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Analyze and process image luminance levels for enhancing contrast and segmentation in computer vision applications.
The ImageLuminanceDetector node is designed to analyze and process the luminance (brightness) levels of an input image. This node is particularly useful for tasks that require an understanding of the light intensity distribution within an image, such as enhancing image contrast, performing image segmentation based on brightness, or preparing images for further processing in computer vision applications. By focusing on the luminance aspect, this node helps in isolating and manipulating the brightness information, which can be crucial for various artistic and technical purposes. The node leverages advanced algorithms to accurately detect and adjust luminance, ensuring high-quality output that can be used in a wide range of AI art and image processing workflows.
The gamma_correction
parameter allows you to adjust the gamma value applied to the image during luminance detection. Gamma correction is a nonlinear operation used to encode and decode luminance or tristimulus values in image processing. Adjusting this parameter can help in enhancing or dimming the brightness of the image. The default value is 1.0, which means no gamma correction is applied. The minimum value is 0.1, and the maximum value is 2.0, with a step size of 0.001. Lower values will make the image appear darker, while higher values will make it appear brighter.
The output parameter IMAGE
represents the processed image with adjusted luminance levels. This output is crucial for further image processing tasks where brightness information is essential. The processed image can be used for various applications, including image enhancement, segmentation, and preparation for machine learning models. The output ensures that the luminance information is accurately represented, providing a high-quality image that meets the requirements of subsequent processing steps.
ValueError: Gamma correction value out of range
TypeError: Invalid input image format
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