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.
Related
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.
http://i.imgur.com/j7hStIG.png
Hi I need help repairing this image using for loops. I know I have to identify the bad pixels first and fill them in. thanks. PS I am very new to matlab
clear
clc
format compact
filenameIN = uigetfile('.bmp','Picture');
noisyRGBarray = imread(filenameIN);
figure(1)
imshow(noisyRGBarray)
y = noisyRGBarray;
[m,n]=size(y)
clean=[];
for i=2:m-1
for j=2:n-1
if y(i,j)% clean add new
clean = [ clean, y(i,j) ]
end
end
end
Im pretty sure the for statemetn is wrong and I do not know wat to do from here. I need help writing the for loop to go through the image matrix to identify the black and white pixels.
Try running a median filter on your image. See here for an example.
If you must use a for loop for learning reasons, please explain what you consider to be a "bad pixel" (black? different from neighbors in some way?), attempt to identify such a pixel based on the criteria you settle on, and adjust the value of that pixel.
In general, you should not adopt the approach of starting with an empty array and growing it one pixel at a time. Rather, create the output image as a copy of the input (clean=noisyRGBarray;) or initialize with zeros (clean=zeros(size(noisyRGBarray))), and modify the bad pixels (clean(i,j,:)=...);
Ok so, I'm making a 2d dungeon crawler and I want to randomize a map for it. Right now it's look like I'm going to use a Random Walk algoritm for the path, combined with a Perlin Noise for different the underworld enviroments (currently only 1, as I'm using my own shitty looking tile set consisting of only 1 rook image and 1 grass image, but whatever :D)
So in figuring out how random walks work, it looks like I'm supposed to do something along the lines of this:
*create two-dimensional array sized after the map.
*pick random start postion and end postiont (I chose to put these on opposite sides of the map, randomly distributed across its side.
*follow these steps until you hit your finish point:
*pick a direction to 'walk' at random (only up, down, left, right because you otherwise I'm left with diagonal passes which the player can't walk through)
*'walk' that direction for a random amount of steps (I randomize amount of steps first then walk one by one for bound checking later, rather than just drawing a line).
*Everytime you 'walk' on a tile, turn that tile to 1 from originally 0.
*repeat above steps until you hit your finish point.
This leaves me with too much open ground, and too much closed ground. What I'm looking for is a path covered with rooms, sort of, but I want to control how big the 'rooms' become. I don't want 'rooms' to get too big, which some become. So I want the feeling of being in an enclosed space, but also I want to use as much of the map grid as possible.
Is a random walk not suited for this? I was thinking about making every step have a certain width to it, maybe that could work.
Or maybe I'm just implementing it wrong! I'm not a math genious sadly ;P
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.
I'm trying to run a distance transform on a thresholded binary image in
order to assist anomaly detection (my hope is that I can detect large
changes around the edges of the object), however for some reason, upon
running my Distance Transform script, I'm getting a strange banding type of
effect. I tested something similar in the Distance Transform demo script in
the samples directory, with the same results. One possible reason I came up
with was that the distance was going beyond the 0-255 scale and therefore
essentially being modulus'ed to keep it within the boundaries. Has anyone
had any experience with this that could advise?
I have posted images and code on my blog if that helps
Thanks in advance,
Ian
One quick way to test your theory: try with a grey scale image that's muted (all values v --> 128+(v-128)/32 or something) and see if that makes the bands much wider or eliminates them completely.
It's always a good idea to nail down what the problem is first, and then try to fix it.
I can't help with the code, but I'd like to point out that the expected result on your blog is probably incorrect as well: look at the sharp black-gray border in the bottom part of the large object: it should not be there, as the maximum difference between two adjacent pixels should be 1.