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I'm new in Matlab and now I have a problem for the implementation of a simple speaker recognition system using PNCC and MFFC.
My problem is on matrix dimension in fact, when I run my program, it give me this error:
Matrix dimensions must agree.
Error in disteu (line 43)
d(n,:) = sum((x(:, n+copies) - y) .^2, 1);
Error in test (line 22)
d = disteu(v, code{l});
Error in main (line 4)
test('C:\Users\Antonio\Documents\MATLAB\test',5, code);
Just for the sake of clarity I have attached my code.
Could anyone help me please?
function d = disteu(x, y)
% DISTEU Pairwise Euclidean distances between columns of two matrices
%
% Input:
% x, y: Two matrices whose each column is an a vector data.
%
% Output:
% d: Element d(i,j) will be the Euclidean distance between two
% column vectors X(:,i) and Y(:,j)
%
% Note:
% The Euclidean distance D between two vectors X and Y is:
% D = sum((x-y).^2).^0.5
% D = sum((x-y).^2).^0.5
[M, N] = size(x);
[M2, P] = size(y);
if (M ~= M2)
y=padarray(y,0,0,'post');
x=padarray(x,21,0,'post');
[M, N] = size(x)
[M2, P] = size(y)
y=padarray(y,0,0,'post');
[M2, P] = size(y)
end
%error('Matrix dimensions do not match.')
d = zeros(N, P);
if (N < P)
copies = zeros(1,P);
for n = 1:N
d(n,:) = sum((x(:, n+copies) - y) .^2, 1);
end
else
copies = zeros(1,N);
for p = 1:P
d(:,p) = sum((x - y(:, p+copies)) .^2, 1)';
end
end
d = d.^0.5;
function [aadDCT] = PNCC(rawdata, fsamp)
ad_x = rawdata;
%addpath voicebox/; % With Spectral Subtraction - default parameters
%ad_x = specsub(rawdata, fsamp);
dLamda_L = 0.999;
dLamda_S = 0.999;
dSampRate = fsamp;
dLowFreq = 200;% Changed to 40 from 200 as low freq is 40 in gabor as well
dHighFreq = dSampRate / 2;
dPowerCoeff = 1 / 15;
iFiltType = 1;
dFactor = 2.0;
dGammaThreshold = 0.005;
iM = 0; % Changed from 2 to 0 as number of frames coming out to be different due to queue
iN = 4;
iSMType = 0;
dLamda = 0.999;
dLamda2 = 0.5;
dDelta1 = 1;
dLamda3 = 0.85;
dDelta2 = 0.2;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Flags
%
bPreem = 1; % pre-emphasis flag
bSSF = 1;
bPowerLaw = 1;
bDisplay = 0;
dFrameLen = 0.025; % 25.6 ms window length, which is the default setting in CMU Sphinx
dFramePeriod = 0.010; % 10 ms frame period
iPowerFactor = 1;
global iNumFilts;
iNumFilts = 40;
if iNumFilts<30
iFFTSize = 512;
else
iFFTSize = 1024;
end
% For derivatives
deltawindow = 2; % to calculate 1st derivative
accwindow = 2; % to calculate 2nd derivative
% numcoeffs = 13; % number of cepstral coefficients to be used
numcoeffs = 13;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Flags
%
%
% Array Queue Ring-buffer
%
global Queue_aad_P;
global Queue_iHead;
global Queue_iTail;
global Queue_iWindow;
global Queue_iNumElem;
Queue_iWindow = 2 * iM + 1;
Queue_aad_P = zeros(Queue_iWindow, iNumFilts);
Queue_iHead = 0;
Queue_iTail = 0;
Queue_iNumElem = 0;
iFL = floor(dFrameLen * dSampRate);
iFP = floor(dFramePeriod * dSampRate);
iNumFrames = floor((length(ad_x) - iFL) / iFP) + 1;
iSpeechLen = length(ad_x);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Pre-emphasis using H(z) = 1 - 0.97 z ^ -1
%
if (bPreem == 1)
ad_x = filter([1 -0.97], 1, double(ad_x));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Obtaning the gammatone coefficient.
%
% Based on M. Snelly's auditory toolbox.
