OpenCV实现图像校正功能

/*-----------------------------------------------------------------------------------------------------------------
Copyright (C),2018---, HUST Liu
 File name: image_solve.h
 Author: 刘峻源 Version: 1 Date: 2018.10.3
 ------------------------------------------------------------------------------------------------------------------
 Description:
 文档矫正项目.cpp的主要函数储存在这里

 
-------  -----------   ------------  ------------   -----------  ---------
函数说明：comMatC用于连接矩阵
 toushibianhuan_gai_fangshebianhuan用于仿射变换
 toushibianhuan用于仿射变换
 main_transform 调用函数来处理图像，包括平移、缩小、旋转、仿射变换和透视变换
 input_solve 用以矫正文档，包括打开图像、滤波、提取边缘、绘制边缘、透视变换矫正文档
--------------------------------------------------------------------------------
 Others:NONE
 Function List: comMatC、toushibianhuan、toushibianhuan_gai_fangshebianhuan、input_solve
 --------------------------------------------------------------------------------
 history: NONE

-------------------------------------------------------------------------------------*/

/*-----------------------------------------------------------------
标准openCV开头         --
引用头文件和命名空间        --
------------------------------------------------------------------*/
#include 
#include 
#include 
#include 
#include 
using namespace std;
using namespace cv;

/*-------------------------------------------------------------------------------
Function: comMatC
 Description:上下连接矩阵，并输出
 --------------------------------------------------------------------------------
 Calls:Create、copyTo
 Called By: main_transform
 Table Accessed: NONE
 Table Updated: NONE
 --------------------------------------------------------------------------------
 Input:
 第一个参数：上面的矩阵，Mat
 第二个参数：下面的矩阵，Mat
 第三个参数：连接后的输出容器，Mat
 Output：输出连接后的矩阵
 Return:输出矩阵
 Others:列数不一致会报错！
---------------------------------------------------------------------------------*/
Mat comMatC(Mat Matrix1, Mat Matrix2, Mat &MatrixCom)
{
 CV_Assert(Matrix1.cols == Matrix2.cols);
 MatrixCom.create(Matrix1.rows + Matrix2.rows, Matrix1.cols, Matrix1.type());
 Mat temp = MatrixCom.rowRange(0, Matrix1.rows);
 Matrix1.copyTo(temp);
 Mat temp1 = MatrixCom.rowRange(Matrix1.rows, Matrix1.rows + Matrix2.rows);
 Matrix2.copyTo(temp1);
 return MatrixCom;
}

