ComfyUI Node: SAM3 Point Segmentation

SAM3 Point Segmentation:

The SAM3Segmentation node is designed to perform click-based interactive segmentation on images using the SAM3 model. This node allows you to segment specific areas of an image by providing precise point or box locations, making it ideal for tasks that require detailed and accurate segmentation. The node is particularly useful for artists and designers who need to isolate parts of an image for further manipulation or analysis. By leveraging the SAM3 model's capabilities, this node offers a user-friendly approach to segmentation, enabling you to achieve high-quality results with minimal effort. The node's primary goal is to facilitate the segmentation process by providing an intuitive interface that mimics the "click to segment" behavior, similar to previous versions like SAM2, while also offering advanced features for more complex segmentation tasks.

SAM3 Point Segmentation Input Parameters:

sam3_model

The sam3_model parameter is essential for the segmentation process as it specifies the SAM3 model to be used. This model should be loaded from the LoadSAM3Model node and is responsible for executing the segmentation task. The model must be configured with enable_inst_interactivity=True to ensure interactive segmentation capabilities. This parameter does not have a default value and must be provided for the node to function correctly.

image

The image parameter is the input image on which the segmentation will be performed. It serves as the canvas for the segmentation task, and the quality and resolution of this image can significantly impact the results. This parameter is mandatory and does not have a default value, as it directly influences the segmentation output.

confidence_threshold

The confidence_threshold parameter determines the minimum confidence score required to retain detections during the segmentation process. It is a floating-point value ranging from 0.0 to 1.0, with a default value of 0.2. A lower threshold, such as 0.2, is recommended for better performance with SAM3's presence scoring, as it allows for more detections to be considered, potentially improving the segmentation results.

SAM3 Point Segmentation Output Parameters:

mask

The mask output provides the binary mask of the segmented area, indicating which parts of the input image have been identified and isolated by the segmentation process. This mask is crucial for further image processing tasks, such as editing or analysis, as it clearly delineates the segmented regions.

mask_logits

The mask_logits output contains the raw logits of the segmentation mask, offering a more detailed representation of the segmentation confidence across the image. This output is useful for advanced users who may want to refine or analyze the segmentation results further.

visualization

The visualization output provides a visual representation of the segmentation results, overlaying the mask on the original image. This output helps you quickly assess the quality and accuracy of the segmentation, making it easier to make adjustments if necessary.

boxes

The boxes output lists the bounding boxes around the detected segments, providing spatial information about the location and size of each segmented area. This output is valuable for tasks that require precise localization of segments within the image.

scores

The scores output contains the confidence scores for each detected segment, indicating the model's certainty about the presence of each segment. These scores can help you decide which segments to keep or discard based on their confidence levels.

SAM3 Point Segmentation Usage Tips:

• Ensure that the sam3_model is loaded with enable_inst_interactivity=True to fully utilize the interactive segmentation capabilities of the node.
• Adjust the confidence_threshold parameter to balance between precision and recall. A lower threshold may yield more segments, while a higher threshold can improve the precision of the detected segments.
• Use high-resolution images for better segmentation results, as the quality of the input image directly affects the accuracy of the segmentation.

SAM3 Point Segmentation Common Errors and Solutions:

Model not loaded

• Explanation: This error occurs when the sam3_model is not properly loaded or configured.
• Solution: Ensure that the SAM3 model is loaded using the LoadSAM3Model node and that enable_inst_interactivity=True is set.

Image input missing

• Explanation: This error arises when the image parameter is not provided.
• Solution: Make sure to input a valid image file for the segmentation process.

Invalid confidence threshold

• Explanation: This error happens when the confidence_threshold is set outside the allowed range of 0.0 to 1.0.
• Solution: Adjust the confidence_threshold to a value within the specified range, ideally starting with the default value of 0.2.