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Merge multiple mask tensors into a unified mask for image segmentation and object detection.
The Masks Combine Batch node is designed to merge multiple mask tensors into a single combined mask. This node is particularly useful when you have several masks representing different regions or features of an image and you want to create a unified mask that encompasses all these regions. By summing the individual masks and ensuring the combined values are clamped between 0 and 1, this node effectively integrates the masks while maintaining the integrity of the mask values. This process is essential for tasks that require a comprehensive mask representation, such as image segmentation, object detection, or any application where multiple mask layers need to be consolidated into one.
This parameter accepts a list of mask tensors that you want to combine. Each mask in the list should be a tensor representing a specific region or feature of an image. The function will stack these masks along a new dimension, sum them, and then clamp the resulting values to ensure they remain between 0 and 1. This ensures that the combined mask accurately represents all the input masks without exceeding the valid value range for mask tensors.
The output is a single combined mask tensor. This tensor is the result of summing all the input masks and clamping the values between 0 and 1. The combined mask can be used in subsequent image processing tasks where a unified mask is required. It effectively represents all the regions or features indicated by the input masks in a single tensor.
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