C++ 使用不同的C++；替换位头的头
我正试图编译一个OpenCV开源代码项目，但它正在使用这些标题C++ 使用不同的C++；替换位头的头,c++,xcode,header,C++,Xcode,Header,我正试图编译一个OpenCV开源代码项目，但它正在使用这些标题 #include <bits/stl_list.h> #include <bits/stl_vector.h> #包括 #包括 我使用的是Xcode，它无法对这些头进行正则化。是否可以将其替换为诸如 #include <list.h> #include <vector.h> #包括 #包括 当我这样做时，它会打断引用位头的部分代码。看起来他们仍然只是引用了一些东西，比如List
#include <bits/stl_list.h>
#include <bits/stl_vector.h>
#包括
#包括
#include <list.h>
#include <vector.h>
#包括
#包括
List#include <iostream>
#include "opencv/cv.h"
#include <opencv2/opencv.hpp>
#include <bits/stl_list.h>
#include <bits/stl_vector.h>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/core/core.hpp"
#include "My_Q.h"
using namespace std;
using namespace cv;
//=========controller parameters=================
const int MAX_CORNERS = 100;
const int frame_queue_size = 50 ;
const float threshold_percent = 0.1;
const bool draw_floor_line = true;
const bool webcam_mode = false;
const string movie_address = "sample_videos/1p1.mp4";//1p1,people,2p1
//======camera calibration parameters============
const float fku = 1000;
const float v0 = 100;
const float h = 1;
//=======global variables========================
int number_of_people;
CvPoint2D32f* features;
int features_number=0;
int* setNumbers;
int* height_from_floor;
void MyLine( Mat img, Point start, Point end )
{
int thickness = 1;
int lineType = 8;
line( img,
start,
end,
Scalar( 0,255, 255 ),
thickness,
lineType );
}
CvPoint2D32f* find_features(Mat image,vector <Rect> Rect_list,
int & features_number)
{
//cout<<"4.2.1"<<endl;
list<CvPoint2D32f> features;
list<int> set_number;
list<int> dist2B;
int RectNumber = Rect_list.size();
int i, j;
int All_features_number=0;
//cout<<"4.2.2"<<endl;
for (i=0; i<RectNumber; i++)
{
Rect r = Rect_list[i];
////=================cropping=================================
//cout<<"4.2.3"<<endl;
cout<<r.x<<','<<r.y<<endl;
//Mat cropped_rectangle(image, r);
Mat cropped_rectangle ;
image(r).copyTo(cropped_rectangle);//crop r to cropped_rectangle
//cout<<"4.2.4"<<endl;
////=================extracting good features=================
IplImage* imgA = new IplImage(cropped_rectangle);//convert matrix
//cropped_rectangle to IplImage
CvSize img_sz = cvGetSize(imgA);
IplImage* eig_image = cvCreateImage( img_sz, IPL_DEPTH_32F, 1 );
//scratch image
IplImage* tmp_image =cvCreateImage(img_sz,IPL_DEPTH_32F,1);
//scratch image
int corner_count = MAX_CORNERS;
//The corner_count indicates the maximum number of points
// for which there
//is space to return. After the routine exits, corner_count
//is overwritten by the number
//of points that were actually found
//cout<<"4.2.5"<<endl;
CvPoint2D32f* cornersA = new CvPoint2D32f[ MAX_CORNERS ];
// good feature points
//cout<<"4.2.6"<<endl;
cvGoodFeaturesToTrack(//fine good features of rectangle
imgA,
eig_image,
tmp_image,
cornersA,
&corner_count,
0.01,
5.0,
0,
3,
0,
0.04
);
//========================
//========================
//cout<<"4.2.7"<<endl;
////===mapping good features to the original picture and add them to the list
//of features and set their set(the rectangle they are belonging)===
for(int k = 0;k<corner_count;k++)
{
int distance = r.height - cornersA[k].y;
dist2B.push_back(distance);
cornersA[k].x= r.x+ cornersA[k].x;//mapping
cornersA[k].y= r.y+ cornersA[k].y;//mapping
features.push_back(cornersA[k]);//adding
