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bccbec79fd
Face alignment (#1199) * This commit will add a new functionality of one millisecond face_alignment to OpenCV. Face alignment is a computer vision technology for identifying the geometric structure of human faces in digital images. Given the location and size of a face, it automatically determines the shape of the face components such as eyes and nose. Added following functions : 1) Application to train a face landmark detector. 2) Application to detect face landmarks using a trained model. 3) Application to swap faces using face landmark detection 4) Application to detect landmarks in a video. Merged the code with a global facemark API. Added Doxygen Documentation for the Class created. Added tutorials for the samples added. Added visualisations depicting error rate and training time. Made desired changes fix fix fix fix fix fix fix fix fix * face: drop duplicated file -face_alignmentImpl.hpp +face_alignmentimpl.hpp * face: minor refactoring - replace license headers - fix usage of "precomp.hpp"
93 lines
3.3 KiB
C++
93 lines
3.3 KiB
C++
#include "opencv2/face.hpp"
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#include "opencv2/videoio.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/objdetect.hpp"
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#include "opencv2/imgproc.hpp"
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#include <iostream>
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#include <vector>
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#include <string>
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using namespace std;
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using namespace cv;
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using namespace cv::face;
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static bool myDetector(InputArray image, OutputArray faces, CascadeClassifier *face_cascade)
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{
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Mat gray;
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if (image.channels() > 1)
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cvtColor(image, gray, COLOR_BGR2GRAY);
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else
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gray = image.getMat().clone();
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equalizeHist(gray, gray);
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std::vector<Rect> faces_;
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face_cascade->detectMultiScale(gray, faces_, 1.4, 2, CASCADE_SCALE_IMAGE, Size(30, 30));
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Mat(faces_).copyTo(faces);
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return true;
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}
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int main(int argc,char** argv){
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//Give the path to the directory containing all the files containing data
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CommandLineParser parser(argc, argv,
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"{ help h usage ? | | give the following arguments in following format }"
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"{ model_filename f | | (required) path to binary file storing the trained model which is to be loaded [example - /data/file.dat]}"
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"{ image i | | (required) path to image in which face landmarks have to be detected.[example - /data/image.jpg] }"
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"{ face_cascade c | | Path to the face cascade xml file which you want to use as a detector}"
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);
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// Read in the input arguments
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if (parser.has("help")){
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parser.printMessage();
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cerr << "TIP: Use absolute paths to avoid any problems with the software!" << endl;
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return 0;
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}
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string filename(parser.get<string>("model_filename"));
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if (filename.empty()){
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parser.printMessage();
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cerr << "The name of the model file to be loaded for detecting landmarks is not found" << endl;
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return -1;
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}
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string image(parser.get<string>("image"));
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if (image.empty()){
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parser.printMessage();
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cerr << "The name of the image file in which landmarks have to be detected is not found" << endl;
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return -1;
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}
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string cascade_name(parser.get<string>("face_cascade"));
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if (cascade_name.empty()){
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parser.printMessage();
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cerr << "The name of the cascade classifier to be loaded to detect faces is not found" << endl;
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return -1;
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}
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Mat img = imread(image);
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//pass the face cascade xml file which you want to pass as a detector
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CascadeClassifier face_cascade;
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face_cascade.load(cascade_name);
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FacemarkKazemi::Params params;
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Ptr<FacemarkKazemi> facemark = FacemarkKazemi::create(params);
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facemark->setFaceDetector((FN_FaceDetector)myDetector, &face_cascade);
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facemark->loadModel(filename);
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cout<<"Loaded model"<<endl;
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vector<Rect> faces;
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resize(img,img,Size(460,460));
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facemark->getFaces(img,faces);
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vector< vector<Point2f> > shapes;
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if(facemark->fit(img,faces,shapes))
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{
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for( size_t i = 0; i < faces.size(); i++ )
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{
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cv::rectangle(img,faces[i],Scalar( 255, 0, 0 ));
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}
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for(unsigned long i=0;i<faces.size();i++){
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for(unsigned long k=0;k<shapes[i].size();k++)
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cv::circle(img,shapes[i][k],5,cv::Scalar(0,0,255),FILLED);
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}
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namedWindow("Detected_shape");
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imshow("Detected_shape",img);
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waitKey(0);
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}
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return 0;
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} |