Getting Started

Getting started

Here is an example of how simple it is to use an OpenCV function from MATLAB to detect faces:

% Load a face detector and an image
cascade = cv.CascadeClassifier('haarcascade_frontalface_alt.xml');
im = imread('myface.jpg');

% Preprocess image
gr = cv.cvtColor(im, 'RGB2GRAY');
gr = cv.equalizeHist(gr);

% Detect faces
boxes = cascade.detect(gr, ...
    'ScaleFactor',1.3, 'MinNeighbors',2, 'MinSize',[30 30]);

% Draw results
for i = 1:numel(boxes)
    im = cv.rectangle(im, boxes{i}, 'Color',[0 255 0], 'Thickness',2);
end
imshow(im);

Would you like to use a camera input? No problem.

% Connect to a camera
camera = cv.VideoCapture();
pause(2);
for i = 1:50
    % Capture and show frame
    frame = camera.read();
    imshow(frame);
    drawnow;
end

Interested in deep learning, here is an example of image identification using a pretrained convolutional neural network:

% load pretrained model, along with list of 1000 recognized categories
dname = fullfile(mexopencv.root(), 'test', 'dnn', 'GoogLeNet');
net = cv.Net('Caffe', ...
    fullfile(dname, 'deploy.prototxt'), ...
    fullfile(dname, 'bvlc_googlenet.caffemodel'));
labels = fileread(fullfile(dname, 'synset_words.txt'));
labels = regexp(strtrim(labels), '\n', 'split');

% open webcam
cap = cv.VideoCapture(0);
hImg = imshow(cap.read());
while ishghandle(hImg)
    % read frame and classify image
    img = cap.read();
    net.setInput(cv.Net.blobFromImages(img, 'Size',[224 224], 'SwapRB',true));
    prob = net.forward();
    [~,idx] = max(prob);

    % show prediction
    set(hImg, 'CData',img)
    title(sprintf('(%.2f%%) %s', prob(idx)*100, labels{idx}))
    drawnow
end
cap.release();

Check out the included samples for more demos (here and here).

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The package already contains over 450 OpenCV functions/classes (covering many opencv and opencv_contrib modules). You can check a list of supported functions in the user documentation.

If you can't find your favorite one, you can easily add a new MEX function through MxArray class. MxArray is a data conversion utility for MATLAB's native array and OpenCV data types.

With this class, your MEX-function is as simple as the following:

#include "mexopencv.hpp"
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
    // Check number of arguments
    nargchk(nrhs==2 && nlhs<=1);

    // Convert input mxArray to cv::Mat and cv::Size
    cv::Mat src(MxArray(prhs[0]).toMat()), dst;
    cv::Size ksize(MxArray(prhs[1]).toSize());

    // Use your favorite OpenCV function
    cv::blur(src, dst, ksize);

    // Convert output from cv::Mat back to mxArray
    plhs[0] = MxArray(dst);
}

Check the developer documentation for further details.