set_number.push_back(i);
}
//cout<<"4.2.8"<<endl;
All_features_number+=corner_count;
}
CvPoint2D32f* features_array = new CvPoint2D32f[All_features_number];
setNumbers = new int[All_features_number];
height_from_floor = new int [All_features_number];
int iter = 0;
//cout<<"4.2.9"<<endl;
while (features.size() > 0)
//copy the features list to the final features array
{
CvPoint2D32f F_point = features.front();
features_array[iter].x = F_point.x;
features_array[iter].y = F_point.y;
features.pop_front();
int SN = set_number.front();
setNumbers[iter] = SN;
set_number.pop_front();
int d = dist2B.front();
height_from_floor[iter] = d;
dist2B.pop_front();
iter++;
}
//cout<<"4.2.10"<<endl;
features_number=All_features_number;
return features_array;
}
int people_floor(int p, bool & status)
{
int sum=0;
int m=0;
for(int i = 0 ; i< features_number;i++)
if (setNumbers[i]== p)
{
sum+=(features[i].y+height_from_floor[i]);
m++;
}
if(m==0)
{
status = false;
return 0;
}
status = true;
return(sum/m);
}
int scaled_people_floor(int p,bool &status)
{
int y = people_floor(p,status);
if(!status)
return -1;
float sum=0;
float m = 0;
for(int i = 0;i<features_number;i++)
{
if(setNumbers[i]==setNumbers[p])
{
for(int j=i+1;j<features_number;j++)
{
if(setNumbers[j]==setNumbers[p])
{
if(height_from_floor[i]-height_from_floor[j]<2)
continue;
float t = (features[i].y-features[j].y)/(height_from_floor[i]-height_from_floor[j]);
if(t<0)
t=-t;
sum+= t;
m++;
}
}
}
}
if(m==0)
{
return(y);
}
float alpha = sum/m;
return(alpha*y);
}
float compute_distance(int v)
{
return((fku*h)/(v-v0));
}
int main (int argc, const char * argv[])
{
int screen_width;
int screen_height;
VideoCapture cap;
if(!webcam_mode)
{
VideoCapture cap1(movie_address);
screen_width = cap1.get(CV_CAP_PROP_FRAME_WIDTH);
screen_height =cap1.get(CV_CAP_PROP_FRAME_HEIGHT);
cap = cap1;
}
else
{
VideoCapture cap2(0);
screen_width = 640;
screen_height =480;
cap2.set(CV_CAP_PROP_FRAME_WIDTH, screen_width);//1024,320,640,160
cap2.set(CV_CAP_PROP_FRAME_HEIGHT, screen_height);//768,240,480,120
cap = cap2;
}
if (!cap.isOpened())
return -1;
namedWindow("video capture", CV_WINDOW_AUTOSIZE);
Mat current_frame;
Mat next_frame;
Mat current_frame_copy;
HOGDescriptor hog;
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
int Threshold=0;
while(1)//main loop:
{
vector<Rect> found, found_filtered;
CvPoint2D32f* tracked_features;
////****************************************************************************
////**********************detection loop: **********************************
while(1)//detection loop
{
cout<<"start detection loop\n";
////--------delete info of previous iteration---------
found_filtered.clear();
//--------------get next frame------------------------
cap >> current_frame;
if(! current_frame.data )// Check for invalid input
{
cout<<"no frame"<<endl;
return 0;
}
current_frame.copyTo(current_frame_copy);
//convert it to the gray
cvtColor(current_frame, current_frame, CV_BGR2GRAY);
//cout<<"2"<<endl;
//---------------detection with HOG algorithm---------
hog.detectMultiScale(current_frame,
found,
0,
Size(8,8),
Size(32,32),
1.05,
2);
cout<<"HOG detection Done"<<endl;
//filter redundant rectangles
for (int i=0; i<found.size(); i++)
{
Rect r = found[i];
int j;
for (j=0; j<found.size(); j++)
if (j!=i && (r & found[j])==r)
