Hi Brayden, I am going to give you an example of how this could be done.
In order to do so, let me create a matrix z that contains peaks and valleys.
[x,y] = meshgrid(linspace(-3,3,100));
z = peaks(linspace(-3,3,100));
surf(x,y,z)
Here, z is 100x100.
Now, let's code a nested loop that goes through all the elements and finds out if they are either a peak or a valley.
peak_row = [];
peak_col = [];
valley_row = [];
valley_col = [];
for row = 2:size(z,1)-1
for col = 2:size(z,2)-1
local_mat = z(row-1:row+1,col-1:col+1);
local_mat = local_mat(:);
local_mat(5) = [];
if all(z(row,col) > local_mat)
peak_row = [peak_row,row];
peak_col = [peak_col,col];
elseif all(z(row,col) < local_mat)
valley_row = [valley_row,row];
valley_col = [valley_col,col];
else
end
end
end
Let us check how many valleys and peaks it found
fprintf('There are %d valley and %d peaks',[length(valley_row),length(peak_row)])
Which makes sense if you carefully look at the plot above.
Let us now, for instance, plot the peaks
surf(x,y,z)
hold on
plot3(x(peak_row(1),peak_col(1)),y(peak_row(1),peak_col(1)),z(peak_row(1),peak_col(1)),'*r')
plot3(x(peak_row(2),peak_col(2)),y(peak_row(2),peak_col(2)),z(peak_row(2),peak_col(2)),'*r')
plot3(x(peak_row(3),peak_col(3)),y(peak_row(3),peak_col(3)),z(peak_row(3),peak_col(3)),'*r')
And the same goes with the valleys.
Of course, this code ignores possible peaks and or valleys that you might find at the edge of the matrix.
You could definitely expand from here and implement that too.
As a last remark, there are functions in matlab that can do that with just a few lines of code (see for instance islocalmax).