/*--------------------------------------------------------------------------------
Function: toushibianhuan
 Description:实现透视变换功能 ，将input_image按tp_Transform_maxtri矩阵变换后输出至另一图像容器中
 -------------------------------------------------------------------------------
 Calls:max、min
 Called By:main_transform
 Table Accessed: NONE
 Table Updated: NONE
 ----------------------------------------------------------------------------------
 Input:
 第一个参数：透视变换输入的图像矩阵，Mat
 第二个参数：输出图像容器矩阵，Mat
 第三个参数：变换矩阵，Mat
 Output：无返回值。在控制台上打印出原图的位置矩阵、变换后的图像坐标矩阵、变换矩阵
 Return:NONE
 Others:NONE
-----------------------------------------------------------------*/
void toushibianhuan(Mat input_image, Mat &output, Mat tp_Translater_maxtri)
{
 int qiu_max_flag;
 int j;
 int i;
 //定义顶点位置矩阵
 Mat position_maxtri(3, 4, CV_64FC1, Scalar(1));
 position_maxtri.at < double >(0, 0) = 0;
 position_maxtri.at < double >(1, 0) = 0;
 position_maxtri.at < double >(1, 1) = 0;
 position_maxtri.at < double >(0, 2) = 0;
 position_maxtri.at < double >(1, 2) = input_image.rows;
 position_maxtri.at < double >(0, 3) = input_image.cols;
 position_maxtri.at < double >(1, 3) = input_image.rows;
 position_maxtri.at < double >(0, 1) = input_image.cols;
 Mat new_corner = tp_Translater_maxtri * position_maxtri;
 //打印并监视三个矩阵
 cout << "coner_maxtri" << new_corner << ";" << endl << endl;
 cout << "pos_maxtri" << position_maxtri << ";" << endl << endl;
 cout << "T_maxtri" << tp_Translater_maxtri << ";" << endl << endl;
 //为了计算图像高度，先初始化最高最低、最左最右点
 double max_kuan = new_corner.at < double >(0, 0) / new_corner.at < double >(2, 0);
 double min_kuan = new_corner.at < double >(0, 0) / new_corner.at < double >(2, 0);
 double max_gao = new_corner.at < double >(1, 0) / new_corner.at < double >(2, 0);
 double min_gao = new_corner.at < double >(1, 0) / new_corner.at < double >(2, 0);
 for (qiu_max_flag = 1; qiu_max_flag < 4; qiu_max_flag++)
 {
 max_kuan = max(max_kuan,
 new_corner.at < double >(0, qiu_max_flag) / new_corner.at < double >(2, qiu_max_flag));
 min_kuan = min(min_kuan,
 new_corner.at < double >(0, qiu_max_flag) / new_corner.at < double >(2, qiu_max_flag));
 max_gao = max(max_gao,
 new_corner.at < double >(1, qiu_max_flag) / new_corner.at < double >(2, qiu_max_flag));
 min_gao = min(min_gao,
 new_corner.at < double >(1, qiu_max_flag) / new_corner.at < double >(2, qiu_max_flag));
 }
 //创建向前映射矩阵 map1, map2
 output.create(int(max_gao - min_gao), int(max_kuan - min_kuan), input_image.type());
 Mat map1(output.size(), CV_32FC1);
 Mat map2(output.size(), CV_32FC1);
 Mat tp_point(3, 1, CV_32FC1, 1);
 Mat point(3, 1, CV_32FC1, 1);
 tp_Translater_maxtri.convertTo(tp_Translater_maxtri, CV_32FC1);
 Mat Translater_inv = tp_Translater_maxtri.inv();
 //核心步骤，将映射阵用矩阵乘法更新出来
 for (i = 0; i < output.rows; i++)
 {
 for (j = 0; j < output.cols; j++)
 {
 point.at(0) = j + min_kuan;
 point.at(1) = i + min_gao;
 tp_point = Translater_inv * point;
 map1.at(i, j) = tp_point.at(0) / tp_point.at(2);
 map2.at(i, j) = tp_point.at(1) / tp_point.at(2);
 }
 }