% In actual C-implementation, we just use a table
%
bGamma = 1;
[wts,binfrqs] = fft2melmx(iFFTSize, dSampRate, iNumFilts, 1, dLowFreq, dHighFreq, iFiltType);
wts = wts';
wts(size(wts, 1) / 2 + 1 : size(wts, 1), : ) = [];
aad_H = wts;
i_FI = 0;
i_FI_Out = 0;
if bSSF == 1
adSumPower = zeros(1, iNumFrames - 2 * iM);
else
adSumPower = zeros(1, iNumFrames);
end
%dLamda_L = 0.998;
aad_P = zeros(iNumFrames, iNumFilts);
aad_P_Out = zeros(iNumFrames - 2 * iM, iNumFilts);
ad_Q = zeros(1, iNumFilts);
ad_Q_Out = zeros(1, iNumFilts);
ad_QMVAvg = zeros(1, iNumFilts);
ad_w = zeros(1, iNumFilts);
ad_w_sm = zeros(1, iNumFilts);
ad_QMVAvg_LA = zeros(1, iNumFilts);
MEAN_POWER = 1e10;
dMean = 5.8471e+08;
dPeak = 2.7873e+09 / 15.6250;
% (1.7839e8, 2.0517e8, 2.4120e8, 2.9715e8, 3.9795e8) 95, 96, 97, 98, 99
% percentile from WSJ-si84
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
dPeakVal = 4e+07;% % 4.0638e+07 --> Mean from WSJ0-si84 (Important!!!)
%%%%%%%%%%%
dMean = dPeakVal;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Obtaining the short-time Power P(i, j)
%
for m = 0 : iFP : iSpeechLen - iFL
ad_x_st = ad_x(m + 1 : m + iFL) .* hamming(iFL);
adSpec = fft(ad_x_st, iFFTSize);
ad_X = abs(adSpec(1: iFFTSize / 2));
aadX(:, i_FI + 1) = ad_X;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Calculating the Power P(i, j)
%
for j = 1 : iNumFilts
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Squared integration
%
if iFiltType == 2
aad_P(i_FI + 1, j) = sum((ad_X .* aad_H(:, j)) .^ 2);
else
aad_P(i_FI + 1, j) = sum((ad_X .^ 2 .* aad_H(:, j)));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Calculating the Power P(i, j)
%
dSumPower = sum(aad_P(i_FI + 1, : ));
if bSSF == 1
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Ring buffer (using a Queue)
%
if (i_FI >= 2 * iM + 1)
Queue_poll();
end
Queue_offer(aad_P(i_FI + 1, :));
ad_Q = Queue_avg();
if (i_FI == 2 * iM)
ad_QMVAvg = ad_Q.^ (1 / 15);
ad_PBias = (ad_Q) * 0.9;
end
if (i_FI >= 2 * iM)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Bias Update
%
for i = 1 : iNumFilts,
if (ad_Q(i) > ad_PBias(i))
ad_PBias(i) = dLamda * ad_PBias(i) + (1 - dLamda) * ad_Q(i);
else
ad_PBias(i) = dLamda2 * ad_PBias(i) + (1 - dLamda2) * ad_Q(i);
end
end
for i = 1 : iNumFilts,
ad_Q_Out(i) = max(ad_Q(i) - ad_PBias(i), 0) ;
if (i_FI == 2 * iM)
ad_QMVAvg2(i) = 0.9 * ad_Q_Out(i);
ad_QMVAvg3(i) = ad_Q_Out(i);
ad_QMVPeak(i) = ad_Q_Out(i);
end
if (ad_Q_Out(i) > ad_QMVAvg2(i))
ad_QMVAvg2(i) = dLamda * ad_QMVAvg2(i) + (1 - dLamda) * ad_Q_Out(i);
else
ad_QMVAvg2(i) = dLamda2 * ad_QMVAvg2(i) + (1 - dLamda2) * ad_Q_Out(i);
end
dOrg = ad_Q_Out(i);
ad_QMVAvg3(i) = dLamda3 * ad_QMVAvg3(i);
if (ad_Q(i) < dFactor * ad_PBias(i))
ad_Q_Out(i) = ad_QMVAvg2(i);
else
if (ad_Q_Out(i) <= dDelta1 * ad_QMVAvg3(i))
ad_Q_Out(i) = dDelta2 * ad_QMVAvg3(i);
end
end
ad_QMVAvg3(i) = max(ad_QMVAvg3(i), dOrg);
ad_Q_Out(i) = max(ad_Q_Out(i), ad_QMVAvg2(i));
end
ad_w = ad_Q_Out ./ max(ad_Q, eps);
for i = 1 : iNumFilts,
if iSMType == 0
ad_w_sm(i) = mean(ad_w(max(i - iN, 1) : min(i + iN ,iNumFilts)));
elseif iSMType == 1
ad_w_sm(i) = exp(mean(log(ad_w(max(i - iN, 1) : min(i + iN ,iNumFilts)))));
elseif iSMType == 2
ad_w_sm(i) = mean((ad_w(max(i - iN, 1) : min(i + iN ,iNumFilts))).^(1/15))^15;
elseif iSMType == 3
ad_w_sm(i) = (mean( (ad_w(max(i - iN, 1) : min(i + iN ,iNumFilts))).^15 )) ^ (1 / 15);
end
end
aad_P_Out(i_FI_Out + 1, :) = ad_w_sm .* aad_P(i_FI - iM + 1, :);
