本篇博客是Dlib库学习的第三篇---人脸对齐。人脸对齐与人脸检测工程建立与配置基本相同,在此不再赘述。可参照我上一篇博客。闲话少说,来点干货。
步骤一:建立并配置工程,参照上一篇博客。
步骤二:下载形状模型文件
下载地址:模型文件
步骤三:具体代码,这段代码也是dlib提供的例子,我自己添加的中文注释!
- // The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
- /*
-
- This example program shows how to find frontal human faces in an image and
- estimate their pose. The pose takes the form of 68 landmarks. These are
- points on the face such as the corners of the mouth, along the eyebrows, on
- the eyes, and so forth.
- ****这个例子展示了怎样在一张图片中找到正脸和他们的姿势.姿势是由68个点的形式组成的.
-
-
- //This face detector is made using the classic Histogram of Oriented
- Gradients (HOG) feature combined with a linear classifier, an image pyramid,
- and sliding window detection scheme.//
- ****人脸检测器的原理
-
- The pose estimator was created by
- using dlib's implementation of the paper://根据这篇论文编写的程序
- One Millisecond Face Alignment with an Ensemble of Regression Trees by
- Vahid Kazemi and Josephine Sullivan, CVPR 2014
- and was trained on the iBUG 300-W face landmark dataset.
-
- Also, note that you can train your own models using dlib's machine learning
- tools. See train_shape_predictor_ex.cpp to see an example.
- ****我们可以训练自己的模型,用train_shape_predictor_ex.exe
-
-
- Finally, note that the face detector is fastest when compiled with at least
- SSE2 instructions enabled. So if you are using a PC with an Intel or AMD
- chip then you should enable at least SSE2 instructions. If you are using
- cmake to compile this program you can enable them by using one of the
- following commands when you create the build project:
- cmake path_to_dlib_root/examples -DUSE_SSE2_INSTRUCTIONS=ON
- cmake path_to_dlib_root/examples -DUSE_SSE4_INSTRUCTIONS=ON
- cmake path_to_dlib_root/examples -DUSE_AVX_INSTRUCTIONS=ON
- This will set the appropriate compiler options for GCC, clang, Visual
- Studio, or the Intel compiler. If you are using another compiler then you
- need to consult your compiler's manual to determine how to enable these
- instructions. Note that AVX is the fastest but requires a CPU from at least
- 2011. SSE4 is the next fastest and is supported by most current machines.
- */
-
-
- #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 <iostream>
-
- using namespace dlib;
- using namespace std;
-
- // ----------------------------------------------------------------------------------------
-
- 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.
- // 这个例子需要一个形状模型文件和一系列的图片.
- 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:///files/shape_predictor_68_face_landmarks.dat.bz2" << endl;//从这个地址下载模型标记点数据
- return 0;
- }
-
- // We need a face detector. We will use this to get bounding boxes for
- // each face in an image.
- //****需要一个人脸检测器,获得一个边界框
- 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.
- //****也需要一个形状预测器,这是一个工具用来预测给定的图片和脸边界框的标记点的位置。
- //****这里我们仅仅从shape_predictor_68_face_landmarks.dat文件加载模型
- shape_predictor sp;//定义个shape_predictor类的实例
- deserialize(argv[1]) >> sp;
-
-
- image_window win, win_faces;
- // Loop over all the images provided on the command line.
- // ****循环所有图片
- for (int i = 2; i < argc; ++i)
- {
- cout << "processing image " << argv[i] << endl;
- array2d<rgb_pixel> img;//注意变量类型 rgb_pixel 三通道彩色图像
- load_image(img, argv[i]);
- // 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);//检测人脸,获得边界框
- cout << "Number of faces detected: " << dets.size() << endl;//检测到人脸的数量
-
- // Now we will go ask the shape_predictor to tell us the pose of
- // each face we detected.
- //****调用shape_predictor类函数,返回每张人脸的姿势
- std::vector<full_object_detection> shapes;//注意形状变量的类型,full_object_detection
- for (unsigned long j = 0; j < dets.size(); ++j)
- {
- full_object_detection shape = sp(img, dets[j]);//预测姿势,注意输入是两个,一个是图片,另一个是从该图片检测到的边界框
- cout << "number of parts: " << shape.num_parts() << endl;
- //cout << "pixel position of first part: " << shape.part(0) << endl;//获得第一个点的坐标,注意第一个点是从0开始的
- //cout << "pixel position of second part: " << shape.part(1) << endl;//获得第二个点的坐标
- /*自己改写,打印出全部68个点*/
- for (int i = 1; i < 69; i++)
- {
- cout << "第 " << i<< " 个点的坐标: " << shape.part(i-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);
- }
-
- // Now let's view our face poses on the screen.
- //**** 显示结果
- win.clear_overlay();
- 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:
- //****我们也能提取每张剪裁后的人脸的副本,旋转和缩放到一个标准尺寸
- 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 << "Hit enter to process the next image..." << endl;
- cin.get();
- }
- }
- catch (exception& e)
- {
- cout << "\nexception thrown!" << endl;
- cout << e.what() << endl;
- }
- }
-
- // ----------------------------------------------------------------------------------------
其他的和上一篇博客相同,祝大家好运!
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