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System Design for RawShines Project

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1. System

MacOS: Apple has implemented the HDR display. We need to first match that (hard task)

2. Programming Language

Python

C++

3. High-Level System Components:

  • Input Module: Accepts and validates the RAW image files.
  • Metadata Extraction Module: Extracts and temporarily stores metadata from the RAW file.
  • Image Processing Engine: Prepares RAW images for manipulations by converting them into an intermediate format.
  • Modification GUI: Provides user-friendly tools and controls for image adjustments.
  • HEIC Converter: Converts the manipulated image into the HEIC format.
  • Output Module: Outputs the processed HEIC image, complete with options for subsequent actions such as saving or sharing.

4. Detailed Design:

  • a. Input Module:
  • File Selection: Facilitates selection or drag-and-drop of RAW files.
  • File Validation: Ensures files adhere to supported RAW formats.
  • Batch Processing: Enables processing of multiple files simultaneously.
  • b. Image Processing Engine:
  • Decoding Engine: Utilizes libraries like libraw for RAW image decoding.
  • Color Correction: Employs algorithms for consistent color grading.
  • Intermediate Format Storage: Ensures quick processing by converting and storing images in an intermediate format.
  • c. Modification GUI:
  • Preview Pane: Real-time display of image modifications.
  • Adjustment Sliders: Controls for brightness, contrast, saturation, etc.
  • Undo/Redo Actions: Grants flexibility in modifications.
  • Presets: Allows quick edits through predefined settings.
  • Zoom & Pan Tools: Facilitates detail inspection.
  • Histogram Display: Dynamic histogram for informed editing.
  • d. HEIC Converter:
  • Conversion Engine: Employs libraries like libheif for HEIC conversion.
  • Compression Settings: Users can set desired compression levels.
  • Profile Embedding: Guarantees color consistency by embedding ICC profiles.
  • e. Output Module:
  • File Saving: Users dictate destination and filename for output.
  • Metadata Preservation: Original metadata remains intact in the converted image.
  • Sharing Options: Direct integration for cloud, social media, or email sharing.

5. System Architecture & Technologies:

  • Platform: Choices include Electron or Qt, based on target audience.
  • Programming Language: Choose between JavaScript (Electron) or C++ (Qt).
  • External Libraries: Utilize libraw and libheif.
  • Database: Integrate SQLite for efficient storage and retrieval.

6. Non-functional Considerations:

  • Performance: Prioritize fast processing, especially for high-res RAW images.
  • Usability: Ensure an intuitive experience, especially for non-tech-savvy photographers.
  • Scalability: Plan for potential growth or feature additions.
  • Security: Prioritize user data security.
  • Extensibility: A modular architecture facilitates future feature additions.
  • Error Handling: Users receive clear messages for unsupported formats or other issues.

7. Testing:

  • Unit Testing: Individual components are rigorously tested.
  • Integration Testing: Modules are tested for seamless interactions.
  • Usability Testing: Gather feedback from photographers on GUI and workflows.
  • Performance Testing: Ensure efficiency with high-res RAW files.