图像拼接在实际的应用场景很广，比如无人机航拍，遥感图像等等，图像拼接是进一步做图像理解基础步骤，拼接效果的好坏直接影响接下来的工作，所以一个好的图像拼接算法非常重要。
再举一个身边的例子吧，你用你的手机对某一场景拍照，但是你没有办法一次将所有你要拍的景物全部拍下来，所以你对该场景从左往右依次拍了好几张图，来把你要拍的所有景物记录下来。那么我们能不能把这些图像拼接成一个大图呢？我们利用opencv就可以做到图像拼接的效果！
比如我们有对这两张图进行拼接。
从上面两张图可以看出，这两张图有比较多的重叠部分，这也是拼接的基本要求。
那么要实现图像拼接需要那几步呢？简单来说有以下几步：
- 对每幅图进行特征点提取
- 对对特征点进行匹配
- 进行图像配准
- 把图像拷贝到另一幅图像的特定位置
- 对重叠边界进行特殊处理
好吧，那就开始正式实现图像配准。
第一步就是特征点提取。现在CV领域有很多特征点的定义，比如sift、surf、harris角点、ORB都是很有名的特征因子，都可以用来做图像拼接的工作，他们各有优势。本文将使用ORB和SURF进行图像拼接，用其他方法进行拼接也是类似的。
基于SURF的图像拼接
用SIFT算法来实现图像拼接是很常用的方法，但是因为SIFT计算量很大，所以在速度要求很高的场合下不再适用。所以，它的改进方法SURF因为在速度方面有了明显的提高（速度是SIFT的3倍），所以在图像拼接领域还是大有作为。虽说SURF精确度和稳定性不及SIFT，但是其综合能力还是优越一些。下面将详细介绍拼接的主要步骤。
1.特征点提取和匹配
1 //提取特征点
2 SurfFeatureDetector Detector(2000);
3 vector<KeyPoint> keyPoint1, keyPoint2;
4 Detector.detect(image1, keyPoint1);
5 Detector.detect(image2, keyPoint2);
6
7 //特征点描述，为下边的特征点匹配做准备
8 SurfDescriptorExtractor Descriptor;
9 Mat imageDesc1, imageDesc2;
10 Descriptor.compute(image1, keyPoint1, imageDesc1);
11 Descriptor.compute(image2, keyPoint2, imageDesc2);
12
13 FlannBasedMatcher matcher;
14 vector<vector<DMatch> > matchePoints;
15 vector<DMatch> GoodMatchePoints;
16
17 vector<Mat> train_desc(1, imageDesc1);
18 matcher.add(train_desc);
19 matcher.train();
20
21 matcher.knnMatch(imageDesc2, matchePoints, 2);
22 cout << "total match points: " << matchePoints.size() << endl;
23
24 // Lowe's algorithm,获取优秀匹配点
25 for (int i = 0; i < matchePoints.size(); i++)
26 {
27 if (matchePoints[i][0].distance < 0.4 * matchePoints[i][1].distance)
28 {
29 GoodMatchePoints.push_back(matchePoints[i][0]);
30 }
31 }
32
33 Mat first_match;
34 drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);
35 imshow("first_match ", first_match);
2.图像配准
这样子我们就可以得到了两幅待拼接图的匹配点集，接下来我们进行图像的配准，即将两张图像转换为同一坐标下，这里我们需要使用findHomography函数来求得变换矩阵。但是需要注意的是，findHomography函数所要用到的点集是Point2f类型的，所有我们需要对我们刚得到的点集GoodMatchePoints再做一次处理，使其转换为Point2f类型的点集。
1 vector<Point2f> imagePoints1, imagePoints2;
2
3 for (int i = 0; i<GoodMatchePoints.size(); i++)
4 {
5 imagePoints2.push_back(keyPoint2[GoodMatchePoints[i].queryIdx].pt);
6 imagePoints1.push_back(keyPoint1[GoodMatchePoints[i].trainIdx].pt);
7 }
这样子，我们就可以拿着imagePoints1, imagePoints2去求变换矩阵了，并且实现图像配准。值得注意的是findHomography函数的参数中我们选泽了CV_RANSAC，这表明我们选择RANSAC算法继续筛选可靠地匹配点，这使得匹配点解更为精确。
1 //获取图像1到图像2的投影映射矩阵 尺寸为3*3
2 Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
3 ////也可以使用getPerspectiveTransform方法获得透视变换矩阵，不过要求只能有4个点，效果稍差
4 //Mat homo=getPerspectiveTransform(imagePoints1,imagePoints2);
5 cout << "变换矩阵为：\n" << homo << endl << endl; //输出映射矩阵
6
7 //图像配准
8 Mat imageTransform1, imageTransform2;
