Closed. This question needs to be more focused。它当前不接受答案。
想改善这个问题吗?更新问题,使其仅关注editing this post的一个问题。
7年前关闭。
Improve this question
假设我有2张图片。两者看起来相同但有所不同。例如,
这是我的代码:
上面的代码用于查找“匹配项”。这是它的结果
您会看到image1(左侧图像)中有一个“钢铁侠3手册”,而image2(右侧图像)中没有。现在,我需要找到image2中缺少的“东西”,并在控制台中打印一条消息。
应该考虑的是,这本《钢铁侠3手册》只是一个例子,在实际情况下,我不知道会丢失什么。
我怎样才能做到这一点?
想改善这个问题吗?更新问题,使其仅关注editing this post的一个问题。
7年前关闭。
Improve this question
假设我有2张图片。两者看起来相同但有所不同。例如,
image1
在图像中有一个小册子,但是image2
没有小册子,但是image2中的所有其他内容都与image1相同。这是我的代码:
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include <opencv2/nonfree/features2d.hpp>
using namespace cv;
void readme();
int main( int argc, char** argv )
{
Mat img_object = imread( "C:/Users/Yohan/Pictures/ironManSpecial2.jpg",CV_LOAD_IMAGE_GRAYSCALE);
Mat img_scene = imread("C:/Users/Yohan/Pictures/noIronMan.jpg",CV_LOAD_IMAGE_GRAYSCALE);
if( !img_object.data || !img_scene.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 400;
SurfFeatureDetector detector( minHessian );
std::vector<KeyPoint> keypoints_object, keypoints_scene;
detector.detect( img_object, keypoints_object );
detector.detect( img_scene, keypoints_scene );
//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat descriptors_object, descriptors_scene;
extractor.compute( img_object, keypoints_object, descriptors_object );
extractor.compute( img_scene, keypoints_scene, descriptors_scene );
//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
std::vector< DMatch > matches;
matcher.match( descriptors_object, descriptors_scene, matches );
double max_dist = 0; double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < descriptors_object.rows; i++ )
{ double dist = matches[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}
printf("-- Max dist : %f \n", max_dist );
printf("-- Min dist : %f \n", min_dist );
//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
std::vector< DMatch > good_matches;
for( int i = 0; i < descriptors_object.rows; i++ )
{ if( matches[i].distance < 3*min_dist )
{ good_matches.push_back( matches[i]); }
}
Mat img_matches;
drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
//-- Localize the object from img_1 in img_2
std::vector<Point2f> obj;
std::vector<Point2f> scene;
for( int i = 0; i < good_matches.size(); i++ )
{
//-- Get the keypoints from the good matches
obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
}
Mat H = findHomography( obj, scene, CV_RANSAC );
//-- Get the corners from the image_1 ( the object to be "detected" )
std::vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
std::vector<Point2f> scene_corners(4);
perspectiveTransform( obj_corners, scene_corners, H);
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
//-- Show detected matches
namedWindow( "Good Matches & Object detection", CV_WINDOW_NORMAL );
imshow( "Good Matches & Object detection", img_matches );
waitKey(0);
return 0;
}
上面的代码用于查找“匹配项”。这是它的结果
您会看到image1(左侧图像)中有一个“钢铁侠3手册”,而image2(右侧图像)中没有。现在,我需要找到image2中缺少的“东西”,并在控制台中打印一条消息。
应该考虑的是,这本《钢铁侠3手册》只是一个例子,在实际情况下,我不知道会丢失什么。
我怎样才能做到这一点?
最佳答案
您可以尝试使用找到的关键点对齐图像,然后找到逐像素差异,或应用模板匹配算法。
关于c++ - 寻找2张图片之间的差异,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/18903953/
10-12 21:54