opencv学习特征提取-编程知识

内容来源于《opencv4应用开发入门、进阶与工程化实践》

图像金字塔

略

拉普拉斯金字塔

对输入图像进行reduce操作会生成不同分辨率的图像，对这些图像进行expand操作，然后使用reduce减去expand之后的结果，就会得到拉普拉斯金字塔图像。

详情可查看https://zhuanlan.zhihu.com/p/80362140

图像金字塔融合

拉普拉斯金字塔通过源图像减去先缩小再放大的图像构成，保留的是残差，为图像还原做准备。

根据拉普拉斯金字塔的定义可以知道，拉普拉斯金字塔的每一层都是一个高斯差分图像。：

原图 = 拉普拉斯金字塔图L0层 + expand(高斯金字塔G1层)，也就是说，可以基于低分辨率的图像与它的高斯差分图像，重建生成一个高分辨率的图像。

详情参考https://zhuanlan.zhihu.com/p/454085730的图像融合部分，讲的很好。

步骤：

生成苹果、橘子的高斯金字塔 $G_{L}$ 和 $G_{R}$
求苹果、橘子的的拉普拉斯金字塔 $L_{apple}$ 和 $L_{orange}$
求mask的高斯金字塔 $G_{mask}$
在每个尺度（分辨率）下，用 $G_{mask}$ 拼接 $L_{apple}$ 和 $L_{orange}$ ，最终得到拼接的拉普拉斯金字塔 $L_{fused}$
生成最低分辨率的起始图（都选取最低分辨率下的 $G_{L}$ 和 $G_{R}$ 根据同分辨率下 $G_{mask}$ 进行拼接，得到最低分辨率下的拼接结果 $O_{min}$
从 $O_{min}$ 开始，利用 $L_{fused}$ 得到最高分辨率的拼接结果

示例代码：

int level = 3;
Mat smallestLevel;
Mat blend(Mat &a, Mat &b, Mat &m) {int width = a.cols;int height = a.rows;Mat dst = Mat::zeros(a.size(), a.type());Vec3b rgb1;Vec3b rgb2;int r1 = 0, g1 = 0, b1 = 0;int r2 = 0, g2 = 0, b2 = 0;int red = 0, green = 0, blue = 0;int w = 0;float w1 = 0, w2 = 0;for (int row = 0; row<height; row++) {for (int col = 0; col<width; col++) {rgb1 = a.at<Vec3b>(row, col);rgb2 = b.at<Vec3b>(row, col);w = m.at<uchar>(row, col);w2 = w / 255.0f;w1 = 1.0f - w2;b1 = rgb1[0] & 0xff;g1 = rgb1[1] & 0xff;r1 = rgb1[2] & 0xff;b2 = rgb2[0] & 0xff;g2 = rgb2[1] & 0xff;r2 = rgb2[2] & 0xff;red = (int)(r1*w1 + r2*w2);green = (int)(g1*w1 + g2*w2);blue = (int)(b1*w1 + b2*w2);// outputdst.at<Vec3b>(row, col)[0] = blue;dst.at<Vec3b>(row, col)[1] = green;dst.at<Vec3b>(row, col)[2] = red;}}return dst;
}vector<Mat> buildGaussianPyramid(Mat &image) {vector<Mat> pyramid;Mat copy = image.clone();pyramid.push_back(image.clone());Mat dst;for (int i = 0; i<level; i++) {pyrDown(copy, dst, Size(copy.cols / 2, copy.rows / 2));dst.copyTo(copy);pyramid.push_back(dst.clone());}smallestLevel = dst;return pyramid;
}vector<Mat> buildLapacianPyramid(Mat &image) {vector<Mat> lp;Mat temp;Mat copy = image.clone();Mat dst;for (int i = 0; i<level; i++) {pyrDown(copy, dst, Size(copy.cols / 2, copy.rows / 2));pyrUp(dst, temp, copy.size());Mat lapaian;subtract(copy, temp, lapaian);lp.push_back(lapaian);copy = dst.clone();}smallestLevel = dst;return lp;
}
void FeatureVectorOps::pyramid_blend_demo(Mat &apple, Mat &orange) {Mat mc = imread("D:/images/mask.png");if (apple.empty() || orange.empty()) {return;}imshow("苹果图像", apple);imshow("橘子图像", orange);vector<Mat> la = buildLapacianPyramid(apple);Mat leftsmallestLevel;smallestLevel.copyTo(leftsmallestLevel);vector<Mat> lb = buildLapacianPyramid(orange);Mat rightsmallestLevel;smallestLevel.copyTo(rightsmallestLevel);Mat mask;cvtColor(mc, mask, COLOR_BGR2GRAY);vector<Mat> maskPyramid = buildGaussianPyramid(mask);Mat samllmask;smallestLevel.copyTo(samllmask);Mat currentImage = blend(leftsmallestLevel, rightsmallestLevel, samllmask);imwrite("D:/samll.png", currentImage);// 重建拉普拉斯金字塔vector<Mat> ls;for (int i = 0; i<level; i++) {Mat a = la[i];Mat b = lb[i];Mat m = maskPyramid[i];ls.push_back(blend(a, b, m));}// 重建原图Mat temp;for (int i = level - 1; i >= 0; i--) {pyrUp(currentImage, temp, ls[i].size());add(temp, ls[i], currentImage);}imshow("高斯金子图像融合重建-图像", currentImage);
}

