94 lines
3.8 KiB
C++
94 lines
3.8 KiB
C++
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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using namespace cv::face;
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static bool myDetector( InputArray image, OutputArray ROIs, CascadeClassifier* face_cascade)
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{
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Mat gray;
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std::vector<Rect> faces;
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if(image.channels()>1){
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cvtColor(image.getMat(),gray,COLOR_BGR2GRAY);
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}
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else{
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gray = image.getMat().clone();
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}
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equalizeHist( gray, gray );
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face_cascade->detectMultiScale( gray, faces, 1.1, 3, 0, Size(30, 30) );
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Mat(faces).copyTo(ROIs);
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return true;
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}
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TEST(CV_Face_FacemarkKazemi, can_create_default) {
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string cascade_name = cvtest::findDataFile("face/lbpcascade_frontalface_improved.xml", true);
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string configfile_name = cvtest::findDataFile("face/config.xml", true);
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CascadeClassifier face_cascade;
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EXPECT_TRUE(face_cascade.load(cascade_name));
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FacemarkKazemi::Params params;
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params.configfile = configfile_name;
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Ptr<FacemarkKazemi> facemark;
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EXPECT_NO_THROW(facemark = FacemarkKazemi::create(params));
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EXPECT_TRUE(facemark->setFaceDetector((cv::face::FN_FaceDetector)myDetector, &face_cascade));
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EXPECT_FALSE(facemark.empty());
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}
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TEST(CV_Face_FacemarkKazemi, can_loadTrainingData) {
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string filename = cvtest::findDataFile("face/lbpcascade_frontalface_improved.xml", true);
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string configfile_name = cvtest::findDataFile("face/config.xml", true);
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CascadeClassifier face_cascade;
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EXPECT_TRUE(face_cascade.load(filename));
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FacemarkKazemi::Params params;
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params.configfile = configfile_name;
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Ptr<FacemarkKazemi> facemark;
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EXPECT_NO_THROW(facemark = FacemarkKazemi::create(params));
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EXPECT_TRUE(facemark->setFaceDetector((cv::face::FN_FaceDetector)myDetector, &face_cascade));
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vector<String> filenames;
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filename = cvtest::findDataFile("face/1.txt", true);
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filenames.push_back(filename);
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filename = cvtest::findDataFile("face/2.txt", true);
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filenames.push_back(filename);
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vector<String> imagenames;
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vector< vector<Point2f> > trainlandmarks,Trainlandmarks;
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vector<Rect> rectangles;
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//Test getData function
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EXPECT_NO_THROW(loadTrainingData(filenames,trainlandmarks,imagenames));
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vector<Mat> trainimages;
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for(unsigned long i=0;i<imagenames.size();i++){
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string img = cvtest::findDataFile(imagenames[i], true);
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Mat src = imread(img);
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EXPECT_TRUE(!src.empty());
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trainimages.push_back(src);
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Trainlandmarks.push_back(trainlandmarks[i]);
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}
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string modelfilename = "face_landmark_model.dat";
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Size scale = Size(460,460);
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EXPECT_TRUE(facemark->training(trainimages,Trainlandmarks,configfile_name,scale,modelfilename));
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}
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TEST(CV_Face_FacemarkKazemi, can_detect_landmarks) {
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string cascade_name = cvtest::findDataFile("face/lbpcascade_frontalface_improved.xml", true);
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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;
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EXPECT_NO_THROW(facemark = FacemarkKazemi::create(params));
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EXPECT_TRUE(facemark->setFaceDetector((cv::face::FN_FaceDetector)myDetector, &face_cascade));
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string imgname = cvtest::findDataFile("face/detect.jpg");
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string modelfilename = cvtest::findDataFile("face/face_landmark_model.dat",true);
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Mat img = imread(imgname);
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EXPECT_TRUE(!img.empty());
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EXPECT_FALSE(facemark.empty());
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EXPECT_NO_THROW(facemark->loadModel(modelfilename));
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vector<Rect> faces;
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//Detect faces in the current image
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EXPECT_TRUE(facemark->getFaces(img,faces));
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//vector to store the landmarks of all the faces in the image
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vector< vector<Point2f> > shapes;
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EXPECT_NO_THROW(facemark->fit(img,faces,shapes));
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shapes.clear();
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}
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}} // namespace
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