://docs.conda.io/en/latest/miniconda.html).
Python 3.11 is required to support YOLO models.
Then create an environment with

conda create -n urobotics python=3.11

activate it with

conda activate urobotics

Then clone the repo in a directory of your choice

git clone https://github.com/berkeleyauv/perception.git

Change into the cloned repo directory and install it

pip3 install -e ./

Install dependencies with

pip3 install -r requirements-classical.txt

If you also need YOLO/torch-based models, install the torch group instead (it includes everything in `requirements-classical.txt`, plus `torch`, `torchvision`, and `ultralytics`)

pip3 install -r requirements-torch.txt

Also, our training data is stored here https://www.dropbox.com/sh/rrbfqfutrmifrxs/AAAfXxlcCtWZmUELp4wXyTIxa?dl=0 so download it and unzip it in the same folder as `perception`.

### Cython
To compile cythonized code, run the following commands after `cd`ing into the folder with Cython `setup.py`

python setup.py build_ext --inplace
    cythonize file_to_cythonize.pyx

## misc:
Misc code, camera calibration etc.

## tasks:
Code for specific tasks like

1. cross: cross detection
2. segmentation:
3. path_marker: path_marker detection
4. spinny_wheel_detection

etc

In order to create your own algorithm to test:

1. Create <your_algo>.py and put it in one of the specific task folders in perception/tasks.

2. Create a class which extends the TaskPerceiver class. perception/tasks/TaskPerceiver.py includes a template with documentation for how to do this.

## vis:
Visualization tools 
Code for testing tasks (Ideally this should be placed a separate folder called `tests`).

After writing the code for your specific task algorithm, you can do one of two things:

1. Add this to the end of <your algorithm>.py file:
    
        if __name__ == '__main__':
            from perception.vis.vis import run
            run(<list of file/directory names>, <new instance of your class>, <save your video?>)
    and then run
    
        python <your algorithm>.py
2. Add this to the perception/__init__.py file:

import <path to your module>
        
        ALGOS = {
            'custom_name': <your module>.<your class reference>
        }
    and then run
    
        python perception/vis/vis.py --algorithm custom_name [--data <path to file/directory>] [--profile <function name>] [--save_video]
    The **algorithm** parameter is required. If **data** isn't specified, it'll default to your webcam. If **profile** isn't specified, it will be off by default. Add the **save_video** tag if you want to save your vis test as an mp4 file.

## wiki:
Flowchart on TaskPerceiver, TaskReceiver, AlgorithmRunner.

perception

perception 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

perception

perception 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README