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Specialized node for extracting key poses from images, streamlining pose detection in visual data analysis.
OpenPoseKeyPose is a specialized node designed to extract and interpret key poses from images, providing a streamlined approach to pose detection. This node is part of the OpenPose family, which is renowned for its ability to analyze human body postures in visual data. The primary goal of OpenPoseKeyPose is to identify and highlight significant poses within an image, making it an invaluable tool for applications that require precise pose estimation, such as animation, virtual reality, and motion capture. By focusing on key poses, this node helps reduce complexity and enhances the efficiency of pose analysis, allowing you to concentrate on the most critical aspects of human movement. Its integration into workflows can significantly improve the accuracy and speed of pose-related tasks, making it a powerful asset for AI artists and developers working with human-centric visual data.
The image
parameter is a string that specifies the path to the image file you wish to analyze for key poses. This parameter is crucial as it determines the source of the visual data that the node will process. The image path should be valid and accessible; otherwise, the node will not be able to perform its function. There is no default value for this parameter, so you must provide a valid path to an image file.
The frame
parameter is an integer that indicates the specific frame of the image sequence to be processed. This is particularly useful when dealing with video files or image sequences where you want to extract key poses from a particular frame. The default value is 0, with a minimum value of -2<sup>
31 and a maximum value of 2<sup>
31. Adjusting this parameter allows you to target specific moments within a sequence for pose detection.
The IMAGE
output parameter provides the processed image with the detected key poses highlighted. This output is essential for visual verification and further processing, as it allows you to see the results of the pose detection directly on the image. The highlighted key poses can be used for various applications, such as animation or analysis.
The MASK
output parameter delivers a mask that corresponds to the detected key poses in the image. This mask is a binary representation where the key poses are marked, enabling you to isolate and manipulate these areas separately from the rest of the image. The mask is particularly useful for tasks that require precise control over pose-related regions, such as compositing or further image processing.
frame
parameter to target specific frames in a sequence, which can be particularly useful for analyzing motion in videos.<image_path>
<sup>
31 to 2<sup>
31 and corresponds to an actual frame in the image sequence.RunComfy is the premier ComfyUI platform, offering ComfyUI online environment and services, along with ComfyUI workflows featuring stunning visuals. RunComfy also provides AI Playground, enabling artists to harness the latest AI tools to create incredible art.