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// This file is part of the OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/face/mace.hpp"
#include <iostream>
using namespace cv;
using namespace cv::face;
using namespace std;
enum STATE {
NEUTRAL,
RECORD,
PREDICT
};
const char *help =
"press 'r' to record images. once N trainimages were recorded, train the mace filter\n"
"press 'p' to predict (twofactor mode will switch back to neutral after each prediction attempt)\n"
"press 's' to save a trained model\n"
"press 'esc' to return\n"
"any other key will reset to neutral state\n";
int main(int argc, char **argv) {
CommandLineParser parser(argc, argv,
"{ help h usage ? || show this help message }"
"{ cascade c || (required) path to a cascade file for face detection }"
"{ pre p || load a pretrained mace filter file, saved from previous session (e.g. my.xml.gz) }"
"{ num n |50| num train images }"
"{ size s |64| image size }"
"{ twofactor t || pass phrase(text) for 2 factor authentification.\n"
" (random convolute images seeded with the crc of this)\n"
" users will get prompted to guess the secrect, additional to the image. }"
);
String cascade = parser.get<String>("cascade");
if (parser.has("help") || cascade.empty()) {
parser.printMessage();
return 1;
} else {
cout << help << endl;
}
String defname = "mace.xml.gz";
String pre = parser.get<String>("pre");
String two = parser.get<String>("twofactor");
int N = parser.get<int>("num");
int Z = parser.get<int>("size");
int state = NEUTRAL;
Ptr<MACE> mace;
if (! pre.empty()) { // load pretrained model, if available
mace = MACE::load(pre);
if (mace->empty()) {
cerr << "loading the MACE failed !" << endl;
return -1;
}
state = PREDICT;
} else {
mace = MACE::create(Z);
if (! two.empty()) {
cout << "'" << two << "' initial passphrase" << endl;
mace->salt(two);
}
}
CascadeClassifier head(cascade);
if (head.empty()) {
cerr << "loading the cascade failed !" << endl;
return -2;
}
VideoCapture cap(0);
if (! cap.isOpened()) {
cerr << "VideoCapture could not be opened !" << endl;
return -3;
}
vector<Mat> train_img;
while(1) {
Mat frame;
cap >> frame;
vector<Rect> rects;
head.detectMultiScale(frame,rects);
if (rects.size()>0) {
Scalar col = Scalar(0,120,0);
if (state == RECORD) {
if (train_img.size() >= size_t(N)) {
mace->train(train_img);
train_img.clear();
state = PREDICT;
} else {
train_img.push_back(frame(rects[0]).clone());
}
col = Scalar(200,0,0);
}
if (state == PREDICT) {
if (! two.empty()) { // prompt for secret on console
cout << "enter passphrase: ";
string pass;
getline(cin, pass);
mace->salt(pass);
state = NEUTRAL;
cout << "'" << pass << "' : ";
}
bool same = mace->same(frame(rects[0]));
if (same) col = Scalar(0,220,220);
else col = Scalar(60,60,60);
if (! two.empty()) {
cout << (same ? "accepted." : "denied.") << endl;
}
}
rectangle(frame, rects[0], col, 2);
}
imshow("MACE",frame);
int k = waitKey(10);
switch (k) {
case -1 : break;
case 27 : return 0;
default : state = NEUTRAL; break;
case 'r': state = RECORD; break;
case 'p': state = PREDICT; break;
case 's': mace->save(defname); break;
}
}
return 0;
}
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