The Hand Detection API is an advanced tool that utilizes image analysis techniques to identify and analyze human hands within an input image. It outputs precise coordinates of hand bone nodes, enabling developers to capture and understand intricate hand gestures and positions.

The API employs complex image processing algorithms to recognize and isolate hands within the input image. It then employs skeletal tracking techniques to accurately determine the positions of the hand's 21 bone nodes, including finger joints and hand articulations.

The API is designed to work with images containing human hands. It's important for the input images to have appropriate aspect ratios and clear visibility of hands. Images with varying lighting conditions, orientations, and backgrounds can also be accommodated, but results might vary.

The API's output includes both the coordinate frame of the detected hand and detailed information about the positions of 21 bone nodes within each hand. This data enables developers to recreate and analyze the gestures and hand positions.

Yes, the API is capable of detecting multiple hands within a single image. It can provide comprehensive information about the bone nodes of each detected hand, allowing for precise analysis of each hand's gesture.

The Hand Detection API returns detailed information about detected hands, including the coordinates of 21 bone nodes for each hand. This data allows developers to analyze hand gestures and movements accurately.

The key fields in the response data include "hand_info," which contains an array of detected hands, and "hand_parts," detailing the coordinates (x, y) and confidence scores for each bone node of the hand.

The response data is structured in JSON format, with a top-level "message" indicating success and a "data" object containing "hand_info." Each hand's details are nested within this object, providing a clear hierarchy for accessing specific information.

The Analyze endpoint primarily accepts an image file as input. Users can customize their requests by adjusting image quality, resolution, and format to optimize detection results.

Users can utilize the returned data by mapping the coordinates of the bone nodes to visualize hand gestures or integrate them into applications for gesture recognition, gaming, or virtual interactions.

Typical use cases include gesture-based controls in AR/VR applications, sign language interpretation, and enhancing user interactions in gaming, where precise hand movement tracking is essential.

Data accuracy is maintained through advanced image processing algorithms and machine learning techniques that continuously improve hand detection capabilities based on diverse training datasets.

If the API returns partial or empty results, users should check the input image quality and ensure hands are clearly visible. Implementing error handling in the application can help manage such scenarios gracefully.

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