9 warpPerspective(image01, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x), image02.rows));
10 //warpPerspective(image01, imageTransform2, adjustMat*homo, Size(image02.cols*1.3, image02.rows*1.8));
11 imshow("直接经过透视矩阵变换", imageTransform1);
12 imwrite("trans1.jpg", imageTransform1);
3. 图像拷贝
拷贝的思路很简单，就是将左图直接拷贝到配准图上就可以了。
1 //创建拼接后的图,需提前计算图的大小
2 int dst_width = imageTransform1.cols; //取最右点的长度为拼接图的长度
3 int dst_height = image02.rows;
4
5 Mat dst(dst_height, dst_width, CV_8UC3);
6 dst.setTo(0);
7
8 imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows)));
9 image02.copyTo(dst(Rect(0, 0, image02.cols, image02.rows)));
10
11 imshow("b_dst", dst);
4.图像融合（去裂缝处理）
从上图可以看出，两图的拼接并不自然，原因就在于拼接图的交界处，两图因为光照色泽的原因使得两图交界处的过渡很糟糕，所以需要特定的处理解决这种不自然。这里的处理思路是加权融合，在重叠部分由前一幅图像慢慢过渡到第二幅图像，即将图像的重叠区域的像素值按一定的权值相加合成新的图像。
1 //优化两图的连接处，使得拼接自然
2 void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
3 {
4 int start = MIN(corners.left_top.x, corners.left_bottom.x);//开始位置，即重叠区域的左边界
5
6 double processWidth = img1.cols - start;//重叠区域的宽度
7 int rows = dst.rows;
8 int cols = img1.cols; //注意，是列数*通道数
9 double alpha = 1;//img1中像素的权重
10 for (int i = 0; i < rows; i++)
11 {
12 uchar* p = img1.ptr<uchar>(i); //获取第i行的首地址
13 uchar* t = trans.ptr<uchar>(i);
14 uchar* d = dst.ptr<uchar>(i);
15 for (int j = start; j < cols; j++)
16 {
17 //如果遇到图像trans中无像素的黑点，则完全拷贝img1中的数据
18 if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0)
19 {
20 alpha = 1;
21 }
22 else
23 {
24 //img1中像素的权重，与当前处理点距重叠区域左边界的距离成正比，实验证明，这种方法确实好
25 alpha = (processWidth - (j - start)) / processWidth;
26 }
27
28 d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);
29 d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);
30 d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);
31
32 }
33 }
34 }
多尝试几张，验证拼接效果
测试一
测试二
测试三
最后给出完整的SURF算法实现的拼接代码。
1 #include "highgui/highgui.hpp"
2 #include "opencv2/nonfree/nonfree.hpp"
3 #include "opencv2/legacy/legacy.hpp"
4 #include <iostream>
5
6 using namespace cv;
7 using namespace std;
8
9 void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst);
10
11 typedef struct
12 {
13 Point2f left_top;
14 Point2f left_bottom;
15 Point2f right_top;
16 Point2f right_bottom;
17 }four_corners_t;
18
19 four_corners_t corners;
20
21 void CalcCorners(const Mat& H, const Mat& src)
22 {
23 double v2[] = { 0, 0, 1 };//左上角
24 double v1[3];//变换后的坐标值
25 Mat V2 = Mat(3, 1, CV_64FC1, v2); //列向量
26 Mat V1 = Mat(3, 1, CV_64FC1, v1); //列向量
27
28 V1 = H * V2;
29 //左上角(0,0,1)
30 cout << "V2: " << V2 << endl;
31 cout << "V1: " << V1 << endl;
32 corners.left_top.x = v1[0] / v1[2];
33 corners.left_top.y = v1[1] / v1[2];
34
35 //左下角(0,src.rows,1)
36 v2[0] = 0;
37 v2[1] = src.rows;
38 v2[2] = 1;
