I am trying to write a function that thresholds a grey-level image F and a threshold value t (0 ≤ t ≤ 255) such that r = 0 for r < t and r = 255 otherwise.
I have tried to implement this, but imshow(r) does not produce an output.
function f = imgThreshold(img, t)
f = img;
if (f < t)
f = 0;
else
f = 1;
end
img = imread('https://i.stack.imgur.com/kP0u2.png');
t = 20;
r = imgThreshold(img, t);
imshow(r);
This should threshold this image. However, it does not do so. What am I doing wrong?
Best would be to use logical indexing:
f(f<t)=0; % set all elements of f<t to 0
f(~(f==0))=1; % Set all elements where f is not 0 (i.e. the rest) to 1
f<t nicely produces a logical matrix adhering to the condition, but subsequently you do either f=1 or f=0, meaning that you set the entirety of f to be a scalar (one or zero), which of course just plots a black or white square. Instead, use the logical matrix as indexing operation into the matrix itself, then assigning the desired value to each true entry, like above.
Also a function definition either goes in its own file, or on the bottom of the script. Thus either you save the function as imgThreshold.m and leave the rest for the script, or first call the script and place function f = imgThreshold(img, t) etc after the call to imshow
Related
I'm new to Matlab and want to write a program that chooses the value of a parameter (P) to minimize the difference between two vectors, where each vector is a variable in a dataframe. The first vector (call it A) is a predetermined vector of 1s and 0s, and the second vector (call it B) has each of its entries determined as an indicator function that depends on the value of the parameter P and other variables in the dataframe. For instance, let C be a third variable in the dataset, so
A = [1, 0, 0, 1, 0]
B = [x, y, z, u, v]
where x = 1 if (C[1]+10)^0.5 - P > (C[1])^0.5 and otherwise x = 0, and similarly, y = 1 if (C[2]+10)^0.5 - P > (C[2])^0.5 and otherwise y = 0, and so on.
I'm not really sure where to start with the code, except that it might be useful to use the fminsearch command. Any suggestions?
Edit: I changed the above by raising to a power, which is closer to the actual example that I have. I'm also providing a complete example in response to a comment:
Let A be as above, and let C = [10, 1, 100, 1000, 1]. Then my goal with the Matlab code would be to choose a value of P to minimize the differences between the coordinates of the vectors A and B, where B[1] = 1 if (10+10)^0.5 - P > (10)^0.5 and otherwise B[1] = 0, and similarly B[2] = 1 if (1+10)^0.5 - P > (1)^0.5 and otherwise B[2] = 0, etc. So I want to choose P to maximize the likelihood that A[1] = B[1], A[2] = B[2], etc.
I have the following setup in Matlab, where ds is the name of my dataset:
ds.B = zeros(size(ds,1),1); % empty vector to fill
for i = 1:size(ds,1)
if ((ds.C(i) + 10)^(0.5) - P > (ds.C(i))^(0.5))
ds.B(i) = 1;
else
ds.B(i) = 0;
end
end
Now I want to choose the value of P to minimize the difference between A and B. How can I do this?
EDIT: I'm also wondering how to do this when the inequality is something like (C[i]+10)^0.5 - P*D[i] > (C[i])^0.5, where D is another variable in my dataset. Now P is a scalar being multiplied rather than just added. This seems more complicated since I can't solve for P exactly. How can I solve the problem in this case?
EDIT 1: It seems fminbnd() isn't optimal, likely due to the stairstep nature of the indicator function. I've updated to test the midpoints of all the regions between indicator function flips, plus endpoints.
EDIT 2: Updated to include dataset D as a coefficient of P.
If you can package your distance calculation up in a single function based on P, you can then search for its minimum.
arraySize = 1000;
ds.A = double(rand([arraySize,1]) > 0.5);
ds.C = rand(size(ds.A));
ds.D = rand(size(ds.A));
B = #(P)double((ds.C+10).^0.5 - P.*ds.D > ds.C.^0.5);
costFcn = #(P)sqrt(sum((ds.A-B(P)).^2));
% Solving the equation (C+10)^0.5 - P*D = C^0.5 for P, and sorting the results
BCrossingPoints = sort(((ds.C+10).^0.5-ds.C.^0.5)./ds.D);
% Taking the average of each crossing point with its neighbors
BMidpoints = (BCrossingPoints(1:end-1)+BCrossingPoints(2:end))/2;
% Appending endpoints onto the midpoints
PsToTest = [BCrossingPoints(1)-0.1; BMidpoints; BCrossingPoints(end)+0.1];
% Calculate the distance from A to B at each P to test
costResult = arrayfun(costFcn,PsToTest);
% Find the minimum cost
[~,lowestCostIndex] = min(costResult);
% Find the optimum P
optimumP = PsToTest(lowestCostIndex);
ds.B = B(optimumP);
semilogx(PsToTest,costResult)
xlabel('P')
ylabel('Distance from A to B')
1.- x is assumed positive real only, because with x<0 then complex values show up.
Since no comment is made in the question it seems reasonable to assume x real and x>0 only.
As requested, P 'the parameter' a scalar, P only has 2 significant states >0 or <0, let's see how is this:
2.- The following lines generate kind-of random A and C.
Then a sweep of p is carried out and distances d1 and d2 are calculated.
d1 is euclidean distance and d2 is the absolute of the difference between A and and B converting both from binary to decimal:
N=10
% A=[1 0 0 1 0]
A=randi([0 1],1,N);
% C=[10 1 1e2 1e3 1]
C=randi([0 1e3],1,N)
p=[-1e4:1:1e4]; % parameter to optimize
B=zeros(1,numel(A));
d1=zeros(1,numel(p)); % euclidean distance
d2=zeros(1,numel(p)); % difference distance
for k1=1:1:numel(p)
B=(C+10).^.5-p(k1)>C.^.5;
d1(k1)=(sum((B-A).^2))^.5;
d2(k1)=abs(sum(A.*2.^[numel(A)-1:-1:0])-sum(B.*2.^[numel(A)-1:-1:0]));
end
figure;
plot(p,d1)
grid on
xlabel('p');title('d1')
figure
plot(p,d2)
grid on
xlabel('p');title('d2')
The only degree of freedom to optimise seems to be the sign of P regardless of |P| value.
3.- f(p,x) has either no root, or just one root, depending upon p
The threshold funtion is
if f(x)>0 then B(k)==1 else B(k)==0
this is
f(p,x)=(x+10)^.5-p-x^.5
Now
(x+10).^.5-p>x.^.5 is same as (x+10).^.5-x.^.5>p
There's a range of p that keeps f(p,x)=0 without any (real) root.
For the particular case p=0 then (x+10).^.5 and x.^.5 do not intersect (until Inf reached = there's no intersection)
figure;plot(x,(x+10).^.5,x,x.^.5);grid on
[![enter image description here][3]][3]
y2=diff((x+10).^.5-x.^.5)
figure;plot(x(2:end),y2);
grid on;xlabel('x')
title('y2=diff((x+10).^.5-x.^.5)')
[![enter image description here][3]][3]
% 005
This means the condition f(x)>0 is always true holding all bits of B=1. With B=1 then d(A,B) turns into d(A,1), a constant.
However, for a certain value of p then there's one root and f(x)>0 is always false keeping all bits of B=0.
In this case d(A,B) the cost function turns into d(A,0) and this is A itself.
4.- P as a vector
The optimization gains in degrees of freedom if instead of P scalar, P is considered as vector.
For a given x there's a value of p that switches B(k) from 0 to 1.
Any value of p below such threshold keeps B(k)=0.
Equivalently, inverting f(x) :
g(p)=(10-p^2)^2/(4*p^2)>x
Values of x below this threshold bring B closer to A because for each element of B it's flipped to the element value of A.
Therefore, it's convenient to consider P as a vector, not a ascalar, and :
For all, or as many (as possible) elements of C to meet c(k)<(10-p^2)^2/(4*p^2) in order to get C=A or
minimize d(A,C)
5.- roots of f(p,x)
syms t positive
p=[-1000:.1:1000];
zp=NaN*ones(1,numel(p));
sol=zeros(1,numel(p));
for k1=1:1:numel(p)
p(k1)
eq1=(t+10)^.5-p(k1)-t^.5-p(k1)==0;
s1=solve(eq1,t);
if ~isempty(s1)
zp(k1)=s1;
end
end
nzp=~isnan(zp);
zp(nzp)
returns
=
620.0100 151.2900 64.5344 34.2225 20.2500 12.7211
8.2451 5.4056 3.5260 2.2500 1.3753 0.7803
0.3882 0.1488 0.0278
clear
n=45; // widht
m=23; // length
// total 990 blocks m*n
a=-2; b=1; // x-limits
c=2; d=4; // y-limits
f=#(x,y) 4.0*x.^3.*y+0.7.*x.^2.*y+2.5.*x+0.2.*y; //function
x=linspace(a,b,n);
y=linspace(c,d,m);
h1=(b-a)/n
h2=(d-c)/m
dA=h1*h2
[X,Y]=meshgrid(x,y); //did a meshgrid cause q wouldnt accept different index bounds without meshgriding.
q=sum((sum(dA*f(X,Y))))
Ive been using a formula for double riemanns at this link.
https://activecalculus.org/multi/S-11-1-Double-Integrals-Rectangles.html
these are the answers
1.I=81.3000.
