I am new to LabVIEW and I am trying to read a code written in LabVIEW. The block diagram is this:
This is the program to input x and y functions into the voltage input. It is meant to give an input voltage in different forms (sine, heartshape , etc.) into the fast-steering mirror or galvano mirror x and y axises.
x and y function controls are for inputting a formula for a function, and then we use "evaluation single value" function to input into a daq assistant.
I understand that { 2*(|-Mpi|)/N }*i + -Mpi*pi goes into the x value. However, I dont understand why we use this kind of formula. Why we need to assign a negative value and then do the absolute value of -M*pi. Also, I don`t understand why we need to divide to N and then multiply by i. And finally, why need to add -Mpi again? If you provide any hints about this I would really appreciate it.
This is just a complicated way to write the code/formula. Given what the code looks like (unnecessary wire bends, duplicate loop-input-tunnels, hidden wires, unnecessary coercion dots, failure to use appropriate built-in 'negate' function) not much care has been given in writing it. So while it probably yields the correct results you should not expect it to do so in the most readable way.
To answer you specific questions:
Why we need to assign a negative value and then do the absolute value
We don't. We can just move the negation immediately before the last addition or change that to a subtraction:
{ 2*(|Mpi|)/N }*i - Mpi*pi
And as #yair pointed out: We are not assigning a value here, we are basically flipping the sign of whatever value the user entered.
Why we need to divide to N and then multiply by i
This gives you a fraction between 0 and 1, no matter how many steps you do in your for-loop. Think of N as a sampling rate. I.e. your mirrors will always do the same movement, but a larger N just produces more steps in between.
Why need to add -Mpi again
I would strongly assume this is some kind of quick-and-dirty workaround for a bug that has not been fixed properly. Looking at the code it seems this +Mpi*pi has been added later on in the development process. And while I don't know what the expected values are I would believe that multiplying only one of the summands by Pi is probably wrong.
I'm using MQL5 (my first code).
I want to use a script that uses MA, but first, I wanted to confirm the value to verify I'm doing correctly. Using a very basic code into script:
double x=0;
x = iMA(Symbol(),Period(),100,0,MODE_SMA,PRICE_CLOSE);
Alert("The actual MA from last 100 points of EURUSD actually is: " + x;
The expected value is near the actual price... 1.23456, but this function is returning 10.00000 or 11.0000.
I believe I'm missing something, and https://www.mql5.com/es/docs/indicators/ima helplink is not quite clear enough.
I already saw another similar function: MA[0] which seems to bring the moving average from specific candle, but, I don't know how to manage the Period range (100) or if is related to Close/Open variables on it. I didn't find any specific helplink to review.
Any ideas are very appreciated!!!
x should be int, it is a handler of the MA. So each indicator when created in MT5 receives its handler, and you can use it later to get what you need. If you need several MA's - create several handlers and give each of them different names (x1, x2 or add some sense). Expert advisors in the default build of MT5 are good examples on what to do.
The iMA function Returns the handle of a specified technical indicator, not the "moving average" value.
For example, to get the value of the Moving average you can use this (in MQ4):
EMA34Handler = iMA(NULL,0,34,0,MODE_EMA,PRICE_CLOSE);
EMA34Value = CopyBuffer(EMA34Handler, 0,0);
I am beginner.I just started coding in codeacademy.In a certain level,the gave me a task which is relatate with threshold.So,my question is what is mouseResponse threshold and why should we set a specific threshold?
The actual question is give below:
1.
Three variables let you experiment with the animation physics: mouseResponseThreshold, friction, and rotationForce.
mouseResponseThreshold affects how close the mouse pointer needs to be to affect the dots that make up the letters. The larger the number, the more powerful the effect of the mouse interaction. Experiment with changing the mouseResponseThreshold to different numbers and running your code!
And the hint is "Try starting out by setting the threshold to 150."
What is mouseResponse threshold
This is distance from the mouse position to your target's position (in this case, the target is the "...dots that make up your letters").
