https://github.com/zingzing001/watermarkremovertool
A cross-platform tool to remove watermarks from PDF and Word documents with ease. Supports batch processing and customizable removal modes. 一个跨平台工具，可轻松从 PDF 和 Word 文档中移除水印。支持批量处理和自定义移除模式。
https://github.com/zingzing001/watermarkremovertool
python watermark watermark-removal watermark-remover watermark-tools
Last synced: 6 months ago
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A cross-platform tool to remove watermarks from PDF and Word documents with ease. Supports batch processing and customizable removal modes. 一个跨平台工具，可轻松从 PDF 和 Word 文档中移除水印。支持批量处理和自定义移除模式。
- Host: GitHub
- URL: https://github.com/zingzing001/watermarkremovertool
- Owner: ZingZing001
- Created: 2024-11-21T07:51:55.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-11-21T08:24:46.000Z (7 months ago)
- Last Synced: 2024-11-21T08:29:28.841Z (7 months ago)
- Topics: python, watermark, watermark-removal, watermark-remover, watermark-tools
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Watermark Remover Tool
[English](README.md) | [简体中文](README_zh-CN.md)
A powerful and user-friendly tool for removing watermarks from PDF and Word documents. This application provides both fast and deep removal modes, ensuring optimal results for various watermark types.
---
## Features
- **Fast Removal:** Quickly removes layer-based watermarks from PDF files.
- **Deep Removal:** Combines advanced image processing techniques to remove text and image-based watermarks.
- **Word Support:** Removes watermarks from `.docx` files.
- **Batch Processing:** Load multiple files and process them in bulk.
- **Customizable Modes:** Choose between "Fast Removal" and "Deep Removal."
- **Progress Tracking:** Visual progress bar and estimated completion time.
- **Cross-Platform:** Works on Windows, macOS, and Linux.
---
## Prerequisites
Ensure you have Python 3.9 or later installed. Additionally, install the dependencies listed in `requirements.txt`.
---
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/watermark-remover.git
cd watermark-remover
```
2. Install the dependencies:
```bash
pip install -r requirements.txt
```
3. Install Poppler for PDF processing:
- **macOS:**
```bash
brew install poppler
```
- **Ubuntu:**
```bash
sudo apt-get install poppler-utils
```
- **Windows:**
Download Poppler binaries from [Poppler for Windows](http://blog.alivate.com.au/poppler-windows/) and add the `bin` folder to your PATH.
---
## Usage
### GUI Mode
1. Launch the tool:
```bash
python prod.py
```
2. Select an output folder for processed files.
3. Load files from a folder to process.
4. Choose removal mode: **Fast Removal** or **Deep Removal**.
5. Select the files to process and click **Execute**.
### Command Line Mode (for testing or integration)
- You can use the functions in `removerPdf.py` and `removerWord.py` programmatically.
### Function Explanation: `is_text_color_rgb` and `is_text_color_hsv`
These two functions are designed to detect black or near-black text (or watermarks) in an image. You can adjust the thresholds to adapt to different kinds of watermarks.
#### **`is_text_color_rgb`**
This function identifies black or near-black pixels in an image using the RGB colour space.
##### **How it works:**
1. **RGB Thresholding:**
- The function checks if the intensity values of all three channels (Red, Green, and Blue) are less than `140`.
- Pixels meeting this condition are considered "dark," representing text or watermark content.
2. **Adjusting for Watermarks:**
- **Increase the threshold (`140 → higher`)**: To detect lighter shades of gray or faint black text.
- **Decrease the threshold (`140 → lower`)**: To focus on strictly darker pixels, excluding lighter marks.
3. **Example Use Case:**
- Ideal for detecting solid black or grayscale text-based watermarks.
##### **Code:**
```python
def is_text_color_rgb(img_array):
# Identify black or near-black pixels in RGB color space
mask = (
(img_array[:, :, 0] < 140) & # Red channel threshold
(img_array[:, :, 1] < 140) & # Green channel threshold
(img_array[:, :, 2] < 140) # Blue channel threshold
)
return mask
```
#### **is_text_color_hsv**
This function identifies black-like or dark regions in the HSV (Hue, Saturation, Value) color space, which is more robust for varying lighting and color tones.