adSumPower(i_FI_Out + 1) = sum(aad_P_Out(i_FI_Out + 1, :));
if adSumPower(i_FI_Out + 1) > dMean
dMean = dLamda_S * dMean + (1 - dLamda_S) * adSumPower(i_FI_Out + 1);
else
dMean = dLamda_L * dMean + (1 - dLamda_L) * adSumPower(i_FI_Out + 1);
end
aad_P_Out(i_FI_Out + 1, :) = aad_P_Out(i_FI_Out + 1, :) / (dMean) * MEAN_POWER;
i_FI_Out = i_FI_Out + 1;
end
else % if not SSF
adSumPower(i_FI + 1) = sum(aad_P(i_FI + 1, :));
if adSumPower(i_FI_Out + 1) > dMean
dMean = dLamda_S * dMean + (1 - dLamda_S) * adSumPower(i_FI_Out + 1);
else
dMean = dLamda_L * dMean + (1 - dLamda_L) * adSumPower(i_FI_Out + 1);
end
aad_P_Out(i_FI + 1, :) = aad_P(i_FI + 1, :) / (dMean) * MEAN_POWER;
end
i_FI = i_FI + 1;
end
%adSorted = sort(adSumPower);
%dMaxPower = adSorted(round(0.98 * length(adSumPower)));
%aad_P_Out = aad_P_Out / dMaxPower * 1e10;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Apply the nonlinearity
%
%dPowerCoeff
if bPowerLaw == 1
aadSpec = aad_P_Out .^ dPowerCoeff;
else
aadSpec = log(aad_P_Out + eps);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% DCT
%
aadDCT = dct(aadSpec')';
%aadDCT(:, numcoeffs+1:iNumFilts) = [];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% MVN
%
% for i = 1 : numcoeffs
% aadDCT( :, i ) = (aadDCT( : , i ) - mean(aadDCT( : , i)))/std(aadDCT(:,i));
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Temporal Derivatives
% calculate 1st derivative (velocity)
dt1 = deltacc(aadDCT', deltawindow);
% calculate 2nd derivative (acceleration)
dt2 = deltacc(dt1, accwindow);
% append dt1 and dt2 to mfcco
aadDCT = [aadDCT'; dt2];
% aadDCT = [aadDCT'; dt2];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Display
%
if bDisplay == 1
figure
aadSpec = idct(aadDCT', iNumFilts);
imagesc(aadSpec); axis xy;
end
aadDCT = aadDCT';
%{
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Writing the feature in Sphinx format
%
[iM, iN] = size(aadDCT);
iNumData = iM * iN;
fid = fopen(szOutFeatFileName, 'wb');
fwrite(fid, iNumData, 'int32');
iCount = fwrite(fid, aadDCT(:), 'float32');
fclose(fid);
%}
end
function dt = deltacc(input, winlen)
% calculates derivatives of a matrix, whose columns are feature vectors
tmp = 0;
for cnt = 1 : winlen
tmp = tmp + cnt*cnt;
end
nrm = 1 / (2*tmp);
dt = zeros(size(input));
rows = size(input,1);
cols = size(input,2);
for col = 1 : cols
for cnt = 1 : winlen
inx1 = col - cnt; inx2 = col + cnt;
if inx1 < 1; inx1 = 1; end
if inx2 > cols; inx2 = cols; end
dt(:, col) = dt(:, col) + (input(:, inx2) - input(:, inx1)) * cnt;
end
end
dt = dt * nrm;
end
function [] = Queue_offer(ad_x)
global Queue_aad_P;
global Queue_iHead;
global Queue_iTail;
global Queue_iWindow;
global Queue_iNumElem;
Queue_aad_P(Queue_iTail + 1, :) = ad_x;
Queue_iTail = mod(Queue_iTail + 1, Queue_iWindow);
Queue_iNumElem = Queue_iNumElem + 1;
if Queue_iNumElem > Queue_iWindow
error ('Queue overflow');
end
end
function [ad_x] = Queue_poll()
global Queue_aad_P;
global Queue_iHead;
global Queue_iTail;
global Queue_iWindow;
global Queue_iNumElem;
if Queue_iNumElem <= 0
error ('No elements');
end
ad_x = Queue_aad_P(Queue_iHead + 1, :);
Queue_iHead = mod(Queue_iHead + 1, Queue_iWindow);
Queue_iNumElem = Queue_iNumElem - 1;
end
function[adMean] = Queue_avg()
global Queue_aad_P;
global Queue_iHead;
global Queue_iTail;
global Queue_iWindow;
global Queue_iNumElem;
global iNumFilts;
adMean = zeros(1, iNumFilts); % Changed from 40 (number of filter banks)