Harris角点检测

角点是图像中亮度变化最强的地方，反映了图像的本质特征。

图像的角点在各个方向上都有很强的梯度变化。

亚像素级别的角点检测

详细请参考https://www.cnblogs.com/qq21497936/p/13096048.html

大概理解是角点一般在边缘上，边缘的梯度与沿边缘方向的的向量正交，也就是内积为0，根据内积为零，角点周围能列出一个方程组，方程组的解就是角点坐标。

opencv亚像素级别定位函数API：

void cv::cornerSubPix(InputArray imageInputOutputArray corners //输入整数角点坐标，输出浮点数角点坐标Size winSize //搜索窗口Size zeroZone TermCriteria criteria //停止条件
)

示例代码

void FeatureVectorOps::corners_sub_pixels_demo(Mat &image) {Mat gray;cvtColor(image, gray, COLOR_BGR2GRAY);int maxCorners = 400;double qualityLevel = 0.01;std::vector<Point2f> corners;goodFeaturesToTrack(gray, corners, maxCorners, qualityLevel, 5, Mat(), 3, false, 0.04);Size winSize = Size(5, 5);Size zeroZone = Size(-1, -1);//opencv迭代终止条件类TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.001);cornerSubPix(gray, corners, winSize, zeroZone, criteria);for (size_t t = 0; t < corners.size(); t++) {printf("refined Corner: %d, x:%.2f, y:%.2f\n", t, corners[t].x, corners[t].y);}
}

HOG特征描述子

详细请参考：https://baijiahao.baidu.com/s?id=1646997581304332534&wfr=spider&for=pc&searchword=HOG%E7%89%B9%E5%BE%81%E6%8F%8F%E8%BF%B0%E5%AD%90

讲的很好。

大概就是以一种特殊的直方图来表示图像特征，直方图存储的是梯度的方向和幅值（x轴是方向，y轴是幅值且加权）。

示例代码：

virtual void cv::HOGDescriptor::compute(InputArray imgstd::vector<float> & descriptorsSize winStride=Size()Size padding=Size()const std::vector<Point> &locations = std::vector<Point>()
)void FeatureVectorOps::hog_feature_demo(Mat &image) {Mat gray;cvtColor(image, gray, COLOR_BGR2GRAY);HOGDescriptor hogDetector;std::vector<float> hog_descriptors;hogDetector.compute(gray, hog_descriptors, Size(8, 8), Size(0, 0));std::cout << hog_descriptors.size() << std::endl;for (size_t t = 0; t < hog_descriptors.size(); t++) {std::cout << hog_descriptors[t] << std::endl;}
}

HOG特征行人检测

opencv基于HOG行人特征描述子的检测函数：

void HOGDescriptor::detectMultiScale(InputArray img,vector<Rect>& foundLocations, double hitThreshold=0, Size winStride=Size(), Size padding=Size(),double scale=1.05,double finalThreshold=2.0,bool useMeanshiftGrouping=false
)
//示例代码
void FeatureVectorOps::hog_detect_demo(Mat &image) {HOGDescriptor *hog = new HOGDescriptor();hog->setSVMDetector(hog->getDefaultPeopleDetector());vector<Rect> objects;hog->detectMultiScale(image, objects, 0.0, Size(4, 4), Size(8, 8), 1.25);for (int i = 0; i < objects.size(); i++) {rectangle(image, objects[i], Scalar(0, 0, 255), 2, 8, 0);}imshow("HOG行人检测", image);
}

ORB特征描述子

没看懂。

描述子匹配

暴力匹配：

再使用暴力匹配之前先创建暴力匹配器：

static Ptr<BFMatcher> cv::BFMatcher::create(int normType=NORM_L2 //计算描述子暴力匹配时采用的计算方法bool crossCheck=false //是否使用交叉验证
)

调用暴力匹配的匹配方法，有两种，最佳匹配和KNN匹配

void cv::DescriptorMatch::match(InputArray queryDescriptorsInputArray trainDescriptorsstd::vector<DMatch> & matchesInputArray mask=noArray
)void cv::DescriptorMatch::knnMatch(InputArray queryDescriptorsInputArray trainDescriptorsstd::vector<DMatch> & matchesint kInputArray mask=noArraybool compactResult =false
)

FLANN匹配：

cv::FlannBasedMatcher::FlannBasedMatcher(const Ptr<flann::IndexParams> & indexParams=makePtr<flann::KDTreeIndexParams>()const Ptr<flann::SearchParams> & searchParams=makePtr<flann::SearchParams>()
)

