semvec/pypi-packages huggingface dataset:
yoctoEC2 0.2.1
A yocto EC2 Python project
Keywords: yoctoEC2 build tools development

# YOCTO EC2

A project with some tools that may help embedded linux developers build linux image with Yocto on EC2.

The idea we may select a very cool EC2 with 16 cores and 32 GB RAM, this cost us ~$0.4 an hour for a build. We only run the EC2 once we start building the linux image and stop this once we are done. So, the EC2 does not cost us more than $30-$40 a month. it's cheaper than you invest super desktop to build the code, plus utility bill and cooler as well.

![](https://lh3.googleusercontent.com/m-zRp4JaYeeZuNh1SOQhn8lvYHwt8VTb5TuDTUmC1A1dPxHnTBC9d7o_8gfsr0cv_VwhVdj3csTdpSXRe9ja8LTczSNzR_7P7sukSwHGPHOMmB1UWQUfd7g-RocsxsUWQTJRU_q7tKU=w482-h766-no)

### THE CURRENT VERSION IS ONLY SUPPORT NXP iMX SOURCE CODE

## Setup AWS

1. Create EC2 instance:

    - Type: c5.4xlarge and ESB 120GB
    - OS: Ubuntu Server 18.04

    Note: Use ubuntu user for everything.

2. Add your ssh public key to the EC2

    - Copy your public key
        ```bash
        ssh-keygen
        cat ~/.ssh/id_rsa.pub | pbcopy
        ```
    - Paste the key to EC2
        ```bash
        vim .ssh/authorized_keys
        ```

## Clone source code and initialize build environment

Clone your code to your EC2

## Installation

```bash
pip install yoctoEC2
```

#### For current version, I suggest you to initialize build environment and accept EULA by manual.

## Yocto build

You may want to modify build script `build.sh` with your project parameters.

Start EC2, build, copy image and Stop EC2

`yocto-ec2 build --instance-id=<instance-id> --project-root=<project-path> --DISTRO=<distro> --MACHINE=<machine> --IMAGE=<image>`


```bash
yocto-ec2 build --instance-id=i-12345678  --project-root=/home/ubuntu/Workspace/iMX6ULEVK/ --script-path=./build.sh --sdcard-image=/home/ubuntu/Workspace/iMX6ULEVK/build/tmp/deploy/images/imx6ulevk/core-image-base-imx6ulevk.sdcard.bz2
```
gamecam-sdrabing 0.8.3
Remote-camera software suite.

# gamecam
Filter out trail-camera images that don't contain an animal (eventually will be able to label exported images as well).

## Motivation
While working for the [Francis Ecology Lab](https://francisecologylab.wixsite.com/francislab), a problem was presented to me. In 2018, there were 800,000+ remote camera images collected for a single experiment. The majority turned out to be false triggers - photos of grass and bushes swaying.

Instead of going through them one by one, I developed this program. Once coded, it only took a week to generate a data table with all the images that contained animals, labeled to genus or species, and that had detection numbers corresponding to one of two common schemes. The data table can be converted into observational units of detections instead of individual images.

## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install gamecam.

```bash
pip install gamecam-sdrabing
```

## Usage
```python
from gamecam import pyrcolate as pyr

# creates data table from folder
jpg_data = pyr.construct_jpg_data(pyr.input_directory())

# Cam objects store all relevant metadata
cam = pyr.Cam(jpg_data)

help(Cam.plot)                     # shows quick-guide for key bindings
cam.plot()                         # interactive plot for image filtering
cam.save(pyr.input_filename())     # creates a .sav file that can be loaded
cam.export(pyr.input_directory())  # exports images and a .csv file to folder
```

## To-Do
Finish gamecam.classipy, which will allow for rapid labeling of exported images.

## Contributing
This is my first project, so I have no idea what I'm doing.
If you have suggestions or tips, please tell me!

## License
[MIT](https://choosealicense.com/licenses/mit/) © Shane Drabing 2019