#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/image_processing/render_face_detections.h>
#include <dlib/image_processing.h>
#include <dlib/gui_widgets.h>
#include <dlib/image_io.h>
#include <dlib/opencv.h>
#include <iostream>
#include<vector>
#include<math.h>
#include <opencv2/opencv.hpp>
using namespace dlib;
using namespace std;
std::vector<string> getTrainimg(string str){
cv::Directory dir;
string path1 = "C:/Users/Administrator/Desktop/表情库/训练/" + str;
string exten1 = "*.jpg";//JAFFE .jpg ,CK .bmp
bool addPath1 = true;
std::vector<string> filenames = dir.GetListFiles(path1, exten1, addPath1);
return filenames;
}
int main(int argc, char** argv)
{
try
{
// This example takes in a shape model file and then a list of images to
// process. We will take these filenames in as command line arguments.
// Dlib comes with example images in the examples/faces folder so give
// those as arguments to this program.
//if (argc == 1)
//{
// cout << "Call this program like this:" << endl;
// cout << "./face_landmark_detection_ex shape_predictor_68_face_landmarks.dat faces/*.jpg" << endl;
// cout << "\nYou can get the shape_predictor_68_face_landmarks.dat file from:\n";
// cout << "http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;
// return 0;
//}
//std::cout << "argc:" << argc << std::endl;
// We need a face detector. We will use this to get bounding boxes for
// each face in an image.
frontal_face_detector detector = get_frontal_face_detector();
// And we also need a shape_predictor. This is the tool that will predict face
// landmark positions given an image and face bounding box. Here we are just
// loading the model from the shape_predictor_68_face_landmarks.dat file you gave
// as a command line argument.
shape_predictor sp;
// std::cout << "argv[1]:" << argv[1] << std::endl;
deserialize("shape_predictor_68_face_landmarks.dat") >> sp;
image_window win;// win_faces;
// Loop over all the images provided on the command line.
std::vector<string> filenames=getTrainimg("happy");
int filesize = filenames.size;
for (int i = 3; i < filesize; ++i)
{
array2d<bgr_pixel> img;
string filename = "C://Users//74205//Desktop//rawImage//";
filename.append(to_string(i));
filename.append(".jpg");
load_image(img, filename);
// Make the image larger so we can detect small faces.
pyramid_up(img);
// Now tell the face detector to give us a list of bounding boxes
// around all the faces in the image.
std::vector<rectangle> dets = detector(img);
cout << "Number of faces detected: " << dets.size() << endl;
// Now we will go ask the shape_predictor to tell us the pose of
// each face we detected.
std::vector<full_object_detection> shapes;
for (unsigned long j = 0; j < dets.size(); ++j)
{
full_object_detection shape = sp(img, dets[j]);
cout << "number of parts: " << shape.num_parts() << endl;
cout << "pixel position of first part: " << shape.part(0) << endl;
cout << "pixel position of second part: " << shape.part(1) << endl;
// You get the idea, you can get all the face part locations if
// you want them. Here we just store them in shapes so we can
// put them on the screen.
shapes.push_back(shape);
cv::Mat temp = dlib::toMat(img);
for (int k = 0; k < 68; ++k){
circle(temp, cvPoint(shapes[j].part(k).x(), shapes[j].part(k).y()), 3, cv::Scalar(0, 0, 255), -1);//j代表检测到的第几个人脸
putText(temp, to_string(k), cvPoint(shapes[j].part(k).x(), shapes[j].part(k).y()), CV_FONT_HERSHEY_PLAIN, 1, cv::Scalar(255, 0, 0), 1, 4);//k代表68个特征点
}
}
// Now let's view our face poses on the screen.
win.clear_overlay();
string newFilename = "C://Users//74205//Desktop//detection//";
newFilename.append(to_string(i));
newFilename.append(".jpg");
const char *file = newFilename.data();
cv::Mat temp = dlib::toMat(img);//将img图像进行保存，转化成mat类型的对象
//将处理好的图像进行由mat形式转化为IplImage形式
IplImage *src;
src = &IplImage(temp);
cvSaveImage(file, src);
win.set_image(img);
//win.add_overlay(render_face_detections(shapes));
//// We can also extract copies of each face that are cropped, rotated upright,
//// and scaled to a standard size as shown here:
//dlib::array<array2d<rgb_pixel> > face_chips;
//extract_image_chips(img, get_face_chip_details(shapes), face_chips);
//win_faces.set_image(tile_images(face_chips));
cout << "加上vector向量之后" << endl;
cout << "Hit enter to process the next image..." << endl;
cin.get();
}
}
catch (exception& e)
{
cout << "\nexception thrown!" << endl;
cout << e.what() << endl;
}
system("pause");
#include <dlib/image_processing/render_face_detections.h>
#include <dlib/image_processing.h>
#include <dlib/gui_widgets.h>
#include <dlib/image_io.h>
#include <dlib/opencv.h>
#include <iostream>
#include<vector>
#include<math.h>
#include <opencv2/opencv.hpp>
using namespace dlib;
using namespace std;
std::vector<string> getTrainimg(string str){
cv::Directory dir;
string path1 = "C:/Users/Administrator/Desktop/表情库/训练/" + str;
string exten1 = "*.jpg";//JAFFE .jpg ,CK .bmp
bool addPath1 = true;
std::vector<string> filenames = dir.GetListFiles(path1, exten1, addPath1);
return filenames;
}
int main(int argc, char** argv)
{
try
{
// This example takes in a shape model file and then a list of images to
// process. We will take these filenames in as command line arguments.
