semvec/pypi-packages huggingface dataset:
CBZpliers 0.2.0
A tool to combine multiple .cbz files into one.
Requires Python: >=3.6

# CBZpliers

Pliers is a Python tool that helps you combine multiple `.cbz` (comic book archive) files into a single file.

## Installation

You will be able to install Pliers directly from PyPI:

```bash
pip install CBZpliers
```
## Usage

You can use the tool from command line

```bash
combine_cbz <cbz_dir> <volume_title> <series_title>
```

<cbz_dir> - path to the directory which contains the files you want to combine. It is recommended to have a separate directory, containing nothing BUT the .cbz files

<volume_title> - name of the final .cbz file

<series_title> - name of the series, only shows in the .xml file inside the .cbz archive, but might be useful for some applications
lucidsim 0.0.1rc1
LucidSim and Benchmark Suite

LucidSim and Benchmark Suite
============================

Experiments
-----------

**1. To generate data**

-  run `weaver.render_node <weaver/render_node.py>`__ in the
   dream-weaver repository
-  run teacher streamer, turn off streaming
-  run vuer_monitor to monitor the images

**2. To Behavior Clone**

.. code-block:: shell

   lucidim.learning --env <env> --algo <algo> --num_timesteps <num_timesteps> --log_path <log_path> --save_path <save_path>

**To Evaluate**

-  run
   `lucidsim_experiments.sweeps.evaluation_sweep <lucidsim_experiments/sweeps/evaluation_sweep.py>`__
   to generate the sweeps
-  run
   `lucidsim_experiments.launch_evaluation.py <lucidsim_experiments/launch_evaluation.py>`__
   to launch the evaluation runs

Environments
------------

LucidSim Environments: each with ``lucidsim``, ``rgb``, ``heightmap``,
``depth`` render.

-  ☐ Parkour (+ cones)
-  ☐ Stairs (+ cones)
-  ☐ Hurdle (+ cones)
-  ☐ Gaps (+ cones)
-  ☐ Chase ball (+ cones)

Real World Benchmarks: each with ``morning``, ``noon``, ``night``,
``depth``,

-  ☐ Curbs
-  ☐ Tables
-  ☐ Stairs
-  ☐ Ramps
-  ☐ Parkour

.. _experiments-1:

Experiments
-----------

-  `Chase -
   domain_rand <lucidsim_experiments/sweeps/chase_domain_rand/README.md>`__
-  `Chase -
   lucidsim <lucidsim_experiments/sweeps/chase_lucidsim/README.md>`__
-  `Chase -
   render <lucidsim_experiments/sweeps/chase_render/README.md>`__
-  `Hallway -
   lucidsim <lucidsim_experiments/sweeps/hallway_lucidsim/README.md>`__
-  `Parkour -
   depth <lucidsim_experiments/sweeps/parkour_depth/README.md>`__
-  `Parkour -
   domain_rand <lucidsim_experiments/sweeps/parkour_domain_rand/README.md>`__
-  `Parkour -
   expert <lucidsim_experiments/sweeps/parkour_expert/README.md>`__
-  `Parkour -
   lucidsim <lucidsim_experiments/sweeps/parkour_lucidsim/README.md>`__
-  `Parkour -
   lucidsim_2_frame <lucidsim_experiments/sweeps/parkour_lucidsim_2_frame/README.md>`__
-  `Parkour -
   lucidsim_3_frame <lucidsim_experiments/sweeps/parkour_lucidsim_3_frame/README.md>`__
-  `Parkour -
   render_2_frame <lucidsim_experiments/sweeps/parkour_render_2_frame/README.md>`__
-  `Parkour -
   render_3_frame <lucidsim_experiments/sweeps/parkour_render_3_frame/README.md>`__
-  `Street -
   lucidsim <lucidsim_experiments/sweeps/street_lucidsim/README.md>`__

**Skybox**: Each environment comes in lightings that corresponds to
different time of day.

-  ☐ daylight

-  ☐ dawn: yellow light, low incidence level (long shadow)

-  morning

-  evening: spotlight, low ambient, streetlight in the background

Setup
-----

Unfortunately, gym is a mess. You need the following versions

.. code-block:: shell

   pip install setuptools==65.5.0 pip==21 wheel==0.38.4
   pip install gym==0.21.0
   pip install gym-dmc==0.2.9
sbcli-ha 0.3.9
CLI for managing SimplyBlock cluster


# Simply Block
[![Docker Image Build](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml)

[![Python Unit Testing](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml)


## Install
Add the package repo from AWS CodeArtifact using [awscli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)

```bash
aws codeartifact login --tool pip --repository sbcli --domain simplyblock --domain-owner 565979732541 --region eu-west-1
```
Install package
```bash
pip install --extra-index-url https://pypi.org/simple sbcli-dev
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
Please see this document
[README.md](../main/simplyblock_cli/README.md)


## Simply Block Web API
Please see this document
[README.md](../main/simplyblock_web/README.md)



### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```

setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.


```
sudo docker compose -f docker-compose-dev.yml up --build -d
```
ritm-annotation 0.8.1
Awesome ritm_annotation created by lucasew

# ritm_annotation

[![Translation status](https://hosted.weblate.org/widget/ritm_annotation/text/svg-badge.svg)](https://hosted.weblate.org/engage/ritm_annotation/)
[![codecov](https://codecov.io/gh/lucasew/ritm_annotation/branch/main/graph/badge.svg?token=ritm_annotation_token_here)](https://codecov.io/gh/lucasew/ritm_annotation)
[![CI](https://github.com/lucasew/ritm_annotation/actions/workflows/main.yml/badge.svg)](https://github.com/lucasew/ritm_annotation/actions/workflows/main.yml)

Tool to do dataset annotation for semantic segmentation datasets.

Based on [SamsungLabs/ritm_interactive_segmentation](https://github.com/SamsungLabs/ritm_interactive_segmentation)

**Work in progress**

## Installation

- Pip
```bash
pip install ritm_annotation
```

- Nix flakes
## Running it using Nix flakes
```
nix run github:lucasew/ritm_annotation -- --help
```

- Docker
```
docker run -ti ghcr.io/lucasew/ritm_annotation:latest bash
```

## Usage

```bash
$ python -m ritm_annotation --help
# or
$ ritm_annotation --help
```

## Pretrained model weights
[Here you can download pretrained weights](https://github.com/SamsungLabs/ritm_interactive_segmentation/releases/tag/v1.0)

In my tests I used `coco_lvis_h18_itermask.pth` (SHA256 5f69cfce354d1507e3850bfc39ee7057c8dd27b6a4910d1d2dc724916b9ee32b)


## Development

Read the [CONTRIBUTING.md](CONTRIBUTING.md) file.