added Comments to explain this code for normies

FaceDetectionLite.cpp

@@ -1,63 +1,70 @@
-#include <opencv2/opencv.hpp>
-#include <opencv2/dnn.hpp>
-#include <iostream>
+#include <opencv2/opencv.hpp> // OpenCV library for computer vision
+#include <opencv2/dnn.hpp>    // OpenCV deep neural networks module
+#include <iostream>           // Standard input-output library
 
-using namespace cv;
-using namespace cv::dnn;
-using namespace std;
+using namespace cv;            // OpenCV namespace
+using namespace cv::dnn;       // OpenCV DNN namespace
+using namespace std;           // Standard C++ namespace
 
+// Path to the pre-trained model configuration and binary files
 const string MODEL_CONFIG = "deploy.prototxt.txt";
 const string MODEL_BINARY = "res10_300x300_ssd_iter_140000.caffemodel";
 
+// Function to detect faces in live video
 void detectFaces() {
+    // Load the pre-trained model for face detection
     Net net = readNetFromCaffe(MODEL_CONFIG, MODEL_BINARY);
 
+    // Access the default camera for video capture
     VideoCapture video(0);
-    if (!video.isOpened()) {
+    if (!video.isOpened()) {  // Check if the camera couldn't be opened
         cerr << "Error: Couldn't open the camera." << endl;
         return;
     }
 
-    Mat frame;
+    Mat frame; // Variable to store video frames
 
-    while (true) {
-        video >> frame;
-        if (frame.empty()) {
+    while (true) { // Continuously process video frames
+        video >> frame; // Capture a frame from the camera
+        if (frame.empty()) { // Check if the frame is empty
             cerr << "Error: Couldn't capture frame." << endl;
             break;
         }
 
+        // Prepare the frame for neural network input
         Mat blob = blobFromImage(frame, 1.0, Size(300, 300), Scalar(104, 177, 123), false, false);
-        net.setInput(blob);
-        Mat detections = net.forward();
+        net.setInput(blob); // Set the frame as input to the neural network
+        Mat detections = net.forward(); // Perform face detection using the neural network
 
+        // Iterate through detected faces and draw rectangles around high-confidence faces (Highly technical stuff) DO NOT TOUCH!!!!!!!!!
         for (int i = 0; i < detections.size[2]; ++i) {
             float* dataPtr = detections.ptr<float>(0);
             float detectionConfidence = dataPtr[i * 7 + 2];
 
-            if (detectionConfidence > 0.5) {
+            if (detectionConfidence > 0.5) { // Check face detection confidence threshold
+                // Extract coordinates for drawing the rectangle around the face of the person
                 int x1 = static_cast<int>(dataPtr[i * 7 + 3] * frame.cols);
                 int y1 = static_cast<int>(dataPtr[i * 7 + 4] * frame.rows);
                 int x2 = static_cast<int>(dataPtr[i * 7 + 5] * frame.cols);
                 int y2 = static_cast<int>(dataPtr[i * 7 + 6] * frame.rows);
 
-                Rect faceRect(x1, y1, x2 - x1, y2 - y1);
+                Rect faceRect(x1, y1, x2 - x1, y2 - y1); // Rectangle around the face cordinates
 
-                rectangle(frame, faceRect, Scalar(0, 255, 0), 2);
+                rectangle(frame, faceRect, Scalar(0, 255, 0), 2); // Draw the rectangle on the frame
             }
         }
 
-        imshow("Face Detection", frame);
+        imshow("Face Detection", frame); // Display the processed frame
 
-        if (waitKey(30) == 27) {
+        if (waitKey(30) == 27) { // Check for 'Esc' key press to exit the program
             cout << "Exiting program." << endl;
             break;
         }
     }
 }
 
+// Main function
 int main() {
-    detectFaces();
+    detectFaces(); // Call the function to detect faces in live video
     return 0;
 }
-