semvec/pypi-packages huggingface dataset:
tkintervideo 0.0.2
A simple Tkinter Python Package For synced audio & video playback.
Requires Python: >=3.6

# tkintervideo

A simple Tkinter Python Package For synced audio & video playback with a real-time API.

## Installation
- From PyPi
    - ```bash
      pip install tkvideo
      ```
- From source
    - ```bash
      pip install -r requirements.txt
      ```
    - Build it normally(refer web).
## Features:

    - Controls Widget Added
    - Timeline seeking.(slider based & button based)
    - Synced audio playback.(Video is synced with audio)
    - Upto any Resolution video playback(using PIL & imageio)
    - TODO: youtube/hls support.
    - In-player real-time audio & video edits.


    - duration
    - fps
    - audio frame rate
    - state detection
    - auto resize(based on window resize)
    - total frames
    - current play position
    - seeking(to a second or a frame)
    - play/pause/stop/resume
    - volume
    - edits(you can edit the video & audio because moviepy & pydub are built-in)
## Usage

```python
from tkvideo.tkvideo import player
from tkvideo.tkvideo.tools import Controls
import tkinter as tk
from tkinter import ttk
import sv_ttk
import time

class App(tk.Tk):
    def __init__(self,*args,**kwargs):
        super().__init__(*args,**kwargs)
        sv_ttk.set_theme('dark')
        self.m_widget = player.Player(self,height=200,width=300)

        self.rowconfigure(0,weight=1)
        self.rowconfigure(1,weight=0)
        self.columnconfigure(0,weight=1)

        self.m_widget.grid(column=0,row=0,sticky=tk.NSEW,padx=5,pady=5)
        self.m_widget.load('output.avi')

        self.controls = Controls(self,self.m_widget)
        self.m_widget.bind("<<Duration>>",self.controls.update_scale)
        self.controls.grid(column=0,row=1,sticky=tk.NSEW,padx=10,pady=10)
myApp = App()
myApp.mainloop()
```

## Contributing

Pull requests are welcome.

## License

[GPL-3](https://choosealicense.com/licenses/gpl-3.0/)
feature-importances 0.0.3
Unupservised Feature Importances
Requires Python: >=3.8.5


# Make sure you have upgraded version of pip
Windows
```
py -m pip install --upgrade pip
```

Linux/MAC OS
```
python3 -m pip install --upgrade pip
```

## Create a project with the following structure
```
packaging_tutorial/
├── LICENSE
├── pyproject.toml
├── README.md
├── setup.cfg
├── src/
│   └── example_package/
│       ├── __init__.py
│       └── example.py
└── tests/
touch LICENSE
touch pyproject.toml
touch setup.cfg
mkdir src/mypackage
touch src/mypackage/__init__.py
touch src/mypackage/main.py
mkdir tests
```

## pyproject.toml 

This file tells tools like pip and build how to create your project

```
[build-system]
requires = [
    "setuptools>=42",
    "wheel"
]
build-backend = "setuptools.build_meta"
```
build-system.requires gives a list of packages that are needed to build your package. Listing something here will only make it available during the build, not after it is installed.

build-system.build-backend is the name of Python object that will be used to perform the build. If you were to use a different build system, such as flit or poetry, those would go here, and the configuration details would be completely different than the setuptools configuration described below.


# Setup.cfg setup
Using setup.cfg is a best practice, but you could have a dynamic setup file using setup.py

```
[metadata]
name = example-pkg-YOUR-USERNAME-HERE
version = 0.0.1
author = Example Author
author_email = author@example.com
description = A small example package
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/pypa/sampleproject
project_urls =
    Bug Tracker = https://github.com/pypa/sampleproject/issues
classifiers =
    Programming Language :: Python :: 3
    License :: OSI Approved :: MIT License
    Operating System :: OS Independent

[options]
package_dir =
    = src
packages = find:
python_requires = >=3.6

[options.packages.find]
where = src

```
# Running the build
### Make sure your build tool is up to date
Windows
```
py -m pip install --upgrade build
```
Linux/MAC OS
```
python3 -m pip install --upgrade build
```


### Create the build
```
py -m build
```













### References
https://packaging.python.org/tutorials/packaging-projects/
autoelbow 0.0.4
Auto Elow For Clustering
Requires Python: >=3.6



# Dependencies 
### Make sure you have install the sklearn ~ Kmeans
import
```
from sklearn.cluster import KMeans
```
### Install the package
```
pip install package_name
```


### Import the package 
```
from autoelbow_rupakbob import autoelbow
```

### Call the package function
```
autoelbow.auto_elbow_search(data)
```
segment-anything-onnx 0.0.5
Run Segment Anything with ONNX models
Requires Python: >=3.8

# segment-anything-onnx

Use ONNX models for Segment Anything inference.

Special thanks to:
- [Viet-Anh NGUYEN](https://github.com/vietanhdev) for creating the [SAM Exporter](https://github.com/vietanhdev/samexporter) libraries from which these files are derived.
- The [Segment Anything](https://github.com/facebookresearch/segment-anything) team at Meta AI Research


## Usage from Source

1. Clone Segment Anything ONNX from Github
    ```bash
    git clone git@github.com:whatstyle-ai/segment-anything-onnx.git
    cd segment-anything-onnx
    pip install -e .
    ```
2. Use the [SAM Exporter](https://github.com/vietanhdev/samexporter) to generate the ONNX models
3. Copy the ONNX models to the segment-anything-onnx/models directory
4. Predict some masks 
    ```bash
    cd segment-anything-onnx
    ./demo.sh
    ```

## Usage from pip install

1. Use the [SAM Exporter](https://github.com/vietanhdev/samexporter) to generate the ONNX models, or obtain the ONNX models from another source
2. Copy the ONNX models to a "models" directory, such as:
    ```
    models/sam_vit_l_0b3195.encoder.onnx
    models/sam_vit_l_0b3195.decoder.onnx
    ```
3. Install Segment Anything ONNX using pip:
    ```bash
    pip install segment-anything-onnx
    ```
4. Predict a mask:
    ```python
    import cv2
    import urllib.request
    import numpy as np

    from segment_anything_onnx.inference import predict_masks


    def load_image(uri):
        if( uri.startswith('https://') or uri.startswith('http://') ):
            req = urllib.request.urlopen(uri)
            arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
            img = cv2.imdecode(arr, -1) # 'Load it as it is'
            return img
        else: 
            return cv2.imread(uri)  # uri is just a local file path


    encoder_model_path = './models/sam_vit_l_0b3195.encoder.onnx'
    decoder_model_path = './models/sam_vit_l_0b3195.decoder.onnx'
    image = load_image( 'https://raw.githubusercontent.com/whatstyle-ai/segment-anything-onnx/main/examples/laura.jpg' )
    prompt = [
        { 'type': 'point', 'data': [1750, 300], 'label': 0 },
        { 'type': 'rectangle', 'data': [611, 655, 2712, 4500] }
    ]
    options = {
        'show': True,
        'output': './output/laura-L.png'
    }

    predict_masks( encoder_model_path, decoder_model_path, image, prompt, options )
    ```
sphinx-veldus-ext 0.2.12
A Sphinx Extension built for veldus.net
Requires Python: >=3.7

# Veldus

View here: [http://veldus.net/](http://veldus.net/)

[PyPi repository](https://pypi.org/project/sphinx-veldus-ext/)

## 0.2.12

* Minor format update to "Open in new tab" for map link