break;
if (j==found.size())
found_filtered.push_back(r);
}
number_of_people = found_filtered.size();
//cout<<"4"<<endl;
//----------extracting good features for tracking------
if(number_of_people>0)//if at least one person found:
{
// cout<<"4.1"<<endl;
for(int i=0;i<found_filtered.size();i++)
//bound rectangles to the screen
{
if(found_filtered[i].x<0)
found_filtered[i].x =0;
if(found_filtered[i].y<0)
found_filtered[i].y =0;
if(found_filtered[i].x+found_filtered[i].width>screen_width)
found_filtered[i].width =screen_width-found_filtered[i].x;
if(found_filtered[i].y+found_filtered[i].height>screen_height)
found_filtered[i].height =screen_height-found_filtered[i].y;
}
try// find the list of features of the people
{
// cout<<"4.2"<<endl;
features = find_features(current_frame,
found_filtered,
features_number);
// cout<<"4.3"<<endl;
}
catch(Exception exp)
{
cout<<"exception find features error"<<endl;
continue;
}
break;
}
//cout<<"4.4.1"<<endl;
//------------showing detection
imshow("video capture", current_frame_copy);
waitKey(10);
cout<<"end detection loop"<<endl;
}
////===========setting the breaking threshold for the tracking=========
//cout<<"4.5"<<endl;
Threshold = features_number*threshold_percent;
//cout<<"4.6, feature_number="<<features_number
// <<",Threshold="<<Threshold<<endl;
My_Q Queue(frame_queue_size,1);
////****************************************************************************
////***********************//tracking loop: ********************************
while(1)//tracking loop
{
cout<<"tracking loop start"<<endl;
////==================check for breaking conditions===============
Queue.AddNew(features_number);
if(Queue.BreakCondition())
{
cout<<"features # are not changing for a while!"<<endl;
break;
}
if(features_number< Threshold)
//check for enough number of features to track
{
cout<<"features less than threshold"<<endl;
break;
//go to the detection loop when number of
//tracked features is less than an arbitrary threshold
}
////======================reading the next frame================
current_frame_copy.setTo(Scalar(0,0,0));
//cout<<"t00"<<endl;
cap >> next_frame;//get the next frame
//cout<<"t01"<<endl;
if(! next_frame.data )// Check for invalid input
{
cout<<"Frame not captured"<<endl;
return 0;
//continue;
}
next_frame.copyTo(current_frame_copy);
cvtColor(next_frame, next_frame, CV_BGR2GRAY);
//convert it to gray scale
//cout<<"t02";
////=====================tracking using LK ==============================
try
{
////-----------LK parameters-------------
char status[features_number];
//shows the status of points. 0 means
// feature lost; 1 means found
float feature_errors[features_number];
//list of lost features
tracked_features= new CvPoint2D32f[ features_number ];
IplImage* imgA = new IplImage(current_frame);
//convert initial matrix image to IplImage
IplImage* imgB = new IplImage(next_frame);
//convert final matrix image to IplImage
CvSize pyr_sz = cvSize( imgA->width+8, imgB->height/3 );
IplImage* pyrA = cvCreateImage( pyr_sz, IPL_DEPTH_32F, 1 );
IplImage* pyrB = cvCreateImage( pyr_sz, IPL_DEPTH_32F, 1 );
int win_size = 10;
cout<<"before LK"<<endl;
//--------------------------------------
cvCalcOpticalFlowPyrLK(
imgA,//initial image
imgB,
//final image,both should be single-channel, 8-bit images.