2.I-left=-87.4287 //-84.5523 my result
3.I-Right=-75.1072
I can't see what im doing wrong. I need input from somebody.
I would debug the integration scheme with a dummy constant function
f = #(x,y) 1.0*ones(size(x))
The result should be the exact total area (b-a)*(d-c) = 6, but your code gives 6.415.
The problem is that you are integrating outside the domain with those limits. You should stop the domain discretization one step before in each dimension:
h1 = (b-a)/n
h2 = (d-c)/m
x = linspace(a, b - h1, n);
y = linspace(c, d - h2, m);
This will give you the expected area for the dummy function:
q = 6.0000
and for the real function, evaluating at the top-left corner, you get:
q = -87.482
You didn't do anything wrong with your code. The difference comes from the resolution of x and y used in you code, since they are not sufficiently high.
For example, when you have n = 5000 and m = 5000
q = sum((sum(dA*f(X,Y)))); % your Riemann sum approach
l = integral2(f,a,b,c,d); % using built-in function for double integral to verify the value of `q`
you will see that the results are very close now
q = -81.329
l = -81.300
I'm trying to plot the Yukawa Potential in Matlab and I want to have my program go through user inputs for the values alpha (called alph in my program) and l. The values I need to use are 0.1, 0.2 and 0.3 for alpha with values of 0, 1 and 2 of l for each value of alpha. I know I could set up a loop for this but it doesn't have to be pretty and I want to test the values one at a time. Anyway I keep getting an error after I input the values for alpha, the error I keep getting is in my function, saying that I don't have enough input arguments. The output should be the T matrix, the Hamiltonian matrix and a plot of the first 10 eigenfunctions.
I've tried going in and simply defining alpha as the numbers I want to look at and the program works fine with displaying the output I'm looking for. I just want to be able to change the values for alpha without having to change the program itself. I haven't had any problems with the l inputs.
r = linspace(0.05,19.95,1999)
n = 1999
dr = 0.05
a = full(gallery("tridiag",n,1,-2,1))
T = -0.5*a/(dr^2)
l = input('Input a value for l.')
alph = input('Input a value for alpha.')
v = arrayfun(#(r) yuk_pot(r,l),r);
V = diag(v)
H = T + V
[O,D] = eig(H);
plot(r,O(:,1),r,O(:,2),r,O(:,3),r,O(:,4),r,O(:,5),r,O(:,6),r,O(:,7),r,O(:,8),r,O(:,9),r,O(:,10))
function v = yuk_pot(r,alph,l)
v = (-exp(-alph*r)/r) + 0.5*(l*(l+1)/(r^2));
end
your function function v = yuk_pot(r,alph,l) has 3 input arguments.
you call it with 2 arguments (r and l)
v = arrayfun(#(r) yuk_pot(r,l),r);
what about the second alph argument?
I've noticed that matlab builtin functions can handle either scalar or vector parameters. Example:
sin(pi/2)
ans =
1
sin([0:pi/5:pi])
ans =
0 0.5878 0.9511 0.9511 0.5878 0.0000
If I write my own function, for example, a piecewise periodic function:
function v = foo(t)
t = mod( t, 2 ) ;
if ( t < 0.1 )
v = 0 ;
elseif ( t < 0.2 )
v = 10 * t - 1 ;
else
v = 1 ;
end
I can call this on individual values:
[foo(0.1) foo(0.15) foo(0.2)]
ans =
0 0.5000 1.0000
however, if the input for the function is a vector, it is not auto-vectorized like the builtin function:
foo([0.1:0.05:0.2])
ans =
1
Is there a syntax that can be used in the definition of the function that indicates that if a vector is provided, a vector should be produced? Or do builtin functions like sin, cos, ... check for the types of their input, and if the input is a vector produce the same result?