39 V2 = Mat(3, 1, CV_64FC1, v2); //列向量
40 V1 = Mat(3, 1, CV_64FC1, v1); //列向量
41 V1 = H * V2;
42 corners.left_bottom.x = v1[0] / v1[2];
43 corners.left_bottom.y = v1[1] / v1[2];
44
45 //右上角(src.cols,0,1)
46 v2[0] = src.cols;
47 v2[1] = 0;
48 v2[2] = 1;
49 V2 = Mat(3, 1, CV_64FC1, v2); //列向量
50 V1 = Mat(3, 1, CV_64FC1, v1); //列向量
51 V1 = H * V2;
52 corners.right_top.x = v1[0] / v1[2];
53 corners.right_top.y = v1[1] / v1[2];
54
55 //右下角(src.cols,src.rows,1)
56 v2[0] = src.cols;
57 v2[1] = src.rows;
58 v2[2] = 1;
59 V2 = Mat(3, 1, CV_64FC1, v2); //列向量
60 V1 = Mat(3, 1, CV_64FC1, v1); //列向量
61 V1 = H * V2;
62 corners.right_bottom.x = v1[0] / v1[2];
63 corners.right_bottom.y = v1[1] / v1[2];
64
65 }
66
67 int main(int argc, char *argv[])
68 {
69 Mat image01 = imread("g5.jpg", 1); //右图
70 Mat image02 = imread("g4.jpg", 1); //左图
71 imshow("p2", image01);
72 imshow("p1", image02);
73
74 //灰度图转换
75 Mat image1, image2;
76 cvtColor(image01, image1, CV_RGB2GRAY);
77 cvtColor(image02, image2, CV_RGB2GRAY);
78
79
80 //提取特征点
81 SurfFeatureDetector Detector(2000);
82 vector<KeyPoint> keyPoint1, keyPoint2;
83 Detector.detect(image1, keyPoint1);
84 Detector.detect(image2, keyPoint2);
85
86 //特征点描述，为下边的特征点匹配做准备
87 SurfDescriptorExtractor Descriptor;
88 Mat imageDesc1, imageDesc2;
89 Descriptor.compute(image1, keyPoint1, imageDesc1);
90 Descriptor.compute(image2, keyPoint2, imageDesc2);
91
92 FlannBasedMatcher matcher;
93 vector<vector<DMatch> > matchePoints;
94 vector<DMatch> GoodMatchePoints;
95
96 vector<Mat> train_desc(1, imageDesc1);
97 matcher.add(train_desc);
98 matcher.train();
99
100 matcher.knnMatch(imageDesc2, matchePoints, 2);
101 cout << "total match points: " << matchePoints.size() << endl;
102
103 // Lowe's algorithm,获取优秀匹配点
104 for (int i = 0; i < matchePoints.size(); i++)
105 {
106 if (matchePoints[i][0].distance < 0.4 * matchePoints[i][1].distance)
107 {
108 GoodMatchePoints.push_back(matchePoints[i][0]);
109 }
110 }
111
112 Mat first_match;
113 drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);
114 imshow("first_match ", first_match);
115
116 vector<Point2f> imagePoints1, imagePoints2;
117
118 for (int i = 0; i<GoodMatchePoints.size(); i++)
119 {
120 imagePoints2.push_back(keyPoint2[GoodMatchePoints[i].queryIdx].pt);
121 imagePoints1.push_back(keyPoint1[GoodMatchePoints[i].trainIdx].pt);
122 }
123
124
125
126 //获取图像1到图像2的投影映射矩阵 尺寸为3*3
127 Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
128 ////也可以使用getPerspectiveTransform方法获得透视变换矩阵，不过要求只能有4个点，效果稍差
129 //Mat homo=getPerspectiveTransform(imagePoints1,imagePoints2);
130 cout << "变换矩阵为：\n" << homo << endl << endl; //输出映射矩阵
131
132 //计算配准图的四个顶点坐标
133 CalcCorners(homo, image01);
