好的，这是我的源代码。此代码将在文件中获取图像，并将其与另一个文件中的图像列表进行比较。在图像文件中，您必须包含一个.txt文件，其中包含您要比较的文件中所有图像的名称。我遇到的问题是这两个图像非常相似但不完全相同。我需要一种方法来进一步细化这些匹配。甚至可能是一种比较这两种形状的全新方式（更大的块，blob等）。我正在考虑的一种方法实际上是制作一个完整的关键点地图，并且只比较关键点，如果它们处于或接近某个对应于两个图像的点。即：比较点（12,200）处的关键点，（x，y）的+ -10像素，并查看另一图像上是否存在类似的关键点。
我需要的是一种从ActualImplant和XrayOfThatSameImplantButASlightlyDifferentSize获得最佳匹配的方法。拜托，谢谢！
PS：你会看到我正在试验Sobel Derivatives和其他类似事情的评论部分。我最终只是在X射线上调整对比度和亮度以获得最佳轮廓。在用于试图匹配任何东西之前，必须对植入物的图像进行相同的操作。
#include "opencv2\highgui\highgui.hpp"
#include "opencv2\features2d\features2d.hpp"
#include "opencv2\imgproc.hpp"
#include <iostream>
#include <fstream>
#include <ctime>
const string defaultDetector = "ORB";
const string defaultDescriptor = "ORB";
const string defaultMatcher = "BruteForce-Hamming";
const string defaultXrayImagePath = "../../xray.png";
const string defaultImplantImagesTextListPath = "../../implantImage.txt";
const string defaultPathToResultsFolder = "../../results";
static void printIntro(const string& appName)
{
cout << "/* *\n"
<< " * Created by: Alex Gatz. 1/11/12. Created for: Xray Implant Identification *\n"
<< " * This code was created to scan a file full of images of differnt implants, generate keypoint maps *\n"
<< " * for each image, and identifywhich image most closely matches a chosen image in another folder *\n"
<< " */ *\n"
<< endl;
cout << endl << "Format:\n" << endl;
cout << "./" << appName << " [detector] [descriptor] [matcher] [xrayImagePath] [implantImagesTextListPath] [pathToSaveResults]" << endl;
cout << endl;
cout << "\nExample:" << endl
<< "./" << appName << " " << defaultDetector << " " << defaultDescriptor << " " << defaultMatcher << " "
<< defaultXrayImagePath << " " << defaultImplantImagesTextListPath << " " << defaultPathToResultsFolder << endl;
}
static void maskMatchesByImplantImgIdx(const vector<DMatch>& matches, int trainImgIdx, vector<char>& mask)
{
mask.resize(matches.size());
fill(mask.begin(), mask.end(), 0);
for (size_t i = 0; i < matches.size(); i++)
{
if (matches[i].imgIdx == trainImgIdx)
mask[i] = 1;
}
}
static void readImplantFilenames(const string& filename, string& dirName, vector<string>& implantFilenames)
{
implantFilenames.clear();
ifstream file(filename.c_str());
if (!file.is_open())
return;
size_t pos = filename.rfind('\\');
char dlmtr = '\\';
if (pos == String::npos)
{
pos = filename.rfind('/');
dlmtr = '/';
}
dirName = pos == string::npos ? "" : filename.substr(0, pos) + dlmtr;
while (!file.eof())
{
string str; getline(file, str);
if (str.empty()) break;
implantFilenames.push_back(str);
}
file.close();
}
static bool createDetectorDescriptorMatcher(const string& detectorType, const string& descriptorType, const string& matcherType,
Ptr<FeatureDetector>& featureDetector,
Ptr<DescriptorExtractor>& descriptorExtractor,
Ptr<DescriptorMatcher>& descriptorMatcher)
{
cout << "< Creating feature detector, descriptor extractor and descriptor matcher ..." << endl;
featureDetector = ORB::create( //All of these are parameters that can be adjusted to effect match accuracy and process time.
10000, //int nfeatures = Maxiumum number of features to retain; max vaulue unknown, higher number takes longer to process. Default: 500
1.4f, //float scaleFactor= Pyramid decimation ratio; between 1.00 - 2.00. Default: 1.2f
6, //int nlevels = Number of pyramid levels used; more levels more time taken to process, but more accurate results. Default: 8
40, //int edgeThreshold = Size of the border where the features are not detected. Should match patchSize roughly. Default: 31
0, //int firstLevel = Should remain 0 for now. Default: 0
4, //int WTA_K = Should remain 2. Default: 2
ORB::HARRIS_SCORE, //int scoreType = ORB::HARRIS_SCORE is the most accurate ranking possible for ORB. Default: HARRIS_SCORE
33 //int patchSize = size of patch used by the oriented BRIEF descriptor. Should match edgeThreashold. Default: 31
);
//featureDetector = ORB::create(); // <-- Uncomment this and comment the featureDetector above for default detector-
//OpenCV 3.1 got rid of the dynamic naming of detectors and extractors.
