Components Required

• Microcontroller: ESP32-CAM
• Camera Module: Integrated camera on the ESP32-CAM
• Power Supply: USB or battery pack
• Optional: LED for indication

Overview

This project aims to implement a face detection system using the ESP32-CAM, which can capture images and process them for face detection using a pre-trained machine learning model.

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Steps to Implement

1. Setup Environment

• Install Arduino IDE: Ensure you have the latest version of the Arduino IDE installed.
• ESP32 Board Support: Add the ESP32 board to your Arduino IDE via the Board Manager.

2. Connect ESP32-CAM

Connect the ESP32-CAM to your computer using a USB-to-serial adapter. Connect the pins as follows:

• GND to GND
• 5V to VCC
• TX to RX
• RX to TX
• GPIO0 to GND (for programming mode)

3. Upload Code

Use the following sample code to implement face detection:

#include "esp_camera.h"
#include 

const char* ssid = "YOUR_SSID";
const char* password = "YOUR_PASSWORD";

void setup() {
    Serial.begin(115200);
    camera_config_t config;
    config.ledc_channel = LEDC_CHANNEL;
    config.ledc_timer = LEDC_TIMER;
    config.pin_d0 = 32;
    config.pin_d1 = 33;
    config.pin_d2 = 34;
    config.pin_d3 = 35;
    config.pin_d4 = 36;
    config.pin_d5 = 37;
    config.pin_d6 = 38;
    config.pin_d7 = 39;
    config.pin_xclk = 0;
    config.pin_pclk = 22;
    config.pin_vsync = 25;
    config.pin_href = 23;
    config.pin_sscb_sda = 21;
    config.pin_sscb_scl = 26;
    config.pin_pwdn = 32;
    config.pin_reset = -1;
    config.xclk_freq_hz = 20000000;
    config.frame_size = FRAMESIZE_SVGA;
    config.jpeg_quality = 12;
    config.fb_count = 2;

    // Initialize the camera
    esp_err_t err = esp_camera_init(&config);
    if (err != ESP_OK) {
        Serial.println("Camera init failed");
        return;
    }

    // Connect to WiFi
    WiFi.begin(ssid, password);
    while (WiFi.status() != WL_CONNECTED) {
        delay(1000);
        Serial.println("Connecting to WiFi...");
    }
    Serial.println("Connected to WiFi");
}

void loop() {
    // Capture an image
    camera_fb_t *fb = esp_camera_fb_get();
    if (!fb) {
        Serial.println("Camera capture failed");
        return;
    }

    // Process image for face detection here (ML model)
    // ...

    // Return the frame buffer
    esp_camera_fb_return(fb);
}
        

4. Machine Learning Model

Integrate a pre-trained face detection model (e.g., Haar Cascades or MobileNet) into the code to process the captured images and detect faces.

5. Testing

Power the ESP32-CAM and access the video stream through your web browser using the IP address provided in the serial monitor.

Conclusion

You have successfully implemented a face detection system using the ESP32-CAM and machine learning. This system can be expanded with more functionalities such as notifications or integration with other IoT devices.