The Body Part Recognition API is a cutting-edge solution for accurately detecting and analyzing human bodies within images. By utilizing advanced algorithms and deep learning techniques, this API is capable of identifying all the human bodies present in a given image and providing detailed information about their key points and positions.
When an input image is processed through the API, it detects and extracts 21 key points for each human body present in the image. These key points include essential landmarks such as the top of the head, ears, eyes, nose, corners of the mouth, neck, shoulders, elbows, wrists, hips, knees, and ankles. The API outputs the coordinate information for each key point, allowing developers to precisely locate and analyze different body parts.
One of the remarkable features of the Body Part Recognition API is its ability to handle complex scenes. It supports multi-person detection, even in scenarios where multiple individuals are present in the image. Additionally, it can accurately identify human bodies in situations involving overlapping body positions, occlusion, back or side views, low or mid-air overhead perspectives, and even large movements. This ensures robust performance across a wide range of real-world scenarios.
The API not only provides the positions of key points but also delivers the number of human bodies detected within the image. This information can be valuable in various applications, such as crowd analysis, behavior monitoring, fitness tracking, and augmented reality experiences.
It's worth noting that the Body Part Recognition API is continuously evolving. The developers behind the API are constantly working on expanding the range of key points to include more detailed body parts and landmarks. By staying tuned to updates from the API provider, developers can expect additional key points that will enhance the level of detail and accuracy in body part recognition.
Integrating the Body Part Recognition API into applications opens up a plethora of possibilities. From enhancing user experiences in gaming and virtual reality to enabling advanced analytics in sports performance tracking, the API provides a robust and reliable solution for extracting vital body part information from images.
Overall, the Body Part Recognition API offers a comprehensive and efficient approach to detecting and analyzing human bodies, providing developers with valuable insights into the positioning and key points of individuals within images.
Pass an image URL and receive all the recognized coordinates for the body parts in the image.
Fitness Tracking Applications: The Body Part Recognition API can be integrated into fitness tracking apps to accurately identify and track specific body parts during exercises or workouts. This allows users to receive detailed feedback on their form, monitor muscle engagement, and optimize their fitness routines.
Augmented Reality (AR) Filters and Effects: AR applications can leverage the API to detect and track users' body parts in real-time, enabling the overlay of virtual objects, filters, or effects that precisely align with different body parts. This enhances the immersive experience and opens up creative opportunities for interactive AR experiences.
Gesture Recognition and Control: By analyzing the positions of key points, the API can be utilized to recognize and interpret various gestures performed by users. This can be applied to control user interfaces, gaming interactions, or virtual character animations, creating intuitive and engaging user experiences.
Sports Performance Analysis: Coaches, trainers, and athletes can employ the API to analyze sports movements and techniques. By tracking the positions of key points during training or competitions, they can evaluate and optimize performance, identify areas for improvement, and prevent injuries by ensuring correct body alignment and movement patterns.
Healthcare and Rehabilitation: The Body Part Recognition API can assist in physical therapy and rehabilitation applications. By accurately tracking body movements and key points, healthcare professionals can provide remote monitoring, guidance, and feedback to patients, ensuring proper exercise form and progress tracking.
Human-Computer Interaction: The API can be integrated into human-computer interaction systems, such as sign language recognition or touchless control interfaces. By detecting and analyzing key points, the API enables accurate interpretation and response to user gestures, enhancing accessibility and interaction possibilities.
Besides the number of API calls, there is no other limitation
Supports complex scenes such as multi-person detection, overlapping body positions, occlusion, back, side, low and mid-air overhead, and large movements.
The positions of 21 key points: top of the head, left ear, right ear, left eye, right eye, nose, left corner of the mouth, right corner of the mouth, neck, left shoulder, right shoulder, left elbow, right elbow, left wrist, right wrist, left hip, right hip, left knee, right knee, left ankle, right ankle. The schematic is below and we are constantly adding more key points, so stay tuned.
Get Body Parts - Endpoint Features
| Object | Description |
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imageUrl |
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Continue with Google
Continue with Github