//These two are one in the same when using ORB, some detectors and extractors are separate
// in which case you would set "descriptorExtractor = descriptorType::create();" or its equivilant.
descriptorExtractor = featureDetector;
descriptorMatcher = DescriptorMatcher::create(matcherType);
cout << ">" << endl;
bool isCreated = !(featureDetector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty());
if (!isCreated)
cout << "Can not create feature detector or descriptor extractor or descriptor matcher of given types." << endl << ">" << endl;
return isCreated;
}
static void manipulateImage(Mat& image) //Manipulates images into only showing an outline!
{
//Sobel Dirivative edge finder
//int scale = 1;
//int delta = 0;
//int ddepth = CV_16S;
////equalizeHist(image, image); //This will equilize the lighting levels in each image.
//GaussianBlur(image, image, Size(3, 3), 0, 0, BORDER_DEFAULT);
//Mat grad_x, grad_y;
//Mat abs_grad_x, abs_grad_y;
////For x
//Sobel(image, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT);
//convertScaleAbs(grad_x, abs_grad_x);
////For y
//Sobel(image, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT);
//convertScaleAbs(grad_y, abs_grad_y);
//addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, image);
//Specific Level adjustment (very clean)
double alpha = 20; //Best Result: 20
int beta = -300; //Best Result: -300
image.convertTo(image, -1, alpha, beta);
}
static bool readImages(const string& xrayImageName, const string& implantFilename,
Mat& xrayImage, vector <Mat>& implantImages, vector<string>& implantImageNames)
{
//TODO: Add a funtion call to automatically adjust all images loaded to best settings for matching.
cout << "< Reading the images..." << endl;
xrayImage = imread(xrayImageName, CV_LOAD_IMAGE_GRAYSCALE); //Turns the image gray while loading.
manipulateImage(xrayImage); //Runs image manipulations
if (xrayImage.empty())
{
cout << "Xray image can not be read." << endl << ">" << endl;
return false;
}
string trainDirName;
readImplantFilenames(implantFilename, trainDirName, implantImageNames);
if (implantImageNames.empty())
{
cout << "Implant image filenames can not be read." << endl << ">" << endl;
return false;
}
int readImageCount = 0;
for (size_t i = 0; i < implantImageNames.size(); i++)
{
string filename = trainDirName + implantImageNames[i];
Mat img = imread(filename, CV_LOAD_IMAGE_GRAYSCALE); //Turns imamges gray while loading.
//manipulateImage(img); //Runs Sobel Dirivitage on implant image.
if (img.empty())
{
cout << "Implant image " << filename << " can not be read." << endl;
}
else
{
readImageCount++;
}
implantImages.push_back(img);
}
if (!readImageCount)
{
cout << "All implant images can not be read." << endl << ">" << endl;
return false;
}
else
cout << readImageCount << " implant images were read." << endl;
cout << ">" << endl;
return true;
}
static void detectKeypoints(const Mat& xrayImage, vector<KeyPoint>& xrayKeypoints,
const vector<Mat>& implantImages, vector<vector<KeyPoint> >& implantKeypoints,
Ptr<FeatureDetector>& featureDetector)
{
cout << endl << "< Extracting keypoints from images..." << endl;
featureDetector->detect(xrayImage, xrayKeypoints);
featureDetector->detect(implantImages, implantKeypoints);
cout << ">" << endl;
}
static void computeDescriptors(const Mat& xrayImage, vector<KeyPoint>& implantKeypoints, Mat& implantDescriptors,
const vector<Mat>& implantImages, vector<vector<KeyPoint> >& implantImageKeypoints, vector<Mat>& implantImageDescriptors,
Ptr<DescriptorExtractor>& descriptorExtractor)
{
cout << "< Computing descriptors for keypoints..." << endl;
descriptorExtractor->compute(xrayImage, implantKeypoints, implantDescriptors);
descriptorExtractor->compute(implantImages, implantImageKeypoints, implantImageDescriptors);
int totalTrainDesc = 0;
for (vector<Mat>::const_iterator tdIter = implantImageDescriptors.begin(); tdIter != implantImageDescriptors.end(); tdIter++)
totalTrainDesc += tdIter->rows;
