ComfyUI Node: ◎ Radiance HDR Histogram

◎ Radiance HDR Histogram:

The HDRHistogram node is designed to analyze High Dynamic Range (HDR) images by generating a histogram that provides insights into the image's dynamic range, clipping indicators, and stops visualization. This node is particularly useful for artists and designers working with HDR content, as it helps in understanding the distribution of luminance or color values across the image. By visualizing the histogram, you can easily identify areas of the image that may be overexposed or underexposed, allowing for more informed adjustments and enhancements. The node leverages the power of Python libraries such as PIL for rendering the histogram, ensuring a detailed and accurate representation of the image's tonal range. This analysis is crucial for achieving the desired visual effects and maintaining the integrity of HDR images in your projects.

◎ Radiance HDR Histogram Input Parameters:

image

This parameter represents the HDR image that you want to analyze. It should be provided as a tensor, which is a multi-dimensional array used to store the image data. The image serves as the primary input for the node, and its quality and characteristics will directly influence the accuracy and detail of the histogram analysis.

mode

The mode parameter determines the type of analysis to be performed on the image. It can take one of three values: "luminance," "rgb," or "log_luminance." The default value is "luminance." When set to "luminance," the node analyzes the brightness levels of the image, which is useful for understanding the overall light distribution. The "rgb" mode analyzes each color channel separately, providing a more detailed view of the color distribution. The "log_luminance" mode applies a logarithmic scale to the luminance values, which can be helpful for images with a wide dynamic range.

show_clipping

This boolean parameter, which defaults to True, controls whether clipping indicators are displayed on the histogram. Clipping indicators highlight areas of the image where pixel values are either too low (clipped to black) or too high (clipped to white), which can result in loss of detail. Enabling this option helps you quickly identify and address potential exposure issues in your HDR images.

stops_range

The stops_range parameter specifies the range of stops to be visualized in the histogram. It is an integer value with a default of 14, and it can be adjusted between a minimum of 8 and a maximum of 24. This parameter affects the granularity of the stops visualization, allowing you to focus on specific ranges of the dynamic range that are most relevant to your analysis.

◎ Radiance HDR Histogram Output Parameters:

histogram

The histogram output is an image that visually represents the distribution of pixel values in the input HDR image. It provides a graphical depiction of the tonal range, allowing you to see how the image's brightness or color values are spread across different levels. This output is crucial for assessing the exposure and contrast of the image and making informed adjustments.

stats

The stats output is a string that contains detailed statistical information about the HDR image. It includes metrics such as the minimum, maximum, and mean pixel values, as well as the dynamic range in stops. Additionally, it provides information on the percentage of pixels that are clipped at the low and high ends of the range. This data is valuable for understanding the overall quality and characteristics of the image.

◎ Radiance HDR Histogram Usage Tips:

• To get a comprehensive view of your HDR image's exposure, use the "luminance" mode for a general analysis and switch to "rgb" mode if you need to examine individual color channels.
• Adjust the stops_range parameter to focus on specific areas of interest within the dynamic range, especially if your image has extreme highlights or shadows.

◎ Radiance HDR Histogram Common Errors and Solutions:

◎ Radiance HDR Histogram error: <error_message>

• Explanation: This error occurs when there is an issue during the histogram analysis process, which could be due to an invalid input image or an unexpected internal error.
• Solution: Ensure that the input image is a valid tensor and that all parameters are set correctly. If the problem persists, check the console for additional error details and consider reloading the image or restarting the application.

Return RED ERROR IMAGE

• Explanation: This indicates that the node encountered a critical error and was unable to generate the histogram. The output is a red image to signal the failure.
• Solution: Verify that the input image is correctly formatted and that all input parameters are within their valid ranges. Review any error messages in the console for further troubleshooting steps.