pyrA,
//buffer allocated to store the pyramid images
pyrB,
features,
////the array contains the points
//for which the motion is to be found
tracked_features,////similar array into
//which the computed new locations of the points
////from features are to be placed
features_number,////feature_number is the number
//of points in the features list
cvSize( win_size,win_size ),
5,
status,
feature_errors,
cvTermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, .3 ),
0
);
cout<<"LK done"<<endl;
////============updating the info after tracking===================
//updating feature points
//removing info (set_number/distance to bottom) of lost features
int new_feature_number = 0;
int * temp_set_number = new int[features_number];
int * temp_distance = new int[features_number];
int j = 0;
for(int i = 0 ;i<features_number;i++)
{
if(status[i] == 1)
{
new_feature_number++;
//counting number of features tracked successfully
temp_set_number[j]=setNumbers[i];
temp_distance[j]= height_from_floor[i];
j++;
}
}
setNumbers = temp_set_number;
height_from_floor = temp_distance;
features_number = new_feature_number;
cout<<"features_number="<<features_number<<endl;
delete[] features;
features = new CvPoint2D32f[ features_number ] ;
for(int i = 0;i<features_number;i++)//update features
{
features[i]=tracked_features[i];
}
delete[] tracked_features;
next_frame.copyTo(current_frame);
cout<<"info updated after LK"<<endl;
}
catch(Exception exp)
{
cout<<"LK-exception"<<endl;
break;
}
////****************************************************************************
////****************************computing distance******************************
for(int p=0;p<number_of_people;p++)
{
bool status;
int v = scaled_people_floor(p,status);
//int v = people_floor(p);
if(status)
{
float d = compute_distance(v);
if(draw_floor_line)
MyLine(current_frame_copy,Point(0,v),Point(screen_width,v));
string dist_text = static_cast<ostringstream*>( &(ostringstream() << d) )->str();
dist_text = dist_text.substr(0,5);
putText(current_frame_copy, "distance: "+dist_text+"m", Point(screen_width-150,screen_height-p*50-20),
CV_FONT_NORMAL, 0.5,CV_RGB((125-p*100)%255,(p*100)%255,(255-p*100)%255),1,1);
}
}
cout<<"computing distance"<<endl;
////================showing points ======================
for(int i=0;i<features_number;i++)
{
int p = setNumbers[i];
circle(current_frame_copy,
features[i],
1,
CV_RGB((125-p*100)%255,(p*100)%255,(255-p*100)%255),
3);
}
imshow("video capture", current_frame_copy);
waitKey(10);
cout<<"tracking loop end"<<endl;
}
cout<<"main loop end"<<endl;
}
return 0;
}
#包括
#包括“opencv/cv.h”
#包括
#包括
#包括
#包括“opencv2/highgui/highgui.hpp”
#包括“opencv2/imgproc/imgproc.hpp”
#包括“opencv2/core/core.hpp”
#包括“My_Q.h”
使用名称空间std；
使用名称空间cv；
//==========控制器参数=================
const int MAX_CORNERS=100；
const int frame_queue_size=50；
常量浮动阈值_百分比=0.1；
const bool draw\u floor\u line=true；
const bool网络摄像头模式=错误；
const string movie\u address=“sample\u videos/1p1.mp4”//1p1，各位，2p1
//=======摄像机校准参数============
常数浮点fku=1000；
常数浮动v0=100；
常数浮点h=1；
//=======全局变量========================
国际人口数量；
CV2D32F*特性；
int特征数=0；
int*集合编号；
int*距地板的高度；
无效MyLine（材料图、点起点、点终点）
{
int厚度=1；
int线型=8；
线路（img，
开始
完,，
标量（0255255），
厚度，
线型）；
}
CvPoint2D32f*查找特征（Mat图像、矢量矩形列表、，
整数和特征（数字）
{
//可能这些头不是标准头。我认为它们是gcc库实现的一部分，有些人认为使用它们而不是标准的东西很酷。你最好使用标准头，找出需要修复的地方。旁注-这些头的标准名称是
和
（无.h后缀）如果库作者编写了#include
，那么您可能会觉得代码质量很低。考虑使用其他库。使用命名空间std；
是库作者希望您失败的另一个警告标志。@user4581301，这一行怎么样？字符串dist\u text=static\u cast（&（ostringstream（）str（）
这是无法挽救的。这些头不是标准头。我认为它们是gcc库实现的一部分，有些人认为使用它们而不是标准的东西是很酷的。你最好使用标准头，并弄清楚需要修复什么（如果有的话）。旁注-这些头的标准名称是
和
（无.h后缀）如果库作者编写了#include
，那么您可能会觉得代码质量很低。考虑使用其他库。使用命名空间std；
是库作者希望您失败的另一个警告信号。@user4581301，这行怎么样？字符串dist\u text=static\u cast（&（ostringstream（）str（））
这是无法挽救的