Getting Started with Google Cloud Vision AI: A Beginner’s Guide

Google Cloud Vision AI unlocks the power of image analysis through its comprehensive set of APIs. This guide provides a foundational understanding for beginners to utilize Vision AI and integrate it into their projects.

1. Setting Up Your Project:

• Create a Google Cloud Platform (GCP) project if you haven’t already. You can do this by visiting the GCP Console: https://console.cloud.google.com/ and following the on-screen instructions.
• Enable billing for your project, as using Vision AI incurs charges based on usage.
• Visit the Vision AI Library: https://cloud.google.com/vision/docs/libraries and choose the client library corresponding to your preferred programming language (Python, Java, Node.js, etc.). Download and install the library according to the instructions provided.

2. Authentication:

Securely access Vision AI by creating an API key. Navigate to the IAM & Admin: https://console.cloud.google.com/iam-admin section of your GCP Console and create a new service account key. Download the JSON file containing the key and store it securely. Detailed steps below.

1. Create a Google Cloud Project (if you haven’t already):

• Go to the Google Cloud Platform Console: https://console.cloud.google.com/
• Click on “Create project” and choose a unique and descriptive name for your project.

2. Enable the Vision AI API:

• In the GCP Console, navigate to the “APIs & Services” section.
• Click on “Library.”
• Search for “Vision AI” and click on it.
• Click on “Enable” to activate the API for your project.

3. Create a Service Account:

• In the GCP Console, go to the “IAM & Admin” section: https://console.cloud.google.com/iam-admin/iam
• Click on “Service accounts.”
• Click on “Create service account.”
• Give your service account a descriptive name (e.g., “vision-ai-access”) and click “Create.”
• Under “Grant this service account access to project,” select the roles you need for Vision AI. The minimum required role is “Vision API Editor.” You can find a more detailed list of roles and their permissions here: https://stackoverflow.com/questions/65291375/google-vision-api-service-account-permission
• Click “Done” to create the service account.

4. Generate a Service Account Key:

• Click on the newly created service account (e.g., “vision-ai-access”).
• Click on “Keys” in the top navigation bar.
• Click on “Add key” and select “JSON” as the key type.
• Click “Create.”
• Download the JSON file and store it securely in a location only authorized users can access. Do not share this key publicly or embed it in your code. This file contains the credentials needed to authenticate your application with Vision AI.

5. Use the Service Account Key in your Code:

• The specific steps for using the key will vary depending on the programming language and library you’re using. Here are general guidelines:
  • Python: Use the google.auth.jwt library to create a service account client and set the credentials using the downloaded JSON file.
  • Java: Use the com.google.cloud:google-cloud-vision library and set the credentials using the JSON file content.
  • Node.js: Use the @google-cloud/vision library and initialize a client instance with the service account credentials from the JSON file.
• Refer to the official documentation for your chosen language and library for detailed instructions on how to use the service account key:
  • Python: https://cloud.google.com/python/docs
  • Java: https://medium.com/@bhandarimansubh/getting-started-with-google-cloud-vision-api-with-java-cacc0b2defa0
  • Node.js: https://cloud.google.com/nodejs/docs/reference/vision/latest

Remember:

• Keep your service account key confidential. Do not share it publicly or embed it in your code.
• Grant only the minimum necessary permissions to the service account.
• Rotate your service account keys regularly to enhance security.

3. Making API Calls:

Once you have the necessary tools, it’s time to interact with the Vision AI API. Here’s an example using PHP to detect labels in an image:

<?php

// Replace with your downloaded JSON file path
$keyFilePath = "path/to/your/key.json";

// Replace with the URL or path to your image
$imageUrl = "https://www.example.com/image.jpg";

require_once __DIR__ . '/vendor/autoload.php';

use Google\Cloud\Vision\V1\ImageAnnotatorClient;

function visionLabelDetection($keyFilePath, $imageUrl) {
  try {
    // Create a Vision client.
    $client = new ImageAnnotatorClient([
        'keyFilePath' => $keyFilePath,
    ]);

    // Build the image object.
    $image = new Google\Cloud\Vision\V1\Image();
    if (strpos($imageUrl, 'http') === 0) {
      $image->setSource(new Google\Cloud\Vision\V1\ImageSource([
          'gcsUri' => $imageUrl,
        ]));
    } else {
      $image->setContent(file_get_contents($imageUrl));
    }

    // Build the feature request.
    $feature = new Google\Cloud\Vision\V1\Feature([
        'type' => Google\Cloud\Vision\V1\Feature\Type::LABEL_DETECTION,
    ]);

    // Create the request.
    $request = new Google\Cloud\Vision\V1\AnnotateImageRequest([
        'image' => $image,
        'features' => [$feature],
    ]);

    // Make the request and handle the response.
    $response = $client->annotateImage($request);
    $labels = $response->getImageContext()->getLabelAnnotations();

    foreach ($labels as $label) {
      echo "Label: " . $label->getDescription() . ", Score: " . $label->getScore() . PHP_EOL;
    }
  } catch (Exception $e) {
    echo "Error: " . $e->getMessage();
  }
}

// Call the function with your key file path and image URL
visionLabelDetection($keyFilePath, $imageUrl);

?>

Things to note:

• Make sure you have the Google Cloud PHP library installed using composer: composer require google/cloud-vision
• Replace path/to/your/key.json with the actual path to your downloaded JSON file containing the API key.
• Replace https://www.example.com/image.jpg with the URL or path to the image you want to analyze.

4. Understanding the Response:

This code snippet sends a request to the Vision AI API, specifying label detection as the desired feature. It then parses the response and prints out the identified labels and their corresponding scores.

5. Exploring Further:

Vision AI offers a wide range of functionalities beyond label detection. Explore the official documentation (https://cloud.google.com/vision/docs) to discover other features like:

• Object detection: Identify and localize objects within an image.
• Text extraction: Extract text embedded within images like documents or signs.
• Landmark recognition: Detect and identify famous landmarks around the world.
• SafeSearch: Moderate and filter inappropriate content based on Google’s SafeSearch criteria.