134 cout << "left_top:" << corners.left_top << endl;
135 cout << "left_bottom:" << corners.left_bottom << endl;
136 cout << "right_top:" << corners.right_top << endl;
137 cout << "right_bottom:" << corners.right_bottom << endl;
138
139 //图像配准
140 Mat imageTransform1, imageTransform2;
141 warpPerspective(image01, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x), image02.rows));
142 //warpPerspective(image01, imageTransform2, adjustMat*homo, Size(image02.cols*1.3, image02.rows*1.8));
143 imshow("直接经过透视矩阵变换", imageTransform1);
144 imwrite("trans1.jpg", imageTransform1);
145
146
147 //创建拼接后的图,需提前计算图的大小
148 int dst_width = imageTransform1.cols; //取最右点的长度为拼接图的长度
149 int dst_height = image02.rows;
150
151 Mat dst(dst_height, dst_width, CV_8UC3);
152 dst.setTo(0);
153
154 imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows)));
155 image02.copyTo(dst(Rect(0, 0, image02.cols, image02.rows)));
156
157 imshow("b_dst", dst);
158
159
160 OptimizeSeam(image02, imageTransform1, dst);
161
162
163 imshow("dst", dst);
164 imwrite("dst.jpg", dst);
165
166 waitKey();
167
168 return 0;
169 }
170
171
172 //优化两图的连接处，使得拼接自然
173 void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
174 {
175 int start = MIN(corners.left_top.x, corners.left_bottom.x);//开始位置，即重叠区域的左边界
176
177 double processWidth = img1.cols - start;//重叠区域的宽度
178 int rows = dst.rows;
179 int cols = img1.cols; //注意，是列数*通道数
180 double alpha = 1;//img1中像素的权重
181 for (int i = 0; i < rows; i++)
182 {
183 uchar* p = img1.ptr<uchar>(i); //获取第i行的首地址
184 uchar* t = trans.ptr<uchar>(i);
185 uchar* d = dst.ptr<uchar>(i);
186 for (int j = start; j < cols; j++)
187 {
188 //如果遇到图像trans中无像素的黑点，则完全拷贝img1中的数据
189 if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0)
190 {
191 alpha = 1;
192 }
193 else
194 {
195 //img1中像素的权重，与当前处理点距重叠区域左边界的距离成正比，实验证明，这种方法确实好
196 alpha = (processWidth - (j - start)) / processWidth;
197 }
198
199 d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);
200 d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);
201 d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);
202
203 }
204 }
205 }
基于ORB的图像拼接
利用ORB进行图像拼接的思路跟上面的思路基本一样，只是特征提取和特征点匹配的方式略有差异罢了。这里就不再详细介绍思路了，直接贴代码看效果。
1 #include "highgui/highgui.hpp"
2 #include "opencv2/nonfree/nonfree.hpp"
3 #include "opencv2/legacy/legacy.hpp"
4 #include <iostream>
5
6 using namespace cv;
7 using namespace std;
8
9 void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst);
10
11 typedef struct
12 {
13 Point2f left_top;
14 Point2f left_bottom;
15 Point2f right_top;
16 Point2f right_bottom;
17 }four_corners_t;
18
19 four_corners_t corners;
20
21 void CalcCorners(const Mat& H, const Mat& src)
