semvec/pypi-packages huggingface dataset:
Deepfake-detector 1.4
A Python library for detecting deepfake images and videos.
Requires Python: >=3

# Deepfake Detector

Deepfake Detector is a Python library designed for detecting deepfake content in images and videos. Leveraging advanced machine learning techniques, it provides an easy-to-use interface for real-time and batch processing of media files.

## Features

- Real-time Deepfake Detection: Analyze live video feeds to detect deepfake content.
- Batch Processing: Process multiple images and videos to classify them as real or fake.
- Customizable Thresholds: Adjust detection sensitivity to suit your specific needs.

## Installation

You can install the `deepfake_detector` package using pip:

```bash
pip install deepfake_detector
```

## Usage

### For Video Detection

Detect deepfake content in a video file. You can provide a video file path or use a webcam.

```python
from deepfake_detector import live_video_prediction, image_prediction

# Set a custom threshold
live_video_prediction(source='name.mp4',threshold=0.5)
```

### For Image Detection

Detect deepfake content in a single image.

```python
from deepfake_detector import live_video_prediction, image_prediction

# Image prediction example
image_prediction('name.png', threshold=0.6)
```

### Customizing Detection Threshold

You can set the threshold to control the sensitivity of the detection.

```python
# Example with a higher sensitivity threshold
live_video_prediction(source='name.mp4',threshold=0.5)
image_prediction('name.png', threshold=0.6)
```

## Paper Reference

For more detailed information about the techniques used in this library, please refer to the following research paper:

- **Title**: [Deepfake Detection Using the Rate of Change between Frames Based on Computer Vision](https://www.mdpi.com/1424-8220/21/21/7367)
- **Journal**: MDPI Sensors
- **Abstract**: This paper explores advanced methods for detecting deepfake media using convolutional neural networks (CNNs). The study provides a comprehensive analysis of various techniques and their effectiveness in identifying manipulated content.

## Contributing

If you would like to contribute to the development of this library, please contact me.

## Contact

For any questions or feedback, please contact:

- **Author**: Adupa Nithin Sai
- **Email**: [adupanithinsai@gmail.com](mailto:adupanithinsai@gmail.com)
- **GitHub**: [https://github.com/saiadupa/Deepfake-detector](https://github.com/saiadupa/Deepfake-detector)

---

Thank you for using Deepfake Detector. We hope you find it useful for your deepfake detection needs!
```
jd-image-processing 0.0.1
Image Processing
Requires Python: >=3.8

# jd_image_process

Description. 
The package jd_image_process is used to:

- Complete the requirements of a challenge in a Data Engineering class at Digital Innovation One.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install jd_image_process

```bash
pip install jd_image_process
```

## Usage

```python
from jd_image_process.utils import io, plot
from jd_image_process.processing import combination, transformation

image1 = io.read_image('image1_path')
image2 = io.read_image('image2_path')

plot.plot_image(image1)
plot.plot_image(image2)

result_image = combination.transfer_histogram(image1, image2)
plot.plot_result(image1, image2, result_image)
```

## Author
Judenilson Araujo

www.judenilson.com.br

## License
[MIT](https://choosealicense.com/licenses/mit/)
cameo-botrun-prompt-tools 1.0.6
cameo_botrun_prompt_tools
Requires Python: >=3.11

# cameo_botrun_prompt_tools
這個套件提供了一系列工具，方便使用者產生與波特人相關的提示詞。

## 安裝
使用 pip 安裝套件：
```
pip install cameo_botrun_prompt_tools
```


## 使用範例

### print_button() 和 print_link() 函式
`print_button()` 和 `print_link()` 函式可以用來生成含有@begin @end的按鈕和連結文字。

```python
from cameo_botrun_prompt_tools import print_button, print_link

print_button("點擊我", "這是一個按鈕")
print_link("https://www.example.com", "訪問範例網站")

# 上面程式會輸出以下文字
# @begin button("點擊我") 這是一個按鈕 @end
# @begin link("https://www.example.com") 訪問範例網站 @end
```

### print_buttons() 和 print_links() 函式
`print_buttons()` 和 `print_links()` 函式可以用來生成多個含有@begin @end的按鈕和連結文字。

```python
from cameo_botrun_prompt_tools import print_buttons, print_links

print_buttons([{"label": "按鈕1", "content": "這是按鈕1"}, {"label": "按鈕2", "content": "這是按鈕2"}])
print_links([{"url": "https://www.example1.com", "content": "訪問範例網站1"}, {"url": "https://www.example2.com", "content": "訪問範例網站2"}])

# 上面程式會輸出以下文字
# @begin button("按鈕1") 這是按鈕1 @end
# @begin button("按鈕2") 這是按鈕2 @end
# @begin link("https://www.example1.com") 訪問範例網站1 @end
# @begin link("https://www.example2.com") 訪問範例網站2 @end
```

### format_info_for_readability() 函式
`format_info_for_readability()`函式可以藉由LLM將複雜資訊(例如API回傳的資訊)整理成方便人類閱讀的格式輸出。

```python
from cameo_botrun_prompt_tools import format_info_for_readability

formatted_info = format_info_for_readability(
    info, prompt="請將以下資訊整理成方便人類閱讀的格式輸出：\n",
    model="gpt-4o-mini"
)
print(formatted_info)
```