Problem
I am currently deep-diving into learning about path planning algorithms using Matlab. I have referred to some videos, and I was intrigued by the animation of formation points along with their nodes, especially for RRT or Rapidly exploring Random Trees.
I wish to perform similar animations so as to get a clearer idea of how the nodes are forming.
What I have tried till now?
I was currently playing with the Matlab example: Plan Mobile Robot paths using RRT.
To display the animation, I made a minor change i.e.,
%% Plot the path
show(occGrid)
hold on
hold on
size_tree = size(solnInfo.TreeData)
for x = 1:size_tree(1)
% Plot the entire search tree
plot(solnInfo.TreeData(x,1), solnInfo.TreeData(x,2),'.');
drawnow
% pause(0.5)
end
The resulting animation is displayed below
Along with points I wish to have the nodes being formed between the points. I was unable to do so.
Can someone help me out with this?
Since, I was tuning the parameters of an existing Matlab example on Path planning of mobile robot
Updated code: RRT planner animation
Final output
In the code you can play with the pause(0.6) inside for loop where you can tune it according to your own needs.
Related
Im very new to matlab and medical imaging in general. For a project I need to finish a script that uses two matrices the "kernel" and "searchArea" to determine the displacement of the interventricular septum of the heart using the blockchaining method with multiple frames of the cardiac cycle. The "kernel" is a fraction of the orginal frame image and the "searchArea" is an area thats surrounds the kernel in the next frame. I have to use cross correlation to do this and already tried the following (this process will be repeated for all the frames):
crossCor = xcorr2(searchArea,kernel);
[max_cc,imax] = max(abs(crossCor(:)));
[ypeak, xpeak] = ind2sub(size(crossCor),imax(1));
dispLat = ypeak-size(kernel,1)
dispAx = xpeak-size(kernel,2)
This code was writen based on the xcorr2 documentation: https://nl.mathworks.com/help/signal/ref/xcorr2.html
The code does work but the results do not meet my expectations, the script that I got provided with uses the lateral displacement (x displacement) and axial displacement (y displacement) to create tracking frames:
The results should show the displacement of the septum just like in frame 1, but you can see that in frame 2 and 3 the displacement is way off.
I do not know if I'm on the right track here of giving the provided script the right displacements, but any advice or nudges in the right direction are welcome!
REMARK:
I am doing this for an introductory medical imaging course, the blue colored mesh which is used to plot the movement is created using speckle tracking, however I did not get any details on how this works which is probably intended. The Matlab script relies on the right input of axial (y) and lateral (x) displacement, the results are wrong, so my written code is probably (partly) wrong. My main intension to post this on stackoverflow is to receive feedback on my written code, because I don't know what I have done wrong.
I would like to follow a curve (with matlab or opencv) and to find the other end of it when it is cut by an empty space like this example, which is simplified to illustrate the problem:
Link to image of cut curve
Real images are more like this one: Link to real image to analyse
To follow the curve, I can use a skeleton and look at the neighbourhood. The problem is that I don't know how to find the other end efficiently.
I don't think that closing or opening operations could help because as shown on the previous image, there are other curves and the two parts of the curve are quite far from each other so it could lead to boundaries between the different curves instead of the two parts.
I was thinking about polynomial evaluation which could be a solution for simple curves but I am not sure about the precision I could get. If I use a skeleton, I have to find exactly the right pixel or to search in a reasonable neighbourhood which would take some time and once again, as there are other curves in the images, I have to be sure that I will find the good one.
That's why I am searching for an existing function which could estimate precisely the trajectory of the curve and give an usefull output to go further and find the second part of the curve.
If that kind of function doesn't exist, I'm open to any other way of analysing the problem if it can help.
I will start to explain with the first image you provided, you can implement common OpenCV function useful for detecting contour(black region in your case as you have binary image) known as cv2.findContours(), which returns the coordinates of the edges of the surface detected then you can plot each detected contour separately in a blank image to get the edge of your desired line.
Now coming to your 2nd image you have to be slightly careful while performing above analysis as there are many tiny lines. get back to me for further help
I'm using the function regionprops to detect the number of trees on a image taked by drone.
First I removed the ground using Blue NDVI:
Image with threshold:
Then I used the function regionprops to detect the number of trees on image:
But there are a problem on region 15, because all trees on that region are connected and it detects as one tree.
I tried to separate the trees on that region using Watershed Segmentation, but its not working:
Am I doing this the wrong way?
Is there a better method to separate the trees?