cout << "Query descriptors count: " << implantDescriptors.rows << "; Total train descriptors count: " << totalTrainDesc << endl;
cout << ">" << endl;
}
static void matchDescriptors(const Mat& xrayDescriptors, const vector<Mat>& implantDescriptors,
vector<DMatch>& matches, Ptr<DescriptorMatcher>& descriptorMatcher)
{
cout << "< Set implant image descriptors collection in the matcher and match xray descriptors to them..." << endl;
//time_t timerBegin, timerEnd;
//time(&timerBegin);
descriptorMatcher->add(implantDescriptors);
descriptorMatcher->train();
//time(&timerEnd);
//double buildTime = difftime(timerEnd, timerBegin);
//time(&timerBegin);
descriptorMatcher->match(xrayDescriptors, matches);
//time(&timerEnd);
//double matchTime = difftime(timerEnd, timerBegin);
CV_Assert(xrayDescriptors.rows == (int)matches.size() || matches.empty());
cout << "Number of imageMatches: " << matches.size() << endl;
//cout << "Build time: " << buildTime << " ms; Match time: " << matchTime << " ms" << endl;
cout << ">" << endl;
}
static void saveResultImages(const Mat& xrayImage, const vector<KeyPoint>& xrayKeypoints,
const vector<Mat>& implantImage, const vector<vector<KeyPoint> >& implantImageKeypoints,
const vector<DMatch>& matches, const vector<string>& implantImagesName, const string& resultDir)
{
cout << "< Save results..." << endl;
Mat drawImg;
vector<char> mask;
for (size_t i = 0; i < implantImage.size(); i++)
{
if (!implantImage[i].empty())
{
maskMatchesByImplantImgIdx(matches, (int)i, mask);
drawMatches(xrayImage, xrayKeypoints, implantImage[i], implantImageKeypoints[i],
matches, drawImg, Scalar::all(-1), Scalar(0, 0, 255), mask, 4);
string filename = resultDir + "/result_" + implantImagesName[i];
if (!imwrite(filename, drawImg))
cout << "Image " << filename << " can not be saved (may be because directory " << resultDir << " does not exist)." << endl;
}
}
cout << ">" << endl;
//After all results have been saved, another function will scan and place the final result in a separate folder.
//For now this save process is required to manually access each result and determine if the current settings are working well.
}
int main(int argc, char** argv)
{
//Intialize variables to global defaults.
string detector = defaultDetector;
string descriptor = defaultDescriptor;
string matcher = defaultMatcher;
string xrayImagePath = defaultXrayImagePath;
string implantImagesTextListPath = defaultImplantImagesTextListPath;
string pathToSaveResults = defaultPathToResultsFolder;
//As long as you have 7 arguments, you can procede
if (argc != 7 && argc != 1)
{
//This will be called if the incorrect amount of commands are used to start the program.
printIntro(argv[1]);
system("PAUSE");
return -1;
}
//As long as you still have 7 arguments, I will set the variables for this
// to the arguments you decided on.
//If testing using XrayID --> Properties --> Debugging --> Command Arguments, remember to start with [detector] as the first command
// C++ includes the [appName] command as the first argument automantically.
if (argc != 1) //I suggest placing a break here and stepping through this to ensure the proper commands were sent in. With a
// GUI this would nto matter because the GUI would structure the input and use a default if no input was used.
{
detector = argv[1];
descriptor = argv[2];
matcher = argv[3];
xrayImagePath = argv[4];
implantImagesTextListPath = argv[5];
pathToSaveResults = argv[6];
}
//Set up cv::Ptr's for tools.
Ptr<FeatureDetector> featureDetector;
Ptr<DescriptorExtractor> descriptorExtractor;
Ptr<DescriptorMatcher> descriptorMatcher;
//Check to see if tools are created, if not true print intro and close program.
if (!createDetectorDescriptorMatcher(detector, descriptor, matcher, featureDetector, descriptorExtractor, descriptorMatcher))
{
printIntro(argv[0]);
system("PAUSE");
return -1;
}
Mat testImage;
vector<Mat> implantImages;
vector<string> implantImagesNames;
//Check to see if readImages completes properly, if not true print intro and close program.
