2020/4/3

今日学习

• 搭好dlib c++的环境
• 三节课
• 写完作业

今日杂碎记录

dlib 的编译

官网教程，稳得一批

CMakeLists.txt 的编写

project(dlib_test) # 工程名字
find_package(OpenCV REQUIRED) # 使用opencv ， 注意括号中的大小写
cmake_minimum_required(VERSION 2.8.12) # cmake 版本

# cmake needs is the dlib source code folder and it will take care of everything.
add_subdirectory(../dlib dlib_build) # 需要编译的添加子目录

include_directories( ${OpenCV_INCLUDE_DIRS} ) 
add_executable(dlib_test main.cpp) # 要编译的文件， 这里的dlib_test 是生成的可执行文件的名字
target_link_libraries( dlib_test ${OpenCV_LIBS} ) # dlib_test 是生成的可执行文件的名字
# Finally, you need to tell CMake that this program, assignment_learning_ex,
# depends on dlib.  You do that with this statement:
target_link_libraries(dlib_test dlib::dlib) # dlib_test 是生成的可执行文件的名字

dlib的CMakeLists.txt编写详细规则
opencv的CMakeLists.txt编写详细规则
更多关于CMakeLists.txt 的编写

C++中的rectangle API

void rectangle(Mat& img, Point pt1,Point pt2,const Scalar& color, int thickness=1, int lineType=8, int shift=0)
 void rectangle(Mat& img, Rect rec, const Scalar& color, int thickness=1, int lineType=8, int shift=0 )

dlib C++ 检测特征点

#include <dlib/opencv.h>
#include <opencv2/opencv.hpp>
#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>
 
using namespace dlib;
using namespace std;
 
int main()
{
	try
	{
		cv::VideoCapture cap(0);
		if (!cap.isOpened())
		{
			cerr << "Unable to connect to camera" << endl;
			return 1;
		}
 
		//image_window win;
 
		// Load face detection and pose estimation models.
		frontal_face_detector detector = get_frontal_face_detector();
		shape_predictor pose_model;
		deserialize("shape_predictor_68_face_landmarks.dat") >> pose_model;
 
		// Grab and process frames until the main window is closed by the user.
		while (cv::waitKey(30) != 27)
		{
			// Grab a frame
			cv::Mat temp;
			cap >> temp;
 
			cv_image<bgr_pixel> cimg(temp); //这一行一定不能少， dlib和opencv中的img格式保存不一样
			// Detect faces 
			std::vector<rectangle> faces = detector(cimg);
			// Find the pose of each face.
			std::vector<full_object_detection> shapes;
			for (unsigned long i = 0; i < faces.size(); ++i)
				shapes.push_back(pose_model(cimg, faces[i]));
	
			if (!shapes.empty()) {
				for (int i = 0; i < 68; i++) {
					circle(temp, cvPoint(shapes[0].part(i).x(), shapes[0].part(i).y()), 3, cv::Scalar(0, 0, 255), -1);
					//	shapes[0].part(i).x();//68个
				}
			}
			//Display it all on the screen
			imshow("Dlib特征点", temp);
 
		}
	}
	catch (serialization_error& e)
	{
		cout << "You need dlib's default face landmarking model file to run this example." << endl;
		cout << "You can get it from the following URL: " << endl;
		cout << "   http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;
		cout << endl << e.what() << endl;
	}
	catch (exception& e)
	{
		cout << e.what() << endl;
	}
}

如果标了数字会是这样的。

dlib 的demo1
dlib 的demo2

明天再使用的方法

FaceDetection
python 与 C++ dlib人脸检测结果对比

刷剧时间长了哇，后悔一秒。