常规智能视频分析（IVS）算法C/C++实现（三）

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人脸检测...

//ivs_dlib_facedetection.h
#pragma once
#include "ivs.h"
#include 
#include 
#include 
using namespace cv;

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include  
#include  

using namespace dlib;	
using namespace std;

//struct CONFIG_ENV
//{
//	//�ϰ찻��set 1, ���e������set 0
//	int detection_region;
//	//��X�����ϰ쪺�I�Ӽ�(�C�Ӱϰ�̦h20���I)
//	int num_region_point[11];
//	//��X�����ϰ쪺�I�y��(x,y)
//	int region_point[11][20][2];
//	//IJ�oĵ�����ܤ֫���ɶ� sec
//	int stay_time[11];
//	//1�N��IJ�o  0�N�������SIJ�o
//	int alert[11];
//	//�z��IJ�o����U���e�� pixel
//	int min_object_width[11];
//	//�z��IJ�o����U������ pixel
//	int min_object_height[11];
//	//�z��IJ�o����W���e�� pixel
//	int max_object_width[11];
//	//�z��IJ�o����W������ pixel
//	int max_object_height[11];
//	//�����쪫�?Ӽ�
//	int num_object;
//	//�����쪫�?y�и�T(x,y,width,height,region)
//	int object_info[64][5];
//};


struct face_info
{
	double score;
	int weight_index;//face direction
	int left;
	int top;
	int right;
	int bottom;
	int width;
	int height;
	int center_x;
	int center_y;
	int nomatch_times;
	int match_times;
	int id;
};
// ----------------------------------------------------------------------------------------

template  using con5d = con;
template  using con5 = con;

template  using downsampler = relu>>>>>>>>;
template  using rcon5 = relu>>;

using net_type = loss_mmod>>>>>>>;

// ----------------------------------------------------------------------------------------

class IVS_FACEDETECTION_DLIB
{
public:
	IVS_FACEDETECTION_DLIB();
	~IVS_FACEDETECTION_DLIB();
	void run_facedetection(matrix& img , std::vector& faces);
	void run_facedetection(matrix& img, std::vector& faces);
	void run_dnn_facedetection(matrix& img , std::vector& faces);
	void run_facedetection_nvr(cv::Mat& frame, CONFIG_ENV& config_env);
private:
	int region_num;
	int tracking(face_info &face1 , face_info &face2);
	frontal_face_detector detector;
	net_type net;
};

//ivs_dlib_facedetection.cpp
#include "ivs_dlib_facedetection.h"


IVS_FACEDETECTION_DLIB::IVS_FACEDETECTION_DLIB()
{
	detector = get_frontal_face_detector();
	deserialize("mmod_human_face_detector.dat") >> net;
	region_num = SET_REGION_A;
}

IVS_FACEDETECTION_DLIB::~IVS_FACEDETECTION_DLIB() {}

//std::vector new_faces_list;
//std::vector faces_list;

void IVS_FACEDETECTION_DLIB::run_facedetection_nvr(cv::Mat& frame, CONFIG_ENV& config_env)
{
	if(frame.channels() == 4)
		cv::cvtColor(frame, frame, CV_BGRA2BGR);
	matrix img;
	int face_number = 0;
	if (config_env.detection_region == 0)
	{//full Screen
		std::vector faces_list;
		matrix img;
		assign_image(img, cv_image(frame));
		run_dnn_facedetection(img, faces_list);
		for (int k = 0; k < faces_list.size(); k++)
		{
			config_env.object_info[face_number][0] = faces_list[k].left;
			config_env.object_info[face_number][1] = faces_list[k].top;
			config_env.object_info[face_number][2] = faces_list[k].width;
			config_env.object_info[face_number][3] = faces_list[k].height;
			config_env.object_info[face_number][4] = 0;
			face_number++;
		}
	}
	else
	{//multi roi
		for (int i = 1; i < region_num; i++)
		{
			if (config_env.num_region_point[i] != 0)
			{
				std::vector faces_list;
				faces_list.clear();
				Rect set_rect;
				set_rect.x = config_env.region_point[i][0][0];
				set_rect.y = config_env.region_point[i][0][1];
				set_rect.width = abs(config_env.region_point[i][2][0] - config_env.region_point[i][0][0]);
				set_rect.height = abs(config_env.region_point[i][2][1] - config_env.region_point[i][0][1]);
				cv::Mat roi_mat = frame(set_rect);
				matrix roi_img;
				assign_image(roi_img, cv_image(roi_mat));
				run_dnn_facedetection(roi_img, faces_list);
				for (int k = 0; k < faces_list.size(); k++)
				{
					faces_list[k].left = faces_list[k].left + set_rect.x;
					faces_list[k].top = faces_list[k].top + set_rect.y;
					faces_list[k].right = faces_list[k].right + set_rect.x;
					faces_list[k].bottom = faces_list[k].bottom + set_rect.y;
					config_env.object_info[face_number][0] = faces_list[k].left;
					config_env.object_info[face_number][1] = faces_list[k].top;
					config_env.object_info[face_number][2] = faces_list[k].width;
					config_env.object_info[face_number][3] = faces_list[k].height;
					config_env.object_info[face_number][4] = i;
					face_number++;
				}
			}
		}
	}
	config_env.num_object = face_number;
}

void IVS_FACEDETECTION_DLIB::run_facedetection(matrix& img, std::vector& faces)
{
	faces.clear();
	std::vectorf_dets;
	detector(img, f_dets, 0);
	for (int i = 0; i < f_dets.size(); i++)
	{
		face_info face;
		face.score = f_dets[i].detection_confidence * 100;
		dlib::rectangle rect = f_dets[i].rect.get_rect();
		face.left = rect.left();
		face.top = rect.top();
		face.right = rect.right();
		face.bottom = rect.bottom();
		face.center_x = round((face.left + face.right) / 2);
		face.center_y = round((face.top + face.bottom) / 2);
		face.width = abs(face.right - face.left);
		face.height = abs(face.bottom - face.top);
		face.weight_index = f_dets[i].weight_index;
		faces.push_back(face);
	}
}

void IVS_FACEDETECTION_DLIB::run_facedetection(matrix& img, std::vector& faces)
{
	detector(img, faces, 0);
}

void IVS_FACEDETECTION_DLIB::run_dnn_facedetection(matrix& img , std::vector& faces)
{
	auto dets = net(img);
	faces.clear();

	for (auto&& d : dets)
	{
		face_info face;
		face.score = d.detection_confidence*100;
		face.left = d.rect.left();
		face.top = d.rect.top();
		face.right = d.rect.right();
		face.bottom = d.rect.bottom();
		face.center_x = round((face.left + face.right) / 2);
		face.center_y = round((face.top + face.bottom) / 2);
		face.width = abs(face.right - face.left);
		face.height = abs(face.bottom - face.top);
		faces.push_back(face);
	}
}


int IVS_FACEDETECTION_DLIB::tracking(face_info &new_face, face_info &old_face)
{
	if (new_face.center_x >= old_face.left && new_face.center_x <= old_face.right)
		if (new_face.center_y >= old_face.top && new_face.center_y <= old_face.bottom)
			return 1;
	return 0;
}