我试图从我的C＃项目中运行快速压缩跟踪算法。我想要的是，（在C＃中）将图像转换为灰度图像到Matlab函数后传递图像
Runtracker
而不是将图像放在Matlab代码中，因为我将应用一些操作来指定包含要开始跟踪的图像的数组的索引。
但是当我通过灰度图像列表时，我得到一个错误说
＆＃34;最佳重载方法匹配＆＃39; trackerNative.FCT.Runtracker（int）＆＃39;有一些无效的论点。＆＃34;
你可以帮我解决这个问题吗？将C＃中的图像列表传递给matlab函数。C＃代码
//to convert to graScale
public double[,] to_gray(Bitmap img)
{
int w = img.Width;
int h = img.Height;
double[,] grayImage = new double[w, h];
for (int i = 0; i < w; i++)
{
for (int x = 0; x < h; x++)
{
Color oc = img.GetPixel(i, x);
grayImage[i, x] = (double)(oc.R + oc.G + oc.B) / 3;
}
}
return grayImage;
}
//to get the files from specified folder
public static String[] GetFilesFrom(String searchFolder, String[] filters, bool isRecursive)
{
List<String> filesFound = new List<String>();
var searchOption = isRecursive ? SearchOption.AllDirectories : SearchOption.TopDirectoryOnly;
foreach (var filter in filters)
{
filesFound.AddRange(Directory.GetFiles(searchFolder, String.Format("*.{0}", filter), searchOption));
}
return filesFound.ToArray();
}
//Button to run the matlab function 'Runtracker'
private void button1_Click(object sender, EventArgs e)
{
FCT obj = new FCT(); //FCT is a matlab class
OpenFileDialog openFileDialog1 = new OpenFileDialog();
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
string file_path = openFileDialog1.FileName;
var filters = new String[] { "png" };
var files = GetFilesFrom(file_path, filters, false);
List< double[,] > imgArr = new List< double[,] >();
for (int i = 0; i < files.Length; i++)
{
Bitmap image = new Bitmap(files[i]);
double[,] grayScale_image = to_gray(image);
imgArr[i] = grayScale_image;
}
object m = obj.Runtracker(imgArr); //the error occured
//Bitmap output = m as Bitmap;
}
}
Matlab代码
function Runtracker(input_imgArr)
clc;clear all;close all;
rand('state',0);
%%
[initstate] = initCt(1);% position of the detected face in the 1st frame
num = length(input_imgArr);% number of frames
%%
x = initstate(1);% x axis at the Top left corner
y = initstate(2);
w = initstate(3);% width of the rectangle
h = initstate(4);% height of the rectangle
%---------------------------
img = imread(input_imgArr(1));
img = double(img);
%%
trparams.init_negnumtrain = 50;%number of trained negative samples
trparams.init_postrainrad = 4;%radical scope of positive samples
trparams.initstate = initstate;% object position [x y width height]
trparams.srchwinsz = 25;% size of search window
%-------------------------
%% Classifier parameters
clfparams.width = trparams.initstate(3);
clfparams.height= trparams.initstate(4);
% feature parameters
% number of rectangle from 2 to 4.
ftrparams.minNumRect =2;
ftrparams.maxNumRect =4;
M = 100;% number of all weaker classifiers, i.e,feature pool
%-------------------------
posx.mu = zeros(M,1);% mean of positive features
negx.mu = zeros(M,1);
posx.sig= ones(M,1);% variance of positive features
negx.sig= ones(M,1);
lRate = 0.85;% Learning rate parameter
%% Compute feature template
[ftr.px,ftr.py,ftr.pw,ftr.ph,ftr.pwt] = HaarFtr(clfparams,ftrparams,M);
%% Compute sample templates
posx.sampleImage = sampleImgDet(img,initstate,trparams.init_postrainrad,1);
negx.sampleImage = sampleImg(img,initstate,1.5*trparams.srchwinsz,4+trparams.init_postrainrad,trpar ams.init_negnumtrain);
%% Feature extraction
iH = integral(img);%Compute integral image
posx.feature = getFtrVal(iH,posx.sampleImage,ftr);
negx.feature = getFtrVal(iH,negx.sampleImage,ftr);
[posx.mu,posx.sig,negx.mu,negx.sig] =
classiferUpdate(posx,negx,posx.mu,posx.sig,negx.mu,negx.sig,lRate);%
update distribution parameters
%% Begin tracking
for i = 2:num
img = imread(input_imgArr(i));
imgSr = img;% imgSr is used for showing tracking results.
img = double(img);
iH = integral(img);%Compute integral image
%% Coarse detection
step = 4; % coarse search step
detectx.sampleImage =
sampleImgDet(img,initstate,trparams.srchwinsz,step);
detectx.feature = getFtrVal(iH,detectx.sampleImage,ftr);
r = ratioClassifier(posx,negx,detectx.feature);% compute the classifier for all samples
clf = sum(r);% linearly combine the ratio classifiers in r to the final classifier
[c,index] = max(clf);
x = detectx.sampleImage.sx(index);
y = detectx.sampleImage.sy(index);
w = detectx.sampleImage.sw(index);
h = detectx.sampleImage.sh(index);
initstate = [x y w h];
%% Fine detection
step = 1;
detectx.sampleImage = sampleImgDet(img,initstate,10,step);
detectx.feature = getFtrVal(iH,detectx.sampleImage,ftr);
r = ratioClassifier(posx,negx,detectx.feature);% compute the classifier for all samples
clf = sum(r);% linearly combine the ratio classifiers in r to the final classifier
[c,index] = max(clf);
x = detectx.sampleImage.sx(index);
y = detectx.sampleImage.sy(index);
w = detectx.sampleImage.sw(index);
h = detectx.sampleImage.sh(index);
initstate = [x y w h];
%% Show the tracking results
imshow(uint8(imgSr));
rectangle('Position',initstate,'LineWidth',4,'EdgeColor','r');
hold on;
text(5, 18, strcat('#',num2str(i)), 'Color','y', 'FontWeight','bold', 'FontSize',20);
set(gca,'position',[0 0 1 1]);
pause(0.00001);
hold off;
%% Extract samples
posx.sampleImage = sampleImgDet(img,initstate,trparams.init_postrainrad,1);
negx.sampleImage =
sampleImg(img,initstate,1.5*trparams.srchwinsz,4+trparams.init_postrainrad,trparams.init_negnumtrain);
%% Update all the features
posx.feature = getFtrVal(iH,posx.sampleImage,ftr);
negx.feature = getFtrVal(iH,negx.sampleImage,ftr);
[posx.mu,posx.sig,negx.mu,negx.sig] = classiferUpdate(posx,negx,posx.mu,posx.sig,negx.mu,negx.sig,lRate);% update distribution parameters
end
end
答案 0 :(得分：0)
这是因为Runtracker期望根据错误的类型为int的参数，并且您传递的是int[]类型的参数，即整数数组。
传递的参数和期望的参数应匹配，以便成功执行该方法。
希望这有帮助。