ComfyUI Node: Get Average Color From Image Jannchie

Get Average Color From Image Jannchie:

The GetAverageColorFromImage node is designed to calculate the average color of an image, providing a simple yet powerful tool for AI artists to analyze and utilize color information from their images. This node can compute the average color using either the mean or mode of the pixel values, offering flexibility depending on the desired outcome. By extracting the average red, green, and blue values, you can gain insights into the overall color composition of an image, which can be particularly useful for tasks such as color grading, palette generation, or image analysis. The node also supports the use of a mask to focus the calculation on specific areas of the image, allowing for more targeted color analysis.

Get Average Color From Image Jannchie Input Parameters:

image

This parameter represents the input image from which the average color will be calculated. The image should be provided as a tensor, which is a multi-dimensional array commonly used in machine learning and image processing. The image tensor should have dimensions corresponding to the batch size, height, width, and color channels (typically RGB).

average

This parameter determines the method used to calculate the average color. It accepts two options: "mean" and "mode". The "mean" option calculates the average color by computing the mean of the pixel values, which provides a smooth and balanced representation of the overall color. The "mode" option calculates the average color by finding the most frequently occurring pixel values, which can highlight dominant colors in the image. The default value is "mean".

mask

This optional parameter allows you to provide a mask that specifies which parts of the image should be included in the average color calculation. The mask should be a tensor with the same height and width as the input image, where each pixel value indicates whether the corresponding pixel in the image should be considered (1) or ignored (0). If no mask is provided, the entire image will be used for the calculation.

Get Average Color From Image Jannchie Output Parameters:

red

This output parameter represents the average red color value of the image or the masked area. The value is a floating-point number between 0 and 255, indicating the intensity of the red channel.

green

This output parameter represents the average green color value of the image or the masked area. The value is a floating-point number between 0 and 255, indicating the intensity of the green channel.

blue

This output parameter represents the average blue color value of the image or the masked area. The value is a floating-point number between 0 and 255, indicating the intensity of the blue channel.

Get Average Color From Image Jannchie Usage Tips:

• To get a balanced representation of the overall color of an image, use the "mean" option for the average parameter.
• If you want to highlight dominant colors in the image, try using the "mode" option for the average parameter.
• Use a mask to focus the average color calculation on specific areas of the image, such as a particular object or region of interest.
• Ensure that the input image tensor is correctly formatted with dimensions corresponding to batch size, height, width, and color channels.

Get Average Color From Image Jannchie Common Errors and Solutions:

Invalid average method: <method>

• Explanation: This error occurs when an unsupported value is provided for the average parameter.
• Solution: Ensure that the average parameter is set to either "mean" or "mode".

Mask dimensions do not match image dimensions

• Explanation: This error occurs when the provided mask tensor does not have the same height and width as the input image tensor.
• Solution: Verify that the mask tensor has the same height and width as the input image tensor.

Input image tensor is not correctly formatted

• Explanation: This error occurs when the input image tensor does not have the expected dimensions (batch size, height, width, and color channels).
• Solution: Ensure that the input image tensor is correctly formatted with the appropriate dimensions.