22 {
23 double v2[] = { 0, 0, 1 };//左上角
24 double v1[3];//变换后的坐标值
25 Mat V2 = Mat(3, 1, CV_64FC1, v2); //列向量
26 Mat V1 = Mat(3, 1, CV_64FC1, v1); //列向量
27
28 V1 = H * V2;
29 //左上角(0,0,1)
30 cout << "V2: " << V2 << endl;
31 cout << "V1: " << V1 << endl;
32 corners.left_top.x = v1[0] / v1[2];
33 corners.left_top.y = v1[1] / v1[2];
34
35 //左下角(0,src.rows,1)
36 v2[0] = 0;
37 v2[1] = src.rows;
38 v2[2] = 1;
39 V2 = Mat(3, 1, CV_64FC1, v2); //列向量
40 V1 = Mat(3, 1, CV_64FC1, v1); //列向量
41 V1 = H * V2;
42 corners.left_bottom.x = v1[0] / v1[2];
43 corners.left_bottom.y = v1[1] / v1[2];
44
45 //右上角(src.cols,0,1)
46 v2[0] = src.cols;
47 v2[1] = 0;
48 v2[2] = 1;
49 V2 = Mat(3, 1, CV_64FC1, v2); //列向量
50 V1 = Mat(3, 1, CV_64FC1, v1); //列向量
51 V1 = H * V2;
52 corners.right_top.x = v1[0] / v1[2];
53 corners.right_top.y = v1[1] / v1[2];
54
55 //右下角(src.cols,src.rows,1)
56 v2[0] = src.cols;
57 v2[1] = src.rows;
58 v2[2] = 1;
59 V2 = Mat(3, 1, CV_64FC1, v2); //列向量
60 V1 = Mat(3, 1, CV_64FC1, v1); //列向量
61 V1 = H * V2;
62 corners.right_bottom.x = v1[0] / v1[2];
63 corners.right_bottom.y = v1[1] / v1[2];
64
65 }
66
67 int main(int argc, char *argv[])
68 {
69 Mat image01 = imread("t1.jpg", 1); //右图
70 Mat image02 = imread("t2.jpg", 1); //左图
71 imshow("p2", image01);
72 imshow("p1", image02);
73
74 //灰度图转换
75 Mat image1, image2;
76 cvtColor(image01, image1, CV_RGB2GRAY);
77 cvtColor(image02, image2, CV_RGB2GRAY);
78
79
80 //提取特征点
81 OrbFeatureDetector surfDetector(3000);
82 vector<KeyPoint> keyPoint1, keyPoint2;
83 surfDetector.detect(image1, keyPoint1);
84 surfDetector.detect(image2, keyPoint2);
85
86 //特征点描述，为下边的特征点匹配做准备
87 OrbDescriptorExtractor SurfDescriptor;
88 Mat imageDesc1, imageDesc2;
89 SurfDescriptor.compute(image1, keyPoint1, imageDesc1);
90 SurfDescriptor.compute(image2, keyPoint2, imageDesc2);
91
92 flann::Index flannIndex(imageDesc1, flann::LshIndexParams(12, 20, 2), cvflann::FLANN_DIST_HAMMING);
93
94 vector<DMatch> GoodMatchePoints;
95
96 Mat macthIndex(imageDesc2.rows, 2, CV_32SC1), matchDistance(imageDesc2.rows, 2, CV_32FC1);
97 flannIndex.knnSearch(imageDesc2, macthIndex, matchDistance, 2, flann::SearchParams());
98
99 // Lowe's algorithm,获取优秀匹配点
100 for (int i = 0; i < matchDistance.rows; i++)
101 {
102 if (matchDistance.at<float>(i, 0) < 0.4 * matchDistance.at<float>(i, 1))
103 {
104 DMatch dmatches(i, macthIndex.at<int>(i, 0), matchDistance.at<float>(i, 0));
105 GoodMatchePoints.push_back(dmatches);
106 }
107 }
108
109 Mat first_match;
110 drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);
111 imshow("first_match ", first_match);
112
113 vector<Point2f> imagePoints1, imagePoints2;
114