Why should I set it
You need to set it so that your code knows at what distance it needs to do a certain operation.
I am working on a vb.net auto-focus routine and have the image processing part worked out, basically I do some edge detection, convert to gray-scale and then measure the standard deviation to work out the most 'in focus' point of the image.
I have done this with a number of images, and it almost comes out as a normal distribution, now I want to start to integrate this with my microscope and a stepper motor.
The concept is that I would move through a lower and upper limit on the stepper motor, and measure the above through live-view, recording the values in a list. In my case the two things I want to record are the position, and the double standard deviation value.
I am wondering what the best way to record these are, should it be
recorded as a typed list, or a dictionary or another method?
Once I record all of these values, I would want to go through the values to conduct some simple analysis of them, so if that was the case
how would I then be able to determine the average, min, max etc?
My first attempt of storing the information was in a typed list, where I had essentially done the below;
Public ZPositions As New List(Of Zfocus)
Public Class Zfocus
Public Position As Integer
Public GreyStDev As Double
End Class
The second way was to use a dictionary;
Public ZPosition As New Dictionary(Of Integer, Double)
However in both cases, I am not sure how I can either pull out a single maximum position value (e.g. Position integer,) or from the dictionary the position value (integer) which (sort of) corrosponds to the best auto-focus position.
The Third added bonus, is to be able to pull out any postions above a
specific value, which may corrospond to having some focus information
within them for focus stacking?
Many thanks
Big thanks to jmcilhinney, this solved my issue and works a treat!
Went with a strongly typed list (the ZFocus list) and then I could do the below;
MaxPosition = ZPositions.First(Function(zp1) zp1.GreyStDev = ZPositions.Max(Function(zp2) zp2.GreyStDev))
This allowed be to set up an auto-focus routine which loops through a number of images (as a test), stores the position (e.g. image number in this case) and the intensity edge information, and at the end then pull out the strongest intensity information which forms the best auto-focus point in my case
I'm attempting to gauge the percentage difference between two images.
Having done a lot of reading I seem to have a number of options but I'm not sure what the best method to follow for:
Ease of coding
Performance.
The methods I've seen are:
Non language specific - academic Image comparison - fast algorithm and Mac specific direct pixel access http://www.markj.net/iphone-uiimage-pixel-color/
Does anyone have any advice about what solutions make most sense for the above two cases and have code samples to show how to apply them?
I've had success calculating the difference between two images using the histogram technique mentioned here. redmoskito's answer in the SO question you linked to was actually my inspiration!
The following is an overview of the algorithm I used:
Convert the images to grayscale—compare one channel instead of three.
Divide each image into an n * n grid of "subimages". Then, for subimage pair:
Calculate their colour composition histograms.
Calculate the absolute difference between the two histograms.
The maximum difference found between two subimages is a measure of the two images' difference. Other metrics could also be used (e.g. the average difference betwen subimages).
As tskuzzy noted in his answer, if your ultimate goal is a binary "yes, these two images are (roughly) the same" or "no, they're not", you need some meaningful threshold value. You could produce such a value by passing images into the algorithm and tweaking the threshold based on its output and how similar you think the images are. A form of machine learning, I suppose.
I recently wrote a blog post on this very topic, albeit as part of a larger goal. I also created a simple iPhone app to demonstrate the algorithm. You can find the source on GitHub; perhaps it will help?
It is really difficult to suggest something when you don't tell us more about the images or the variations. Are they shapes? Are they the different objects and you want to know what class of objects? Are they the same object and you want to distinguish the object instance? Are they faces? Are they fingerprints? Are the objects in the same pose? Under the same illumination?
When you say performance, what exactly do you mean? How large are the images? All in all it really depends. With what you've said if it is only ease of coding and performance I would suggest to just find the absolute value of the difference of pixels. That is super easy to code and about as fast as it gets, but really unlikely to work for anything other than the most synthetic examples.
