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// FindRotation-angle.cpp : 定義控制臺應(yīng)用程序的入口點(diǎn)。

//

// findContours.cpp : 定義控制臺應(yīng)用程序的入口點(diǎn)。

//

#include "stdafx.h"

#include

#include

#include

#include

#include

#include

#pragma comment(lib,"opencv_core2410d.lib")

#pragma comment(lib,"opencv_highgui2410d.lib")

#pragma comment(lib,"opencv_imgproc2410d.lib")

#define PI 3.1415926

using namespace std;

using namespace cv;

int hough_line(Mat src)

{

//【1】載入原始圖和Mat變量定義

Mat srcImage = src;//imread("1.jpg"); //工程目錄下應(yīng)該有一張名為1.jpg的素材圖

Mat midImage,dstImage;//臨時(shí)變量和目標(biāo)圖的定義

//【2】進(jìn)行邊緣檢測和轉(zhuǎn)化為灰度圖

Canny(srcImage, midImage, 50, 200, 3);//進(jìn)行一此canny邊緣檢測

cvtColor(midImage,dstImage, CV_GRAY2BGR);//轉(zhuǎn)化邊緣檢測后的圖為灰度圖

//【3】進(jìn)行霍夫線變換

vector lines;//定義一個(gè)矢量結(jié)構(gòu)lines用于存放得到的線段矢量集合

HoughLinesP(midImage, lines, 1, CV_PI/180, 80, 50, 10 );

//【4】依次在圖中繪制出每條線段

for( size_t i = 0; i < lines.size(); i++ )

{

Vec4i l = lines[i];

line( dstImage, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(186,88,255), 1, CV_AA);

}

//【5】顯示原始圖

imshow("【原始圖】", srcImage);

//【6】邊緣檢測后的圖

imshow("【邊緣檢測后的圖】", midImage);

//【7】顯示效果圖

imshow("【效果圖】", dstImage);

//waitKey(0);

return 0;

}

int main()

{

// Read input binary image

char *image_name = "test.jpg";

cv::Mat image = cv::imread(image_name,0);

if (!image.data)

return 0;

cv::namedWindow("Binary Image");

cv::imshow("Binary Image",image);

// 從文件中加載原圖

IplImage *pSrcImage = cvLoadImage(image_name, CV_LOAD_IMAGE_UNCHANGED);

// 轉(zhuǎn)為2值圖

cvThreshold(pSrcImage,pSrcImage,200,255,cv::THRESH_BINARY_INV);

image = cv::Mat(pSrcImage,true);

cv::imwrite("binary.jpg",image);

// Get the contours of the connected components

std::vector<:vector>> contours;

cv::findContours(image,

contours, // a vector of contours

CV_RETR_EXTERNAL, // retrieve the external contours

CV_CHAIN_APPROX_NONE); // retrieve all pixels of each contours

// Print contours' length

std::cout << "Contours: " << contours.size() << std::endl;

std::vector<:vector>>::const_iterator itContours= contours.begin();

for ( ; itContours!=contours.end(); ++itContours)

{

std::cout << "Size: " << itContours->size() << std::endl;

}

// draw black contours on white image

cv::Mat result(image.size(),CV_8U,cv::Scalar(255));

cv::drawContours(result,contours,

-1, // draw all contours

cv::Scalar(0), // in black

2); // with a thickness of 2

cv::namedWindow("Contours");

cv::imshow("Contours",result);

// Eliminate too short or too long contours

int cmin= 100; // minimum contour length

int cmax= 1000; // maximum contour length

std::vector<:vector>>::const_iterator itc= contours.begin();

while (itc!=contours.end()) {

if (itc->size() < cmin || itc->size() > cmax)

itc= contours.erase(itc);

else

++itc;

}

// draw contours on the original image

cv::Mat original= cv::imread(image_name);

cv::drawContours(original,contours,

-1, // draw all contours

cv::Scalar(255,255,0), // in white

2); // with a thickness of 2

cv::namedWindow("Contours on original");

cv::imshow("Contours on original",original);

// Let's now draw black contours on white image

result.setTo(cv::Scalar(255));

cv::drawContours(result,contours,

-1, // draw all contours

cv::Scalar(0), // in black

1); // with a thickness of 1

image= cv::imread("binary.jpg",0);

//imshow("lll",result);

//waitKey(0);

// testing the bounding box

//

//霍夫變換進(jìn)行直線檢測，此處使用的是probabilistic Hough transform(cv::HoughLinesP)而不是standard Hough transform(cv::HoughLines)

cv::Mat result_line(image.size(),CV_8U,cv::Scalar(255));

result_line = result.clone();

hough_line(result_line);

//Mat tempimage;

//【2】進(jìn)行邊緣檢測和轉(zhuǎn)化為灰度圖

//Canny(result_line, tempimage, 50, 200, 3);//進(jìn)行一此canny邊緣檢測

//imshow("canny",tempimage);

//waitKey(0);

//cvtColor(tempimage,result_line, CV_GRAY2BGR);//轉(zhuǎn)化邊緣檢測后的圖為灰度圖

vector lines;

cv::HoughLinesP(result_line,lines,1,CV_PI/180,80,50,10);

for(int i = 0; i < lines.size(); i++)

{

line(result_line,cv::Point(lines[i][0],lines[i][1]),cv::Point(lines[i][2],lines[i][3]),Scalar(0,0,0),2,8,0);

}

cv::namedWindow("line");

cv::imshow("line",result_line);

//waitKey(0);

/

//

//std::vector<:vector>>::const_iterator itc_rec= contours.begin();

//while (itc_rec!=contours.end())

//{

//cv::Rect r0= cv::boundingRect(cv::Mat(*(itc_rec)));

//cv::rectangle(result,r0,cv::Scalar(0),2);

//++itc_rec;

//}

//cv::namedWindow("Some Shape descriptors");

//cv::imshow("Some Shape descriptors",result);

CvBox2D End_Rage2D;

CvPoint2D32f rectpoint[4];

CvMemStorage *storage = cvCreateMemStorage(0); //開辟內(nèi)存空間

CvSeq* contour = NULL; //CvSeq類型 存放檢測到的圖像輪廓邊緣所有的像素值，坐標(biāo)值特征的結(jié)構(gòu)體以鏈表形式

cvFindContours( pSrcImage, storage, &contour, sizeof(CvContour),CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE);//這函數(shù)可選參數(shù)還有不少

for(; contour; contour = contour->h_next) //如果contour不為空，表示找到一個(gè)以上輪廓，這樣寫法只顯示一個(gè)輪廓

//如改為for(; contour; contour = contour->h_next) 就可以同時(shí)顯示多個(gè)輪廓

{

End_Rage2D = cvMinAreaRect2(contour);

//代入cvMinAreaRect2這個(gè)函數(shù)得到最小包圍矩形 這里已得出被測物體的角度，寬度,高度，和中點(diǎn)坐標(biāo)點(diǎn)存放在CvBox2D類型的結(jié)構(gòu)體中，

//主要工作基本結(jié)束。

for(int i = 0;i< 4;i++)

{

//CvArr* s=(CvArr*)&result;

//cvLine(s,cvPointFrom32f(rectpoint[i]),cvPointFrom32f(rectpoint[(i+1)%4]),CV_G(0,0,255),2);

line(result,cvPointFrom32f(rectpoint[i]),cvPointFrom32f(rectpoint[(i+1)%4]),Scalar(125),2);

}

cvBoxPoints(End_Rage2D,rectpoint);

std::cout <

}

cv::imshow("lalalal",result);

cv::waitKey();

return 0;

}

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