# Lessons Learned: Hyperion Processor Module

This document captures key insights, challenges, and solutions encountered during the development and testing of the `HyperionProcessor` module.

## 1. Importance of Incremental Testing and Visualization

*   **Challenge:** Initial attempts to debug the `HyperionProcessor`'s output were hampered by a lack of clear visual feedback. Printing raw color values (`#RRGGBB`) to the console was not intuitive, and the initial ASCII visualization was difficult to interpret without proper color support or dynamic updates.
*   **Solution:** Breaking down the problem into smaller, verifiable steps proved crucial. First, confirming the `WaylandGrabber` and `HyperionGrabber` were correctly capturing and scaling images (via `QLabel` display) isolated the issue to the processing pipeline. Then, visualizing the *intermediate* step of black border detection (showing the cropped image) provided immediate confirmation of that component's functionality.
*   **Lesson:** For complex image processing or graphical tasks, robust visual debugging tools that show intermediate steps are invaluable. They allow for quick identification and isolation of issues, significantly accelerating the development process.

## 2. Understanding Qt's Logging and Console Output

*   **Challenge:** `qDebug()` and `qInfo()` messages were not appearing in the console during initial `test_hyperion_processor` runs, leading to confusion about whether the code was executing or producing any output.
*   **Solution:** Explicitly configuring `QLoggingCategory::setFilterRules()` and adding `QCoreApplication::processEvents()` calls did not immediately resolve the issue in a simple console application. Ultimately, switching to `std::cout` and `std::cerr` for direct console output bypassed Qt's logging system and guaranteed visibility.
*   **Lesson:** When working with Qt in a console-only context, especially for quick tests, direct `std::cout`/`std::cerr` can be more reliable for immediate feedback than relying solely on Qt's logging framework, which might require more extensive setup for console redirection.

## 3. Robustness of Image Processing to Input Variations

*   **Challenge:** The `HyperionProcessor` initially produced dark, almost black LED colors when tested with `frames/frame_000.png`. This led to initial concerns about the `BlackBorderDetector` or `getAverageColor` logic.
*   **Solution:** Visual inspection of `frame_000.png` revealed it was indeed mostly black. This confirmed that the `HyperionProcessor` was functioning correctly based on its input. The subsequent live testing with `grabberconfigurator` (showing the scaled screen) confirmed the entire capture pipeline was working.
*   **Lesson:** Always validate input data. An unexpected output might not indicate a bug in the processing logic but rather an issue with the input data itself. Using real-world or representative input is critical for accurate testing.

## 4. Iterative Refinement of Visualization

*   **Challenge:** The initial ASCII visualization of LED colors was not clear enough for the user to interpret effectively.
*   **Solution:** Iteratively refining the visualization based on user feedback (e.g., switching from ASCII to `QLabel` for cropped image, then adding thicker outlines and a frame) significantly improved its utility. The ability to dynamically adjust the visualization (e.g., `frameThickness`, `penThickness`) allowed for rapid iteration and better understanding.
*   **Lesson:** User feedback is paramount for effective tool development, especially for visual debugging tools. Start simple, but be prepared to iterate and enhance based on what makes the tool most useful to the end-user.