iPos = Queue_iHead;
for i = 1 : Queue_iNumElem
adMean = adMean + Queue_aad_P(iPos + 1 ,: );
iPos = mod(iPos + 1, Queue_iWindow);
end
adMean = adMean / Queue_iNumElem;
end
function test(testdir, n, code)
for k = 1:n % read test sound file of each speaker
file = sprintf('%ss%d.wav', testdir, k);
[s, fs] = audioread(file);
%x = s + 0.01*randn(length(s),1); %AWGN Noise
%[SNR1] = snr(s);
%[SNR2] = snr(x) ;
v = PNCC(s, fs); % Compute MFCC's
distmin = inf;
k1 = 0;
for l = 1:length(code) % each trained codebook, compute distortion
d = disteu(v, code{l});
dist = sum(min(d,[],2)) / size(d,1);
if dist < distmin
distmin = dist;
k1 = l;
end
end
msg = sprintf('speaker%d -->> s%d', k, k1);
disp(msg);
end
function r = vqlbg(d,k)
%
% Inputs: d contains training data vectors (one per column)
% k is number of centroids required
e = .01;
r = mean(d, 2);
dpr = 10000;
for i = 1:log2(k)
r = [r*(1+e), r*(1-e)];
while (1 == 1)
z = interdists(d, r);
[m,ind] = min(z, [], 2);
t = 0;
for j = 1:2^i
r(:, j) = mean(d(:, find(ind == j)), 2);
x = interdists(d(:, find(ind == j)), r(:, j));
for q = 1:length(x)
t = t + x(q);
end
end
if (((dpr - t)/t) < e)
break;
else
dpr = t;
end
end
end %Output: r contains the result VQ codebook (k columns, one for each centroids)
I'm trying to implement an inversion counter in MATLAB using MergeSort, but for some reason, some of the answers are way off. For example, the number of inversions in [3, 4, 8, 1] is 3, but I'm getting 2. However, the array is being sorted correctly, so I think that the way that I'm counting the split inversions is the problem.
Here's my code:
function [count, sorted] = mergesort(A)
% count is the number of inversions; sorted is the sorted array.
n = length(A);
if n == 1
count = 0;
sorted = A;
else
m = ceil(n/2);
[count1, sorted1] = mergesort(A(1:m));
[count2, sorted2] = mergesort(A(m+1:n));
[crosscount, sorted] = merge(sorted1, sorted2);
count = count1 + count2 + crosscount;
end
end
function [crosscount, z] = merge(x, y)
n = length(x); m = length(y); z = zeros(1, n+m);
ix = 1;
iy = 1;
crosscount = 0;
for iz = 1:(n+m);
if ix > n
z(iz) = y(iy);
iy = iy + 1;
elseif iy > m
z(iz) = x(ix);
ix = ix + 1;
crosscount = crosscount + (n + 1 - ix); %this might be wrong
elseif x(ix) <= y(iy)
z(iz) = x(ix);
ix = ix + 1;
elseif x(ix) > y(iy)
z(iz) = y(iy);
iy = iy + 1;
crosscount = crosscount + 1; %im pretty sure this is right
end
end
end
Alright, so a friend helped me figure it out. My intuition was correct, but I needed help from an actual programmer to understand where I went wrong:
elseif iy > m
z(iz) = x(ix);
ix = ix + 1;
crosscount = crosscount + (n + 1 - ix); **%this might be wrong - this is actually wrong, since you don't want to count if you're traversing beyond the edge of the right array, and since the actual counting is happening in the last if case**
elseif x(ix) <= y(iy)
z(iz) = x(ix);
ix = ix + 1;
elseif x(ix) > y(iy)
z(iz) = y(iy);
iy = iy + 1;
crosscount = crosscount + **(n + 1 - ix)** **this is right because you're counting the remaining numbers in your left array that are also greater than y(iy) and just adding that to your count**
end
The value for the columns is 51 and 50, but when we use anything more than that the waitbar freezes due to index out of bound exception since its a large image and it wont fit in there, so the matlab dosent shut using the waitbar or anything. Need a way to shut the matlab when it encounters any error.
