标签：变换 corners top bot Point2f OpenCV int 透视 cv
OpenCV 可自动调整参数的透视变换:
在shiter大牛的基础之上，对于他的程序做了一定的修改。
首先，通过两个循环使得霍夫变换两个参数：角度的分辨率和点个数的阈值可以变换，这样就不必对于每一张图像都手动的设置阈值。
其次，过滤掉了两个距离很近的直线，使得能够正确找到物体的四个轮廓的直线。
#include
#include
#include
#include
#pragma comment(lib,"opencv_core2413d.lib")
#pragma comment(lib,"opencv_highgui2413d.lib")
#pragma comment(lib,"opencv_imgproc2413d.lib")
cv::Point2f center(0,0);
cv::Point2f computeIntersect(cv::Vec4i a, cv::Vec4i b)
{
int x1 = a[0], y1 = a[1], x2 = a[2], y2 = a[3], x3 = b[0], y3 = b[1], x4 = b[2], y4 = b[3];
float denom;
if (float d = ((float)(x1 - x2) * (y3 - y4)) - ((y1 - y2) * (x3 - x4)))
{
cv::Point2f pt;
pt.x = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / d;
pt.y = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / d;
return pt;
}
else
return cv::Point2f(-1, -1);
}
//确定四个点的中心线
void sortCorners(std::vector& corners,
cv::Point2f center)
{
std::vector top, bot;
for (int i = 0; i < corners.size(); i++)
{
if (corners[i].y < center.y)
top.push_back(corners[i]);
else
bot.push_back(corners[i]);
}
corners.clear();
if (top.size() == 2 && bot.size() == 2){
cv::Point2f tl = top[0].x > top[1].x ? top[1] : top[0];
cv::Point2f tr = top[0].x > top[1].x ? top[0] : top[1];
cv::Point2f bl = bot[0].x > bot[1].x ? bot[1] : bot[0];
cv::Point2f br = bot[0].x > bot[1].x ? bot[0] : bot[1];
corners.push_back(tl);
corners.push_back(tr);
corners.push_back(br);
corners.push_back(bl);
}
}
//计算直线端点的距离
bool Disserence(int a,int b)
{
if (a * a + b * b < 100)
{
return true;
}
else
{
return false;
}
}
int main()
{
cv::Mat src = cv::imread("001.jpg");
if (src.empty())
return -1;
cv::Mat bw;
cv::cvtColor(src, bw, CV_BGR2GRAY);
cv::blur(bw, bw, cv::Size(3, 3));
cv::Canny(bw, bw, 100, 100, 3);
std::vector lines;
std::vector corners;
std::vector approx;
int HoughThre = 20;
int HoughTheta = 30;
/*
void HoughLinesP(InputArray image,OutputArray lines, double rho, double theta, int threshold, double minLineLength=0,double maxLineGap=0 )
image为输入图像，要求是8位单通道图像
lines为输出的直线向量，每条线用4个元素表示，即直线的两个端点的4个坐标值
rho和theta分别为距离和角度的分辨率
threshold为阈值，即步骤3中的阈值
minLineLength为最小直线长度，在步骤5中要用到，即如果小于该值，则不被认为是一条直线
maxLineGap为最大直线间隙，在步骤4中要用到，即如果有两条线段是在一条直线上，但它们之间因为有间隙，所以被认为是两个线段，如果这个间隙大于该值，则被认为是两条线段，否则是一条。
*/
for(;HoughTheta <= 180;HoughTheta = HoughTheta + 30)
{
HoughThre = 30;
for(;HoughThre < 300;HoughThre++)
{
lines.clear();
corners.clear();
approx.clear();
cv::HoughLinesP(bw, lines, 1, CV_PI/HoughTheta, HoughThre, 30, 50); //需要不断的变更霍夫变换的参数，才可以使得刚好找到四条直线，确定出边缘
// Expand the lines
for (int i = 0; i < lines.size(); i++)
{
cv::Vec4i v = lines[i];
lines[i][0] = 0;
lines[i][1] = ((float)v[1] - v[3]) / (v[0] - v[2]) * -v[0] + v[1];
lines[i][2] = src.cols;
lines[i][3] = ((float)v[1] - v[3]) / (v[0] - v[2]) * (src.cols - v[2]) + v[3];
}
//删除距离过近的两条直线
std::set ErasePt;
for (int i = 0; i < lines.size(); i++)
{
for (int j = i + 1; j < lines.size(); j++)
{