{"message":"success","data":{"person_num":4,"person_info":[{"location":{"top":329.16717529297,"height":398.73376464844,"score":0.99482369422913,"left":83.935195922852,"width":184.01873779297},"body_parts":{"left_ear":{"y":386.134765625,"x":231.357421875,"score":0.82972419261932},"left_wrist":{"y":522.169921875,"x":246.904296875,"score":0.83682531118393},"top_head":{"y":347.267578125,"x":215.810546875,"score":0.83453011512756},"left_shoulder":{"y":432.775390625,"x":231.357421875,"score":0.82867068052292},"right_mouth_corner":{"y":401.681640625,"x":204.150390625,"score":0.85599774122238},"left_elbow":{"y":479.416015625,"x":243.017578125,"score":0.84861755371094},"left_mouth_corner":{"y":401.681640625,"x":219.697265625,"score":0.86684691905975},"right_knee":{"y":603.791015625,"x":161.396484375,"score":0.75707840919495},"right_hip":{"y":529.943359375,"x":141.962890625,"score":0.79752606153488},"neck":{"y":413.341796875,"x":200.263671875,"score":0.80784571170807},"left_knee":{"y":599.904296875,"x":176.943359375,"score":0.77508634328842},"right_wrist":{"y":506.623046875,"x":106.982421875,"score":0.87072920799255},"right_ankle":{"y":677.638671875,"x":169.169921875,"score":0.81904417276382},"left_eye":{"y":382.248046875,"x":223.583984375,"score":0.90387237071991},"left_ankle":{"y":665.978515625,"x":165.283203125,"score":0.62612092494965},"right_elbow":{"y":463.869140625,"x":122.529296875,"score":0.83316296339035},"left_hip":{"y":533.830078125,"x":180.830078125,"score":0.79427003860474},"nose":{"y":393.908203125,"x":211.923828125,"score":0.89168190956116},"right_ear":{"y":378.361328125,"x":184.716796875,"score":0.857102394104},"right_shoulder":{"y":417.228515625,"x":157.509765625,"score":0.80837500095367},"right_eye":{"y":382.248046875,"x":204.150390625,"score":0.86407351493835}}},{"location":{"top":102.56463623047,"height":599.99090576172,"score":0.99173533916473,"left":130.89923095703,"width":246.42929077148},"body_parts":{"left_ear":{"y":170.5546875,"x":315.0234375,"score":0.88487130403519},"left_wrist":{"y":416.6484375,"x":350.1796875,"score":0.66441202163696},"top_head":{"y":123.6796875,"x":291.5859375,"score":0.86020267009735},"left_shoulder":{"y":246.7265625,"x":344.3203125,"score":0.87362891435623},"right_mouth_corner":{"y":188.1328125,"x":279.8671875,"score":0.90730309486389},"left_elbow":{"y":328.7578125,"x":356.0390625,"score":0.81442075967789},"left_mouth_corner":{"y":188.1328125,"x":297.4453125,"score":0.91256886720657},"right_knee":{"y":539.6953125,"x":256.4296875,"score":0.62648755311966},"right_hip":{"y":416.6484375,"x":256.4296875,"score":0.70029890537262},"neck":{"y":211.5703125,"x":291.5859375,"score":0.90270125865936},"left_knee":{"y":539.6953125,"x":320.8828125,"score":0.59291833639145},"right_wrist":{"y":387.3515625,"x":162.6796875,"score":0.86518681049347},"right_ankle":{"y":639.3046875,"x":244.7109375,"score":0.71336472034454},"left_eye":{"y":164.6953125,"x":297.4453125,"score":0.92144113779068},"left_ankle":{"y":645.1640625,"x":297.4453125,"score":0.68017560243607},"right_elbow":{"y":328.7578125,"x":197.8359375,"score":0.80988317728043},"left_hip":{"y":422.5078125,"x":320.8828125,"score":0.7311646938324},"nose":{"y":176.4140625,"x":285.7265625,"score":0.91195154190063},"right_ear":{"y":164.6953125,"x":262.2890625,"score":0.9107311964035},"right_shoulder":{"y":235.0078125,"x":227.1328125,"score":0.88326698541641},"right_eye":{"y":164.6953125,"x":279.8671875,"score":0.90164983272552}}},{"location":{"top":296.86605834961,"height":420.52432250977,"score":0.97877311706543,"left":363.30679321289,"width":149.34535217285},"body_parts":{"left_ear":{"y":356.4365234375,"x":464.2236328125,"score":0.89165931940079},"left_wrist":{"y":520.8896484375,"x":480.6689453125,"score":0.85524916648865},"top_head":{"y":315.3232421875,"x":443.6669921875,"score":0.85186338424683},"left_shoulder":{"y":409.8837890625,"x":472.4462890625,"score":0.81712436676025},"right_mouth_corner":{"y":372.8818359375,"x":427.2216796875,"score":0.88265591859818},"left_elbow":{"y":467.4423828125,"x":493.0029296875,"score":0.82253921031952},"left_mouth_corner":{"y":372.8818359375,"x":443.6669921875,"score":0.86625397205353},"right_knee":{"y":603.1162109375,"x":410.7763671875,"score":0.8492546081543},"right_hip":{"y":525.0009765625,"x":410.7763671875,"score":0.8053075671196},"neck":{"y":389.3271484375,"x":439.5556640625,"score":0.86626297235489},"left_knee":{"y":607.2275390625,"x":443.6669921875,"score":0.87249583005905},"right_wrist":{"y":500.3330078125,"x":381.9970703125,"score":0.81565093994141},"right_ankle":{"y":664.7861328125,"x":418.9990234375,"score":0.84910094738007},"left_eye":{"y":352.3251953125,"x":447.7783203125,"score":0.90972018241882},"left_ankle":{"y":668.8974609375,"x":443.6669921875,"score":0.82913535833359},"right_elbow":{"y":455.1083984375,"x":381.9970703125,"score":0.82046729326248},"left_hip":{"y":529.1123046875,"x":451.8896484375,"score":0.81305986642838},"nose":{"y":360.5478515625,"x":435.4443359375,"score":0.86751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curl --location --request POST 'https://zylalabs.com/api/2107/body+part+recognition+api/1901/get+body+parts?imageUrl=https://seaout.oss-us-west-1.aliyuncs.com/sample/2_Screenshot-145.png' --header 'Authorization: Bearer YOUR_API_KEY'
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Authorization
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[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
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The Body Part Recognition API leverages advanced algorithms and deep learning techniques to achieve high accuracy in detecting and tracking key points. However, it is essential to provide clear and well-captured images for optimal results.
Yes, the Body Part Recognition API is designed to handle complex scenarios, including images with overlapping human bodies. It can accurately detect and extract key points for each individual, providing valuable information even in crowded scenes.
The response time of the API depends on factors such as image size, processing power, and network speed. Generally, the API offers fast response times, enabling real-time or near real-time applications. However, larger images or high volumes of requests may increase processing time.
The Body Part Recognition API is designed to work with various types of clothing and body positions. It can accurately detect key points regardless of clothing color, style, or body orientation. However, excessively loose or obstructive clothing may affect the accuracy of the results.
Currently, the Body Part Recognition API supports the detection and tracking of key points in static images. It does not provide real-time analysis or tracking for videos. However, developers can extract frames from videos and process them individually using the API for key point detection and analysis.
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