semvec/pypi-packages huggingface dataset:
image-manipulation-datasets 1.1.0
A collection of image manipulation dataset classes implemented in PyTorch
Requires Python: >=3.9

# Image Manipulation Datasets (IMDS)

This Python package provides PyTorch-compatible dataset classes for common image manipulation datasets used in digital forensics and deepfake detection research.

## Supported Datasets

- **CASIA 2.0** - Forgery classification dataset with 4,795 images
- **Defacto** - Collection of manipulation datasets:
  - Copy/Move (~19,000 forgeries)
  - Splicing (~105,000 forgeries) 
  - Inpainting (~25,000 forgeries)
- **Coverage** - Copy-move forgery database with similar genuine objects
- **IMD2020** - Real-life manipulated images from the Internet (2,010 images)

## Installation

```bash
pip install image-manipulation-datasets
```

## Quick Start

```python
from imds import casia
from torch.utils.data import DataLoader

# Load any dataset
dataset = casia.CASIA2(data_dir='data/CASIA2.0', split='train')
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

for images, masks in dataloader:
    # images: torch.Tensor shape (batch_size, 3, H, W)
    # masks: torch.Tensor shape (batch_size, 1, H, W) 
    pass
```

## Documentation

For comprehensive API documentation, usage examples, and advanced features, see:

**[📖 API Documentation](API_DOCUMENTATION.md)**

The documentation includes:
- Complete API reference for all dataset classes
- Usage examples and common patterns
- Directory structure requirements for each dataset
- Performance optimization tips
- Error handling guidelines

## Sample Quality

Datasets are not always perfect. Of the available datasets, COVERAGE, CASIA 2, and Defacto Splicing had images and masks that didn't match in size, though they have been verified as pairs. For this reason, the dataset classes resize the masks to the size of the original image, with the hopes that the masks line up correctly with the image. This is unverified as it would require manually verifying each of the over 110,000 image and mask pairs.
pythons_musicals 1.1.0.4
This library can play sounds and your music.
Requires Python: >=3.2

## Open

 image: https://i.pinimg.com/originals/0f/4e/86/0f4e86b2085cecc78942b05279c961d3.png


## About

This Python's library can "play" the notes and she can download YOUR music.

It's got two modules: `note` and `load_music`

## Installing
Enter in your bash:
```bash
pip install pythons_musicals
```

## Call the functions

Note module's call is:

```py
import pythons_musicals

<yourNoteVariable> = pythons_musicals.note.load(<note>)

if __name__ == "__main__":
    <yourNoteVariable>.play()
```

and load_music module's call is:

```py

    import pythons_musicals

    <SoundVariable> = pythons_muscals.load_music.load(<yourDirectory>)

    if __name__ == "__main__":
        <SoundVariable>.start()
```

ATTENTION!
------

Note module's got method `play()`, and load_music module's got method `start()`
streamlit-typed-session 0.1.0.post2
Add your description here
Requires Python: <4,>=3.10

# Streamlit Typed Session

This package is made for people who like the safety of Python static type-checkers but also have to work with Streamlit at the same time.

**Caution** this project is under heavy development.

## Guide

The current main API was inspired by `pydantic-settings`.

To start you just need to import `streamlit_typed_session.SessionBase` and `streamlit_typed_session.StateVar` and declare a class as in the example below.

```py
from streamlit_typed_session import SessionBase, StateVar


class SessionModel(SessionBase):
    number: StateVar[int]

session_state = SessionModel()
```

After this you can use `session_state` as a normal class.

Under the hood the attributes of `SessionModel` are turned into descriptors, that modify the underlying `st.session_state`.

For a more detailed example check [`scripts/streamlit_test.py`](./scripts/streamlit_test.py)

### Why `StateVar`?

`StateVar` is a "syntactic sugar", its underlying type is `T | UnsetType`. Every attribute that does not have a default value _should_ have `UnsetType` in its type hints. This is required as if the attribute is not present in the underlying session state (and does not have a default value) they will return `Unset` instead of raising an exception. By providing `UnsetType` as a type hint you can rely on static type-checkers to warn you if an attribute might be unset.