ComfyUI Node: SEGM Detector (combined)

SEGM Detector (combined):

The SegmDetectorCombined_v2 node is designed to facilitate the detection and segmentation of objects within an image using a combined segmentation detector. This node leverages advanced machine learning models to identify and segment objects, providing a mask that highlights the detected regions. The primary goal of this node is to simplify the process of object segmentation, making it accessible to AI artists who may not have a deep technical background. By adjusting parameters such as threshold and dilation, you can fine-tune the detection process to suit your specific needs, ensuring accurate and detailed segmentation results.

SEGM Detector (combined) Input Parameters:

segm_detector

This parameter specifies the segmentation detector model to be used for the detection process. The model is responsible for analyzing the image and identifying the objects to be segmented. It is crucial to select a well-trained and appropriate model for your specific use case to achieve optimal results.

image

The image parameter is the input image that you want to process. This image will be analyzed by the segmentation detector to identify and segment objects. Ensure that the image is of good quality and relevant to the objects you wish to detect.

threshold

The threshold parameter determines the confidence level required for an object to be considered detected. It is a floating-point value ranging from 0.0 to 1.0, with a default value of 0.5. A higher threshold means that only objects with higher confidence scores will be detected, which can reduce false positives but may miss some objects. Conversely, a lower threshold will detect more objects but may include more false positives.

dilation

The dilation parameter controls the dilation process applied to the detected masks. It is an integer value ranging from -512 to 512, with a default value of 0. Dilation can help in refining the edges of the detected masks, making them more precise. Positive values will expand the mask, while negative values will contract it. Adjust this parameter based on the level of detail required for your segmentation.

SEGM Detector (combined) Output Parameters:

MASK

The MASK output parameter provides the resulting mask from the segmentation process. This mask is a binary image where the detected objects are highlighted. The mask can be used for further processing, visualization, or as input for other nodes in your workflow. It is essential for understanding which regions of the image contain the detected objects.

SEGM Detector (combined) Usage Tips:

• Adjust the threshold parameter to balance between detecting all possible objects and minimizing false positives. Start with the default value and fine-tune based on your specific requirements.
• Use the dilation parameter to refine the edges of the detected masks. Experiment with different values to achieve the desired level of detail and precision.
• Ensure that the input image is of high quality and relevant to the objects you want to detect. Poor quality images can lead to inaccurate segmentation results.

SEGM Detector (combined) Common Errors and Solutions:

[Impact Pack] ERROR: SegmDetectorForEach does not allow image batches.

• Explanation: This error occurs when you try to process a batch of images instead of a single image.
• Solution: Ensure that you are providing a single image as input to the node. If you need to process multiple images, consider looping through them individually.

mask is None

• Explanation: This error indicates that the segmentation detector did not return any mask, possibly due to a very high threshold or an issue with the model.
• Solution: Lower the threshold value to ensure that more objects are detected. Verify that the segmentation model is correctly loaded and appropriate for the input image.