How to Master Computer Vision with OpenCV 2.0: Tips and Tricks

// Use the namespace cv using namespace cv; // Define the main function int main() // Read an image from a file Mat image = imread("image.jpg"); // Check if the image is valid if (image.empty()) // Print an error message std::cout

// Use the namespace cv using namespace cv; // Define the main function int main() // Create a VideoCapture object to read the video from a file VideoCapture cap("video.mp4"); // Check if the video is valid if (!cap.isOpened()) // Print an error message std::cout > frame; // Check if the frame is valid if (frame.empty()) // Break the loop break; // Resize the frame to half its original size resize(frame, frame, Size(width / 2, height / 2)); // Flip the frame horizontally flip(frame, frame, 1); // Write the frame to the output file writer

// Use the namespace cv using namespace cv; // Define the main function int main() // Read an image from a file Mat image = imread("image.jpg"); // Check if the image is valid if (image.empty()) // Print an error message std::cout

// Use the namespace cv using namespace cv; // Define the main function int main() // Read an image from a file Mat image = imread("image.jpg"); // Check if the image is valid if (image.empty()) // Print an error message std::cout > contours; // Create a vector of integers to store the hierarchy of contours vector hierarchy; // Find the contours in the edge image using the external mode and the simple approximation method findContours(edges, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); // Draw the contours on the original image using a green color and a thickness of 2 drawContours(image, contours, -1, Scalar(0, 255, 0), 2); // Create a window with a name namedWindow("Contours Image", WINDOW_AUTOSIZE); // Show the image on the window imshow("Contours Image", image); // Wait for a key press waitKey(0); // Return a success code return 0; ``` To compile and run this program, you need to specify the path to the opencv libraries and include them in your command. For example, if you are using Linux and g++, you can use the following command: ```bash g++ -I /path/to/opencv/include -L /path/to/opencv/lib -lopencv_core -lopencv_highgui -lopencv_imgcodecs -lopencv_imgproc -o detect_contours detect_contours.cpp ``` Then, you can run the program with: ```bash ./detect_contours ``` You should see a window with the original image with the contours drawn on it. ### How to recognize objects and faces with OpenCV 2.0 Another common and interesting task of computer vision is to recognize objects and faces in images, which are specific types of objects or regions of interest that have some semantic meaning or identity. OpenCV 2.0 provides various functions and classes for this purpose, such as: - matchTemplate for finding matches of a template image in a larger image using various methods and metrics - HoughCircles for finding circles in images using the Hough transform algorithm - CascadeClassifier for detecting objects or faces in images using pre-trained or custom cascade classifiers based on Haar-like features or local binary patterns (LBP) - face::FaceRecognizer for recognizing faces in images using pre-trained or custom face recognizers based on eigenfaces, fisherfaces, or local binary patterns histograms (LBPH) --- finds the matches of a template image of a coin in it using the normalized cross-correlation method, draws a rectangle around each match with a threshold of 0.9, finds the circles in the image using the Hough transform algorithm with a minimum radius of 20 and a maximum radius of 50, draws a circle around each circle with a blue color and a thickness of 2, detects the faces in the image using a pre-trained Haar cascade classifier for frontal faces, draws a rectangle around each face with a red color and a thickness of 2, and displays it on a window, you can use the following code: ```cpp // Include the header file #include

// Use the namespace cv using namespace cv; // Define the main function int main() // Read an image from a file Mat image = imread("image.jpg"); // Check if the image is valid if (image.empty()) // Print an error message std::cout (i, j); // Check if the value is above a threshold of 0.9 if (value > 0.9) // Draw a rectangle around the match with a green color and a thickness of 2 rectangle(image, Point(j, i), Point(j + template.cols, i + template.rows), Scalar(0, 255, 0), 2); // Create a Mat object to store the grayscale image Mat gray; // Convert the image to grayscale cvtColor(image, gray, COLOR_BGR2GRAY); // Create a vector of Vec3f to store the circles vector circles; // Find the circles in the grayscale image using the Hough transform algorithm with a minimum radius of 20 and a maximum radius of 50 HoughCircles(gray, circles, HOUGH_GRADIENT, 1, gray.rows / 8, 200, 100, 20, 50); // Loop through each circle in the vector for (size_t i = 0; i faces; // Detect the faces in the grayscale image using the classifier with a scale factor of 1.1 and a minimum number of neighbors of 3 face_cascade.detectMultiScale(gray, faces, 1.1, 3); // Loop through each face in the vector for (size_t i = 0; i

// Use the namespace cv using namespace cv; // Define the main function int main() { // Read a pre-trained CNN model from a Caffe file dnn::Net net = dnn::readNetFromCaffe("bvlc_googlenet.prototxt", "bvlc_googlenet.caffemodel"); // Check if the model is valid if (net.empty()) // Print an error message std::cout << "Could not load or find the model" << std::endl; // Return an error code return -1; // Read an image from a file Mat image = imread("image.jpg"); // Check if the image is valid if (image.empty()) // Print an error message std::cout << "Could not open or find the image" << std::endl; // Return an error code return -1; // Create a Mat object to store the resized image Mat resized; // Resize the image to match the input size of the model (224x224) resize(image, resized, Size(224, 224)); // Create a Mat object to store the blob image Mat blob; // Convert the resized image to a blob image with a scale factor of 1.0, a mean value of 104, 117, 123 for each channel, and a swap of red and blue channels blob = dnn::blobFromImage(resized, 1.0,