// Dlib comes with example images in the examples/faces folder so give
// those as arguments to this program.
//if (argc == 1)
//{
// cout << "Call this program like this:" << endl;
// cout << "./face_landmark_detection_ex shape_predictor_68_face_landmarks.dat faces/*.jpg" << endl;
// cout << "\nYou can get the shape_predictor_68_face_landmarks.dat file from:\n";
// cout << "http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;
// return 0;
//}
//std::cout << "argc:" << argc << std::endl;
// We need a face detector. We will use this to get bounding boxes for
// each face in an image.
frontal_face_detector detector = get_frontal_face_detector();
// And we also need a shape_predictor. This is the tool that will predict face
// landmark positions given an image and face bounding box. Here we are just
// loading the model from the shape_predictor_68_face_landmarks.dat file you gave
// as a command line argument.
shape_predictor sp;
// std::cout << "argv[1]:" << argv[1] << std::endl;
deserialize("shape_predictor_68_face_landmarks.dat") >> sp;
image_window win;// win_faces;
// Loop over all the images provided on the command line.
std::vector<string> filenames=getTrainimg("happy");
int filesize = filenames.size;
for (int i = 3; i < filesize; ++i)
{
array2d<bgr_pixel> img;
string filename = "C://Users//74205//Desktop//rawImage//";
filename.append(to_string(i));
filename.append(".jpg");
load_image(img, filename);
// Make the image larger so we can detect small faces.
pyramid_up(img);
// Now tell the face detector to give us a list of bounding boxes
// around all the faces in the image.
std::vector<rectangle> dets = detector(img);
cout << "Number of faces detected: " << dets.size() << endl;
// Now we will go ask the shape_predictor to tell us the pose of
// each face we detected.
std::vector<full_object_detection> shapes;
for (unsigned long j = 0; j < dets.size(); ++j)
{
full_object_detection shape = sp(img, dets[j]);
cout << "number of parts: " << shape.num_parts() << endl;
cout << "pixel position of first part: " << shape.part(0) << endl;
cout << "pixel position of second part: " << shape.part(1) << endl;
// You get the idea, you can get all the face part locations if
// you want them. Here we just store them in shapes so we can
// put them on the screen.
shapes.push_back(shape);
cv::Mat temp = dlib::toMat(img);
for (int k = 0; k < 68; ++k){
circle(temp, cvPoint(shapes[j].part(k).x(), shapes[j].part(k).y()), 3, cv::Scalar(0, 0, 255), -1);//j代表检测到的第几个人脸
putText(temp, to_string(k), cvPoint(shapes[j].part(k).x(), shapes[j].part(k).y()), CV_FONT_HERSHEY_PLAIN, 1, cv::Scalar(255, 0, 0), 1, 4);//k代表68个特征点
}
}
// Now let's view our face poses on the screen.
win.clear_overlay();
string newFilename = "C://Users//74205//Desktop//detection//";
newFilename.append(to_string(i));
newFilename.append(".jpg");
const char *file = newFilename.data();
cv::Mat temp = dlib::toMat(img);//将img图像进行保存，转化成mat类型的对象
//将处理好的图像进行由mat形式转化为IplImage形式
IplImage *src;
src = &IplImage(temp);
cvSaveImage(file, src);
win.set_image(img);
//win.add_overlay(render_face_detections(shapes));
//// We can also extract copies of each face that are cropped, rotated upright,
//// and scaled to a standard size as shown here:
//dlib::array<array2d<rgb_pixel> > face_chips;
//extract_image_chips(img, get_face_chip_details(shapes), face_chips);
//win_faces.set_image(tile_images(face_chips));
cout << "加上vector向量之后" << endl;
cout << "Hit enter to process the next image..." << endl;
cin.get();
}
}
catch (exception& e)
{
cout << "\nexception thrown!" << endl;
cout << e.what() << endl;
}
system("pause");
}
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