我的想法很简单。我正在使用mexopencv并尝试查看当前是否存在与数据库中存储的任何图像匹配的对象。我正在使用OpenCV DescriptorMatcher函数来训练我的图像。
这是一个代码段，我希望在this之上构建，它是使用mexopencv一对一匹配的图像，也可以扩展为图像流。
function hello
detector = cv.FeatureDetector('ORB');
extractor = cv.DescriptorExtractor('ORB');
matcher = cv.DescriptorMatcher('BruteForce-Hamming');
train = [];
for i=1:3
train(i).img = [];
train(i).points = [];
train(i).features = [];
end;
train(1).img = imread('D:\test\1.jpg');
train(2).img = imread('D:\test\2.png');
train(3).img = imread('D:\test\3.jpg');
for i=1:3
frameImage = train(i).img;
framePoints = detector.detect(frameImage);
frameFeatures = extractor.compute(frameImage , framePoints);
train(i).points = framePoints;
train(i).features = frameFeatures;
end;
for i = 1:3
boxfeatures = train(i).features;
matcher.add(boxfeatures);
end;
matcher.train();
camera = cv.VideoCapture;
pause(3);%Sometimes necessary
window = figure('KeyPressFcn',@(obj,evt)setappdata(obj,'flag',true));
setappdata(window,'flag',false);
while(true)
sceneImage = camera.read;
sceneImage = rgb2gray(sceneImage);
scenePoints = detector.detect(sceneImage);
sceneFeatures = extractor.compute(sceneImage,scenePoints);
m = matcher.match(sceneFeatures);
%{
%Comments in
img_no = m.imgIdx;
img_no = img_no(1);
%I am planning to do this based on the fact that
%on a perfect match imgIdx a 1xN will be filled
%with the index of the training
%example 1,2 or 3
objPoints = train(img_no+1).points;
boxImage = train(img_no+1).img;
ptsScene = cat(1,scenePoints([m.queryIdx]+1).pt);
ptsScene = num2cell(ptsScene,2);
ptsObj = cat(1,objPoints([m.trainIdx]+1).pt);
ptsObj = num2cell(ptsObj,2);
%This is where the problem starts here, assuming the
%above is correct , Matlab yells this at me
%index exceeds matrix dimensions.
end [H,inliers] = cv.findHomography(ptsScene,ptsObj,'Method','Ransac');
m = m(inliers);
imgMatches = cv.drawMatches(sceneImage,scenePoints,boxImage,boxPoints,m,...
'NotDrawSinglePoints',true);
imshow(imgMatches);
%Comment out
%}
flag = getappdata(window,'flag');
if isempty(flag) || flag, break; end
pause(0.0001);
end
现在的问题是
imgIdx是一个1xN的矩阵，它包含不同训练索引的索引，这是显而易见的。只有在完全匹配时，矩阵imgIdx才会完全被匹配的图像索引填充。 因此，如何使用此矩阵选择正确的图像索引。也在这两行中，我得到索引超出矩阵维的误差。
ptsObj = cat(1,objPoints([m.trainIdx]+1).pt);
ptsObj = num2cell(ptsObj,2);
这是显而易见的，因为在调试时，我清楚地看到
m.trainIdx的大小大于objPoints，即我正在访问的点不应该访问，因此索引超出了关于
imgIdx的使用的文档很少，因此任何对此主题有知识的人都需要帮助。这些是我使用的图像。
Image1
Image2
Image3
@Amro回复后的第一次更新:
With the ratio of min distance to distance at 3.6 , I get the following response.
With the ratio of min distance to distance at 1.6 , I get the following response.
最佳答案
我认为用代码解释起来更容易，所以在这里:)
%% init
detector = cv.FeatureDetector('ORB');
extractor = cv.DescriptorExtractor('ORB');
matcher = cv.DescriptorMatcher('BruteForce-Hamming');
urls = {
'http://i.imgur.com/8Pz4M9q.jpg?1'
'http://i.imgur.com/1aZj0MI.png?1'
'http://i.imgur.com/pYepuzd.jpg?1'
};
N = numel(urls);
train = struct('img',cell(N,1), 'pts',cell(N,1), 'feat',cell(N,1));
%% training
for i=1:N
% read image
train(i).img = imread(urls{i});
if ~ismatrix(train(i).img)
train(i).img = rgb2gray(train(i).img);
end
% extract keypoints and compute features
train(i).pts = detector.detect(train(i).img);
train(i).feat = extractor.compute(train(i).img, train(i).pts);
% add to training set to match against
matcher.add(train(i).feat);
end
% build index
matcher.train();
%% testing
% lets create a distorted query image from one of the training images
% (rotation+shear transformations)
t = -pi/3; % -60 degrees angle
tform = [cos(t) -sin(t) 0; 0.5*sin(t) cos(t) 0; 0 0 1];
img = imwarp(train(3).img, affine2d(tform)); % try all three images here!
% detect fetures in query image
pts = detector.detect(img);
feat = extractor.compute(img, pts);
% match against training images
m = matcher.match(feat);
% keep only good matches
%hist([m.distance])
m = m([m.distance] < 3.6*min([m.distance]));
% sort by distances, and keep at most the first/best 200 matches
[~,ord] = sort([m.distance]);
m = m(ord);
m = m(1:min(200,numel(m)));
% naive classification (majority vote)
tabulate([m.imgIdx]) % how many matches each training image received
idx = mode([m.imgIdx]);
% matches with keypoints belonging to chosen training image
mm = m([m.imgIdx] == idx);
% estimate homography (used to locate object in query image)
ptsQuery = num2cell(cat(1, pts([mm.queryIdx]+1).pt), 2);
ptsTrain = num2cell(cat(1, train(idx+1).pts([mm.trainIdx]+1).pt), 2);
[H,inliers] = cv.findHomography(ptsTrain, ptsQuery, 'Method','Ransac');
% show final matches
imgMatches = cv.drawMatches(img, pts, ...
train(idx+1).img, train(idx+1).pts, ...
mm(logical(inliers)), 'NotDrawSinglePoints',true);
% apply the homography to the corner points of the training image
[h,w] = size(train(idx+1).img);
corners = permute([0 0; w 0; w h; 0 h], [3 1 2]);
p = cv.perspectiveTransform(corners, H);
p = permute(p, [2 3 1]);
% show where the training object is located in the query image
opts = {'Color',[0 255 0], 'Thickness',4};
imgMatches = cv.line(imgMatches, p(1,:), p(2,:), opts{:});
imgMatches = cv.line(imgMatches, p(2,:), p(3,:), opts{:});
imgMatches = cv.line(imgMatches, p(3,:), p(4,:), opts{:});
imgMatches = cv.line(imgMatches, p(4,:), p(1,:), opts{:});
imshow(imgMatches)
结果:
请注意，由于您没有发布任何测试图像(在您的代码中，您正在从网络摄像头获取输入)，因此我通过扭曲一个训练图像并将其用作查询图像来创建了一个。我正在使用某些MATLAB工具箱中的函数(
imwarp等)，但是这些对于演示来说不是必需的，您可以将它们替换为等效的OpenCV函数...我必须说这种方法不是最可靠的方法。考虑使用其他技术，例如bag-of-word model，OpenCV已经使用implements了。
关于matlab - DescriptorMatcher mexopencv中imgIdx的问题，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/20717025/