115 for (int i = 0; i<GoodMatchePoints.size(); i++)
116 {
117 imagePoints2.push_back(keyPoint2[GoodMatchePoints[i].queryIdx].pt);
118 imagePoints1.push_back(keyPoint1[GoodMatchePoints[i].trainIdx].pt);
119 }
120
121
122
123 //获取图像1到图像2的投影映射矩阵 尺寸为3*3
124 Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
125 ////也可以使用getPerspectiveTransform方法获得透视变换矩阵，不过要求只能有4个点，效果稍差
126 //Mat homo=getPerspectiveTransform(imagePoints1,imagePoints2);
127 cout << "变换矩阵为：\n" << homo << endl << endl; //输出映射矩阵
128
129 //计算配准图的四个顶点坐标
130 CalcCorners(homo, image01);
131 cout << "left_top:" << corners.left_top << endl;
132 cout << "left_bottom:" << corners.left_bottom << endl;
133 cout << "right_top:" << corners.right_top << endl;
134 cout << "right_bottom:" << corners.right_bottom << endl;
135
136 //图像配准
137 Mat imageTransform1, imageTransform2;
138 warpPerspective(image01, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x), image02.rows));
139 //warpPerspective(image01, imageTransform2, adjustMat*homo, Size(image02.cols*1.3, image02.rows*1.8));
140 imshow("直接经过透视矩阵变换", imageTransform1);
141 imwrite("trans1.jpg", imageTransform1);
142
143
144 //创建拼接后的图,需提前计算图的大小
145 int dst_width = imageTransform1.cols; //取最右点的长度为拼接图的长度
146 int dst_height = image02.rows;
147
148 Mat dst(dst_height, dst_width, CV_8UC3);
149 dst.setTo(0);
150
151 imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows)));
152 image02.copyTo(dst(Rect(0, 0, image02.cols, image02.rows)));
153
154 imshow("b_dst", dst);
155
156
157 OptimizeSeam(image02, imageTransform1, dst);
158
159
160 imshow("dst", dst);
161 imwrite("dst.jpg", dst);
162
163 waitKey();
164
165 return 0;
166 }
167
168
169 //优化两图的连接处，使得拼接自然
170 void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
171 {
172 int start = MIN(corners.left_top.x, corners.left_bottom.x);//开始位置，即重叠区域的左边界
173
174 double processWidth = img1.cols - start;//重叠区域的宽度
175 int rows = dst.rows;
176 int cols = img1.cols; //注意，是列数*通道数
177 double alpha = 1;//img1中像素的权重
178 for (int i = 0; i < rows; i++)
179 {
180 uchar* p = img1.ptr<uchar>(i); //获取第i行的首地址
181 uchar* t = trans.ptr<uchar>(i);
182 uchar* d = dst.ptr<uchar>(i);
183 for (int j = start; j < cols; j++)
184 {
185 //如果遇到图像trans中无像素的黑点，则完全拷贝img1中的数据
186 if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0)
187 {
188 alpha = 1;
189 }
190 else
191 {
192 //img1中像素的权重，与当前处理点距重叠区域左边界的距离成正比，实验证明，这种方法确实好
193 alpha = (processWidth - (j - start)) / processWidth;
194 }
195
196 d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);
197 d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);
198 d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);
199
200 }
201 }
202 }
