I'm attempting to reproduce the ARCamera's project point function, but for some reason the values are not matching up properly. I am taking the ARCamera's projection matrix and view matrix and applying basic CG perspective transform math, (PV) * p, but the NDC values do not match the pixel values given from the ARCamera's project point function. Any ideas? Am I forgetting something?
Some more detail:
Basically, I'm trying to take an ARFrame a the click of a button, and then trying to replicate the functionality of https://developer.apple.com/documentation/arkit/arcamera/2923538-projectpoint. I'm attempting to do this with https://developer.apple.com/documentation/arkit/arcamera/2887458-projectionmatrix and https://developer.apple.com/documentation/arkit/arcamera/2921672-viewmatrix, making sure all of the inputs match for both parts. CG size is used to transform the coordinates from NDC space to image space.
EDIT: Solution found, check comments below.
The problem turned out to be projection_matrix sometimes does not correctly find the device orientation. The correct approach is to use projectionMatrix(for:viewportSize:zNear:zFar:).
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I can make 2D dimensional netcdf maps of some quantity. I open it in panoply and there is color map of that quantity. But I cannot visualize some boolean value.
Can I somehow mark particular grid points with some symbol on the map (it can be diamond, square, triangle... whatever), is there a way how to do it in Fortran90? I accept also python related help.
Again: I mean there would be color map (from real values) (which I can do) and at the same time some values will have e. g. triangle on it.
If I understand the question correctly, then you can easily do that with Python and using some plotting library (e.g Matplotlib). With Fortran it is extremely tricky as it does not natively support plotting in my mind.
Basically with Python you just have to :
read the wanted variables (coordinates and the field itself)
make the map of the field i.e make the plot
find the locations you want to highlight and just add those locations to the plot
I have been working on a piece of code that takes in a curve (cloud of points with x,y coordinates only for now) and parameterises it to approximate the given shape with nurbs. The issue I have is that the resultant parameterised curve is linear(!) between the first two control points and only between the other ones approximates the input curve. Any idea on why that would happen (i.e. the linear segment between the first two control points)?
Also, the system wouldn't let me post a picture. Hope the problem is clear enough though..
Your software system most probably uses multiple start and end points. This leads to visually straight lines at the given control points. These are in fact not really linear going, they only look like.
Thanks for replying and looking at my problem, but I have found the bug in my code. I used the number of points from the input curve rather than the number of control points wanted (which have similar variable names in my code) to compute the knot vector and thus the problem propagated from that point onwards.
If there is a given 2d array of an image, where threshold has been done and now is in binary information.
Is there any particular way to process this image to that I get multiple blob's coordinates on the image?
I can't use openCV because this process needs to run simultaneously on 10+ simulated robots on a custom simulator in C.
I need the blobs xy coordinates, but first I need to find those multiple blobs first.
Simplest criteria of pixel group size should be enough. But I don't have any clue how to start the coding.
PS: Single blob should be no problem. Problem is multiple blobs.
Just a head start ?
Have a look at QuickBlob which is a small, standalone C library that sounds perfectly suited for your needs.
QuickBlob comes with a small command-line tool (csv-blobs) that outputs the position and size of each blob found within the input image:
./csv-blobs white image.png
X,Y,size,color
28.37,10.90,41,white
51.64,10.36,42,white
...
Here's an example (output image is produced thanks to the show-blobs.py tiny Python utility that comes with QuickBlob):
You can go through the binary image labeling the connected parts with an algorithm like the following:
Create a 2D array of ints, labelArray, that will hold the labels of the connected regions and initiate it to all zeros.
Iterate over each binary pixel, p, row by row
A. If p is true and the corresponding value for this position in the labelArray is 0 (unlabeled), assign it to a new label and do a breadth-first search that will add all surrounding binary pixels that are also true to that same label.
The only issue now is if you have multiple blobs that are touching each other. Because you know the size of the blobs, you should be able to figure out how many blobs are in a given connected region. This is the tricky part. You can try doing a k-means clustering at this point. You can also try other methods like using binary dilation.
I know that I am very late to the party, but I am just adding this for the benefipeople who are researching this problem.
Here is a nice description that might fit your needs.
http://www.mcs.csueastbay.edu/~grewe/CS6825/Mat/BinaryImageProcessing/BlobDetection.htm
I have to do some image processing but I don't know where to start. My problem is as follows :-
I have a 2D fiber image (attached with this post), in which the fiber edges are denoted by white color and the inside of the fiber is black. I want to choose any black pixel inside the fiber, and travel from it along the length of the fiber. This will involve comparing the contrast with the surrounding pixels and then travelling in the desired direction. My main aim is to find the length of the fiber
So can someone please tell me atleast where to start? I have made a rough algorithm in my mind on how to approach my problem but I don't know even which software/library to use.
Regards
Adi
EDIT1 - Instead of OpenCV, I started using MATLAB since I found it much easier. I applied the Hough Transform and then Houghpeaks function with max no. of peaks = 100 so that all fibers are included. After that I got the following image. How do I find the length now?
EDIT2 - I found a research article on how to calculate length using Hough Transform but I'm not able to implement it in MATLAB. Someone please help
If your images are all as clean as the one you posted, it's quite an easy problem.
