Landmark detection is a technique used in computer vision to identify and locate specific, known structures or features in an image or video. This can include buildings, monuments, natural features, and other distinctive objects.
The output of a landmark detection system may include the location and shape of the detected landmarks, as well as a confidence score indicating how likely the detection is to be correct. Landmark detection is used in a variety of applications, including robotics, augmented reality, and self-driving cars.
The first step to use Landmark Detection is to set Axios, a promise-based HTTP client for the browser and Node.js, that will allow you to call Eden AI API.
Next, you'll need to initialize the File System module in order to access local files on your computer.
Finally, you'll need to create your multipart/formdata parameters form:
To perform Landmark Detection, you will need to create an account on Eden AI for free. Then, you will be able to get your API key directly from the homepage with free credits offered by Eden AI.
For example, we called two different Landmark Detection engines. Once the parameters values are passed, you can configure your request:
Then, you just need to launch the request and print the result:
Here is an example of a Landmark Detection API response:
Using Landmark Detection with Eden AI API is quick and easy.
We offer a unified API for all providers: simple and standard to use, with a quick switch between providers and an access to the specific features of each provider.
The JSON output format is the same for all suppliers thanks to Eden AI's standardisation work. The response elements are also standardised thanks to Eden AI's powerful matching algorithms.
With Eden AI you have the possibility to integrate a third party platform: we can quickly develop connectors. To go further and customize your Landmark Detection request with specific parameters, check out our documentation.