if (!readImages(xrayImagePath, implantImagesTextListPath, testImage, implantImages, implantImagesNames))
{
printIntro(argv[0]);
system("PAUSE");
return -1;
}
vector<KeyPoint> xrayKeypoints;
vector<vector<KeyPoint> > implantKeypoints;
detectKeypoints(testImage, xrayKeypoints, implantImages, implantKeypoints, featureDetector);
Mat xrayDescriptors;
vector<Mat> implantTestImageDescriptors;
computeDescriptors(testImage, xrayKeypoints, xrayDescriptors, implantImages, implantKeypoints, implantTestImageDescriptors,
descriptorExtractor);
vector<DMatch> imageMatches;
matchDescriptors(xrayDescriptors, implantTestImageDescriptors, imageMatches, descriptorMatcher);
saveResultImages(testImage, xrayKeypoints, implantImages, implantKeypoints, imageMatches, implantImagesNames, pathToSaveResults);
system("PAUSE");
return 0;
}
答案 0 :(得分：0)
该图像看起来很像人造臀部。如果你正在处理医学影像，你一定要查看The Insight Toolkit（ITK），它有许多为这个领域的特定需求而设计的特殊功能。您可以在真实世界图像和模板数据之间进行简单的模型图像注册，以找到最佳结果。我认为使用这种方法可以获得比上述基于点的测试更好的结果。
这种配准执行一组参数的迭代优化（在这种情况下，仿射变换），寻求找到模型到图像数据的最佳映射。
上面的示例拍摄了一张固定图像，并尝试找到将运动图像映射到其上的变换。变换是2D仿射变换（在这种情况下为旋转和平移），其参数是运行优化器的结果，该优化器最大化匹配度量。度量标准衡量固定图像和变换后的运动图像的匹配程度。插值器采用运动图像并应用变换将其映射到固定图像上。
在示例图像中，固定图像可以是original X-ray，动态图像可以是actual implant。您可能需要添加缩放以进行完全仿射变换，因为两者的大小不同。
度量标准衡量转换的运动图像与固定图像的匹配程度，因此您需要确定匹配有效的容差或最小度量标准。如果图像非常不同，则度量标准将非常低并且可以被拒绝。
输出是一组变换参数，输出图像是应用于运动图像的最终最佳变换（不是图像的组合）。结果基本上告诉你在X射线中发现植入物的位置。
答案 1 :(得分：0)
尝试以下代码。希望这对您有所帮助。
#include <opencv2/nonfree/nonfree.hpp>
#include <iostream>
#include <dirent.h>
#include <ctime>
#include <stdio.h>
using namespace cv;
using namespace std;
int main(int argc, const char *argv[])
{
double ratio = 0.9;
Mat image1 = imread("Image1_path);
Mat image2 = cv::imread("Image2_path");
Ptr<FeatureDetector> detector;
Ptr<DescriptorExtractor> extractor;
// TODO default is 500 keypoints..but we can change
detector = FeatureDetector::create("ORB");
extractor = DescriptorExtractor::create("ORB");
vector<KeyPoint> keypoints1, keypoints2;
detector->detect(image1, keypoints1);
detector->detect(image2, keypoints2);
cout << "# keypoints of image1 :" << keypoints1.size() << endl;
cout << "# keypoints of image2 :" << keypoints2.size() << endl;
Mat descriptors1,descriptors2;
extractor->compute(image1,keypoints1,descriptors1);
extractor->compute(image2,keypoints2,descriptors2);
cout << "Descriptors size :" << descriptors1.cols << ":"<< descriptors1.rows << endl;
vector< vector<DMatch> > matches12, matches21;
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
matcher->knnMatch( descriptors1, descriptors2, matches12, 2);
matcher->knnMatch( descriptors2, descriptors1, matches21, 2);
//BFMatcher bfmatcher(NORM_L2, true);
//vector<DMatch> matches;
//bfmatcher.match(descriptors1, descriptors2, matches);
double max_dist = 0; double min_dist = 100;
for( int i = 0; i < descriptors1.rows; i++)
{
double dist = matches12[i].data()->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);
cout << "Matches1-2:" << matches12.size() << endl;
cout << "Matches2-1:" << matches21.size() << endl;
std::vector<DMatch> good_matches1, good_matches2;
for(int i=0; i < matches12.size(); i++)
{
if(matches12[i][0].distance < ratio * matches12[i][1].distance)
good_matches1.push_back(matches12[i][0]);
}
for(int i=0; i < matches21.size(); i++)
{
if(matches21[i][0].distance < ratio * matches21[i][1].distance)
good_matches2.push_back(matches21[i][0]);
}
cout << "Good matches1:" << good_matches1.size() << endl;
cout << "Good matches2:" << good_matches2.size() << endl;
// Symmetric Test
std::vector<DMatch> better_matches;
for(int i=0; i<good_matches1.size(); i++)
{
for(int j=0; j<good_matches2.size(); j++)
{
if(good_matches1[i].queryIdx == good_matches2[j].trainIdx && good_matches2[j].queryIdx == good_matches1[i].trainIdx)
{
better_matches.push_back(DMatch(good_matches1[i].queryIdx, good_matches1[i].trainIdx, good_matches1[i].distance));
break;
}
}
}
cout << "Better matches:" << better_matches.size() << endl;
double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;
// show it on an image
Mat output;
drawMatches(image1, keypoints1, image2, keypoints2, better_matches, output);
imshow("Matches result",output);
waitKey(0);
return 0;
}