ComfyUI Node: Image Histogram

Image Histogram:

The LF_ImageHistogram node is designed to generate histograms for the RGB channels and their sum from an input image tensor. This node is particularly useful for analyzing the color distribution within an image, providing valuable insights into the image's color composition. By converting the image tensor into histograms, you can better understand the intensity distribution of each color channel (Red, Green, and Blue) and their combined effect. This can be beneficial for tasks such as image enhancement, color correction, and other image processing applications. The node formats the histogram data into a structured dataset, making it easy to integrate and utilize in further processing or analysis workflows.

Image Histogram Input Parameters:

image

The image parameter expects an input image tensor in the shape [1, H, W, 3]. This tensor represents the image you want to analyze, where H is the height, W is the width, and 3 corresponds to the RGB color channels. The image tensor should be normalized, with pixel values typically ranging from 0 to 1. This parameter is crucial as it serves as the source data from which the histograms are generated. There are no specific minimum, maximum, or default values for this parameter, but it must be a valid image tensor in the specified shape.

Image Histogram Output Parameters:

image

The image output parameter returns the original input image tensor. This allows you to pass the image along to subsequent nodes in your workflow without needing to re-load or re-process the image. It ensures that the image data remains accessible for further operations or analysis.

dataset

The dataset output parameter provides a JSON-formatted dataset containing the histogram data for the RGB channels and their sum. This dataset includes the histograms for the Red, Green, and Blue channels, as well as a combined histogram that represents the sum of all three channels. The dataset is structured to be easily interpretable and can be used for various analytical purposes, such as visualizing the color distribution or performing statistical analysis on the image's color composition.

Image Histogram Usage Tips:

• Ensure that your input image tensor is correctly normalized and in the shape [1, H, W, 3] to avoid any processing errors.
• Utilize the dataset output to visualize the histograms and gain insights into the color distribution of your image, which can be helpful for tasks like color correction or enhancement.
• Combine this node with other image processing nodes to create a comprehensive analysis pipeline, leveraging the histogram data for more informed decision-making.

Image Histogram Common Errors and Solutions:

Invalid image tensor shape

• Explanation: The input image tensor is not in the expected shape [1, H, W, 3].
• Solution: Ensure that your image tensor is correctly formatted and normalized, with the shape [1, H, W, 3].

Tensor conversion error

• Explanation: There was an issue converting the image tensor to a numpy array.
• Solution: Verify that the input tensor is on the CPU and properly normalized. If necessary, move the tensor to the CPU using .cpu() before passing it to the node.

Histogram calculation error

• Explanation: An error occurred while calculating the histograms for the RGB channels.
• Solution: Check the integrity of the input image tensor and ensure it contains valid pixel values. If the issue persists, try using a different image to see if the problem is specific to the input data.