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我一直在使用Opencv的時間。這一次，我遇到了一個讓我非常惱火的問題。 其實，我有一個模板圖像，我想使用匹配來識別它在我的相機流，但我面對這樣的控制檯錯誤：使用SURF描述符的miniflann.cpp中的錯誤
OpenCV Error: Unsupported format or combination of formats (type=0
) in unknown function, file ..\..\..\opencv\modules\flann\src\miniflann.cpp, lin
e 299
其實這是代碼，它編譯良好，但錯誤出現在執行中。
#include "stdafx.h"
#include <stdio.h>
#include <iostream>
#include <fstream>
#include <string>
#include "opencv2/core/core.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/features2d/features2d.hpp"
//#include "opencv2/legacy/legacy.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
using namespace cv;
using namespace std;
int main()
{
//reference image
Mat object = imread("tel_tmpl.jpg", CV_LOAD_IMAGE_GRAYSCALE);
if(!object.data)
{
std::cout<< "Error reading object " << std::endl;
return -1;
}
char key = 'a';
int framecount = 0;
SurfFeatureDetector detector(500);
SurfDescriptorExtractor extractor;
FlannBasedMatcher matcher;
Mat frame, des_object, image;
Mat des_image, img_matches, H;
std::vector<KeyPoint> kp_object;
std::vector<Point2f> obj_corners(4);
std::vector<KeyPoint> kp_image;
std::vector<vector<DMatch > > matches;
std::vector<DMatch > good_matches;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
//compute detectors and descriptors of reference image
detector.detect(object, kp_object);
extractor.compute(object, kp_object, des_object);
//create video capture object
VideoCapture cap(0);
//Get the corners from the object
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint(object.cols, 0);
obj_corners[2] = cvPoint(object.cols, object.rows);
obj_corners[3] = cvPoint(0, object.rows);
//wile loop for real time detection
while (key != 27)
{
//capture one frame from video and store it into image object name 'frame'
cap >> frame;
if (framecount < 5)
{
framecount++;
continue;
}
//converting captured frame into gray scale
cvtColor(frame, image, CV_RGB2GRAY);
//extract detectors and descriptors of captured frame
detector.detect(image, kp_image);
extractor.compute(image, kp_image, des_image);
//find matching descriptors of reference and captured image
matcher.knnMatch(des_object, des_image, matches, 2);
//finding matching keypoints with Euclidean distance 0.6 times the distance of next keypoint
//used to find right matches
for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++)
{
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
//Draw only "good" matches
drawMatches(object, kp_object, frame, kp_image, good_matches, img_matches,
Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
//3 good matches are enough to describe an object as a right match.
if (good_matches.size() >= 3)
{
for(int i = 0; i < good_matches.size(); i++)
{
//Get the keypoints from the good matches
obj.push_back(kp_object[ good_matches[i].queryIdx ].pt);
scene.push_back(kp_image[ good_matches[i].trainIdx ].pt);
}
try
{
H = findHomography(obj, scene, CV_RANSAC);
}
catch(Exception e){}
perspectiveTransform(obj_corners, scene_corners, H);
//Draw lines between the corners (the mapped object in the scene image)
line(img_matches, scene_corners[0] + Point2f(object.cols, 0), scene_corners[1] + Point2f(object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[1] + Point2f(object.cols, 0), scene_corners[2] + Point2f(object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[2] + Point2f(object.cols, 0), scene_corners[3] + Point2f(object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[3] + Point2f(object.cols, 0), scene_corners[0] + Point2f(object.cols, 0), Scalar(0, 255, 0), 4);
}
//Show detected matches
imshow("Good Matches", img_matches);
//clear array
good_matches.clear();
key = waitKey(1);
}
return 0;
}
在此先感謝