if (Disserence(abs(lines[i][0] - lines[j][0]),abs(lines[i][1] - lines[j][1])) && (Disserence(abs(lines[i][2] - lines[j][2]),abs(lines[i][3] - lines[j][3]))))
{
ErasePt.insert(j);
}
}
}
// std::vector::iterator it = lines.end();
int Num = lines.size();
while (Num != 0)
{
std::set::iterator j = ErasePt.find(Num);
if (j != ErasePt.end())
{
lines.erase(lines.begin() + Num - 1);
}
Num--;
}
if (lines.size() != 4)
{
continue;
}
//计算直线的交点，保存在图像范围内的部分
for (int i = 0; i < lines.size(); i++)
{
for (int j = i+1; j < lines.size(); j++)
{
cv::Point2f pt = computeIntersect(lines[i], lines[j]);
if (pt.x >= 0 && pt.y >= 0 && pt.x <= src.cols && pt.y <= src.rows) //保证交点在图像的范围之内
corners.push_back(pt);
}
}
if (corners.size() != 4)
{
continue;
}
cv::approxPolyDP(cv::Mat(corners), approx, cv::arcLength(cv::Mat(corners), true) * 0.02, true);
//if (approx.size() != 4)
//{
// std::cout << "The object is not quadrilateral!" << std::endl;
// return -1;
//}
if (lines.size() == 4 && corners.size() == 4 && approx.size() == 4)
{
break;
}
// std::cout<<".";
}
std::cout< if (lines.size() == 4 && corners.size() == 4 && approx.size() == 4)
break;
if (HoughTheta == 180 && HoughThre >= 299)
{
return -1;
}
}
cv::Mat dst = src.clone();
//for (int i = 0; i < lines.size(); i++)
//{
// cv::Vec4i v = lines[i];
// cv::line(dst, cv::Point(v[0], v[1]), cv::Point(v[2], v[3]), CV_RGB(0,255,0));
//}
//cvNamedWindow("image",0);
//cv::imshow("image", dst);
//cvWaitKey();
// Get mass center
for (int i = 0; i < corners.size(); i++)
center += corners[i];
center *= (1. / corners.size());
sortCorners(corners, center);
if (corners.size() == 0){
std::cout << "The corners were not sorted correctly!" << std::endl;
return -1;
}
// Draw lines
for (int i = 0; i < lines.size(); i++)
{
cv::Vec4i v = lines[i];
cv::line(dst, cv::Point(v[0], v[1]), cv::Point(v[2], v[3]), CV_RGB(0,255,0));
}
cvNamedWindow("image",0);
cv::imshow("image", dst);
cv::waitKey();
// Draw corner points
cv::circle(dst, corners[0], 3, CV_RGB(255,0,0), 2);
cv::circle(dst, corners[1], 3, CV_RGB(0,255,0), 2);
cv::circle(dst, corners[2], 3, CV_RGB(0,0,255), 2);
cv::circle(dst, corners[3], 3, CV_RGB(255,255,255), 2);
// Draw mass center
cv::circle(dst, center, 3, CV_RGB(255,255,0), 2);
cv::Mat quad = cv::Mat::zeros(300, 220, CV_8UC3);
std::vector quad_pts;
quad_pts.push_back(cv::Point2f(0, 0));
quad_pts.push_back(cv::Point2f(quad.cols, 0));
quad_pts.push_back(cv::Point2f(quad.cols, quad.rows));
quad_pts.push_back(cv::Point2f(0, quad.rows));
cv::Mat transmtx = cv::getPerspectiveTransform(corners, quad_pts);
cv::warpPerspective(src, quad, transmtx, quad.size());
cv::imshow("image", dst);
cv::imshow("quadrilateral", quad);
cv::waitKey();
return 0;
}
转自：https://blog.51cto.com/u_13565704/5341151
标签：变换,corners,top,bot,Point2f,OpenCV,int,透视,cv 来源： https://www.cnblogs.com/Jack-Elvis/p/16397738.html
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