看一看拼接效果，我觉得还是不错的。
看一下这一组图片，这组图片产生了鬼影，为什么？因为两幅图中的人物走动了啊！所以要做图像拼接，尽量保证使用的是静态图片，不要加入一些动态因素干扰拼接。
opencv自带的拼接算法stitch
opencv其实自己就有实现图像拼接的算法，当然效果也是相当好的，但是因为其实现很复杂，而且代码量很庞大，其实在一些小应用下的拼接有点杀鸡用牛刀的感觉。最近在阅读sticth源码时，发现其中有几个很有意思的地方。
1.opencv stitch选择的特征检测方式
一直很好奇opencv stitch算法到底选用了哪个算法作为其特征检测方式，是ORB，SIFT还是SURF？读源码终于看到答案。
1 #ifdef HAVE_OPENCV_NONFREE
2 stitcher.setFeaturesFinder(new detail::SurfFeaturesFinder());
3 #else
4 stitcher.setFeaturesFinder(new detail::OrbFeaturesFinder());
5 #endif
在源码createDefault函数中（默认设置），第一选择是SURF,第二选择才是ORB（没有NONFREE模块才选），所以既然大牛们这么选择，必然是经过综合考虑的，所以应该SURF算法在图像拼接有着更优秀的效果。
2.opencv stitch获取匹配点的方式
以下代码是opencv stitch源码中的特征点提取部分，作者使用了两次特征点提取的思路：先对图一进行特征点提取和筛选匹配（1->2），再对图二进行特征点的提取和匹配（2->1），这跟我们平时的一次提取的思路不同，这种二次提取的思路可以保证更多的匹配点被选中，匹配点越多，findHomography求出的变换越准确。这个思路值得借鉴。
1 matches_info.matches.clear();
2
3 Ptr<flann::IndexParams> indexParams = new flann::KDTreeIndexParams();
4 Ptr<flann::SearchParams> searchParams = new flann::SearchParams();
5
6 if (features2.descriptors.depth() == CV_8U)
7 {
8 indexParams->setAlgorithm(cvflann::FLANN_INDEX_LSH);
9 searchParams->setAlgorithm(cvflann::FLANN_INDEX_LSH);
10 }
11
12 FlannBasedMatcher matcher(indexParams, searchParams);
13 vector< vector<DMatch> > pair_matches;
14 MatchesSet matches;
15
16 // Find 1->2 matches
17 matcher.knnMatch(features1.descriptors, features2.descriptors, pair_matches, 2);
18 for (size_t i = 0; i < pair_matches.size(); ++i)
19 {
20 if (pair_matches[i].size() < 2)
21 continue;
22 const DMatch& m0 = pair_matches[i][0];
23 const DMatch& m1 = pair_matches[i][1];
24 if (m0.distance < (1.f - match_conf_) * m1.distance)
25 {
26 matches_info.matches.push_back(m0);
27 matches.insert(make_pair(m0.queryIdx, m0.trainIdx));
28 }
29 }
30 LOG("\n1->2 matches: " << matches_info.matches.size() << endl);
31
32 // Find 2->1 matches
33 pair_matches.clear();
34 matcher.knnMatch(features2.descriptors, features1.descriptors, pair_matches, 2);
35 for (size_t i = 0; i < pair_matches.size(); ++i)
36 {
37 if (pair_matches[i].size() < 2)
38 continue;
39 const DMatch& m0 = pair_matches[i][0];
40 const DMatch& m1 = pair_matches[i][1];
41 if (m0.distance < (1.f - match_conf_) * m1.distance)
42 if (matches.find(make_pair(m0.trainIdx, m0.queryIdx)) == matches.end())
43 matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
44 }
45 LOG("1->2 & 2->1 matches: " << matches_info.matches.size() << endl);
这里我仿照opencv源码二次提取特征点的思路对我原有拼接代码进行改写，实验证明获取的匹配点确实较一次提取要多。
1 //提取特征点
2 SiftFeatureDetector Detector(1000); // 海塞矩阵阈值，在这里调整精度，值越大点越少，越精准
3 vector<KeyPoint> keyPoint1, keyPoint2;
4 Detector.detect(image1, keyPoint1);
5 Detector.detect(image2, keyPoint2);
6
7 //特征点描述，为下边的特征点匹配做准备
