特徵檢測

目錄

• 目標
• 理論
• 程式碼
• 解釋
• 結果

上一個教程： 在亞畫素級別檢測角點位置
下一個教程： 特徵描述

原始作者	Ana Huamán
相容性	OpenCV >= 3.0

目標

在本教程中，您將學習如何

• 使用 cv::FeatureDetector 介面來尋找興趣點。具體來說：
  • 使用 cv::xfeatures2d::SURF 及其函式 cv::xfeatures2d::SURF::detect 來執行檢測過程
  • 使用函式 cv::drawKeypoints 繪製檢測到的關鍵點

警告

您需要 OpenCV contrib 模組才能使用 SURF 特徵（替代方案是 ORB、KAZE 等特徵）。

理論

程式碼

C++

本教程的程式碼如下所示。您也可以從 此處 下載

#include <iostream>
#include "opencv2/core.hpp"
#ifdef HAVE_OPENCV_XFEATURES2D
#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( int argc, char* argv[] )
{
    CommandLineParser parser( argc, argv, "{@input | box.png | input image}" );
    Mat src = imread( samples::findFile( parser.get<String>( "@input" ) ), IMREAD_GRAYSCALE );
    if ( src.empty() )
    {
cout << "Could not open or find the image!\n" << endl;
cout << "Usage: " << argv[0] << " <Input image>" << endl;
        return -1;
    }
 
    //-- Step 1: Detect the keypoints using SURF Detector
    int minHessian = 400;
    Ptr<SURF> detector = SURF::create( minHessian );
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 );
 
cv::Mat::empty
    return 0;
}
#else
int main()
{
std::cout << "This tutorial code needs the xfeatures2d contrib module to be run." << std::endl;
    return 0;
}
#endif

Java

本教程的程式碼如下所示。您也可以從 此處 下載

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfKeyPoint;
import org.opencv.features2d.Features2d;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.xfeatures2d.SURF;
 
class SURFDetection {
    public void run(String[] args) {
String filename = args.length > 0 ? args[0] : "../data/box.png";
Mat src = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
        if (src.empty()) {
System.err.println("Cannot read image: " + filename);
System.exit(0);
        }
 
        //-- Step 1: Detect the keypoints using SURF Detector
        double hessianThreshold = 400;
        int nOctaves = 4, nOctaveLayers = 3;
        boolean extended = false, upright = false;
SURF detector = SURF.create(hessianThreshold, nOctaves, nOctaveLayers, extended, upright);
MatOfKeyPoint keypoints = new MatOfKeyPoint();
detector.detect(src, keypoints);
 
        //-- Draw keypoints
Features2d.drawKeypoints(src, keypoints, src);
 
        //-- Show detected (drawn) keypoints
HighGui.imshow("SURF Keypoints", src);
HighGui.waitKey(0);
 
System.exit(0);
    }
}
 
public class SURFDetectionDemo {
    public static void main(String[] args) {
        // 載入本地 OpenCV 庫
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
 
        new SURFDetection().run(args);
    }
}

Python

本教程的程式碼如下所示。您也可以從 此處 下載

from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
 
parser = argparse.ArgumentParser(description='Code for Feature Detection tutorial.')
parser.add_argument('--input', help='Path to input image.', default='box.png')
args = parser.parse_args()
 
src = cv.imread(cv.samples.findFile(args.input), cv.IMREAD_GRAYSCALE)
if src is None
print('Could not open or find the image:', args.input)
exit(0)
 
#-- Step 1: Detect the keypoints using SURF Detector
minHessian = 400
detector = cv.xfeatures2d_SURF.create(hessianThreshold=minHessian)
keypoints = detector.detect(src)
 
#-- Draw keypoints
img_keypoints = np.empty((src.shape[0], src.shape[1], 3), dtype=np.uint8)
cv.drawKeypoints(src, keypoints, img_keypoints)
 
#-- Show detected (drawn) keypoints
cv.imshow('SURF Keypoints', img_keypoints)
 
cv.waitKey()

解釋

結果

1. 這是應用於 box.png 影像的特徵檢測結果

1. 這是 box_in_scene.png 影像的結果