ComfyUI Node: SEGS Classify

SEGS Classify:

ImpactSEGSClassify is a node designed to classify segmented images based on specific criteria, allowing you to filter and organize segments according to predefined or manually set expressions. This node is particularly useful for AI artists who need to manage and manipulate image segments efficiently. By leveraging classification models, it can automatically label and score segments, making it easier to apply conditional logic to include or exclude segments based on their attributes. This functionality is essential for tasks that require precise control over image segmentation, such as creating complex compositions or applying targeted effects.

SEGS Classify Input Parameters:

segs

This parameter represents the segmented images that you want to classify. It is a tuple where the first element is the shape of the original image, and the second element is a list of segments. Each segment contains various attributes like cropped image, mask, confidence score, and bounding box. The quality and accuracy of the classification depend on the segments provided.

classifier

This parameter is the classification model used to label and score the segments. The classifier processes each cropped image segment and returns a set of labels and scores. The choice of classifier can significantly impact the classification results, so it's important to select a model that is well-suited to your specific task.

ref_image_opt

This optional parameter is the reference image from which segments can be cropped if they are not already provided. It ensures that all segments have a corresponding cropped image for classification. If not provided, the node will attempt to use the cropped images already present in the segments.

preset_expr

This parameter allows you to choose a preset expression for classification. If set to 'Manual expr', the node will use the expression provided in the manual_expr parameter. Otherwise, it will use the selected preset expression. This flexibility enables you to quickly apply common classification criteria or define custom ones.

manual_expr

This parameter is used to define a custom classification expression when preset_expr is set to 'Manual expr'. The expression should follow a specific pattern to be correctly interpreted by the node. This allows for highly customized classification logic tailored to your specific needs.

SEGS Classify Output Parameters:

filtered_SEGS

This output contains the segments that meet the classification criteria defined by the expression. It is a tuple where the first element is the shape of the original image, and the second element is a list of segments that passed the classification filter. These segments can be further processed or used in subsequent nodes.

remained_SEGS

This output contains the segments that do not meet the classification criteria. Similar to filtered_SEGS, it is a tuple with the shape of the original image and a list of segments that were excluded by the classification filter. These segments can be reviewed or reclassified as needed.

provided_labels

This output is a list of all the labels that were provided by the classifier during the classification process. It gives you an overview of the different categories identified in the segments, which can be useful for understanding the classification results and making adjustments if necessary.

SEGS Classify Usage Tips:

• Ensure that the segments provided in the segs parameter are of high quality and accurately represent the areas of interest in your image to achieve the best classification results.
• Choose a classifier that is well-suited to your specific task. For example, if you are working with human portraits, use a classifier trained on facial features.
• Use the manual_expr parameter to define custom classification logic that precisely matches your requirements. This can be particularly useful for complex projects where preset expressions are not sufficient.
• Review the provided_labels output to understand the classification results and make any necessary adjustments to your classifier or expression.

SEGS Classify Common Errors and Solutions:

Invalid expression pattern

• Explanation: The expression provided in manual_expr does not match the expected pattern.
• Solution: Ensure that your expression follows the correct format, typically involving a comparison operator and two operands.

Missing cropped image

• Explanation: A segment does not have a cropped image, and no reference image is provided.
• Solution: Provide a reference image in the ref_image_opt parameter or ensure that all segments include a cropped image.

Classifier not found

• Explanation: The specified classifier is not available or incorrectly configured.
• Solution: Verify that the classifier is correctly specified and available in your environment. Check for any typos or configuration issues.

Segments not classified

• Explanation: None of the segments meet the classification criteria.
• Solution: Review your classification expression and ensure it is correctly defined. Adjust the criteria to be more inclusive if necessary.