Learn AI Bootcamp Materials

3_TestCLI

Estimated Time: 20-25 minutes

Exploring Cosmos DB

Azure Cosmos DB is our resilient NoSQL PaaS solution and is incredibly useful for storing loosely structured data like we have with our image metadata results. There are other possible choices (Azure Table Storage, SQL Server), but Cosmos DB gives us the flexibility to evolve our schema freely (like adding data for new services), query it easily, and can be quickly integrated into Azure Search (which we’ll do in a later lab).

Lab 3.1 (optional): Understanding CosmosDBHelper

Cosmos DB is not a focus of this workshop, but if you’re interested in what’s going on - here are some highlights from the code we will be using:

• Navigate to the CosmosDBHelper.cs class in the ImageStorageLibrary. Review the code and the comments. Many of the implementations used can be found in the Getting Started guide.
• Go to TestCLI’s Util.cs and review the ImageMetadata class (code and comments). This is where we turn the ImageInsights we retrieve from Cognitive Services into appropriate Metadata to be stored into Cosmos DB.
• Finally, look in Program.cs in TestCLI and at ProcessDirectoryAsync. First, we check if the image and metadata have already been uploaded - we can use CosmosDBHelper to find the document by ID and to return null if the document doesn’t exist. Next, if we’ve set forceUpdate or the image hasn’t been processed before, we’ll call the Cognitive Services using ImageProcessor from the ProcessingLibrary and retrieve the ImageInsights, which we add to our current ImageMetadata.

Once all of that is complete, we can store our image - first the actual image into Blob Storage using our BlobStorageHelper instance, and then the ImageMetadata into Cosmos DB using our CosmosDBHelper instance. If the document already existed (based on our previous check), we should update the existing document. Otherwise, we should be creating a new one.

Lab 3.2: Loading Images using TestCLI

We will implement the main processing and storage code as a command-line/console application because this allows you to concentrate on the processing code without having to worry about event loops, forms, or any other UX related distractions. Feel free to add your own UX later.

Once you’ve set your Cognitive Services API keys, your Azure Blob Storage Connection String, and your Cosmos DB Endpoint URI and Key in your TestCLI’s settings.json, you can run the TestCLI.

Run TestCLI, then open Command Prompt and navigate to “C:\LearnAI-Bootcamp\lab01.1-computer_vision\resources\code\Starting-ImageProcessing\TestCLI\bin\Debug” folder (Hint: use the “cd” command to change directories). Then enter TestCLI.exe. You should get the following result:

    > TestCLI.exe

    Usage:  [options]

    Options:
    -force            Use to force update even if file has already been added.
    -settings         The settings file (optional, will use embedded resource settings.json if not set)
    -process          The directory to process
    -query            The query to run
    -? | -h | --help  Show help information

By default, it will load your settings from settings.json (it builds it into the .exe), but you can provide your own using the -settings flag. To load images (and their metadata from Cognitive Services) into your cloud storage, you can just tell TestCLI to -process your image directory as follows:

    > TestCLI.exe -process c:\learnai-bootcamp\lab01.1-computer_vision\resources\sample_images

Once it’s done processing, you can query against your Cosmos DB directly using TestCLI as follows:

    > TestCLI.exe -query "select * from images"

Take some time to look through the sample_images (you can find them in resources/sample_images) and compare the images to the results in your application.

Continue to 4_Challenge_and_Closing

Back to README