#### **How it works:**
1. **HSV Conversion:**
- The image is converted to the HSV color space.
- Hue (H) is ignored because black is not dependent on specific colors. Instead, Saturation (S) and Value (V) are analyzed.
2. **Thresholding:**
- Saturation (S < 40): Ensures the region is not colorful (low saturation means grayscale or black).
- Value (V < 160): Ensures the region is dark (lower values indicate darker pixels).
3. **Adjusting for Watermarks**:
- Increase Saturation Threshold (S < 40 → higher): Includes slightly tinted watermarks.
- Decrease Saturation Threshold (S < 40 → lower): Focuses strictly on grayscale or black regions.
- Increase Value Threshold (V < 160 → higher): Includes lighter shades of text or watermark.
- Decrease Value Threshold (V < 160 → lower): Focuses strictly on darker marks.
4. **Example Use Case:**
- Particularly useful for detecting faintly tinted or dark watermarks.
#### **Code:**
```python
def is_text_color_hsv(img_array):
# Convert the RGB image to HSV
hsv_img = cv2.cvtColor(img_array, cv2.COLOR_RGB2HSV)
# Identify dark or black-like regions in HSV space
mask = (hsv_img[:, :, 1] < 40) & (hsv_img[:, :, 2] < 160) # Saturation and Value thresholds
return mask
```
### Customizing for Different Watermarks
By modifying the threshold values, you can adapt the functions to detect specific types of watermarks:
1. **Light Gray Watermarks:**
- Increase 140 in is_text_color_rgb and V < 160 in is_text_color_hsv to include lighter shades.
2. **Faint Colored Watermarks:**
- Increase the S threshold in is_text_color_hsv to include more color.
3. **Dark and Clear Watermarks:**
- Lower all thresholds (R/G/B < 140, S < 40, V < 160) to focus on darker and clearer watermarks.
---
## File Structure
- **prod.py**: Main GUI application file.
- **removerPdf.py**: Functions for processing and removing watermarks from PDF files.
- **removerWord.py**: Functions for processing and removing watermarks from Word documents.
- **requirements.txt**: List of required Python libraries.
---
## Dependencies
The tool depends on the following Python libraries:
```text
PyQt5
pymupdf
pdf2image
numpy
scikit-image
Pillow
python-docx
```
Install these dependencies using `pip install -r requirements.txt`.
---
## Exampler
- Before
- After
---
## Known Issues
### Memory Usage:
- Processing large PDFs may consume a significant amount of memory. The tool saves intermediate images to the disk to mitigate this.
### Responsiveness:
- The GUI may become unresponsive during intensive operations in Deep Removal mode.
---
## Special Thanks
A heartfelt thank you to the authors and maintainers of the following libraries and tools that made this project possible:
- **[PyQt5](https://pypi.org/project/PyQt5/):** For enabling the creation of a modern and user-friendly GUI.
- **[PyMuPDF](https://pymupdf.readthedocs.io/):** For providing robust tools to manipulate and analyze PDF documents.
- **[pdf2image](https://pypi.org/project/pdf2image/):** For seamless PDF-to-image conversion.
- **[NumPy](https://numpy.org/):** For efficient array manipulation and mathematical operations.
- **[scikit-image](https://scikit-image.org/):** For advanced image processing and manipulation capabilities.
- **[Pillow](https://pillow.readthedocs.io/):** For versatile image manipulation and saving functionalities.
- **[python-docx](https://python-docx.readthedocs.io/):** For enabling the manipulation of Word documents.
- **[Poppler](https://poppler.freedesktop.org/):** For handling PDF rendering and conversion.
Your hard work and dedication have not only made this project possible but also helped countless developers worldwide to create innovative solutions.
**Thank you for your invaluable contributions to the open-source community! ❤️**
---
## License
This project is licensed under the MIT License. See LICENSE for details.
## Credits
Developed by Zhang Johnson.