人脸关键点定位之后，就可以通过眼睛的位置获知人脸的姿态，在大量的论文中都强调过，将人脸对齐之后，能够显著的提升识别的准确率，因此，在前面的工作基础上，这里做一些许的调整，就可以实现对齐功能。
这个很简单，因此只贴一下关键代码：
首先，在mainwindow.h文件中的private slots:中添加成员变量：
void preprocess(Mat& img, const vector<Point2f> &p, const Rect & r,
Point& e_L, Point& e_R, Point& e_C, Mat & face);然后，添加preprocess函数的实现部分：
void MainWindow::preprocess(Mat& img, const vector<Point2f> & p, const Rect & r,
Point& e_L, Point& e_R, Point& e_C, Mat & face)
{
e_L = Point(0, 0);
e_R = Point(0, 0);
e_C = Point(0, 0);
// 获取左右眼的中心坐标
// 36-41左眼
for (int m = 36; m < 42; m++)
{
e_L.x += (int)p[m].x;
e_L.y += (int)p[m].y;
circle(img, p[m], 1, Scalar(0, 255, 0));
}
e_L.x /= 6;
e_L.y /= 6;
// 42-47右眼
for (int n = 42; n < 48; n++)
{
e_R.x += (int)p[n].x;
e_R.y += (int)p[n].y;
circle(img, p[n], 1, Scalar(0, 255, 0));
}
e_R.x /= 6;
e_R.y /= 6;
circle(img, e_L, 1, Scalar(0, 0, 255), 3);
circle(img, e_R, 1, Scalar(0, 0, 255), 3);
e_L -= Point(r.x, r.y);
e_R -= Point(r.x, r.y);
// 人眼中心
e_C.x = (e_L.x + e_R.x) * 0.5f;
e_C.y = (e_L.y + e_R.y) * 0.5f;
// 人脸对齐
// 计算两个眼睛之间的角度
double dy = (e_R.y - e_L.y);
double dx = (e_R.x - e_L.x);
double len = sqrt(dx*dx + dy*dy);
double angle = atan2(dy, dx) * 180.0 / CV_PI;
double desiredLen = (DESIRED_RIGHT_EYE_X - 0.16);
double scale = desiredLen * DESIRED_FACE_WIDTH / len;
Mat rot_mat = getRotationMatrix2D(e_C, angle, scale);
// Shift the center of the eyes to be the desired center between the eyes.
rot_mat.at<double>(0, 2) += DESIRED_FACE_WIDTH * 0.5f - e_C.x;
rot_mat.at<double>(1, 2) += DESIRED_FACE_HEIGHT * DESIRED_LEFT_EYE_Y - e_C.y;
Mat warped = Mat(DESIRED_FACE_HEIGHT, DESIRED_FACE_WIDTH, CV_8U, Scalar(128)); // Clear the output image to a default grey.
Mat roi = img(r);
warpAffine(roi, warped, rot_mat, warped.size());
//equalizeHist(warped, warped);
Mat mask = Mat(warped.size(), CV_8U, Scalar(0)); // Start with an empty mask.
Point faceCenter = Point( DESIRED_FACE_WIDTH/2, cvRound(DESIRED_FACE_HEIGHT * FACE_ELLIPSE_CY) );
Size size = Size( cvRound(DESIRED_FACE_WIDTH * FACE_ELLIPSE_W), cvRound(DESIRED_FACE_HEIGHT * FACE_ELLIPSE_H) );
ellipse(mask, faceCenter, size, 0, 0, 360, Scalar(255), CV_FILLED);
//imshow("mask", mask);
// Use the mask, to remove outside pixels.
Mat dstImg = Mat(warped.size(), CV_8U, Scalar(128)); // Clear the output image to a default gray.
// Apply the elliptical mask on the face.
warped.copyTo(dstImg, mask); // Copies non-masked pixels from filtered to dstImg.
//imshow("dstImg", dstImg);
face = warped;
}最后，显示部分代码位于readFrame函数中：
void MainWindow::readFrame()
{
cap >> frame;
Mat gray, frame1, frame2;
vector<Rect> faces;
cvtColor(frame, gray, COLOR_BGR2GRAY);
//cvtColor(frame, frame1, COLOR_BGR2RGB);
//QImage img((const uchar*)frame1.data,
//frame1.cols, frame1.rows,
//frame1.cols * frame1.channels(),
//QImage::Format_RGB888);
// ui->label->setPixmap(QPixmap::fromImage(img));
equalizeHist(gray, gray);
//-- Detect faces
face_cascade.detectMultiScale( gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(60, 60) );
vector< vector<Point2f> > landmarks;
// Run landmark detector
bool success = facemark->fit(frame,faces,landmarks);
Mat face;
if(success)
{
// If successful, render the landmarks on the face
for(int i = 0; i < landmarks.size(); i++)
{
// cout << landmarks[i].size() << endl;
// drawLandmarks(frame, landmarks[i]);
preprocess(frame, landmarks[i], faces[i],
eye_L, eye_R, eye_C, face);
int width = ui->label_2->width();
int height = ui->label_2->height();
cv::resize(face, face, cv::Size(width, height));
cvtColor(face, frame2, COLOR_BGR2RGB);
QImage img1((const uchar*)frame2.data,
frame2.cols, frame2.rows,
frame2.cols * frame2.channels(),
QImage::Format_RGB888);
ui->label_2->setPixmap(QPixmap::fromImage(img1));
rectangle(frame, faces[i], Scalar(0, 255, 0), 2);
}
}
cvtColor(frame, frame1, COLOR_BGR2RGB);
QImage img((const uchar*)frame1.data,
frame1.cols, frame1.rows,
frame1.cols * frame1.channels(),
QImage::Format_RGB888);
ui->label->setPixmap(QPixmap::fromImage(img));
}效果如下：