 remap(input_image, output, map1, map2, CV_INTER_LINEAR);
}

/*--------------------------------------------------------------------------------
Function: toushibianhuan_gai_fangshebianhuan
 Description:实现仿射变换功能 ，将input_image按Translater_maxtri矩阵变换后输出至另一图像容器中
 ------------------------------------------------------------------------------------
 Calls:comMatC、max、min
 Called By:main_transform
 Table Accessed: NONE
 Table Updated: NONE
 ------------------------------------------------------------------------------------
 Input:
 第一个参数：透视变换输入的图像矩阵，Mat
 第二个参数：输出图像矩阵，Mat
 第三个参数：变换矩阵，Mat
 Output：无返回值。在控制台上打印出原图的位置矩阵、变换后的图像坐标矩阵、变换矩阵
 Return:NONE
 Others:NONE
-------------------------------------------------------------------------------*/
void toushibianhuan_gai_fangshebianhuan(Mat input_image, Mat &output, Mat Translater_maxtri)
{
 int width = 0;
 int height = 0;
 Mat tp_Translater_maxtri;
 Mat position_maxtri(3, 4, CV_64FC1, Scalar(1));
 Mat one_vector(1, 3, CV_64FC1, Scalar(0));
 one_vector.at(0, 2) = 1.;
 comMatC(Translater_maxtri, one_vector, tp_Translater_maxtri);
 position_maxtri.at < double >(1, 1) = 0;
 position_maxtri.at < double >(0, 2) = 0;
 position_maxtri.at < double >(0, 0) = 0;
 position_maxtri.at < double >(1, 0) = 0;
 position_maxtri.at < double >(0, 3) = input_image.cols;
 position_maxtri.at < double >(1, 3) = input_image.rows;
 position_maxtri.at < double >(0, 1) = input_image.cols;
 position_maxtri.at < double >(1, 2) = input_image.rows;
 Mat new_corner = tp_Translater_maxtri * position_maxtri;
 cout << "coner_maxtri" << new_corner << ";" << endl << endl;
 cout << "pos_maxtri" << position_maxtri << ";" << endl << endl;
 cout << "T_maxtri" << tp_Translater_maxtri << ";" << endl << endl;
 double max_kuan = new_corner.at < double >(0, 0) / new_corner.at < double >(2, 0);
 double min_kuan = new_corner.at < double >(0, 0) / new_corner.at < double >(2, 0);
 double max_gao = new_corner.at < double >(1, 0) / new_corner.at < double >(2, 0);
 double min_gao = new_corner.at < double >(1, 0) / new_corner.at < double >(2, 0);
 for (int flag = 1; flag < 4; flag++)
 {
 max_kuan = max(max_kuan, new_corner.at < double >(0, flag) / new_corner.at < double >(2, flag));
 min_kuan = min(min_kuan, new_corner.at < double >(0, flag) / new_corner.at < double >(2, flag));
 max_gao = max(max_gao, new_corner.at < double >(1, flag) / new_corner.at < double >(2, flag));
 min_gao = min(min_gao, new_corner.at < double >(1, flag) / new_corner.at < double >(2, flag));
 }
 output.create(int(max_gao - min_gao), int(max_kuan - min_kuan), input_image.type());
 Mat map1(output.size(), CV_32FC1);
 Mat map2(output.size(), CV_32FC1);
 Mat tp_point(3, 1, CV_32FC1, 1);
 Mat point(3, 1, CV_32FC1, 1);
 tp_Translater_maxtri.convertTo(tp_Translater_maxtri, CV_32FC1);
 Mat Translater_inv = tp_Translater_maxtri.inv();
 for (int i = 0; i < output.rows; i++)
 {
 for (int j = 0; j < output.cols; j++)
 {
 point.at(1) = i + min_gao;
 point.at(0) = j + min_kuan;
 tp_point = Translater_inv * point;
 map1.at(i, j) = tp_point.at(0) / tp_point.at(2);
 map2.at(i, j) = tp_point.at(1) / tp_point.at(2);
 }
 }
 remap(input_image, output, map1, map2, CV_INTER_LINEAR);
}

