The Hand Detection API introduces a remarkable breakthrough in image analysis. Designed to decipher the subtleties of human hands, this API is capable of transforming an input image into a canvas of gestures and motion. Leveraging advanced image processing techniques, it accurately detects hands within the image, regardless of their orientation or position.
Upon analysis, the API provides a comprehensive output that includes both the coordinate frame and intricate details of the hand's skeletal structure. With remarkable precision, it extracts 21 bone node coordinates for each hand, intricately mapping the articulation and positions of each finger joint. This detailed information unlocks a wealth of possibilities, from gesture recognition to immersive virtual interactions.
The Hand Detection API finds diverse applications across industries. In augmented and virtual reality, it can enable captivating interactions, where users' hand movements translate into dynamic digital experiences. In robotics, the API can facilitate human-robot collaboration by comprehending gestures and commands. Additionally, the API can be integrated into applications aimed at enhancing sign language interpretation, healthcare diagnostics, and even gaming experiences.
Developers can seamlessly integrate the Hand Detection API by following the comprehensive documentation. The API offers endpoints, request and response formats, and code examples in various programming languages, making the integration process intuitive and efficient.
As technology continues to bridge the gap between the digital and physical worlds, the Hand Detection API stands as a testament to this evolution. By decoding the language of hands, it paves the way for new dimensions of interactivity and communication, where images and motion intertwine to create a more immersive and engaging experience.
Pass the image and receive an analysis of the hand.
Gesture-Based Interaction in AR/VR:
The Hand Detection API can power gesture-based interactions in augmented and virtual reality environments. Users can control digital elements, navigate interfaces, and manipulate objects through natural hand gestures, enhancing the immersive experience.
Human-Robot Collaboration:
Robotics applications can utilize the API to enable human-robot collaboration. Robots equipped with cameras can interpret human hand gestures, allowing for intuitive communication and control in industries like manufacturing, healthcare, and logistics.
Sign Language Interpretation:
The API can be employed to develop applications that facilitate real-time interpretation of sign language. By accurately detecting hand movements and finger positions, the API enhances communication for the hearing-impaired community.
Gaming and Interactive Entertainment:
Gaming experiences can be enriched by integrating the API. Players can control in-game actions, characters, and elements using their hand gestures, creating a more dynamic and immersive gaming environment.
Healthcare Rehabilitation and Diagnostics:
In healthcare, the API can aid in rehabilitation and diagnostics. Physical therapy applications can use hand gesture data to assess patients' progress and tailor exercises. Moreover, the API could assist in assessing conditions affecting hand mobility and dexterity.
Besides the number of API calls allowed per plan, there are no other limitations.
Hand Recognition
Analyze - Endpoint Features
Object | Description |
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imageUrl |
[Required] |
{"message":"success","data":{"hand_info":[{"hand_parts":{"4":{"y":204,"x":486,"score":0.81871610879898},"10":{"y":321,"x":454,"score":0.8176451921463},"5":{"y":242,"x":422,"score":0.63888543844223},"11":{"y":359,"x":491,"score":0.79886507987976},"12":{"y":390,"x":523,"score":0.81205052137375},"7":{"y":321,"x":497,"score":0.83726966381073},"18":{"y":343,"x":391,"score":0.81639087200165},"13":{"y":305,"x":380,"score":0.67881566286087},"0":{"y":226,"x":263,"score":0.59736984968185},"8":{"y":353,"x":529,"score":0.8176703453064},"19":{"y":364,"x":422,"score":0.78116250038147},"9":{"y":274,"x":406,"score":0.72501480579376},"15":{"y":369,"x":465,"score":0.86385977268219},"16":{"y":396,"x":497,"score":0.85061377286911},"6":{"y":289,"x":470,"score":0.82305908203125},"3":{"y":194,"x":433,"score":0.7212952375412},"17":{"y":321,"x":353,"score":0.74342161417007},"2":{"y":167,"x":385,"score":0.66624814271927},"14":{"y":343,"x":428,"score":0.8819363117218},"1":{"y":173,"x":327,"score":0.49955746531487},"20":{"y":390,"x":454,"score":0.85869860649109}},"location":{"top":167,"height":229,"score":16.048545837402,"left":263,"width":266}}],"hand_num":1},"code":0}
curl --location --request POST 'https://zylalabs.com/api/2504/hand+detection+api/2485/analyze?imageUrl=https://uploads-ssl.webflow.com/577065f4e06b550b0c190c5c/583bb3ca5b8693a10835b1f3_Sophie%27s%20hand_BEN7244.jpg' --header 'Authorization: Bearer YOUR_API_KEY'
Header | Description |
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Authorization
|
[Required] Should be Bearer access_key . See "Your API Access Key" above when you are subscribed. |
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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.
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