opencv学习 特征提取

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：

1. 生成苹果、橘子的高斯金字塔和
2.  求苹果、橘子的的拉普拉斯金字塔和
3. 求mask的高斯金字塔
4. 在每个尺度（分辨率）下，用拼接和，最终得到拼接的拉普拉斯金字塔
5. 生成最低分辨率的起始图（都选取最低分辨率下的和 根据同分辨率下 进行拼接，得到最低分辨率下的拼接结果 
6. 从开始，利用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(row, col);
			rgb2 = b.at(row, col);
			w = m.at(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);

			// output
			dst.at(row, col)[0] = blue;
			dst.at(row, col)[1] = green;
			dst.at(row, col)[2] = red;
		}
	}
	return dst;
}

vector buildGaussianPyramid(Mat &image) {
	vector pyramid;
	Mat copy = image.clone();
	pyramid.push_back(image.clone());
	Mat dst;
	for (int i = 0; i buildLapacianPyramid(Mat &image) {
	vector lp;
	Mat temp;
	Mat copy = image.clone();
	Mat dst;
	for (int i = 0; i la = buildLapacianPyramid(apple);
	Mat leftsmallestLevel;
	smallestLevel.copyTo(leftsmallestLevel);

	vector lb = buildLapacianPyramid(orange);
	Mat rightsmallestLevel;
	smallestLevel.copyTo(rightsmallestLevel);

	Mat mask;
	cvtColor(mc, mask, COLOR_BGR2GRAY);

	vector 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 ls;
	for (int i = 0; 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 image
    InputOutputArray 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 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 img
    std::vector & descriptors
    Size winStride=Size()
    Size padding=Size()
    const std::vector &locations = std::vector()
)

void FeatureVectorOps::hog_feature_demo(Mat &image) {
	Mat gray;
	cvtColor(image, gray, COLOR_BGR2GRAY);
	HOGDescriptor hogDetector;
	std::vector 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& 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 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 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 queryDescriptors
    InputArray trainDescriptors
    std::vector & matches
    InputArray mask=noArray
)

void cv::DescriptorMatch::knnMatch(
    InputArray queryDescriptors
    InputArray trainDescriptors
    std::vector & matches
    int k
    InputArray mask=noArray
    bool compactResult =false
)

FLANN匹配：

cv::FlannBasedMatcher::FlannBasedMatcher(
    const Ptr & indexParams=makePtr()
    const Ptr & searchParams=makePtr()
)

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 box_kpts;
	std::vector 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 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 srcPoints
    OutputArray dstPoints
    int method=0
    double ransacReprojThreshold=3
    OutputArray 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 src
    OutputArray dst
    InputArray 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 box_kpts;
	std::vector 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 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H
	std::vector obj_pts;
	std::vector scene_pts;
	for (size_t i = 0; i < matches.size(); i++)
	{
		//-- Get the keypoints from the good matches
		obj_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 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 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 matches
	namedWindow("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);
}