1. Install coactive
Request a demo to gain access to the Coactive platform. Once you've been granted access, download our latest sdk and install it in your local python environment
Next, load the authentication credentials necessary for calling the coactive APIs. Note that these variables are environment specific and will be provided by Coactive.
3. Initialize a dataset on coactive
To get started, provide coactive access to your visual data. The code below provides access to your data lake and creates a dataset on coactive.
Once coactive has access to your data lake, it will convert your unstructured visual data to semantically meaningful representations. This enables fast access to all your visual data and the core functionalities of coactive, such as intelligent search and real-time analytics.
The code below provides additional metadata about the semantic representations of your data.
4. Search your images
You can use coactive's intelligent search to quickly find the visual assets you are looking for. coactive's APIs allow you to semantically search through millions of images, videos and any associated metadata using text and/or images as shown below.
5. Define a visual concept
You can define a custom visual concept, such as
green_dress, by simply providing a few example images. These visual concepts define which images are considered
green_dress and not
green_dress. This binary classification is our definition of a visual concept.
In the code below, we create the concept
green_dress concept from the images found in the intelligent search above.
While it may seem simple, there are nuances to each visual concept (e.g., is an oil painting of a green dress considered
green_dress or not
green_dress?). These nuances are often crucial and are rarely captured by off-the-shelf image classification solutions.
coactive allows you to quickly and seamlessly define visual concepts, giving you complete control over the definition (and nuances) of your visual taxonomy and classification.
Moreover, coactive allows you to efficiently update these visual concepts as your tasks or visual data change over time.
6. Run real-time queries over your visual data
coactive's real-time analytics engine allows you to query your unstructured visual data using SQL by providing a structured view of your visual data (i.e. rows = visual asset, columns = metadata).
Visual concepts combined with the standard capabilities of SQL can easily answer analytical questions spanning visual and structured data. No heavy lifting or deep technical expertise is required!
Here is how coactive can help you answer analytical questions about the
green_dress concept created using standard SQL syntax:
Are there more examples of
green_dress images were uploaded? Does this vary by category?
green_dress a viral/growing trend?
The code below shows how you can run any of the queries above.
See our tutorials for the fastest way to get started in our most common use cases: