How can I pass output of a function in another function

I have 2 function and one script.

The script basically calls 'BallDetection' function and stores the output in 3 matrices as Zvec,Avec,Dvec.Now I want to pass the values of Avec and Dvec in a second function called MyUpdatedEKFFun.

So how do I do that is there any other method you can suggest will be very helpful.

Thanks.

[Alex Mcaulley]

What's wrong with:

[Zvec,Avec,Dvec] = BallDetection(inputs);
[outputs] = MyUpdatedEKFFun(Avec,Dvec);

[Tipu Sultan]

I am getting only 4 values as output from the function 'MyUpdatedEKFFun'.

R =

   21.0802    0.6929   22.7885   17.8350


A =

   -1.8633   -6.0621   14.2185   23.8241

It should have 10 outputs.

[Alex Mcaulley]

Please, attach the functions and the script.

[Tipu Sultan]

The script is as follows:

D = 'C:\Users\Tipu_Standard\Desktop\Khepera_updated_pics';
S = dir(fullfile(D,'original*.jpg')); % pattern to match filenames.
N = numel(S);%Calculate number of elements in S
%C = cell(1,N,N);%Create ecll array here 1 by N cell array of of empty matrices
Zvec = zeros(1,10);
Dvec = zeros(1,10);
Avec = zeros(1,10);
for k = 1:N
    F = fullfile(D,S(k).name);
    I = imread(F);
    %C{:,:,k} = BallDetection(I);
    %[Zvec(k),Dvec(k),Avec(k)] = fun1(I);
    [Zvec(k),Dvec(k),Avec(k)] = BallDetection(I);
end

BallDetection function is as follows:

function [Z,range,Angle] = BallDetection(I)
%function [z,distance,angle] = fun1(I)
%function [Zvec,Dvec,Avec] = fun1(I)
    B=rgb2gray(I);
    A=adapthisteq(B);
    % Display the original image
    figure,imshow(A);
    distance=0;
    angle=0;
    rmin=8;
    rmax=30;
    % Find all the circles with radius >= 8 and radius <= 30
    [centersdark,radiidark] = imfindcircles(A,[rmin rmax],'Objectpolarity','dark','Sensitivity',0.85);
    k=numel(radiidark);
    viscircles(centersdark,radiidark ,'EdgeColor','b');          
    if(k==0)
        z=0;
    else
        z=1;
        p=2*radiidark;
        f=2.1;
        w=62;
        h=144;
        s=2.88;
        %distance=(f*w*h)/(p*s);
        distance=(f*w*h)/(p*s);
        a=(192/2)-centersdark(1); 
        b=144-centersdark(2);
        %angle=atan2(a,b)*(180/pi);
        angle=atan2(a,b)*(180/pi);
    end
    %tri = [z,distance,angle]
    Z = z;
    range = distance;
    Angle = angle;
end

MyUpdatedEKFFun is as follows:

function [X,Y,R,A] = MyUpdatedEKFFun(Avec,Dvec,t)
%% Function to implement Extended Kalman Filter
% Initialize S,Esimator Vector,Pre allocate memory to diferent partial derivatives

S = [100 0 0 0 0 0;
    0 100 0 0 0 0;
    0 0 100 0 0 0;
    0 0 0 100 0 0;
    0 0 0 0 100 0;
    0 0 0 0 0 100];

prev_S = S; 

Big_lambda = eye(3);

a=0;
b=0;
c=0;
p=0;
q=0;
s=0;

est_vec=[ a ; b; c; p; q; s];

%theta = [ 31 24 18] ;
%t= [ 1 1.1 1.2 ];
%r= [330 364 379];

%% Initialize angles,range,time
% Intialize angles,range and time from the input given to the function when
% called the function