That being said I would like to point you to: DHOG, GLOH, SURF and SIFT.
You can use fairly basic subtraction technique that the lads above suggested. #carlosdc has hit the nail on the head with regard to the type of image this basic technique can be used for. I have attached an example so you can see the results for yourself.
The first shows a image from a simulation at some time t. A second image was subtracted away from the first which was taken some (simulation) time later t + dt. The subtracted image (in black and white for clarity) then shows how the simulation has changed in that time. This was done as described above and is very powerful and easy to code.
Hope this aids you in some way
This is some old nasty FORTRAN, but should give you the basic approach. It is not that difficult at all. Due to the fact that I am doing it on a two colour pallette you would do this operation for R, G and B. That is compute the intensities or values in each cell/pixal, store them in some array. Do the same for the other image, and subtract one array from the other, this will leave you with some coulorfull subtraction image. My advice would be to do as the lads suggest above, compute the magnitude of the sum of the R, G and B componants so you just get one value. Write that to array, do the same for the other image, then subtract. Then create a new range for either R, G or B and map the resulting subtracted array to this, the will enable a much clearer picture as a result.
* =============================================================
SUBROUTINE SUBTRACT(FNAME1,FNAME2,IOS)
* This routine writes a model to files
* =============================================================
* Common :
INCLUDE 'CONST.CMN'
INCLUDE 'IO.CMN'
INCLUDE 'SYNCH.CMN'
INCLUDE 'PGP.CMN'
* Input :
CHARACTER fname1*(sznam),fname2*(sznam)
* Output :
integer IOS
* Variables:
logical glue
character fullname*(szlin)
character dir*(szlin),ftype*(3)
integer i,j,nxy1,nxy2
real si1(2*maxc,2*maxc),si2(2*maxc,2*maxc)
* =================================================================
IOS = 1
nomap=.true.
ftype='map'
dir='./pictures'
! reading first image
if(.not.glue(dir,fname2,ftype,fullname))then
write(*,31) fullname
return
endif
OPEN(unit2,status='old',name=fullname,form='unformatted',err=10,iostat=ios)
read(unit2,err=11)nxy2
read(unit2,err=11)rad,dxy
do i=1,nxy2
do j=1,nxy2
read(unit2,err=11)si2(i,j)
enddo
enddo
CLOSE(unit2)
! reading second image
if(.not.glue(dir,fname1,ftype,fullname))then
write(*,31) fullname
return
endif
OPEN(unit2,status='old',name=fullname,form='unformatted',err=10,iostat=ios)
read(unit2,err=11)nxy1
read(unit2,err=11)rad,dxy
do i=1,nxy1
do j=1,nxy1
read(unit2,err=11)si1(i,j)
enddo
enddo
CLOSE(unit2)
! substracting images
if(nxy1.eq.nxy2)then
nxy=nxy1
do i=1,nxy1
do j=1,nxy1
si(i,j)=si2(i,j)-si1(i,j)
enddo
enddo
else
print *,'SUBSTRACT: Different sizes of image arrays'
IOS=0
return
endif
* normal finishing
IOS=0
nomap=.false.
return
* exceptional finishing
10 write (*,30) fullname
return
11 write (*,32) fullname
return
30 format('Cannot open file ',72A)
31 format('Improper filename ',72A)
32 format('Error reading from file ',72A)
end
! =============================================================
Hope this is of some use. All the best.
Out of the methods described in your first link, the histogram comparison method is by far the simplest to code and the fastest. However key point matching will provide far more accurate results since you want to know a precise number describing the difference between two images.
To implement the histogram method, I would do the following:
Compute the red, green, and blue histograms of each image
Add up the differences between each bucket
If the difference is above a certain threshold, then the percentage is 0%
Otherwise the colors found in the images are similar. So then do a pixel by pixel comparison and convert the difference into a percentage.
I don't know any precise algorithms for finding the key points of an image. However once you find them for each image you can do a pixel by pixel comparison for each of the key points.