About the API:
Object detection is a computer vision technique that allows us to identify and locate objects in an image or video. With this kind of identification and localization, object detection can be used to count objects in a scene and determine and track their precise locations, all while accurately labeling them.
This API will receive an image URL and it will be delivering the position of the object it recognizes and, also, a label of the object. Ex "Chair", or "Car".
Also, this API has the ability to retrieve a new modified image with all the located objects and it's positions.
Object Recognition APIs are vastly used in different places such as:
Because state-of-the-art object detection techniques can accurately identify and track multiple instances of a given object in a scene, these techniques naturally lend themselves to automated video surveillance systems.
For instance, object detection models are capable of tracking multiple people at once, in real-time, as they move through a given scene or across video frames. From retail stores to industrial factory floors, this kind of granular tracking could provide invaluable insights into security, worker performance and safety, retail foot traffic, and more.
Crowd counting is another valuable application of object detection. For densely populated areas like theme parks, malls, and city squares, object detection can help businesses and municipalities more effectively measure different kinds of traffic—whether on foot, in vehicles, or otherwise.
This ability to localize and track people as they maneuver through various spaces could help businesses optimize anything from logistics pipelines and inventory management, to store hours, to shift scheduling, and more. Similarly, object detection could help cities plan events, dedicate municipal resources, etc.
Real-time car detection models are key to the success of autonomous vehicle systems. These systems need to be able to identify, locate, and track objects around them in order to move through the world safely and efficiently.
And while tasks like image segmentation can be (and often are) applied to autonomous vehicles, object detection remains a foundational task that underpins current work on making self-driving cars a reality.
Besides the number of API calls per month, there are no other limitations.
Retrieve the positions of the detected objects and their labels.
Get Coordenates - Endpoint Features
| Object | Description |
|---|---|
url |
[Required] The image URL that you want to extract the objects and their positions from. |
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{"results":[{"score":0.8230468034744263,"label":"sports car, sport car"},{"score":0.056396935135126114,"label":"racer, race car, racing car"},{"score":0.04765290021896362,"label":"grille, radiator grille"},{"score":0.030311841517686844,"label":"car wheel"},{"score":0.015034670941531658,"label":"convertible"}]}
curl --location --request POST 'https://zylalabs.com/api/130/object+recognition+api/173/get+coordenates?url=https://www.cinch.co.uk/static/33916de99a6cf53e7a430f9f862a3296/99791/home-hero.png' --header 'Authorization: Bearer YOUR_API_KEY'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
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