/*------------------------------------------------------------------------------
Function: main_transform
 Description:实现缩小、平移、旋转的仿射变换功能 ，加以展示且将图片保存在当前工程目录下
 ---------------------------------------------------------------------------
 Calls: resize、 warpAffine、 Size 、 Scalar 、getRotationMatrix2D、 namedWindow、
 toushibianhuan_gai_fangshebianhuan、 imshow、 imwrite、waitKey、printf、warpPerspective、fangshebianhuan
 Called By: main
 Table Accessed: NONE
 Table Updated: NONE
 --------------------------------------------------------------------------------
 Input:
 第一个参数：float类型的旋转角度值（非弧度）
 第二个参数：向右平移的像素，int类型
 第三个参数：向下平移的像素，int类型
 第四个参数：读取图像路径，const char类型
 第五个参数：x方向伸缩比例，float类型
 第六个参数：y方向伸缩比例，float类型
 Output：仿射变换、透视变换后的图像保存于当前工程目录下,各参数已经设置好，矫正效果不佳
 Return:无返回值
 Others:NONE
---------------------------------------------------------------------------*/
void main_transform(float angle, int right_translate, int down_translate,
 const char* road_read_image, float x_tobe, float y_tobe)
{
 Point2f input_image1[3] = { Point2f(50,50),Point2f(200,50),Point2f(50,200) };
 Point2f dst1[3] = { Point2f(0,100),Point2f(200,50),Point2f(180,300) };
 Point2f input_image[4] = { Point2f(100,50),Point2f(100,550),Point2f(350,50),Point2f(350,550) };
 Point2f dst[4] = { Point2f(100,50),Point2f(340,550),Point2f(350,80),Point2f(495,550) };
 Mat kernel2 = getPerspectiveTransform(input_image, dst);
 Mat kernel = getAffineTransform(input_image1, dst1);
 Mat one_vector(1, 3, CV_64FC1, Scalar(0));
 Mat Temp_kernel;
 one_vector.at(0, 2) = 1.;
 comMatC(kernel, one_vector, Temp_kernel);
 float all_tobe = x_tobe / 2 + y_tobe / 2;
 Mat old_image = imread(road_read_image);
 Mat new_min_image;
 Mat new_translation_image;
 Mat rotate_image;
 Mat translater(2, 3, CV_32F, Scalar(0));
 Mat rotater;
 Mat fangshe_image;
 Mat toushi_image;
 vector compression_params;
 resize(old_image, new_min_image, Size(), x_tobe, y_tobe, INTER_CUBIC);
 translater.at(0, 0) = 1;
 translater.at(1, 1) = 1;
 translater.at(0, 2) = right_translate;
 translater.at(1, 2) = down_translate;
 warpAffine(new_min_image, new_translation_image, translater,
 Size(new_min_image.cols*1.5, new_min_image.rows*1.5));
 Point rotate_center = Point(new_translation_image.cols / 3, new_translation_image.rows / 2);
 rotater = getRotationMatrix2D(rotate_center, angle, all_tobe);
 warpAffine(new_translation_image, rotate_image, rotater, Size(),
 INTER_CUBIC | CV_WARP_FILL_OUTLIERS, BORDER_CONSTANT, Scalar(0));
 //warpAffine(new_translation_image, fangshe_image, kernel, Size(new_translation_image.cols*1.5, new_translation_image.rows*1.5));
 //这是OpenCV自带的仿射变换.........
 compression_params.push_back(IMWRITE_PNG_COMPRESSION);
 toushibianhuan_gai_fangshebianhuan(new_translation_image, fangshe_image, kernel);
 toushibianhuan(fangshe_image, toushi_image, kernel2);

 //warpPerspective(fangshe_image, toushi_image, kernel2, Size(new_translation_image.cols, new_translation_image.rows));
 //这是openCV的透视变换
 compression_params.push_back(9);
 namedWindow("new_min_image");
 imshow("new_min_image", new_min_image);
 imwrite("task2_1放缩.png", old_image, compression_params);
 namedWindow("new_translation_image");
 imshow("new_translation_image", new_translation_image);
 bool flags = imwrite("task2_1平移.png", new_translation_image, compression_params);
 namedWindow("rotate_image");
 imshow("rotate_image", rotate_image);
 imwrite("task2_1旋转.png", rotate_image, compression_params);
 namedWindow("fangshe_image");
 imshow("fangshe_image", fangshe_image);
 imwrite("task2_1仿射.png", fangshe_image, compression_params);
 namedWindow("toushi_image");
 imshow("toushi_image", toushi_image);
 imwrite("task2_1透视.png", toushi_image, compression_params);
 printf("%d", flags);

