ufacedetect.cpp 8.71 KB
Newer Older
wester committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
#include "opencv2/objdetect.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/core/ocl.hpp"
#include <iostream>

using namespace std;
using namespace cv;

static void help()
{
    cout << "\nThis program demonstrates the cascade recognizer. Now you can use Haar or LBP features.\n"
            "This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.\n"
            "It's most known use is for faces.\n"
            "Usage:\n"
            "./ufacedetect [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]\n"
               "   [--nested-cascade[=nested_cascade_path this an optional secondary classifier such as eyes]]\n"
               "   [--scale=<image scale greater or equal to 1, try 1.3 for example>]\n"
               "   [--try-flip]\n"
               "   [filename|camera_index]\n\n"
            "see facedetect.cmd for one call:\n"
            "./ufacedetect --cascade=\"../../data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml\" --scale=1.3\n\n"
            "During execution:\n\tHit any key to quit.\n"
            "\tUsing OpenCV version " << CV_VERSION << "\n" << endl;
}

void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
                    CascadeClassifier& nestedCascade,
                    double scale, bool tryflip );

string cascadeName = "../../data/haarcascades/haarcascade_frontalface_alt.xml";
string nestedCascadeName = "../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml";

int main( int argc, const char** argv )
{
    VideoCapture capture;
    UMat frame, image;
    Mat canvas;

    string inputName;
    bool tryflip;

    CascadeClassifier cascade, nestedCascade;
    double scale;

    cv::CommandLineParser parser(argc, argv,
        "{cascade|../../data/haarcascades/haarcascade_frontalface_alt.xml|}"
        "{nested-cascade|../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}"
        "{help h ||}{scale|1|}{try-flip||}{@filename||}"
    );
    if (parser.has("help"))
    {
        help();
        return 0;
    }
    cascadeName = parser.get<string>("cascade");
    nestedCascadeName = parser.get<string>("nested-cascade");
    scale = parser.get<double>("scale");
    tryflip = parser.has("try-flip");
    inputName = parser.get<string>("@filename");
    if ( !parser.check())
    {
        parser.printErrors();
        help();
        return -1;
    }

    if ( !nestedCascade.load( nestedCascadeName ) )
        cerr << "WARNING: Could not load classifier cascade for nested objects" << endl;
    if( !cascade.load( cascadeName ) )
    {
        cerr << "ERROR: Could not load classifier cascade" << endl;
        help();
        return -1;
    }

    cout << "old cascade: " << (cascade.isOldFormatCascade() ? "TRUE" : "FALSE") << endl;

    if( inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1) )
    {
a  
Kai Westerkamp committed
81 82 83
        int c = inputName.empty() ? 0 : inputName[0] - '0';
        if(!capture.open(c))
            cout << "Capture from camera #" <<  c << " didn't work" << endl;
wester committed
84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
    }
    else
    {
        if( inputName.empty() )
            inputName = "../data/lena.jpg";
        image = imread( inputName, 1 ).getUMat(ACCESS_READ);
        if( image.empty() )
        {
            if(!capture.open( inputName ))
                cout << "Could not read " << inputName << endl;
        }
    }

    if( capture.isOpened() )
    {
        cout << "Video capturing has been started ..." << endl;
        for(;;)
        {
            capture >> frame;
            if( frame.empty() )
                break;

            detectAndDraw( frame, canvas, cascade, nestedCascade, scale, tryflip );

a  
Kai Westerkamp committed
108
            int c = waitKey(10);
wester committed
109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130
            if( c == 27 || c == 'q' || c == 'Q' )
                break;
        }
    }
    else
    {
        cout << "Detecting face(s) in " << inputName << endl;
        if( !image.empty() )
        {
            detectAndDraw( image, canvas, cascade, nestedCascade, scale, tryflip );
            waitKey(0);
        }
        else if( !inputName.empty() )
        {
            /* assume it is a text file containing the
            list of the image filenames to be processed - one per line */
            FILE* f = fopen( inputName.c_str(), "rt" );
            if( f )
            {
                char buf[1000+1];
                while( fgets( buf, 1000, f ) )
                {
a  
Kai Westerkamp committed
131
                    int len = (int)strlen(buf), c;
wester committed
132 133 134 135 136 137 138 139
                    while( len > 0 && isspace(buf[len-1]) )
                        len--;
                    buf[len] = '\0';
                    cout << "file " << buf << endl;
                    image = imread( buf, 1 ).getUMat(ACCESS_READ);
                    if( !image.empty() )
                    {
                        detectAndDraw( image, canvas, cascade, nestedCascade, scale, tryflip );
a  
Kai Westerkamp committed
140
                        c = waitKey(0);
wester committed
141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
                        if( c == 27 || c == 'q' || c == 'Q' )
                            break;
                    }
                    else
                    {
                        cerr << "Aw snap, couldn't read image " << buf << endl;
                    }
                }
                fclose(f);
            }
        }
    }

