✨ PixelPerfect: Image Resolution Optimizer & Editor 📸

Enhance, Optimize, and Analyze Your Digital Images with AI & Simplicity!

PixelPerfect is a comprehensive, web-based image processing application designed to empower users with advanced tools for enhancing, optimizing, and even forensically analyzing their digital images. Built with Python and Streamlit, it provides a user-friendly interface to leverage powerful AI models and traditional image manipulation techniques for various needs, from content creation to basic digital forensics.

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🚀 Key Features

• AI-Powered Image Enhancement (Super-Resolution):
  • Upscales images by 4x using the state-of-the-art Real-ESRGAN AI model (PyTorch-based).
  • Includes adjustable post-enhancement sharpening for crystal-clear results.
• Efficient Image Compression:
  • Reduce file sizes with controllable JPEG compression quality.
• Flexible Image Resizing:
  • Resize images by percentage scale or to custom pixel dimensions with aspect ratio locking.
• Comprehensive Adjustments & Filters:
  • Fine-tune images with sliders for brightness, contrast, saturation, and hue.
  • Apply a range of pre-defined filters (Blur, Sharpen, Emboss, Contour, etc.).
• Intelligent Denoising:
  • Reduce noise in images using the advanced Non-Local Means algorithm.
• Automatic Background Removal:
  • Effortlessly remove backgrounds from images using the rembg AI library.
• 🕵️ Basic Image Forensics:
  • Error Level Analysis (ELA): Detect potential image tampering by analyzing compression inconsistencies in JPEG images.
  • Image Hash Generation: Compute MD5 and SHA-256 cryptographic hashes for integrity verification of original images.
• Non-Destructive Workflow:
  • Robust Undo/Redo functionality for all editing operations.
  • Option to Restore to Original at any point.
• Batch Processing:
  • Apply enhancements or compression to multiple images simultaneously and download results as a convenient ZIP archive.
• Intuitive Web Interface:
  • User-friendly design with clear sidebar controls and tabbed feature organization.

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💡 Why PixelPerfect?

• AI at Your Fingertips: Leverage powerful deep learning models without complex setups.
• Privacy-First: All processing is done in-memory per session; no image data is persistently stored on any server.
• Versatile Tool: From enhancing old photos to optimizing web assets and even checking image authenticity.
• Open Source: Transparent, extensible, and built with widely used Python libraries.

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⚙️ Installation & Setup

Follow these steps to get PixelPerfect running on your local machine.

Prerequisites

• Python 3.8+ (Python 3.10 or 3.11 recommended)
• Git (if cloning the repository)

Step-by-Step Guide

1. Clone the repository:

   git clone [https://github.com/yourusername/PixelPerfect.git](https://github.com/yourusername/PixelPerfect.git) # Replace with your repo URL
   cd PixelPerfect

2. Create and activate a Python virtual environment: It's highly recommended to use a virtual environment to manage dependencies.

   python -m venv venv
   # On Windows:
   .\venv\Scripts\activate
   # On macOS/Linux:
   source venv/bin/activate

3. Install project dependencies: This will install Streamlit, Pillow, OpenCV, Real-ESRGAN, rembg, and all other necessary libraries.

   pip install -r requirements.txt

   Important Note for GPU (NVIDIA CUDA) Users: If you have an NVIDIA GPU and want to leverage it for faster AI enhancement (highly recommended for performance), you might need to install torch and torchvision with CUDA support manually after the above step, as requirements.txt might default to CPU versions.

   • First, deactivate your venv (deactivate), then reactivate.
   • Uninstall existing torch and torchvision if any:

     pip uninstall torch torchvision torchaudio

   • Visit PyTorch's official website and copy the pip command specific to your CUDA version (e.g., pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118).
   • After installing PyTorch with CUDA, you might need to reinstall realesrgan: pip install realesrgan.

4. Download AI Models (Automatic): The necessary Real-ESRGAN (weights/RealESRGAN_x4plus.pth) and rembg models will be downloaded automatically the first time you run the application and use their respective features. Ensure you have an active internet connection for the first run.

5. Run the application:

   streamlit run app.py

   Your default web browser should open a new tab with the PixelPerfect application (usually at http://localhost:8501).

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🚀 Usage

1. Upload Image(s): Use the "Upload one or more images" button in the sidebar.
   • For single image editing, upload one file.
   • For batch processing, upload multiple files.
2. Select a Feature Tab: Navigate through the tabs in the main content area (e.g., "Image Enhancement," "Image Compression," "Basic Image Forensics").
3. Adjust Parameters: Use sliders, dropdowns, and input fields to configure the desired operation.
4. Apply Operation: Click the "Apply" or "Generate" button within the tab.
5. View Results: The "Processed Image" panel (right side) will update to show the result. For forensic tools like ELA, it will display the analysis output.
6. History: Use "Undo," "Redo," and "Restore to Original" buttons in the sidebar to manage your editing history (for single images).
7. Download:
   • For single processed images or forensic results, select your desired format in the sidebar "Download Options" and click "Download Current Image."
   • For batch processed images, a dedicated "Download All [X] Images (ZIP)" button will appear within the feature tab after processing is complete.

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💻 Technologies Used

• Python: Core programming language.
• Streamlit: Web application framework for interactive UI.
• Pillow (PIL Fork): General image processing and manipulation.
• OpenCV (cv2): Advanced image processing (Hue, Denoising).
• PyTorch (torch): Deep learning framework for AI models.
• Real-ESRGAN (realesrgan): AI model for super-resolution image enhancement.
• rembg: AI library for automatic background removal.
• hashlib: For cryptographic hash generation (MD5, SHA-256).
• io: Python's standard library for in-memory byte streams.
• zipfile: Python's standard library for creating ZIP archives.
• numpy: Numerical computing for efficient array operations.
• ONNX Runtime: (Used internally by rembg) High-performance inference engine for ONNX models.

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🤝 A big thanks to my teammates Anisha Singh, Preeti Nishad, and Abhishek Kumar for their collaboration, problem-solving, and shared vision in making this project a reality.

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💡 Future Enhancements

We envision several exciting future enhancements for PixelPerfect:

• Advanced Editing: Interactive cropping, selective adjustments, text/shape overlays, content-aware fill.
• More AI Models: Image inpainting, style transfer, face restoration, image colorization.
• Improved UX: Interactive image viewer with zoom/pan, custom preset saving, drag-and-drop uploads.
• Integration: Cloud storage (Google Drive, Dropbox), user accounts, API exposure.
• Deeper Forensics: Comprehensive metadata analysis, advanced copy-move forgery detection.
• Performance: Asynchronous processing, client-side rendering (WebAssembly).

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📧 Contact

For any questions, feedback, or collaborations, please feel free to reach out to Amar Sharma at [email protected]

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📸 Demo

👉 Full project details + demo also available on my LinkedIn : https://www.linkedin.com/in/amar-sharma-363897253?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app