Build an AI Camera with Google Vision and ESP32 CAM

Build an AI Camera with Google Vision and ESP32 CAM

Table of Contents

1. Introduction
2. Hardware Components
   • ESP32 Cam Board 2.8 inch
   • TFT Display
   • FTDI Module
3. Connecting to a Server
   • Laptop as Server
   • Google Cloud Vision API
4. Demo of AI Camera
5. Implementing Google Vision Functionalities with ESP32 Camera Module
6. Why ESP32 Cam Module?
7. Setting Up Google Vision API and Node.js Installation
8. Creating a GCP Project and Enabling Billing
9. Setting Up Google Vision API
10. Creating a Service Account and Getting Key
11. Setting Up the GCP Server
12. Installing Node.js and Dependencies
13. Writing the Code for ESP32 Cam
14. testing the AI Camera
15. Conclusion

📷 Creating an AI Camera using ESP32 Cam Module

In this article, we will take you through the step-by-step process of creating an AI camera using the ESP32 Cam module. We will discuss the hardware components required, how to connect to a server, and how to implement Google Vision functionalities with the ESP32 camera module. We will also provide a detailed guide on setting up the Google Cloud Platform (GCP) and installing Node.js for the project. By the end of this article, you will have a fully functional AI camera that can detect and label objects using machine learning algorithms.

1. Introduction

Artificial Intelligence (AI) has revolutionized various industries, and one of its applications is in the field of computer vision. With the ESP32 Cam module and the Google Cloud Vision API, we can create an AI camera that can identify objects and provide labels for them. This project combines hardware and software to create a powerful tool for Image Recognition and processing.

2. Hardware Components

To create the AI camera, we will need the following hardware components:

• ESP32 Cam Board 2.8 inch: This board serves as the main controller for the camera module.
• TFT Display: We will use a 2.8-inch SPI TFT LCD display to view the captured images.
• FTDI Module: The FTDI module is used to power and program the ESP32 board.

3. Connecting to a Server

For the AI camera to work, we need to connect it to a server. In this project, we will use a laptop as our server, which will be connected to the Google Cloud Vision API. The API will perform the image recognition and return the labels using machine learning algorithms. By uploading an image to the server, the AI camera will be able to detect and label objects.

4. Demo of AI Camera

Before diving into the implementation details, let's first take a look at the demo of the AI camera. In the demo, we will upload an image to the camera's dashboard and observe the labels detected by the Google Cloud Vision API. This will give you an idea of how the AI camera functions and the accuracy of its object detection capabilities.

5. Implementing Google Vision Functionalities with ESP32 Camera Module

In this section, we will discuss how to implement the Google Vision functionalities with the ESP32 camera module. We will utilize the ESP32 Cam module's image processing capabilities and integrate it with the Google Cloud Vision API. This will enable the AI camera to detect objects in real-time and provide accurate labels for them.

6. Why ESP32 Cam Module?

The ESP32 Cam module was chosen for this project due to its ideal solution for image processing in IoT applications. With its 32-bit CPU and built-in Wi-Fi and Bluetooth capabilities, the ESP32 Cam module provides a reliable and efficient platform for AI camera development. Its compatibility with various libraries and easy integration with other components make it the perfect choice for this project.

7. Setting Up Google Vision API and Node.js Installation

To make use of the Google Cloud Vision API, we need to set up a GCP project and install Node.js on our server. This section will guide you through the process of creating a GCP project, enabling billing, setting up the Google Vision API, creating a service account, and obtaining the key. We will also cover the installation of Node.js and the necessary dependencies for the project.

8. Creating a GCP Project and Enabling Billing

To access the Google Cloud Vision API, we need to create a GCP project and enable billing for it. In this step, you will learn how to create a new project, give it a name, and enable the necessary services. Although the GCP platform does require a billing-enabled version, this project will not incur any costs. You will also receive an exciting offer of a hundred-dollar coupon if you sign up using the link provided in the description.

9. Setting Up Google Vision API

Once the GCP project is created, we can proceed with setting up the Google Vision API. This involves enabling the API and creating a service account. The service account will provide the necessary credentials for accessing the API. We will guide you through the step-by-step process of enabling the API and creating the service account.

10. Creating a Service Account and Getting Key

To access the Google Vision API, we need a service account key. In this section, you will learn how to create a service account and obtain the key. The service account will act as a representative of our application and will have the necessary permissions to interact with the API. The key will provide the credentials required for authentication.

11. Setting Up the GCP Server

In order to use the Google Vision API, we need to set up a server. In this project, we will be using a laptop as our server. We will guide you through the process of installing Node.js, setting up the required folders and files, and installing the necessary dependencies. This will ensure that our server is capable of handling the requests from the ESP32 camera module.

12. Installing Node.js and Dependencies

Before we can start writing code for the AI camera, we need to install Node.js and the required dependencies. This section will guide you through the process of installing Node.js on your computer and installing the necessary packages and libraries for the project. We will also show you how to set up the project structure and organize the files and folders.

13. Writing the Code for ESP32 Cam

With the server set up and the necessary dependencies installed, we can now start writing the code for the ESP32 Cam module. This section will provide you with the code snippets and explanations for each part of the code. We will cover topics such as capturing images, sending requests to the server, and displaying the labels on the TFT display.

14. Testing the AI Camera

Once the code is written and uploaded to the ESP32 Cam module, we can test the AI camera. In this section, we will guide you through the testing process, including capturing images, sending them to the server, and observing the labels detected by the Google Cloud Vision API. We will provide examples using various objects to showcase the accuracy and reliability of the AI camera.

15. Conclusion

In conclusion, we have successfully created an AI camera using the ESP32 Cam module and the Google Cloud Vision API. This project combines hardware and software to create a powerful tool for image recognition and processing. By following the step-by-step instructions provided in this article, you can build your own AI camera and explore the possibilities of computer vision. Start capturing and detecting objects with your DIY AI camera today!

Pros:

• Accurate object detection using Google Cloud Vision API
• Easy integration with ESP32 Cam module
• Possibility for further customization and development

Cons:

• Requires basic knowledge of programming and electronics
• Time-consuming setup and installation process

Highlights:

• Create an AI camera using the ESP32 Cam module
• Connect to a server and use Google Cloud Vision API for object detection
• Implement Google Vision functionalities with the ESP32 Cam module
• Step-by-step guide on setting up the GCP and installing Node.js
• Write code for capturing images, sending requests, and displaying labels

FAQ

Q: Can I use a different camera module instead of the ESP32 Cam module? A: The ESP32 Cam module is recommended for this project due to its compatibility and image processing capabilities. However, with some modifications to the code, you may be able to use a different camera module.

Q: How accurate is the object detection of the AI camera? A: The accuracy of the AI camera depends on various factors, including the quality of the captured image and the performance of the Google Cloud Vision API. In general, the API provides reliable and accurate results.

Q: Can I customize the AI camera to detect specific objects? A: Yes, you can customize the AI camera to detect specific objects by training the machine learning model with the desired dataset. This requires additional knowledge and resources in machine learning.