    return 0;
}

void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
                    CascadeClassifier& nestedCascade,
                    double scale, bool tryflip )
{
    double t = 0;
    vector<Rect> faces, faces2;
    const static Scalar colors[] =
    {
        Scalar(255,0,0),
        Scalar(255,128,0),
        Scalar(255,255,0),
        Scalar(0,255,0),
        Scalar(0,128,255),
        Scalar(0,255,255),
        Scalar(0,0,255),
        Scalar(255,0,255)
    };
    static UMat gray, smallImg;

    t = (double)getTickCount();

    cvtColor( img, gray, COLOR_BGR2GRAY );
    double fx = 1 / scale;
    resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR );
    equalizeHist( smallImg, smallImg );

    cascade.detectMultiScale( smallImg, faces,
        1.1, 3, 0
        //|CASCADE_FIND_BIGGEST_OBJECT
        //|CASCADE_DO_ROUGH_SEARCH
        |CASCADE_SCALE_IMAGE,
        Size(30, 30) );
    if( tryflip )
    {
        flip(smallImg, smallImg, 1);
        cascade.detectMultiScale( smallImg, faces2,
                                 1.1, 2, 0
                                 //|CASCADE_FIND_BIGGEST_OBJECT
                                 //|CASCADE_DO_ROUGH_SEARCH
                                 |CASCADE_SCALE_IMAGE,
                                 Size(30, 30) );
a  
Kai Westerkamp committed
198
        for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
wester committed
199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257
        {
            faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
        }
    }
    t = (double)getTickCount() - t;
    img.copyTo(canvas);

    double fps = getTickFrequency()/t;
    static double avgfps = 0;
    static int nframes = 0;
    nframes++;
    double alpha = nframes > 50 ? 0.01 : 1./nframes;
    avgfps = avgfps*(1-alpha) + fps*alpha;

    putText(canvas, format("OpenCL: %s, fps: %.1f", ocl::useOpenCL() ? "ON" : "OFF", avgfps), Point(50, 30),
            FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0,255,0), 2);

    for ( size_t i = 0; i < faces.size(); i++ )
    {
        Rect r = faces[i];
        vector<Rect> nestedObjects;
        Point center;
        Scalar color = colors[i%8];
        int radius;

        double aspect_ratio = (double)r.width/r.height;
        if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
        {
            center.x = cvRound((r.x + r.width*0.5)*scale);
            center.y = cvRound((r.y + r.height*0.5)*scale);
            radius = cvRound((r.width + r.height)*0.25*scale);
            circle( canvas, center, radius, color, 3, 8, 0 );
        }
        else
            rectangle( canvas, Point(cvRound(r.x*scale), cvRound(r.y*scale)),
                       Point(cvRound((r.x + r.width-1)*scale), cvRound((r.y + r.height-1)*scale)),
                       color, 3, 8, 0);
        if( nestedCascade.empty() )
            continue;
        UMat smallImgROI = smallImg(r);
        nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
            1.1, 2, 0
            //|CASCADE_FIND_BIGGEST_OBJECT
            //|CASCADE_DO_ROUGH_SEARCH
            //|CASCADE_DO_CANNY_PRUNING
            |CASCADE_SCALE_IMAGE,
            Size(30, 30) );

        for ( size_t j = 0; j < nestedObjects.size(); j++ )
        {
            Rect nr = nestedObjects[j];
            center.x = cvRound((r.x + nr.x + nr.width*0.5)*scale);
            center.y = cvRound((r.y + nr.y + nr.height*0.5)*scale);
            radius = cvRound((nr.width + nr.height)*0.25*scale);
            circle( canvas, center, radius, color, 3, 8, 0 );
        }
    }
    imshow( "result", canvas );
}