8 SiftDescriptorExtractor Descriptor;
9 Mat imageDesc1, imageDesc2;
10 Descriptor.compute(image1, keyPoint1, imageDesc1);
11 Descriptor.compute(image2, keyPoint2, imageDesc2);
12
13 FlannBasedMatcher matcher;
14 vector<vector<DMatch> > matchePoints;
15 vector<DMatch> GoodMatchePoints;
16
17 MatchesSet matches;
18
19 vector<Mat> train_desc(1, imageDesc1);
20 matcher.add(train_desc);
21 matcher.train();
22
23 matcher.knnMatch(imageDesc2, matchePoints, 2);
24
25 // Lowe's algorithm,获取优秀匹配点
26 for (int i = 0; i < matchePoints.size(); i++)
27 {
28 if (matchePoints[i][0].distance < 0.4 * matchePoints[i][1].distance)
29 {
30 GoodMatchePoints.push_back(matchePoints[i][0]);
31 matches.insert(make_pair(matchePoints[i][0].queryIdx, matchePoints[i][0].trainIdx));
32 }
33 }
34 cout<<"\n1->2 matches: " << GoodMatchePoints.size() << endl;
35
36 #if 1
37
38 FlannBasedMatcher matcher2;
39 matchePoints.clear();
40 vector<Mat> train_desc2(1, imageDesc2);
41 matcher2.add(train_desc2);
42 matcher2.train();
43
44 matcher2.knnMatch(imageDesc1, matchePoints, 2);
45 // Lowe's algorithm,获取优秀匹配点
46 for (int i = 0; i < matchePoints.size(); i++)
47 {
48 if (matchePoints[i][0].distance < 0.4 * matchePoints[i][1].distance)
49 {
50 if (matches.find(make_pair(matchePoints[i][0].trainIdx, matchePoints[i][0].queryIdx)) == matches.end())
51 {
52 GoodMatchePoints.push_back(DMatch(matchePoints[i][0].trainIdx, matchePoints[i][0].queryIdx, matchePoints[i][0].distance));
53 }
54
55 }
56 }
57 cout<<"1->2 & 2->1 matches: " << GoodMatchePoints.size() << endl;
58 #endif
最后再看一下opencv stitch的拼接效果吧~速度虽然比较慢，但是效果还是很好的。
1 #include <iostream>
2 #include <opencv2/core/core.hpp>
3 #include <opencv2/highgui/highgui.hpp>
4 #include <opencv2/imgproc/imgproc.hpp>
5 #include <opencv2/stitching/stitcher.hpp>
6 using namespace std;
7 using namespace cv;
8 bool try_use_gpu = false;
9 vector<Mat> imgs;
10 string result_name = "dst1.jpg";
11 int main(int argc, char * argv[])
12 {
13 Mat img1 = imread("34.jpg");
14 Mat img2 = imread("35.jpg");
15
16 imshow("p1", img1);
17 imshow("p2", img2);
18
19 if (img1.empty() || img2.empty())
20 {
21 cout << "Can't read image" << endl;
22 return -1;
23 }
24 imgs.push_back(img1);
25 imgs.push_back(img2);
26
27
28 Stitcher stitcher = Stitcher::createDefault(try_use_gpu);
29 // 使用stitch函数进行拼接
30 Mat pano;
31 Stitcher::Status status = stitcher.stitch(imgs, pano);
32 if (status != Stitcher::OK)
33 {
34 cout << "Can't stitch images, error code = " << int(status) << endl;
35 return -1;
36 }
37 imwrite(result_name, pano);
38 Mat pano2 = pano.clone();
39 // 显示源图像，和结果图像
40 imshow("全景图像", pano);
41 if (waitKey() == 27)
42 return 0;
43 }
