Learn Google Vision AI API in Python

Table of Contents

1. Introduction
2. Setting up a Google Cloud project
3. Authentication setup
4. Enabling Google Vision AI API
5. Creating a Python virtual environment
6. Installing Google Vision AI API Python Package
7. Label detection example
8. Landmark detection example
9. Object detection example
10. Conclusion

How to Use Google Vision AI API in Python

In this tutorial, we will learn how to use Google Vision AI API in Python. Google Vision AI is a Google Cloud product that simplifies image analytics and classification Based on its own pre-trained models. With Google Vision AI, we can perform tasks such as face detection, data object extraction from images, and handwriting extraction from images.

Setting up a Google Cloud project

To get started, we need to Create a Google Cloud project. Open your browser and navigate to console.cloud.google.com. If you don't have an account, sign up for one for free. Once you're on the Google Cloud home page, click on the project drop-down and select "Create a new project." For this tutorial, we'll name the project "Vision AI API demo."

Authentication setup

After creating the project, we need to set up authentication. Click on the hamburger menu, go to "APIs and Services," and then click on "Credentials." On the top, click on "Create credentials" and choose "Service account." Give your service account a name and provide a description to specify the purpose of this account. Once you assign a name to the service account, click on "Create" and Continue. Choose the role for the service account and click on "Continue." Finally, click on "Done" to finish the service account setup.

Enabling Google Vision AI API

To enable Google Vision AI API, go back to the navigation menu, click on "APIs and Services," and then click on "Library." Search for "Cloud Vision API" and make sure that You enable the Cloud Vision API service.

Creating a Python virtual environment

To create a Python virtual environment, go to one of your local directories and pick a location where you want to create the Python project folder. Launch your terminal and make sure that the terminal location is pointing to the location where you want to create the Python virtual environment. To create the Python virtual environment, use the command "python -m venv" followed by the virtual environment name. For this tutorial, we'll name the environment "Vision AI demo." Once the environment is created, activate the environment by running the activate file in the scripts folder.

Installing Google Vision AI API Python package

To install the Google Vision AI API Python package, use the command "pip install --upgrade google-cloud-vision." Once the package and its dependencies are installed, we can move on to the examples.

Label detection example

One of the tasks we can perform with Google Vision AI is label detection. Label detection allows us to detect and extract labels from images. To perform label detection, we need to create a client object and pass in the image file. We can then call the "label_detection" method on the client object to perform label detection. The method returns a list of labels detected in the image.

Landmark detection example

Another task we can perform with Google Vision AI is landmark detection. Landmark detection allows us to detect and extract landmarks from images. To perform landmark detection, we need to create a client object and pass in the image file. We can then call the "landmark_detection" method on the client object to perform landmark detection. The method returns a list of landmarks detected in the image.

Object detection example

Finally, we can perform object detection with Google Vision AI. Object detection allows us to detect and extract objects from images. To perform object detection, we need to create a client object and pass in the image file. We can then call the "object_detection" method on the client object to perform object detection. The method returns a list of objects detected in the image.

Conclusion

In this tutorial, we learned how to use Google Vision AI API in Python. We covered setting up a Google Cloud project, authentication setup, enabling Google Vision AI API, creating a Python virtual environment, and installing the Google Vision AI API Python package. We also covered label detection, landmark detection, and object detection examples.

Highlights

• Google Vision AI simplifies image analytics and classification based on its own pre-trained models.
• We can perform tasks such as face detection, data object extraction from images, and handwriting extraction from images with Google Vision AI.
• To use Google Vision AI API in Python, we need to create a Google Cloud project, set up authentication, enable Google Vision AI API, create a Python virtual environment, and install the Google Vision AI API Python package.
• We can perform label detection, landmark detection, and object detection with Google Vision AI.

FAQ

Q: What is Google Vision AI? A: Google Vision AI is a Google Cloud product that simplifies image analytics and classification based on its own pre-trained models.

Q: What tasks can we perform with Google Vision AI? A: We can perform tasks such as face detection, data object extraction from images, and handwriting extraction from images with Google Vision AI.

Q: How do we use Google Vision AI API in Python? A: To use Google Vision AI API in Python, we need to create a Google Cloud project, set up authentication, enable Google Vision AI API, create a Python virtual environment, and install the Google Vision AI API Python package. We can then perform tasks such as label detection, landmark detection, and object detection.