The Hand Recognition API returns detailed information about detected hands in images, including the coordinate frame for each hand and the coordinates of 21 bone nodes, which represent key points on the hand.

The key fields in the response data include "hand_info," which contains an array of detected hands, and "hand_parts," which provides the coordinates (x, y) and confidence scores for each of the 21 bone nodes.

The response data is structured as a JSON object. It includes a "code" indicating the status of the request and a "data" object containing "hand_info," which lists the detected hands and their corresponding bone node coordinates.

The API provides information on hand detection, including the location and orientation of each hand, as well as detailed coordinates for 21 specific points on the hand, useful for applications like gesture recognition and virtual interaction.

Users can customize their requests by providing different image URLs to the POST Hand Recognition endpoint. The API processes the specified image and returns hand detection data based on the content of that image.

Typical use cases include virtual and augmented reality applications for gesture tracking, human-computer interaction for alternative input methods, gaming for immersive experiences, and medical research for analyzing hand movements in patients.

The Hand Recognition API employs advanced computer vision algorithms that are designed to handle various lighting conditions and hand poses, ensuring high accuracy in hand detection and tracking across diverse scenarios.

Users can expect a consistent JSON structure with a "code" field and a "data" object. Each detected hand will have a corresponding "hand_parts" object containing coordinates and scores, indicating the reliability of each detected point.

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