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From RGB to HSV — and Back Again
- Images are stored with color information represented in RGB space, with pixels having red, green, and blue values.
- RGB values are mixed additively to indicate colors, where 0 values produce black and 100% values result in white.
- Images may also have an alpha channel for transparency, with zero being fully transparent and 100% being fully opaque.
- The HSV color model represents colors by hue, saturation, and value, allowing separate control of color tone, intensity, and brightness.
- HSV model simplifies color processing, enabling easy transitions between colors while maintaining a consistent brightness.
- Python's OpenCV library facilitates color space conversion and manipulation for image processing tasks.
- Examples include isolating colors in an image using HSV masks, converting colors between different color spaces, and transitioning between colors.
- A utility function can convert colors between RGB and HSV, showcasing consistent color conversion accuracy.
- Transitioning colors with constant brightness and saturation using HSV interpolation provides smoother color transitions compared to direct RGB interpolation.
- Full source code for implementation details can be found in the provided GitHub repository for further exploration.
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