semvec/pypi-packages huggingface dataset:
ODLabeler 0.3.12
A tool for object detection, labeling and visualization
Requires Python: >=3.6

# OD-Labeler

ODLabeler is a powerful tool for object detection labeling and visualization. It provides an intuitive graphical user interface for labeling objects in images using the YOLO-World model and supports various output formats such as YOLO, COCO, CSV, and XML.

## Features

- Select a YOLO-World model for object detection
- Choose an images folder for labeling
- Specify output directory for labeled data
- Define object categories for detection
- Use Slicing Adaptive Inference (SAHI) for improved detection
- Select device type (CPU or GPU) for inference
- Customize train/validation split ratio
- Adjust confidence level and non-maximum suppression (IoU) threshold
- Visualize input image statistics and output detection results

You can install OD-Labeler using pip:

```bash
pip install ODLabeler

```

## Usage

To launch the OD-Labeler application, run the following command:

```bash
odlabeler
```

1. Select a YOLO-World model file (.pt) for object detection.
2. Choose the folder containing the images you want to label.
3. Specify the output directory where the labeled data will be saved.
4. Enter the object categories you want to detect, separated by commas.
5. Configure additional options such as SAHI, device type, output format, train/validation split, confidence level, and NMS threshold.
6. Click the "Start" button to begin the labeling process.
7. Monitor the progress and view the detection results in the application.

## Update

You can upgrade OD-Labeler using pip:

```bash
pip install --upgrade ODLabeler

```

## License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). See the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the [GitHub repository](https://github.com/Ziad-Algrafi/ODLabeler).

## Acknowledgements

ODLabeler is built using the following open-source libraries:

- [CustomTkinter](https://github.com/TomSchimansky/CustomTkinter)
- [Ultralytics YOLO](https://github.com/ultralytics/ultralytics)
- [Matplotlib](https://matplotlib.org)
- [OpenCV](https://opencv.org)
- [PyTorch](https://pytorch.org)

## Contact

For any questions or inquiries, please contact the maintainer at - ZiadAlgrafi@gmail.com.
tijatools 0.1.2
A customizable progress ring for terminal applications.
Requires Python: >=3.6

# tijatools

## Descriptions

The tijatools is a Python package that allows you to display dynamic and customizable progress rings in the terminal. It's an ideal tool for providing visual feedback on the status of long-running operations.

## Installation

```bash
pip install tijatools

## Ussage
```bash
import asyncio
from tijatools import ProgressRing, AnimationMode

async def do_some_work(duration):
    await asyncio.sleep(duration)

class ATest:
    async def run_test(self, duration):
        ring = ProgressRing(rotate_symbols=['1', '2', '3', '4', '3', '2', '1'])
        print("ROTATE:")
        await ring.run_with_animation(do_some_work(duration), AnimationMode.ROTATE)
        print("BOUNCE:")
        await ring.run_with_animation(do_some_work(duration), AnimationMode.BOUNCE, shape="x")
        print("BOUNCE2:")
        await ring.run_with_animation(do_some_work(duration), AnimationMode.BOUNCE2)
        print("LOADING:")
        await ring.run_with_animation(do_some_work(duration), AnimationMode.LOADING)

if __name__ == "__main__":
    test = ATest()
    asyncio.run(test.run_test(10))
constrainedlr 0.2.0
Constrained Linear Regression with sklearn-compatible API
Requires Python: >=3.8

# Constrained Linear Regression
<a href="https://pypi.org/project/constrainedlr" target="_blank">
    <img src="https://img.shields.io/pypi/v/constrainedlr?color=%2334D058&label=pypi%20package" alt="Package version">
</a>
<a href="https://pypi.org/project/constrainedlr" target="_blank">
    <img src="https://img.shields.io/pypi/pyversions/constrainedlr.svg?color=%2334D058" alt="Supported Python versions">
</a>

constrainedlr is a drop-in replacement for `scikit-learn`'s `linear_model.LinearRegression` with the extended capability to apply constraints on the model's coefficients, such as signs and lower/upper bounds.

## Installation
```bash
pip install constrainedlr
```

## Example Usage

### Coefficients sign constraints
```python
from constrainedlr import ConstrainedLinearRegression

model = ConstrainedLinearRegression()

model.fit(
    X_train,
    y_train,
    coefficients_sign_constraints={0: "positive", 2: "negative"},
    intercept_sign_constraint="positive",
)

y_pred = model.predict(X_test)

print(model.coef_, model.intercept_)
```

### Coefficients range constraints
```python
from constrainedlr import ConstrainedLinearRegression

model = ConstrainedLinearRegression()

model.fit(
    X_train,
    y_train,
    coefficients_range_constraints={
        0: {"lower": 2},  # 1st coefficient must be 2 or higher
        2: {"upper": 10},  # 3rd coefficient must be smaller than 10
        3: {"lower": 0.1, "upper": 0.5},  # 4th coefficient must be between 0.1 and 0.5
    },
)

y_pred = model.predict(X_test)

print(model.coef_)
```

See more in the [documentation](https://tsitsimis.github.io/constrainedlr/)


### Licence
MIT