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FaceDetection.cpp

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+#include <opencv2/opencv.hpp>
+#include <opencv2/dnn.hpp>
+#include <opencv2/face.hpp>
+#include <iostream>
+#include <filesystem>
+#include <unordered_map>
+
+using namespace cv;
+using namespace cv::dnn;
+using namespace cv::face;
+using namespace std;
+namespace fs = std::filesystem;
+
+const string FACES_FOLDER = "faces";
+const string MODEL_CONFIG = "deploy.prototxt.txt";
+const string MODEL_BINARY = "res10_300x300_ssd_iter_140000.caffemodel";
+
+void recognizeFaces(const vector<Mat>& knownFaceImages, const vector<string>& knownFaceNames) {
+    Net net = readNetFromCaffe(MODEL_CONFIG, MODEL_BINARY);
+
+    Ptr<LBPHFaceRecognizer> model = LBPHFaceRecognizer::create(1, 8, 8, 8, 123.0);
+
+    unordered_map<string, int> labelMapping;
+    vector<int> labels;
+
+    int labelIndex = 0;
+    for (const auto& name : knownFaceNames) {
+        if (labelMapping.find(name) == labelMapping.end()) {
+            labelMapping[name] = labelIndex++;
+        }
+        labels.push_back(labelMapping[name]);
+    }
+
+    model->train(knownFaceImages, labels);
+
+    VideoCapture video(0);
+    if (!video.isOpened()) {
+        cerr << "Error: Couldn't open the camera." << endl;
+        return;
+    }
+
+    Mat frame;
+    vector<Rect> faces;
+
+    while (true) {
+        video >> frame;
+        if (frame.empty()) {
+            cerr << "Error: Couldn't capture frame." << endl;
+            break;
+        }
+
+        Mat blob = blobFromImage(frame, 1.0, Size(300, 300), Scalar(104, 177, 123), false, false);
+        net.setInput(blob);
+        Mat detections = net.forward();
+
+        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) {
+                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);
+
+                Mat faceROI = frame(faceRect);
+
+                cvtColor(faceROI, faceROI, COLOR_BGR2GRAY);
+                resize(faceROI, faceROI, Size(100, 100), 1.0, 1.0, INTER_CUBIC);
+
+                int predictedLabel = -1;
+                double predictionConfidence = 0.0;
+                model->predict(faceROI, predictedLabel, predictionConfidence);
+
+                string personName = "Unknown";
+                if (predictionConfidence < 100.0) {
+                    for (const auto& entry : labelMapping) {
+                        if (entry.second == predictedLabel) {
+                            personName = entry.first;
+                            break;
+                        }
+                    }
+                }
+
+                rectangle(frame, faceRect, Scalar(0, 255, 0), 2);
+                putText(frame, "Person: " + personName, Point(faceRect.x, faceRect.y - 10),
+                        FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255, 255, 255), 1);
+            }
+        }
+
+        imshow("Face Recognition", frame);
+
+        if (waitKey(30) == 27) {
+            cout << "Exiting program." << endl;
+            break;
+        }
+    }
+}
+
+int main() {
+    vector<Mat> knownFaceImages;
+    vector<string> knownFaceNames;
+
+    for (const auto& entry : fs::directory_iterator(FACES_FOLDER)) {
+        if (entry.path().extension() != ".jpg")
+            continue;
+
+        Mat image = imread(entry.path().string(), IMREAD_GRAYSCALE);
+        if (image.empty()) {
+            cerr << "Error: Couldn't read image " << entry.path().string() << endl;
+            continue;
+        }
+
+        string personName = entry.path().stem().string();
+
+        knownFaceImages.push_back(image);
+        knownFaceNames.push_back(personName);
+    }
+
+    if (knownFaceImages.empty()) {
+        cerr << "Error: No face images found in the 'faces' folder." << endl;
+        return 1;
+    }
+
+    recognizeFaces(knownFaceImages, knownFaceNames);
+
+    return 0;
+}
+

FaceDetectionLite.cpp

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+#include <opencv2/opencv.hpp>
+#include <opencv2/dnn.hpp>
+#include <iostream>
+
+using namespace cv;
+using namespace cv::dnn;
+using namespace std;
+
+const string MODEL_CONFIG = "deploy.prototxt.txt";
+const string MODEL_BINARY = "res10_300x300_ssd_iter_140000.caffemodel";
+
+void detectFaces() {
+    Net net = readNetFromCaffe(MODEL_CONFIG, MODEL_BINARY);
+
+    VideoCapture video(0);
+    if (!video.isOpened()) {
+        cerr << "Error: Couldn't open the camera." << endl;
+        return;
+    }
+
+    Mat frame;
+
+    while (true) {
+        video >> frame;
+        if (frame.empty()) {
+            cerr << "Error: Couldn't capture frame." << endl;
+            break;
+        }
+
+        Mat blob = blobFromImage(frame, 1.0, Size(300, 300), Scalar(104, 177, 123), false, false);
+        net.setInput(blob);
+        Mat detections = net.forward();
+
+        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) {
+                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);
+
+                rectangle(frame, faceRect, Scalar(0, 255, 0), 2);
+            }
+        }
+
+        imshow("Face Detection", frame);
+
+        if (waitKey(30) == 27) {
+            cout << "Exiting program." << endl;
+            break;
+        }
+    }
+}
+
+int main() {
+    detectFaces();
+    return 0;
+}
+