The very first technique I'd try is using a Hough Transform to estimate the line parameters, and there is a good implementation of the algorithm in OpenCV. After you have them, you can estimate their length any way you want, based on whatever other constraints you have.
Problem is two-fold as I see it:
1) locate start and end point from your starting position.
2) decide length between start and end points
Since I don't know your input data I assume it's pixel data with a 0..1 data on each pixel representing it's "whiteness".
In order to find end points I would do some kind of WALKER/AI that tries to walk in different locations, knowing original pos and last traversed direction then continuing along that route until "forward arc" is all white. This assumes fiber is somewhat straight (is it?).
Once you got start and end points you can input these into a a* path finding algorithm and give black pixels a low value and white very high. Then find shortest distance between start and end point, that is the length of the fiber.
Kinda hard to give more detail since I have no idea what techniques you gonna use and some example input data.
Assumptions:
-This image can be considered a binary image where there are only 0s(black) and 1s(white).
-all the fibers are straight and their starting and ending points are on borders.
-we can come up with a limit for thickness in fiber(thickness of white lines).
Under these assumptions:
start scanning the image border(start from wherever you want in whichever direction you want...just be consistent) until you encounter with the first white pixel.At this point your program will understand that this is definitely a starting point. By knowing this, you will gather all the white pixels until you reach a certain limit(or a threshold). The idea here is, if there is a fiber,you will get the angle between the fiber and the border the starting point is on...of course the more pixels you get(the inner you get)the surer you will be in the end. This is the trickiest part. after somehow ending up with a line...you need to calculate the angle(basic trigonometry). Since you know the starting point, the width/height of the image and the angle(or cos/sin of those) you will have the exact coordinate of the end point. Be advised...the exactness here is not really what you might have understood because we may(the thing is we will) have calculation errors in cos/sin values. So you need to hold the threshold as long as possible. So your end point will not be a point actually but rather an area indicating possibility that the ending point is somewhere inside that area. The rest is just simple maths.
Obviously you can put too much detail in this method like checking the both white lines that makes the fiber and deciding which one is longer or you can allow some margin for error since those lines will not be straight properly...this is where a conceptual thickness comes to the stage etc.
Programming:
C# has nice stuff and easy for you to use...I'll put some code here...
newBitmap = new Bitmap(openFileDialog1.FileName);
for (int x = 0; x < newBitmap.Width; x++)
{
for (int y = 0; y < newBitmap.Height; y++)
{
Color originalColor = newBitmap.GetPixel(x, y);//gets the pixel value...
//things go here...
}
}
you'll get the image from a openfiledialog and bitmap the image. inside the nested for loop this code scans the image left-to-right however you can change this...
Since you know C++ and C, I would recommend OpenCV
. It is open-source so if you don't trust anyone like me, you won't have a problem ;). Also if you want to use C# like #VictorS. Mentioned I would use EmguCV which is the C# equivilant of OpenCV. Tutorials for OpenCV are included and for EmguCV can be found on their website. Hope this helps!
Download and install the latest version of 3Dslicer,
Load your data and go the the package>EM segmenter without Atlas>
Choose your anatomical tree in 2 different labels, the back one which is your purpose, the white edges.
The choose the whole 2D image as your ROI and click on segment.
Here is the result, I labeled the edges in green and the black area in white
You can modify your tree and change the structures you define.
You can give more samples to your segmentation to make it more accurate.
I have to make an application that recognizes inside an black and white image a piece of tetris given by the user. I read the image to analyze into an array.
How can I do something like this using C?
Assuming that you already loaded the images into arrays, what about using regular expressions?
You don't need exact shape matching but approximately, so why not give it a try!
Edit: I downloaded your doc file. You must identify a random pattern among random figures on a 2D array so regex isn't suitable for this problem, lets say that's the bad news. The good news is that your homework is not exactly image processing, and it's much easier.
It's your homework so I won't create the code for you but I can give you directions.
You need a routine that can create a new piece from the original pattern/piece rotated. (note: with piece I mean the 4x4 square - all the cells of it)
You need a routine that checks if a piece matches an area from the 2D image at position x,y - the matching area would have corners (x-2, y-2, x+1, y+1).
You search by checking every image position (x,y) for a match.
Since you must use parallelism you can create 4 threads and assign to each thread a different rotation to search.
You might not want to implement that from scratch (unless required, of course) ... I'd recommend looking for a suitable library. I've heard that OpenCV is good, but never done any work with machine vision myself so I haven't tested it.
Search for connected components (i.e. using depth-first search; you might want to avoid recursion if efficiency is an issue; use your own stack instead). The largest connected component should be your tetris piece. You can then further analyze it (using the shape, the size or some kind of border description)
Looking at the shapes given for tetris pieces in Wikipedia, called "I,J,L,O,S,T,Z", it seems that the ratios of the sides of the bounding box (easy to find given a binary image and C) reveal whether you have I (4:1) or O (1:1); the other shapes are 2:3.
To detect which of the remaining shapes you have (J,L,S,T, or Z), it looks like you could collect the length and position of the shape's edges that fall on the bounding box's edges. Thus, T would show 3 and 1 along the 3-sides, and 1 and 1 along the 2 sides. Keeping track of the positions helps distinguish J from L, S from Z.