Eyecloud AI Camera

Eyecloud AI Camera Component Documentation

Overview

The Eyecloud AI Camera is a cutting-edge IoT component that combines high-resolution imaging with advanced artificial intelligence capabilities. This camera is designed for various applications, including object detection, facial recognition, and video analytics. This documentation provides an overview of the Eyecloud AI Camera's features, technical specifications, and code examples to help developers integrate it into their projects.

Technical Specifications

Resolution: 1080p (1920x1080) at 30fps
 Sensor: 1/2.8" CMOS
 Lens: 2.8mm, F/2.0
 Field of View: 130
 Connectivity: Wi-Fi, Ethernet, and USB
 Operating System: Linux-based
 AI Capabilities: Object detection, facial recognition, and video analytics

Code Examples

### Example 1: Object Detection using Python and OpenCV

In this example, we will use the Eyecloud AI Camera to detect objects in a scene using OpenCV.

Hardware Requirements:

Eyecloud AI Camera
 Raspberry Pi or similar single-board computer
 Internet connection

Software Requirements:

OpenCV (version 4.5 or later)
 Python 3.7 or later

Code:
```python
import cv2

# Initialize the Eyecloud AI Camera
cap = cv2.VideoCapture('http://eyecloud-camera-ip:8080/video')

while True:
    # Read a frame from the camera
    ret, frame = cap.read()
    
    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
    # Perform object detection using the Eyecloud AI Camera's built-in AI capabilities
    objects = cap.get_objects(gray)
    
    # Draw bounding boxes around detected objects
    for obj in objects:
        x, y, w, h = obj['rect']
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
    
    # Display the output
    cv2.imshow('Object Detection', frame)
    
    # Exit on key press
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release resources
cap.release()
cv2.destroyAllWindows()
```
This example demonstrates how to use the Eyecloud AI Camera to detect objects in a scene using OpenCV. The `get_objects()` method is used to retrieve the detected objects, and bounding boxes are drawn around them using OpenCV's drawing functions.

### Example 2: Facial Recognition using Java and Face4J

In this example, we will use the Eyecloud AI Camera to recognize faces using Face4J.

Hardware Requirements:

Eyecloud AI Camera
 Raspberry Pi or similar single-board computer
 Internet connection

Software Requirements:

Face4J (version 3.3 or later)
 Java 8 or later

Code:
```java
import java.awt.image.BufferedImage;
import java.io.IOException;
import java.net.URL;

import javax.imageio.ImageIO;

import org.face4j.Face4J;
import org.face4j.FaceDetector;
import org.face4j.model.Face;

public class FacialRecognitionExample {
    public static void main(String[] args) throws IOException {
        // Initialize the Face4J library
        Face4J face4j = new Face4J();
        
        // Initialize the Eyecloud AI Camera
        URL cameraUrl = new URL("http://eyecloud-camera-ip:8080/video");
        BufferedImage image = ImageIO.read(cameraUrl);
        
        // Perform facial recognition using the Eyecloud AI Camera's built-in AI capabilities
        FaceDetector detector = face4j.getFaceDetector();
        Face[] faces = detector.detectFaces(image);
        
        // Print the number of detected faces
        System.out.println("Detected " + faces.length + " faces");
        
        // Iterate through the detected faces
        for (Face face : faces) {
            // Get the face bounding box
            int x = face.getBoundingBox().x;
            int y = face.getBoundingBox().y;
            int w = face.getBoundingBox().width;
            int h = face.getBoundingBox().height;
            
            // Print the face bounding box coordinates
            System.out.println("Face at (" + x + ", " + y + ") with size " + w + "x" + h);
        }
    }
}
```
This example demonstrates how to use the Eyecloud AI Camera to recognize faces using Face4J. The `FaceDetector` class is used to detect faces in the captured image, and the bounding box coordinates are printed to the console.

These code examples demonstrate the capabilities of the Eyecloud AI Camera and provide a starting point for developers to integrate it into their projects. For more information, please refer to the Eyecloud AI Camera's datasheet and documentation.