HCI Practical Assignment 1

CMakeLists.txt

@@ -13,7 +13,7 @@ source_group("" FILES ${INCLUDE} ${SOURCES} ${HEADERS})
 source_group("Source Files" FILES "src/main.cpp") 
 
 # OpenCV package
-find_package(OpenCV 4.0 REQUIRED core highgui imgproc imgcodecs PATHS "$ENV{OPENCVDIR}/build")
+find_package(OpenCV 4.0 REQUIRED core highgui imgproc imgcodecs PATHS "$ENV{OPENCV_DIR}/build")
 
 # Turn on the ability to create folders to organize projects (.vcproj)
 # It creates "CMakePredefinedTargets" folder by default and adds CMake defined projects like INSTALL.vcproj and ZERO_CHECK.vcproj

README.md

@@ -2,7 +2,7 @@
 **Dealine**: 27.09.2021
 
 Please put your name here:  
-**Name:** .......
+**Name:** Clichici Calin
 ## Problem 1.1
 ### Calculate Frames-per-Second (FPS) (Points 30)
 1. Fork the current repository
@@ -13,6 +13,10 @@ Please put your name here:
 ### Note
 MacOS users may need to launch the application with the administrator rights, to grant access to the web-camera.
 
+**Solution:**
+The FPS varies from 17 FPS to 25 FPS during runtime, with a total average of 24.54 FPS at the end.
+
+
 ## Problem 1.2
 ### Face detection (Points 70)
 1. Read the OpenCV documentation about Viola-Jones face detector: [Cascade Classifier](https://docs.opencv.org/4.2.0/db/d28/tutorial_cascade_classifier.html)  
@@ -21,6 +25,9 @@ MacOS users may need to launch the application with the administrator rights, to
 ### Note
 Please do not copy-paste the example code from the OpenCV documentation, but try to understand the example code and implement the solution to the problem by yourself.
 
+**Solution:**
+The implemented face detection algorithm as explained by the OpenCV documentation resulted in a heavy decline in FPS, now averaging between 1 FPS and 3 FPS during runtime, with a total average of 2.49 FPS at the end. Overall, it is a 90% reduction in performance.
+
 ## Submission
 Please submit the assignment by making a pull request.
 **Important** : Please make sure that

src/main.cpp

@@ -1,20 +1,144 @@
 #include "types.h"
+#include <time.h>>
+#include <chrono>
 
-int main() {
+Mat detect(Mat img);
+
+using namespace cv;
+
+CascadeClassifier face_cascade;
+CascadeClassifier eyes_cascade;
+
+int main(int argc, const char** argv) {
+
+	// Set up parser
+	CommandLineParser parser(argc, argv,
+		"{help h||}"
+		"{face_cascade|data/haarcascades/haarcascade_frontalface_alt.xml|Path to face cascade.}"
+		"{eyes_cascade|data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|Path to eyes cascade.}"
+		"{camera|0|Camera device number.}");
+
+	parser.about("\nThis program demonstrates using the cv::CascadeClassifier class to detect objects (Face + eyes) in a video stream.\n"
+		"You can use Haar or LBP features.\n\n");
+	parser.printMessage();
+
+	// Set up cascades
+	String face_cascade_name = samples::findFile(parser.get<String>("face_cascade"));
+	String eyes_cascade_name = samples::findFile(parser.get<String>("eyes_cascade"));
+
+	// Load the cascades
+	if (!face_cascade.load(face_cascade_name))
+	{
+		std::cout << "--(!)Error loading face cascade\n";
+		return -1;
+	};
+	if (!eyes_cascade.load(eyes_cascade_name))
+	{
+		std::cout << "--(!)Error loading eyes cascade\n";
+		return -1;
+	};
+
+	// Check if camera is available
 	VideoCapture camera;
 	if (!camera.open(0)) {
 		printf("Can't find a camera\n");
 		return 1;
 	};
 
+	// Number of frames to capture
+	int num_frames = 1;
+	double fps;
+
+	// Start and end times for FPS estimation
+	time_t start, end;
+
+	// Start and end timers for elapsed time estiamtion
+	time_t timerStart, timerEnd;
+
 	// Main loop
 	Mat img;
-	for(;;) {
+
+	// Start timer for elapsed time estimation
+	timerStart = clock();
+	for(;;) 
+	{
+		// Start clock for FPS estimation
+		// Happens before image processing begins
+		start = clock();
+
+		// Get image from camera
 		camera >> img;
+
+		// Detect face and draw recognition
+		img = detect(img);
+
+		// Show the image with facial regonition features
 		imshow("Camera", img);
+
+		// End clock for FPS estimation
+		// Happens after image processing ends
+		end = clock();
+
+		// Detect quit key
 		int key = waitKey(5);
 		if (key == 27 || key == 'q') break;
+
+		// Calculate number of seconds elapsed during image processing
+		double seconds = (double(end) - double(start)) / double(CLOCKS_PER_SEC);
+
+		// Calculate average FPS
+		fps = double(num_frames) / double(seconds);
+
+		// Take current clock value
+		timerEnd = clock();
+
+		// Recalculate number of seconds to estimate elapsed time so far
+		seconds = (double(timerEnd) - double(timerStart)) / double(CLOCKS_PER_SEC);
+
+		// If elapsed time greater than 2 seconds, print FPS and reset timer 
+		if (seconds >+ 2)
+		{
+			timerStart = clock();
+			std::cout << "Average FPS: " << fps << "\n";
+		}
 	}
+
 	camera.release();
 	return 0;
 }
+
+// Function to detect face and eyes
+Mat detect(Mat img)
+{
+	// Initialize blank mat for gray image
+	Mat img_gray;
+	cvtColor(img, img_gray, COLOR_BGR2GRAY);
+	equalizeHist(img_gray, img_gray);
+
+	// Initialize vector for faces
+	std::vector<Rect> faces;
+	face_cascade.detectMultiScale(img_gray, faces);
+
+	// Process and recognize face and eyes
+	for (size_t i = 0; i < faces.size(); i++)
+	{
+		Point center(faces[i].x + faces[i].width / 2, faces[i].y + faces[i].height / 2);
+		ellipse(img, center, Size(faces[i].width / 2, faces[i].height / 2), 0, 0, 360, Scalar(255, 0, 255), 4);
+
+		Mat faceROI = img_gray(faces[i]);
+
+		// In each face, detect eyes
+		std::vector<Rect> eyes;
+		eyes_cascade.detectMultiScale(faceROI, eyes);
+
+		for (size_t j = 0; j < eyes.size(); j++)
+		{
+			Point eye_center(faces[i].x + eyes[j].x + eyes[j].width / 2, faces[i].y + eyes[j].y + eyes[j].height / 2);
+			int radius = cvRound((eyes[j].width + eyes[j].height) * 0.25);
+			circle(img, eye_center, radius, Scalar(255, 0, 0), 4);
+		}
+	}
+
+	// Return image with detection circles
+	return img;
+}