}

/*----------------------------------------------------------------------------
Function: getCrossPoint
 Description:求两直线的交点
 -----------------------------------------------------------------------------
 Calls: NONE
 Called By: input_solve
 Table Accessed: NONE
 Table Updated: NONE
 -----------------------------------------------------------------------------
 Input:
 第一个参数：由两点表示的类型为Vec4i的直线A
 第二个参数：由两点表示的类型为Vec4i的直线B
 Output：Point2f的点
 Return:Point2f的点
 Others:NONE
--------------------------------------------------------------------------------*/
Point2f getCrossPoint(Vec4i LineA, Vec4i LineB)
{
 double ka, kb;
 //求出LineA斜率
 ka = (double)(LineA[3] - LineA[1]) / (double)(LineA[2] - LineA[0]); 
 //求出LineB斜率
 kb = (double)(LineB[3] - LineB[1]) / (double)(LineB[2] - LineB[0]); 


 Point2f crossPoint;
 crossPoint.x = (ka*LineA[0] - LineA[1] - kb * LineB[0] + LineB[1]) / (ka - kb);
 crossPoint.y = (ka*kb*(LineA[0] - LineB[0]) + ka * LineB[1] - kb * LineA[1]) / (ka - kb);
 return crossPoint;
}

/*----------------------------------------------------------------------
Function: input_solve
 Description:用于打开图像、滤波、提取边缘、绘制边缘、透视变换矫正文档的函数。注意，本函数中图像
 处理过程中的参数已经调整完毕
 ------------------------------------------------------------------------
 Calls: imread、resize、morphologyEx、blur、Canny、HoughLines、warpPerspective
 Called By: main
 Table Accessed: NONE
 Table Updated: NONE
 -------------------------------------------------------------------------
 Input:
 第一个参数：输入图像的路径
 Output：经过文档矫正后的图像
 Return:NONE
 Others:矫正图像保存于当前目录下：
 "C:/Users/liujinyuan/source/repos/作业2_2/作业2_2/task2_2矫正.png"
---------------------------------------------------------------*/
void input_solve(const char* image_road)
{
 //定义保存图像参数向量
 vector compression_params;
 compression_params.push_back(IMWRITE_PNG_COMPRESSION);
 compression_params.push_back(9);
 //获取闭运算滤波的核
 Mat element = getStructuringElement(MORPH_RECT, Size(5, 5));
 Mat new_min_image;
 Mat last_kernel;
 //获取灰度图
 Mat old_image = imread(image_road,0);
 vectorlines;
 vectorconers;
 vectorlines_2pt(10);
 Point pt1, pt2,pt3,pt4,pt5,pt6;
 Mat last_image;
 Mat new_min_image2;
 resize(old_image, new_min_image, Size(), 0.5, 0.5, INTER_CUBIC);
 resize(old_image, new_min_image2, Size(), 0.5, 0.5, INTER_CUBIC);
 //闭运算滤波
 morphologyEx(new_min_image, new_min_image, MORPH_CLOSE, element);
 blur(new_min_image,new_min_image,Size(10,10));
 Canny(new_min_image, new_min_image,8.9,9,3 );
 HoughLines(new_min_image,lines,1,CV_PI/180,158,0,0);
 //利用这个循环，可以绘制霍夫变换获取直线的效果图，但是为了简洁性我暂时删去了创建窗口绘制的代码
 for (rsize_t i = 0 ; i < lines.size(); i++)
 {
 if (i!=lines.size()-2)
 {
 float zhongxinjuli = lines[i][0], theta = lines[i][1];
 double cos_theta = cos(theta), sin_theta = sin(theta);
 double x0 = zhongxinjuli * cos_theta, y0 = zhongxinjuli * sin_theta;
 pt1.x = cvRound(x0 - 1000 * sin_theta);
 pt1.y = cvRound(y0 + 1000 * cos_theta);
 pt2.x = cvRound(x0 + 1000 * sin_theta);
 pt2.y = cvRound(y0 - 1000 * cos_theta);
 line(new_min_image, pt1, pt2, Scalar(255, 255, 255), 1, LINE_AA);
 }
 }
 //获取霍夫变换直线的交点
 for (rsize_t flag = 0,flag2=0; flag < lines.size(); flag++)
 {
 if (flag != lines.size() - 2)
 {
 float zx_juli = lines[flag][0], theta2 = lines[flag][1];
 double cos_theta2 = cos(theta2), sin_theta2 = sin(theta2);
 double x1 = zx_juli * cos_theta2, y1 = zx_juli * sin_theta2;
 lines_2pt[flag2][0]= cvRound(x1 - 1000 * sin_theta2);
 lines_2pt[flag2][1] = cvRound(y1 +1000 * cos_theta2);
 lines_2pt[flag2][2] = cvRound(x1 + 1000 * sin_theta2);
 lines_2pt[flag2][3] = cvRound(y1 - 1000 * cos_theta2);
 flag2++;
 }
 }
 for(int flag3=0;flag3<4;flag3++)
 {
 cout << "line_vector=" <

