semvec/pypi-packages huggingface dataset:
imageObjects 0.13.6
Helper Objects for working with open computer vision and image projects
License: MIT
Requires Python: >=3.6


# imageObjects

This acts as an API of sorts for CV2, with some common methods cast into objects that work with images, contours and 
more. All source code at the [github repo page][repo]

### ContourObject
open-cv python has contours, a extremely useful component that can be extracted from images. However, these components 
are in a ndarray format that can then be processed by a list of methods, rather than an object which has a list of 
methods. This library changes that around and allows for the contour extracted via open cv python to be placed in an 
object which can then be manipulated. 

### ImageObject
There are lots of common image functions that have been mapped to images, so that a loaded image can have these as 
properties or as methods inherently rather than having to code them yourself.

### LineObject
This will search for lines within an ImageObject which you can then utilise as a mask or manipulate with ImageObjects

### ImageMaker
This can be used to make new ImageObjects, when you don't have anything to load or custom ImageObjects like text-boxes

### common
Whilst common is mostly designed for internal use, there are some common methods here that may save you re-defining them
yourself or from other packages


[repo]: https://github.com/sbaker-dev/imageObjects
sumo-tasks-pipeline 4.0
Simple Python interface for a traffic simulator: SUMO
License: MIT
Requires Python: >=3.7,<4.0

# sumo-tasks-pipeline
- - -

Run SUMO simulators as easy as possible!

The package `sumo-tasks-pipeline` enables you to run a traffic simulator [SUMO](https://sumo.dlr.de/docs/index.html) efficiently 
and to interact with Python easily.

# Example

Just three lines to run a SUMO simulation.

```python
from sumo_tasks_pipeline import LocalSumoController, SumoConfigObject
from pathlib import Path

path_config = Path().cwd().joinpath('tests/resources/config_complete')
sumo_controller = LocalSumoController(sumo_command='/usr/local/bin/sumo')
sumo_config = SumoConfigObject(scenario_name='example', path_config_dir=path_config, config_name='grid.sumo.cfg')
sumo_result_obj = sumo_controller.start_job(sumo_config)
```

See `examples` directory to know more.

# Features

- Possible to resume your tasks. The feature is useful when you run simulators on Google Colab.
- Possible to save SUMO simulation result to Google Cloud Storage (GCS). No worries even when your local storage is small.
- Possible to run SUMO simulations with multiple machines if you use GCS as the storage backend.

# Requirement

- python > 3.5
- docker 
- docker-compose

# Install

## Pull the image (or build of a docker image with SUMO)

The existing image is on the [Dockerhub](https://hub.docker.com/repository/docker/kensukemi/sumo-ubuntu18).

```shell
docker pull kensukemi/sumo-ubuntu18
```

If you prefer to build with yourself, you run the following command.

```shell
docker-compose build 
```

## Install a python package

```shell
make install
```


# For developer

```shell
pytest tests
```

# license and credit

The source code is licensed MIT. The website content is licensed CC BY 4.0.


```
@misc{sumo-docker-pipeline,
  author = {Kensuke Mitsuzawa},
  title = {sumo-tasks-pipeline},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Kensuke-Mitsuzawa/sumo_docker_pipeline}},
}
```