【opencv/feature2d module】(五)Feature Detection

说在前面

• opencv版本：4.0.1
• 操作系统：win10
• vs版本：2017
• 官方文档：Feature Detection
• 其他说明：自学，记录，demo

补充

• 若无法使用xfeatures2d模块，且已经编译过opencv contirb，请重新编译，并且将OPENCV_ENABLE_NONFREE设置为true，详见【OpenCV/编译】Windows编译OpenCV+contrib&坑
• 貌似是因为有些算法是有专利的。

Feature Detection

• 检测特征我们首先我们得提取特征，那么哪些特征是我们关注的呢？
  • 这些特征在图像缩放、噪声、高亮度等环境下依然能被检测到，这些区域通常在图像中高对比度的地方，例如边；[ 原文：To perform reliable recognition, it is important that the features extracted from the training image be detectable even under changes in image scale, noise and illumination. Such points usually lie on high-contrast regions of the image, such as object edges.]
  • 这些特征的相对位置不会因为在不同的图像中而改变。[原文：Another important characteristic of these features is that the relative positions between them in the original scene shouldn’t change from one image to another. ]

Scale-invariant feature transform(SIFT)

• wikipedia
• opencv
• 优快云（优快云上有大量关于SIFT的博，例如这个

Speeded Up Robust Features(SURF)

• 对SIFT的改进
• wikipedia
• 同样一大堆，例如这个

Code

#include <iostream>
#include <opencv2\core.hpp>
#include <opencv2\highgui.hpp>
#include <opencv2\features2d.hpp>
#include <opencv2\xfeatures2d.hpp>

using namespace cv;
using namespace cv::xfeatures2d;
using std::cout;
using std::endl;
int main()
{
	Mat src = imread("1.jpg", IMREAD_GRAYSCALE);
	if (src.empty())
	{
		cout << "Could not open or find the image!\n" << endl;
		return -1;
	}
	//-- Step 1: Detect the keypoints using SURF Detector
	int minHessian = 400;
	Ptr<SURF> detector = SURF::create(minHessian);//SURF
	//Ptr<SIFT> detector = SIFT::create();//SIFT
	std::vector<KeyPoint> keypoints;
	detector->detect(src, keypoints);
	//-- Draw keypoints
	Mat img_keypoints;
	drawKeypoints(src, keypoints, img_keypoints);
	//-- Show detected (drawn) keypoints
	imshow("SURF Keypoints", img_keypoints);
	waitKey();
	return 0;
}

Result

• SIFT

• SURF

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(oﾟvﾟ)ノ

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