//

// Created by fuzr1 on 2017/8/20.

//

//first step add below

#include

#include

#include

#include

#include

#include

#include

//first step end

//second step

//#include "stdafx.h"

//second step end

#include "opencv2/highgui/highgui.hpp"

#include "opencv2/calib3d/calib3d.hpp"

#include "opencv2/imgproc/imgproc.hpp"

#include "opencv2/features2d/features2d.hpp"

using namespace cv;

using namespace std;

#define LOG_TAG "CombinePicture"

#define LOGD(...) ((void)__android_log_print(ANDROID_LOG_DEBUG, LOG_TAG, __VA_ARGS__))

//third step

#ifdef __cplusplus

extern "C" {

#endif

//third step end

//计算原始图像点位在通过矩阵变换后在目标图像上对应位置

Point2f getTransformPoint(const Point2f originalPoint, const Mat &transformMaxtri);

// fiveth step

JNIEXPORT jlong JNICALL Java_com_lenovo_camera_orbmatch_MainActivity_doCombinePicture(JNIEnv *env, jclass clz, jlong img1, jlong img2)

//int main()

//fiveth step end

{

Mat image01 = Mat(*(Mat*)img1);

cvtColor(image01, image01, CV_BGRA2BGR);

Mat image02 = Mat(*(Mat*)img2);

cvtColor(image02, image02, CV_BGRA2BGR);

if (image01.empty() || image02.empty())

{

// printf("the loader the picture failed");

// waitKey();

LOGD("there is some image input wrong");

return 0;//图像没有所有读取成功

}

//imshow("拼接图像1", image01);

// imshow("拼接图像2", image02);

double time = getTickCount();

//灰度图转换

Mat image1, image2;

cvtColor(image01, image1, CV_RGB2GRAY);

cvtColor(image02, image2, CV_RGB2GRAY);

//提取特征点

//SiftFeatureDetector siftDetector(800); // 海塞矩阵阈值

Ptr fastDetector = FastFeatureDetector::create();

vector keyPoint1, keyPoint2;

fastDetector->detect(image1, keyPoint1);

fastDetector->detect(image2, keyPoint2);

//特征点描述，为下边的特征点匹配作准备

Ptr BriskDescriptor = BRISK::create();

Mat imageDesc1, imageDesc2;

BriskDescriptor->compute(image1, keyPoint1, imageDesc1);

BriskDescriptor->compute(image2, keyPoint2, imageDesc2);

//得到匹配特征点，并提取最优配对

Ptr matcher = DescriptorMatcher::create("BruteForce");

//FlannBasedMatcher matcher;

vector matchePoints;

matcher->match(imageDesc1, imageDesc2, matchePoints, Mat());

if (matchePoints.size() < 10)

{

LOGD("the match point is below 10");

//waitKey();

return 0;

}

sort(matchePoints.begin(), matchePoints.end()); //特征点排序，opencv按照匹配点准确度排序

//获取排在前N个的最优匹配特征点

vector imagePoints1, imagePoints2;

for (int i = 0; i<10; i++)

{

imagePoints1.push_back(keyPoint1[matchePoints[i].queryIdx].pt);

imagePoints2.push_back(keyPoint2[matchePoints[i].trainIdx].pt);

}

//获取图像1到图像2的投影映射矩阵，尺寸为3*3

Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);

Mat adjustMat;

adjustMat = (Mat_(3, 3) << 1.0, 0, image01.cols, 0, 1.0, 0, 0, 0, 1.0);//向后偏移image01.cols矩阵

//Mat adjustMat =Mat::eye(cv::Size(3,3),CV_64F);

// adjustMat.at(0, 2) = image01.cols;

Mat adjustHomo = adjustMat*homo;//矩阵相乘，先偏移

//获取最强配对点(就是第一个配对点)在原始图像和矩阵变换后图像上的对应位置，用于图像拼接点的定位

Point2f originalLinkPoint, targetLinkPoint, basedImagePoint;

originalLinkPoint = keyPoint1[matchePoints[0].queryIdx].pt;

targetLinkPoint = getTransformPoint(originalLinkPoint, adjustHomo);

basedImagePoint = keyPoint2[matchePoints[0].trainIdx].pt;

//图像配准

Mat imageTransform;

//将图片1进行映射到图像2，原本映射后x值为负值，可是把映射矩阵向后偏移image01.cols矩阵

//咱们很难判断出拼接后图像的大小尺寸，为了尽量保留原来的像素，咱们尽量的大一些，对于拼接后的图片能够进一步剪切无效或者不规则的边缘

warpPerspective(image01, imageTransform, adjustMat*homo, Size(image02.cols + image01.cols + 10, image02.rows));

//在最强匹配点的位置处衔接，最强匹配点左侧是图1，右侧是图2，这样直接替换图像衔接很差，光线有突变

//Mat ROIMat = image02(Rect(Point(basedImagePoint.x, 0), Point(image02.cols, image02.rows)));

//ROIMat.copyTo(Mat(imageTransform1, Rect(targetLinkPoint.x, 0, image02.cols - basedImagePoint.x + 1, image02.rows)));

//在最强匹配点左侧的重叠区域进行累加，是衔接稳定过渡，消除突变

Mat image1Overlap, image2Overlap; //图1和图2的重叠部分

image1Overlap = imageTransform(Rect(Point(targetLinkPoint.x - basedImagePoint.x, 0), Point(targetLinkPoint.x, image02.rows)));

image2Overlap = image02(Rect(0, 0, image1Overlap.cols, image1Overlap.rows));

Mat image1ROICopy = image1Overlap.clone(); //复制一份图1的重叠部分

for (int i = 0; i

{

for (int j = 0; j

{

double weight;

weight = (double)j / image1Overlap.cols; //随距离改变而改变的叠加系数

image1Overlap.at(i, j)[0] = (1 - weight)*image1ROICopy.at(i, j)[0] + weight*image2Overlap.at(i, j)[0];

image1Overlap.at(i, j)[1] = (1 - weight)*image1ROICopy.at(i, j)[1] + weight*image2Overlap.at(i, j)[1];

image1Overlap.at(i, j)[2] = (1 - weight)*image1ROICopy.at(i, j)[2] + weight*image2Overlap.at(i, j)[2];

}

}

Mat ROIMat = image02(Rect(Point(image1Overlap.cols, 0), Point(image02.cols, image02.rows))); //图2中不重合的部分

ROIMat.copyTo(Mat(imageTransform, Rect(targetLinkPoint.x, 0, ROIMat.cols, image02.rows))); //不重合的部分直接衔接上去

time = getTickCount() - time;

time /= getTickFrequency();

LOGD("match time=%f\n", time);

// namedWindow("拼接结果", 0);

// imshow("拼接结果", imageTransform);

// imwrite("matchResult.jpg", imageTransform);

// waitKey();

// return 0;

Mat *ret = new Mat(imageTransform);

return (jlong) ret;

}

//计算原始图像点位在通过矩阵变换后在目标图像上对应位置

Point2f getTransformPoint(const Point2f originalPoint, const Mat &transformMaxtri)

{

Mat originelP, targetP;

originelP = (Mat_(3, 1) << originalPoint.x, originalPoint.y, 1.0);

targetP = transformMaxtri*originelP;

float x = targetP.at(0, 0) / targetP.at(2, 0);

float y = targetP.at(1, 0) / targetP.at(2, 0);

return Point2f(x, y);

}

//forrth step

#ifdef __cplusplus

}

#endif

//fourth step end