Image Similarity Search API is a powerful tool that allows developers to compare images based on their visual content and retrieve similar images from a database or the web. This technology leverages advanced algorithms to analyze the visual features of images, such as colors, textures, and shapes, and identify similarities between them.
The Image Similarity Search API works by extracting key features from an input image and comparing them with features from other images in a dataset. It employs techniques like deep learning and computer vision to understand the content of images and measure their similarity.
When a query image is provided to the API, it processes the image and generates a feature vector representing its visual characteristics. Then, it searches through a collection of images to find those with similar feature vectors. The similarity between images is typically measured using distance metrics like Euclidean distance or cosine similarity.
For an in-depth comparison of the top APIs for enhancing visual content analysis, delve into our article “Best Image Similarity Search Solutions of 2024”.
To get started with the Eden AI API, you need to sign up for an account on the Eden AI platform. Once registered, you will get an API key that grants you access to the diverse set of image Similarity providers available on the platform.
Before diving into the code, decide where your query image is located:
Now, let's get to the code. Depending on your image source choice, you'll use different code snippets.
Using File URL
If you're using a file hosted online, here's the Python code snippet:
Ensure to replace "🔗 URL of your image"
with the actual URL of your image. The image you specify here will be used as the query for the similarity search.
Using Local File
If your image is stored locally, use the following code snippet:
Replace "🖼️ path/to/your/image.png"
with the actual path to your image file. This image will serve as the query for the similarity search.
Additionally, you can change the value of "providers"
in both codes to any supported provider on Eden AI you want to use for the image similarity search.
By following these steps, you can harness the power of Eden AI's Image Similarity Search API to find visually similar images with ease. Whether you are working with images hosted online or stored locally, Eden AI provides a seamless and efficient way to integrate image similarity search into your projects. Experiment with different providers and customize the search to suit your specific needs, making the most out of this powerful tool.
In the previous tutorial, we learned how to use the Eden AI Image Similarity Search API to find similar images using a URL or local file. Now, by learning how to add new images to your dataset, you can continually update and refine your image library, making your similarity searches even more effective. Whether you are adding images from an online source or uploading them directly from your device, these steps will help you manage your dataset with ease.
Original Code from Eden AI Documentation
Before we dive into the specific cases, here is the original code from Eden AI documentation:
When adding images via URL, you send the image URL to the API endpoint, which then processes and adds the image to your dataset.
Modified Code Example
Here is how you can modify the code to add an image via URL:
"providers"
, "image_name"
, and "file_url"
to the payload."authorization"
header since it's not required for URL uploads.“requests.post”
method with payload and headers to send the request to the API endpoint.
When adding images from a local file, you need to send the file data directly to the API.
Modified Code Example
Here is how you can modify the code to add an image via local file:
"providers"
and "image_name"
to the payload."authorization"
header remains unchanged as it's still required for file uploads."accept"
and "content-type"
since it is not required for local file uploads."requests.post"
method with both payload, files, and headers to send the request to the API endpoint.
By following these steps, you can easily add new images to your dataset for image similarity search with Eden AI. Keeping your image library updated will enhance the accuracy and relevance of your searches, providing better results over time. Whether you're adding images via URL or local file, Eden AI's API simplifies the process, allowing you to focus on building and refining your application.
To help you visualize these steps, we have prepared a video tutorial demonstrating both how to run an image similarity search and how to add images to your dataset. Watch the video below to follow along and see the process in action:
Using Eden AI API is quick and easy.
We offer a unified API for all providers: simple and standard to use, with a quick switch that allows you to have access to all the specific features very easily (diarization, timestamps, noise filter, etc.).
The JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
With Eden AI you can integrate a third-party platform: we can quickly develop connectors. To go further and customize your API request with specific parameters, check out our documentation.
You can see Eden AI documentation here.
The Eden AI team can help you with your Image Similarity Search integration project. This can be done by :
You can directly start building now. If you have any questions, feel free to schedule a call with us!
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