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Grayscale Image

Converts a color image to grayscale by removing color information while preserving luminance.

Common Properties​

  • Name - The custom name of the node.
  • Color - The custom color of the node.
  • Delay Before (sec) - Waits in seconds before executing the node.
  • Delay After (sec) - Waits in seconds after executing node.
  • Continue On Error - Automation will continue regardless of any error. The default value is false.
info

If the ContinueOnError property is true, no error is caught when the project is executed, even if a Catch node is used.

Inputs​

  • In Path - Path to the input color image file.
  • Out Path - Path where the grayscale output image will be saved.

Options​

This node does not have any configurable options.

Output​

  • result - Boolean indicating success or failure of the grayscale conversion operation.

How It Works​

The Grayscale Image node uses ImageMagick library to convert color images to grayscale. When executed, the node:

  1. Validates input and output file paths
  2. Loads the specified input image
  3. Applies the grayscale conversion algorithm which converts each pixel to a shade of gray based on its luminance
  4. Saves the resulting grayscale image to the output path

Requirements​

  • Valid input image file at the specified path
  • Output path must be writable

Error Handling​

The node will return specific errors in the following cases:

  • Empty or invalid input file path
  • Input file not found at the specified path
  • Empty or invalid output file path

Usage Notes​

  • Grayscale conversion preserves image details while removing color information
  • The conversion uses a standard luminance calculation that properly weights the red, green, and blue channels
  • File size may be reduced after conversion, especially for images with simple color palettes
  • Grayscale images are often used for:
    • Document processing
    • OCR applications
    • Reducing computational complexity in image analysis
    • Creating consistent visual styles
  • Processing is generally fast as it's a straightforward pixel transformation
  • The output image will have the same dimensions as the input image
  • Metadata and other image properties are preserved during the conversion