h = waitbar(0,'Progress','Name','Calculating Feature Heights...',...
'CreateCancelBtn','setappdata(gcbf,''canceling'',1)');
setappdata(h,'canceling',0); %initiallizes waitbar
s1 = size(A);
s2 = size(B);
if (s1(1) < s2(1))
n = s1(1);
else
n = s2(1); % ensures that bounds of i are within the bounds of both images
end
for i = 21:1:n % sets bounds for rows
if getappdata(h,'canceling') %checks for user pushing the cancel button on the waitbar
break
end
waitbar(i/(n-1),h) %progress bar
for j = 61:1:(m-60) % sets bounds for columns
if A(i,j) == A(i,j-1) %if adjacent pixels are the same,
Z(i,j) = Z(i,j-1); %they have the same height
disp(i,j) = disp(i,j-l);
elseif A((i), j) == B(i, j) && A(i,j) ~= A(i,j-1) && A(i,j-1) == B(i,j-1)
Z(i,j) = Z0; %condiions for pixels/features in the 'focal plane'
disp(i,j) = 0;
else
for l = 1:1:20 %sets scan range in rows for disparity
for k = 1:1:60 %sets disparity scan range in cols
if (A(i,j) == B(i-l, j-k) && B(i-l, j-k-1) == B(i-l, j-k))
Z(i,j) = Z(i-l,(j-k-1)); %allows for multipixel features
disp(i,j) = disp(i-l,(j-k-1));
break
elseif (A(i, j) == B(i-l, j-k) && B(i-l, j-k-1) ~= B(i-l, j-k))
xA = [i j];
xB = [i-l j-k];
d = xB-xA;
Z(i,j) = Z0 - (fl*shift)/sqrt((d(1)^2)+(d(2)^2));
disp(i,j) = sqrt((d(1)^2)+(d(2)^2));
break
elseif (A(i,j) == B(i-l, j+k) && B(i-l, j+k-1) == B(i-l, j+k))
Z(i,j) = Z(i-l,(j+k-1));
disp(i,j) = disp(i-l,(j+k-1));
break
elseif (A(i, j) == B(i-l, j+k) && B(i-l, j+k-1) ~= B(i-l, j+k))
xA = [i j];
xB = [i-l j+k];
d = xB-xA;
Z(i,j) = Z0 - (fl*shift)/sqrt((d(1)^2)+(d(2)^2));
disp(i,j) = sqrt((d(1)^2)+(d(2)^2));
break
else
continue
end
end
end
end
end
end
delete(h)
Use a try/catch block.
try
% whatever that might error
catch
delete(h)
end
i have to find the mid points of each lane in a binary iamge , i wrote a code but that is too long and its give error when ever i change the pic of road . i have to save the mid points of each lane and then by finding slope and intercept i have to draw lines on that binary image.
here is the code
x=imread('C:\users\guest\documents\matlab\1.png');
[q,r]= size(x);
n4=zeros(q,r);
midpoint= zeros (720,2); % Array to store midlle points of road lane.
% finding mid points of both lanes.