%theta = [theta1 theta2 theta3 theta4 theta5 theta6 theta7 theta8 ]; % theta defines the angles measured from the 'BallDetection' function
%t= [ t1 t2 t3 t4 t5 t6 t7 t8 ]; % t defines the time of each image captured
%r= [r1 r2 r3 r4 r5 r6 r7 r8 ]; % r defines distance measured from the 'BallDetection' function

theta=Avec;
tm=t;
r=Dvec;
%% Input Measurement vector
% Create a matrix with the angles,range,time as input measurement vector

x = [ r; theta ; tm]; % Input Measurement vector
%dif_x=zeros(2,3);
%% Preallocation various matrices
% Preallocate memory and create measurement vector

dif_a = arrayfun(@(t) [t^2 t 1 0 0 0; 0 0 0 t^2 t 1],t,'UniformOutput',false); % New Measurement vector

%time=2;
time=1.1;

dif_x = zeros(2, 3, 3);  % Pre-allocation
w = zeros(2, 1, 3);  % Pre-allocation
%pred_x = zeros(3, 3, 3);
W = zeros(2, 2); % Pre-allocation
y = zeros(2, 1, 3); % Pre-allocation
K = zeros(6, 2, 3); % Pre-allocation

%% Time update (Prediction)
% Repeat until the predefined condition is met and update estimator vector,Kalman gain,'S'.
for i=1:9
    %% Calculte various matrices for a given iteration
    % Calculate different partial dervatives,noise,kalman gain,measurement
    % vector,new estimator vector
    
     dif_x(:, :, i) = [-cos(theta(i)), r(i).*sin(theta(i)),  2*a.*t(i)+b;...
                       -sin(theta(i)), -r(i).*cos(theta(i)), 2*p.*t(i)+q];

     W(:,:,i) = dif_x(:,:,i) * Big_lambda(i) * dif_x(:,:,i)';
     
     pred_x = x(:,i) + [ 21.15; 0.06; 0.08] % Predicted Input Measurement Vector
     
     w(:,:,i) = dif_x(:,:,i) * (x(:,i) - pred_x); % Measurement noise vector
     
     y(:,:,i) = dif_a{i} * est_vec + w(:,:,i); % New Measurement vector
     
     K(:,:,i) = prev_S*dif_a{i}'/((W(:,:,i) + dif_a{i}*prev_S*dif_a{i}')); % Kalman Gain
     %% Update 
     % Update until a precondition is met
     
     S_new = (eye(6) - (K(:,:,i) * dif_a{i})) * prev_S; % Upadte 'S'
          
     prev_S = S_new; 
     
     est_vec_new = est_vec +  K(:,:,i)*(y(:,:,i)-dif_a{i}*est_vec); % Measue new estimator vector
     
     cond = abs(est_vec_new - est_vec);
     
     if  cond < 0.003 % Check the condition
         break 
     end
     
     est_vec = est_vec_new; % Update the estimator vector
     
     a=est_vec(1,1); % Time rate of change of velocity
     b=est_vec(2,1); % Velocity corresponding direction
     c=est_vec(3,1); % Initial displacement with respect to S5
     p=est_vec(4,1); % Time rate of change of velocity
     q=est_vec(5,1); % Velocity corresponding direction
     s=est_vec(6,1); % Initial displacement with respect to S3
     
     %X(i) = a*time.^2+b*time+c; % To calculate x co-ordinate for t>=3
     %Y(i) = p*time.^2+q*time+s; % To calculate y co-ordinate for t>=3
     
     %figure,plot(t,meas_equa1)
     %new_t=[t,time];
%figure,plot(r_cosTheta,r_sineTheta)
%% Co-ordinate determenation
% Calculate X,Y co-ordinates for t>=time

X(i) = a*time.^2+b*time+c % To calculate x co-ordinate for t>=time
Y(i) = p*time.^2+q*time+s % To calculate y co-ordinate for t>=time
R(i) = (X(i)^2+Y(i)^2) % To calculate next Range
A(i) = pred_x(2,1) + atan(Y(i)/X(i)) - 5 % To calculate new theta
time = time + 0.1
%plot(pred_x,R)
%hold on
plot(theta,r,'--bo')
hold on
plot(A,R,'--ro')
end

end

Thanks in advance.