You need to change your syntax slightly to be able to handle data of any size. I typically use logical filters to vectorise if-statements, as you're trying to do:
function v = foo(t)
v = zeros(size(t));
t = mod( t, 2 ) ;
filt1 = t<0.1;
filt2 = ~filt1 & t<0.2;
filt3 = ~filt1 & ~filt2;
v(filt1) = 0;
v(filt2) = 10*t(filt2)-1;
v(filt3) = 1;
In this code, we've got three logical filters. The first picks out all elements such that t<0.1. The second picks out all of the elements such that t<0.2 that weren't in the first filter. The final filter gets everything else.
We then use this to set the vector v. We set every element of v that matches the first filter to 0. We set everything in v which matches the second filter to 10*t-1. We set every element of v which matches the third filter to 1.
For a more comprehensive coverage of vectorisation, check the MATLAB help page on it.
A simple approach that minimizes the number of operations is:
function v = foo(t)
t = mod(t, 2);
v = ones(size(t)) .* (t > 0.1);
v(t < 0.2) = 10*t(t < 0.2) - 1;
end
If the vectors are large, it might be faster to do ind = t < 0.2, and use that in the last line. That way you only search through the array once. Also, the multiplication might be substituted by an extra line with logical indices.
I repeatedly hit the same problem, thus I was looking for a more generic solution and came up with this:
%your function definition
c={#(t)(mod(t,2))<0.1,0,...
#(t)(mod(t,2))<0.2,#(t)(10 * t - 1),...
true,1};
%call pw which returns the function
foo=pw(c{:});
%example evaluation
foo([0.1:0.05:0.2])
Now the code for pw
function f=pw(varargin)
for ip=1:numel(varargin)
switch class(varargin{ip})
case {'double','logical'}
varargin{ip}=#(x)(repmat(varargin{ip},size(x)));
case 'function_handle'
%do nothing
otherwise
error('wrong input class')
end
end
c=struct('cnd',varargin(1:2:end),'fcn',varargin(2:2:end));
f=#(x)pweval(x,c);
end
function y=pweval(x,p)
todo=true(size(x));
y=x.*0;
for segment=1:numel(p)
mask=todo;
mask(mask)=logical(p(segment).cnd(x(mask)));
y(mask)=p(segment).fcn(x(mask));
todo(mask)=false;
end
assert(~any(todo));
end
So I am trying to go through a for loop that will increment .1 every time and will do this until the another variable h is less than or equal to zero. Then I am suppose to graph this h variable along another variable x. The code that I wrote looks like this:
O = 20;
v = 200;
g = 32.2;
for t = 0:.1:12
% Calculate the height
h(t) = (v)*(t)*(sin(O))-(1/2)*(g)*(t^2);
% Calculate the horizontal location
x(t) = (v)*(t)*cos(O);
if t > 0 && h <= 0
break
end
end
The Error that I keep getting when running this code says "Attempted to access h(0); index must be a positive integer or logical." I don't understand what exactly is going on in order for this to happen. So my question is why is this happening and is there a way I can solve it, Thank you in advance.
You're using t as your loop variable as well as your indexing variable. This doesn't work, because you'll try to access h(0), h(0.1), h(0.2), etc, which doesn't make sense. As the error says, you can only access variables using integers. You could replace your code with the following:
t = 0:0.1:12;
for i = 1:length(t)
% use t(i) instead of t now
end
I will also point out that you don't need to use a for loop to do this. MATLAB is optimised for acting on matrices (and vectors), and will in general run faster on vectorised functions rather than for loops. For instance, your equation for h could be replaced with the following:
O = 20;
v = 200;
g = 32.2;
t = 0:0.1:12;
h = v * t * sin(O) - 0.5 * g * t.^2;
The only difference is that you have to use the element-wise square (.^2) rather than the normal square (^2). This means that MATLAB will square each element of the vector t, rather than multiplying the vector t by itself.
In short:
As the error says, t needs to be an integer or logical.
But your t is t=0:0.1:12, therefore a decimal value.
O = 20;
v = 200;
g = 32.2;
for t = 0:.1:12
% Calculate the height
idx_t = 1:numel(t);
h(idx_t) = (v)*(t)*(sin(O))-(1/2)*(g)*(t^2);
% Calculate the horizontal location
x(idx_t) = (v)*(t)*cos(O);
if t > 0 && h <= 0
break
end
end
Look this question's answer for more options: Subscript indices must either be real positive integers or logical error