for n3=540:720
s=x(n3,:);
startIndex =1;
lastIndex =1280;
pixelsRow =s;
FirstWhiteStart=0; FirstWhiteEnd=0; SecondWhiteStart=0; SecondWhiteEnd=0;
for k=1:1280
if (pixelsRow(k) == 1)&&(FirstWhiteStart == 0)
FirstWhiteStart =k;
elseif (pixelsRow(k)==0)&&(FirstWhiteStart>0)&&(FirstWhiteEnd==0)
FirstWhiteEnd=k-1;
elseif (pixelsRow(k)== 1)&&(FirstWhiteEnd>0)&&(SecondWhiteStart==0)
SecondWhiteStart=k;
elseif (pixelsRow(k)==0)&&(SecondWhiteStart>0)&&(SecondWhiteEnd==0)
SecondWhiteEnd=k-1;
end
end
m1=(FirstWhiteStart + FirstWhiteEnd)./2; % first lanes middle point
m1r = round(m1);
if (m1r <= 1)
mp= sub2ind(size(midpoint),n3,1);
midpoint(mp) = 0;
elseif (m1r > 1)
indices = sub2ind(size(n4),n3,m1r);
n4(indices) = 1;
if (m1r >=640)
mp= sub2ind(size(midpoint),n3,2);
midpoint(mp) = m1r;
elseif (m1r <= 640)
mp= sub2ind(size(midpoint),n3,1);
midpoint(mp) = m1r;
end
end
m2=(SecondWhiteStart + SecondWhiteEnd+1)./2; % second lane middle point.
m2r = round(m2);
if (m2r <= 1)
indices = sub2ind(size(n4),n3,m2r);
n4(indices) = 0;
mp= sub2ind(size(midpoint),n3,1);
midpoint(mp) = 0;
elseif (m2r > 1)
indices = sub2ind(size(n4),n3,m2r);
n4(indices) = 1;
if (m2r >=640)
mp= sub2ind(size(midpoint),n3,2);
midpoint(mp) = m2r;
elseif (m2r <=640)
mp= sub2ind(size(midpoint),n3,1);
midpoint(mp) = m2r;
end
end
end
pairpoints = nchoosek([540:720],2);
var1 = zeros (16290,2); % array to store variables a and b of first lane.
var2 = zeros (16290,2); % array to stote variables a and b of second lane.
% calling middle points previously stored in array,putting in equation.
for n = 1 : 16290
x1 = pairpoints(n,1); %value of frst row
x2 = pairpoints(n,2); %value of 2nd row
y1 = midpoint (pairpoints(n,1), 1); %rows of midpoint matrix are specified from pairpoints location martix
y2 = midpoint (pairpoints(n,2), 1);
z1 = midpoint (pairpoints(n,1), 2);
z2 = midpoint (pairpoints(n,2), 2);
a1 = (y2 - y1) ./ (x2 - x1);
a2 = (z2 - z1) ./ (x2 - x1);
b1=(((x2)*(y1)) - (x1)*(y2)) ./ (x2 - x1);
b2=(((x2)*(z1)) - (x1)*(z2)) ./ (x2 - x1);
% variables a and b of first lane.
line = sub2ind(size(var1),n,1);
var1(line) = a1;
line = sub2ind(size(var1),n,2);
var1(line) = b1;
% variables A and b of second lane.
line = sub2ind(size(var2),n,1);
var2(line) = a2;
line = sub2ind(size(var2),n,2);
var2(line) = b2;
end
v11=round(var1);
v22=round(var2);
% eleminating zeros from array.
[i,j] = find(v11);
a1 = v11(i,1);
a1= a1(a1~=0);
b1 = v11(i,2);
b1= b1(b1~=0);
a11=median(a1)
b11=median(b1)
% eleminating zeros from array.
[k,l] = find(v22);
row = i;
a2 = v22(k,1);
a2= a2(a2~=0);
b2 = v22(k,2);
b2= b2(b2~=0);
a22=median(a2)
b22=median(b2)
%assign variables
lin=zeros(720,2);
% implementation of final line equation.
for x1 = 1:720
% equation becomes (w = eh + f) as actual was (y = ax + b)
y1 = (a11 * x1) + b11;
y2 = (a22 * x1) + b22;
col = sub2ind( size(lin),x1,1); % equation for first lane.
lin(col)= y1;
col = sub2ind( size(lin),x1,2); % equation for second lane.
lin(col)= y2;
end
array=lin;
r= 1:720;
c= 1:1280;
x(r,c)= 0;
imshow(x);
imwrite(x,'a.png');
image =imread('C:\users\guest\documents\matlab\a.png');
for r1 = 1:720
for c = 1:2;
if array(r1,c) < 0;
lin(r1,c) = abs (array(r1,c));
image(r1,lin(r1,c))= 0;
elseif array(r1,c) > 0;
image(r1,lin(r1,c))= 1;
end
end
end
imshow(image)
I am Trying to convert a MATLAB code to C++ using MATLAB coder but this error apears:
Error indenting generated C code
The error points to the name of the function itself and has no more explanations in it. can someone tell me what is this error?
here is the function i want to conver:
function [Report_Clustered,ClusterCounter_new]=InitClusterGenerator_test(Report_In,~,FreqEpsilon,DegreeEpsilon,~,ClusterCounter_old, BlockCount, Report_old)
Report_M = zeros(size(Report_In,1),size(Report_In,2),4);
for i=1:size(Report_In,1)
for j=1:size(Report_In,2)
Report_M(i,j,1)=Report_In(i,j,1);
Report_M(i,j,2)=Report_In(i,j,2);
Report_M(i,j,3)=0; % Cluster number that the point belongs to.