示例代码：

void FeatureVectorOps::orb_match_demo(Mat &box, Mat &box_in_scene) {// ORB特征提取auto orb_detector = ORB::create();std::vector<KeyPoint> box_kpts;std::vector<KeyPoint> scene_kpts;Mat box_descriptors, scene_descriptors;orb_detector->detectAndCompute(box, Mat(), box_kpts, box_descriptors);orb_detector->detectAndCompute(box_in_scene, Mat(), scene_kpts, scene_descriptors);// 暴力匹配auto bfMatcher = BFMatcher::create(NORM_HAMMING, false);std::vector<DMatch> matches;bfMatcher->match(box_descriptors, scene_descriptors, matches);Mat img_orb_matches;drawMatches(box, box_kpts, box_in_scene, scene_kpts, matches, img_orb_matches);imshow("ORB暴力匹配演示", img_orb_matches);// FLANN匹配auto flannMatcher = FlannBasedMatcher(new flann::LshIndexParams(6, 12, 2));flannMatcher.match(box_descriptors, scene_descriptors, matches);Mat img_flann_matches;drawMatches(box, box_kpts, box_in_scene, scene_kpts, matches, img_flann_matches);namedWindow("FLANN匹配演示", WINDOW_FREERATIO);cv::namedWindow("FLANN匹配演示", cv::WINDOW_NORMAL);imshow("FLANN匹配演示", img_flann_matches);
}

基于特征的对象检测

特征描述子匹配之后，可以根据返回的各个DMatch中的索引得到关键点对，然后拟合生成从对象到场景的变换矩阵H。根据矩阵H可以求得对象在场景中的位置，从而完成基于特征的对象检测。

opencv中求得单应性矩阵的API：

Mat cv::findHomograph(InputArray srcPointsOutputArray dstPointsint method=0double ransacReprojThreshold=3OutputArray mask=noArray()const int maxIters=2000;const double confidence=0.995
)

有了变换矩阵H ，可以运用透视变换函数求得场景中对象的四个点坐标并绘制出来。

透视变换函数：

void cv::perspectiveTransform(InputArray srcOutputArray dstInputArray m
)

示例代码：

void FeatureVectorOps::find_known_object(Mat &book, Mat &book_on_desk) {// ORB特征提取auto orb_detector = ORB::create();std::vector<KeyPoint> box_kpts;std::vector<KeyPoint> scene_kpts;Mat box_descriptors, scene_descriptors;orb_detector->detectAndCompute(book, Mat(), box_kpts, box_descriptors);orb_detector->detectAndCompute(book_on_desk, Mat(), scene_kpts, scene_descriptors);// 暴力匹配auto bfMatcher = BFMatcher::create(NORM_HAMMING, false);std::vector<DMatch> matches;bfMatcher->match(box_descriptors, scene_descriptors, matches);// 好的匹配std::sort(matches.begin(), matches.end());const int numGoodMatches = matches.size() * 0.15;matches.erase(matches.begin() + numGoodMatches, matches.end());Mat img_bf_matches;drawMatches(book, box_kpts, book_on_desk, scene_kpts, matches, img_bf_matches);imshow("ORB暴力匹配演示", img_bf_matches);// 单应性求Hstd::vector<Point2f> obj_pts;std::vector<Point2f> scene_pts;for (size_t i = 0; i < matches.size(); i++){//-- Get the keypoints from the good matchesobj_pts.push_back(box_kpts[matches[i].queryIdx].pt);scene_pts.push_back(scene_kpts[matches[i].trainIdx].pt);}Mat H = findHomography(obj_pts, scene_pts, RANSAC);std::cout << "RANSAC estimation parameters: \n" << H << std::endl;std::cout << std::endl;H = findHomography(obj_pts, scene_pts, RHO);std::cout << "RHO estimation parameters: \n" << H << std::endl;std::cout << std::endl;H = findHomography(obj_pts, scene_pts, LMEDS);std::cout << "LMEDS estimation parameters: \n" << H << std::endl;// 变换矩阵得到目标点std::vector<Point2f> obj_corners(4);obj_corners[0] = Point(0, 0); obj_corners[1] = Point(book.cols, 0);obj_corners[2] = Point(book.cols, book.rows); obj_corners[3] = Point(0, book.rows);std::vector<Point2f> scene_corners(4);perspectiveTransform(obj_corners, scene_corners, H);// 绘制结果Mat dst;line(img_bf_matches, scene_corners[0] + Point2f(book.cols, 0), scene_corners[1] + Point2f(book.cols, 0), Scalar(0, 255, 0), 4);line(img_bf_matches, scene_corners[1] + Point2f(book.cols, 0), scene_corners[2] + Point2f(book.cols, 0), Scalar(0, 255, 0), 4);line(img_bf_matches, scene_corners[2] + Point2f(book.cols, 0), scene_corners[3] + Point2f(book.cols, 0), Scalar(0, 255, 0), 4);line(img_bf_matches, scene_corners[3] + Point2f(book.cols, 0), scene_corners[0] + Point2f(book.cols, 0), Scalar(0, 255, 0), 4);//-- Show detected matchesnamedWindow("基于特征的对象检测", cv::WINDOW_NORMAL);imshow("基于特征的对象检测", img_bf_matches);
}