/*-------------------------------------------------------------------------------------------------------------------------------------
Copyright (C),2018---, HUST Liu
 File name:文档矫正项目.cpp
 Author: 刘峻源 Version: 1 Date:2018.10.3
 Description:
   Part1
 根据作业（2）中的任务（1）（2）
 做了以下工作：
 （1）经过仿射变换，图片缩小平移旋转
 （2）调用函数进行仿射、透视变换
 （3）实现函数来做透视变换、仿射变换
------------    ----------    -----------   --------------------   ---- 
 Part2
 根据作业（2）中的任务（3）（4）
 做了以下工作：
 （1）利用读入灰度图像，并且经过滤波、边缘提取、霍夫变换提取边缘直线、得到
  纸张位置（即4个顶点）
 （2）利用透视变换矫正文档
---------  *  ----------  *  --------------- * --------------- * ---------
 具体的任务过程：
 Part1
 调用OpenCV内的函数，编写main_transform函数实现缩小、平移、旋转和仿射变换功能
 （实际上后来openCV的仿射、透视我注释掉了，不用它的函数了）
 实现仿射、透视变换函数 toushibianhuan、toushibianhuan_gai_fangshebianhuan，并在main_transform
 中调用
 注意：在main中调用标头.h文件中的main_transfom函数实现缩小、平移、旋转和仿射、透视变换！！！
------------    ----------   ------------------    -----------------  ---------
 任务过程：
 Part2
 在input_solve中，利用imread读入灰度图，调用blur、morphologyEx滤波，利用canny提取
 边缘，调用HoughLines获取边缘直线，调用getCrossPoint获取直线交点，调用
 getPerspectiveTransform获取变换矩阵，调用warpPerspective实现透视变换
 注意：编写input_solve函数来实现处理功能，本cpp是在main中调用标头.h 中的input_solve函数！！！
------------------------------------------------------------------------------------------
 Others:  图像输入路径：作业2_2/作业2_2/task2.png
 输出图像保存路径：工程文件夹：作业2_2/作业2_2
 注意：在其他环境运行时一定要弄好更改读入路径！！！！
 May Function List: main、main_transform、input_solve
-----------------------------------------------------------------------------------------------
 History:
 as folwing
 ----- -------------   ----------------   ------------------  --------------  -----
 1.2018.10.3 
 2.by 刘峻源
 3.description:在工程作业2_1中将 comMatC、toushibianhuan、toushibianhuan_gai_fangshebianhuan移入头文件
 image_solve.h中
 ----- -------------   ----------------   ------------------  --------------  -----
 1.2018.10.4
 2.by 刘峻源
 3.description:在工程作业2_2中将 main_transfom写入main,去掉main_transform函数的waitKey(0)

 ----- -------------   ----------------   ------------------  --------------  -----
 --------------------------------------------------------------------------------*/


 /*-----------------------------------------------------------------
  标准openCV开头     --
---------------------------------------------------------------------
引用头文件和命名空间        --
------------------------------------------------------------------*/

#include 
#include 
#include 
#include 
#include "标头.h"
using namespace std;
using namespace cv;
int main()
{
 main_transform(90, 0, 100, "task2.png", 0.5, 0.5);
 input_solve("task2.png");
 waitKey(0);
}