Report_M(i,j,4)=0;
Report_In{i,j}
end
end
ClusterCounter = 0;
for i=1:size(Report_M,1)
for j=1:size(Report_M,2)
if (Report_M(i,j,3) == 0)
ClusterCounter = ClusterCounter + 1;
Report_M(i,j,3) = ClusterCounter;
for ii=1:size(Report_M,1)
for jj=1:size(Report_M,2)
if (Report_M(ii,jj,3) == 0)
if (abs(Report_M(i,j,1)-Report_M(ii,jj,1))<FreqEpsilon &&...
(abs(Report_M(i,j,2)-Report_M(ii,jj,2)) <DegreeEpsilon ||...
abs(-360 + Report_M(i,j,2)-Report_M(ii,jj,2)) <DegreeEpsilon ||...
abs(360 + Report_M(i,j,2)-Report_M(ii,jj,2)) <DegreeEpsilon))
Report_M(ii,jj,3) = ClusterCounter;
end
end
end
end
end
end
end
if (BlockCount> 20 && ClusterCounter<4)
warning = 1;
end
ClusterCounter_new = ClusterCounter;
%clear Report_new;
flag = 0;
Report_new = zeros(ClusterCounter,size (Report_M, 2),4);
index = zeros(1, ClusterCounter_new);
for i = 1: size (Report_M, 1)
for j = 1: size (Report_M, 2)
for k = 1: ClusterCounter_new
if (Report_M(i,j,3) == k)
index(1,k) = index(1,k) + 1;
Report_new(k,index(1,k), 1:3) = Report_M(i,j,1:3);
flag = flag + 1;
end
end
end
end
for j = 1: size (Report_new, 2)
for i = 1: size (Report_new, 1)
if (Report_new(i,j,1) == 0)
Report_new(i,j,1:3) = Report_new(i,1,1:3);
end
end
end
%Report_new = Report;
MedoidF_old = zeros(1, size(Report_old,1));
MedoidA_old = zeros(1, size(Report_old,1));
for i=1:size(Report_old,1)
SumF = 0;
SumA = 0;
MinAngle = 361;
MaxAngle = -1;
for j=1:size(Report_old,2)
SumF = SumF + Report_old(i,j,1);
SumA = SumA + Report_old(i,j,2);
if Report_old(i,j,2) > MaxAngle
MaxAngle = Report_old(i,j,2);
elseif Report_old(i,j,2) < MinAngle
MinAngle = Report_old(i,j,2);
end
end
MedoidF_old(1, i) = SumF/size(Report_old,2);
if (MaxAngle - MinAngle) > 350
MedoidA_old(1, i) = 0;
else
MedoidA_old(1, i) = SumA/size(Report_old,2);
end
end
MedoidF_new = zeros(1, size(Report_new,1));
MedoidA_new = zeros(1, size(Report_new,1));
for i=1:size(Report_new,1)
SumF = 0;
SumA = 0;
MinAngle = 361;
MaxAngle = -1;
for j=1:size(Report_new,2)
SumF = SumF + Report_new(i,j,1);
SumA = SumA + Report_new(i,j,2);
if Report_new(i,j,2) > MaxAngle
MaxAngle = Report_new(i,j,2);
elseif Report_new(i,j,2) < MinAngle
MinAngle = Report_new(i,j,2);
end
end
MedoidF_new(1, i) = SumF/size(Report_new,2);
if (MaxAngle - MinAngle) > 350
MedoidA_new(1, i) = 0;
else
MedoidA_new(1, i) = SumA/size(Report_new,2);
end
end
TempCluster = zeros(1, size(Report_new, 1));
CurrentCluster = ClusterCounter_old;
for i = 1: 1: size(Report_new,1)
for j = 1: 1: size(Report_old,1)
if (abs(MedoidF_old(1,j)-MedoidF_new(1,i))<FreqEpsilon &&...
(abs(MedoidA_old(1,j)-MedoidA_new(1,i))<DegreeEpsilon ||...
abs(360 + MedoidA_old(1,j)-MedoidA_new(1,i))<DegreeEpsilon ||...
abs(-360 + MedoidA_old(1,j)-MedoidA_new(1,i))<DegreeEpsilon)) %%if the new cluster is the rest of an old cluster use the old one's index for it
TempCluster(1,i) = Report_old(j,1,3);
end
end
%%this part is for seperating the clusters which where in the collision state in the past time
if (TempCluster(1,i)>0) %%if the new cluster is one of the old ones the index should be set
for j = 1:1:size(Report_new, 2)
Report_new(i,j,3) = TempCluster(1,i);
Report_new(i,j,4) = 1;% Alive
end
else %%first search if the new cluster is a part of a newly found cluster found before this one
for j = 1: 1: i-1
if (abs(MedoidF_new(1,j)-MedoidF_new(1,i))<FreqEpsilon &&...
(abs(MedoidA_new(1,j)-MedoidA_new(1,i))<DegreeEpsilon ||...
abs(360 + MedoidA_new(1,j)-MedoidA_new(1,i))<DegreeEpsilon ||...
abs(-360 + MedoidA_new(1,j)-MedoidA_new(1,i))<DegreeEpsilon)) %%if the new cluster is the rest of an old cluster use the old one's index for it
TempCluster(1,i) = Report_new(j,1,3);
end
end
end
if (TempCluster(1,i)>0) %%if the new cluster is one of the old ones the index should be set
for j = 1:1:size(Report_new, 2)
Report_new(i,j,3) = TempCluster(1,i);
Report_new(i,j,4) = 1;% Alive
end
else %%new cluster is just began so it needs a new index
CurrentCluster = CurrentCluster + 1;
ClusterCounter_new = CurrentCluster;
TempCluster(1,i) = CurrentCluster;
for j = 1:1:size(Report_new, 2)
Report_new(i,j,3) = TempCluster(1,i);
Report_new(i,j,4) = 1; % Alive
end
end
end
NewClusters = zeros(1, size (Report_new, 1));
for i = 1: size(Report_new, 1)
NewClusters (1,i) = Report_new(i,1,3);
end
OldClusters = zeros(1, size (Report_old, 1));
OldClustersLine = zeros(1, size (Report_old, 1));
for i = 1: size(Report_old, 1)
OldClusters (1,i) = Report_old(i,1,3);
OldClustersLine (1, i) = i;
end
NumberOfDead = 0;
%clear AddDead;
AddDead = zeros (16,size(Report_new, 2),4);
if (BlockCount>10)
for i = 1: size (OldClusters, 2)
IsDead = 1;
for j = 1: size (NewClusters, 2)
if OldClusters(1, i) == NewClusters(1,j)
IsDead = 0;
end
end
if (IsDead == 1)
NumberOfDead = NumberOfDead + 1;
%clear TempLine;
TempLine = zeros(1, size(Report_old,2), 4);
TempLine(1,:,1:3) = Report_old(OldClustersLine(1, i),:,1:3);
for k= 1: size(TempLine, 2)
TempLine(1,k,4) = 0; % Dead
end
TempSize = size(TempLine, 2);
Thresh = size(Report_new, 2);
if (TempSize >= Thresh)
AddDead (NumberOfDead, 1:Thresh, 1:4) = TempLine(1,1:Thresh, 1:4);
else
for l = 1: Thresh-TempSize
TempLine(1, TempSize+l, 1:4) = TempLine(1, TempSize, 1:4);
end
AddDead (NumberOfDead, 1:Thresh, 1:4) = TempLine(1,1:Thresh, 1:4);
end
end
end
xR = size (Report_new,1);
if (NumberOfDead == 0)
Report_Clustered = zeros (size(Report_new,1),size(Report_new,2),size(Report_new,3));
else
Report_Clustered = zeros (size(Report_new,1) + NumberOfDead,size(Report_new,2),size(Report_new,3));
end
Report_Clustered (1:size(Report_new,1), :, :) = Report_new(:,:,:);
for i = 1: NumberOfDead
Report_Clustered(xR + i, :) = AddDead(i, :);
end
end
and I'm using matlab 2012a
Tnx.
From what you've said in the comments, it appears that you simply need to call
clear functions
from the command line before recompiling the function to allow Matlab to overwrite the files. See this Matlab forum or the documentation for clear for more detail.