If anyone can help me with this problem I will appreciate. Here is the region 15 without the ground:
If it helps, here is the Gradient Magnitude image:
It has been some time since this question was asked. I hope it is not too late for an answer. I see a general problem of using watershed segmentation in similar questions. Sometimes the objects are apart, not touching each other like in this example . In such cases, only blurring the image is enough to use watershed segmentation. Sometimes the objects are located closely and touch each other, thus the boundaries of objects are not clear like in this example. In such cases, using distance transform-->blur-->watershed helps. In this question, the logical approach should be using distance transform. However, this time the boundaries are not clear due to shadows on and nearby the trees. In such cases, it is good to use any information that helps to separate the objects as in here or emphasise objects itself.
In this question, I suggest using colour information to emphasise tree pixels.
Here are the MATLAB codes and results.
im=imread('https://i.stack.imgur.com/aBHUL.jpg');
im=im(58:500,86:585,:);
imOrig=im;
%% Emphasize trees
im=double(im);
r=im(:,:,1);
g=im(:,:,2);
b=im(:,:,3);
tmp=((g-r)./(r-b));
figure
subplot(121);imagesc(tmp),axis image;colorbar
subplot(122);imagesc(tmp>0),axis image;colorbar
%% Transforms
% Distance transform
im_dist=bwdist(tmp<0);
% Blur
sigma=10;
kernel = fspecial('gaussian',4*sigma+1,sigma);
im_blured=imfilter(im_dist,kernel,'symmetric');
figure
subplot(121);imagesc(im_dist),axis image;colorbar
subplot(122);imagesc(im_blured),axis image;colorbar
% Watershed
L = watershed(max(im_blured(:))-im_blured);
[x,y]=find(L==0);
figure
subplot(121);
imagesc(imOrig),axis image
hold on, plot(y,x,'r.','MarkerSize',3)
%% Local thresholding
trees=zeros(size(im_dist));
centers= [];
for i=1:max(L(:))
ind=find(L==i & im_blured>1);
mask=L==i;
[thr,metric] =multithresh(g(ind),1);
trees(ind)=g(ind)>thr*1;
trees_individual=trees*0;
trees_individual(ind)=g(ind)>thr*1;
s=regionprops(trees_individual,'Centroid');
centers=[centers; cat(1,[],s.Centroid)];
end
subplot(122);
imagesc(trees),axis image
hold on, plot(y,x,'r.','MarkerSize',3)
subplot(121);
hold on, plot(centers(:,1),centers(:,2),'k^','MarkerFaceColor','r','MarkerSize',8)
You could try out a marker-based watershed. Vanilla watershed transforms never work out of the box in my experience. One way to perform one would be to first create a distance map of the segmented area by using imdist(). Then you could suppress local maxima by calling imhmax(). Then calling watershed() will usually perform noticeably better.
Here's a sample script on how to do it:
bwTrees = imopen(bwTrees, strel('disk', 10));
%stabilize the borders to lessen oversegmentation
distTrees = -bwDist(~bwTrees); %Distance transform
distTrees(~bwTrees) = -Inf; %set background to -Inf
distTrees = imhmin(distTrees, 3); %suppress local minima
basins = watershed(distTrees);
ridges = basins == 0;
segmentedTrees = bwTrees & ~ridges; %segment
segmentedTrees = imopen(segmentedTrees, strel('disk', 2));
%remove 'segmentation trash' caused by oversegmentation near the borders.
I fiddled around with the parameters for ~10min but got fairly poor results:
You'd need to pour work into this. Mostly in pre- and post-processing via the morphology. More curvature would help the segmentation, if you could lower the sensitivity of the segmentation in the first part. The size of the h-minima transform is also an paramater of interest. You can probably get adequate results this way.
Probably a better approach would come from the world of clustering techniques. If you have or can find a way to estimate the number of trees in the forest you should be able to use traditional clustering methods to segment out the trees. A Gaussian mixture model or a k-means with k-trees would probably work much better than a marker based watershed if you get even nearly the right amount of trees. Normally I'd estimate the number of trees based on the number of suppressed maxima on a h-maxima transform, but your labels might be a bit too sausagey for that. It's worth a try though.
I'm using the function regionprops to detect the number of trees on a image taked by drone.
First I removed the ground using Blue NDVI:
Image with threshold:
Then I used the function regionprops to detect the number of trees on image:
But there are a problem on region 15, because all trees on that region are connected and it detects as one tree.
I tried to separate the trees on that region using Watershed Segmentation, but its not working:
Am I doing this the wrong way?
Is there a better method to separate the trees?
If anyone can help me with this problem I will appreciate. Here is the region 15 without the ground:
If it helps, here is the Gradient Magnitude image:
It has been some time since this question was asked. I hope it is not too late for an answer. I see a general problem of using watershed segmentation in similar questions. Sometimes the objects are apart, not touching each other like in this example . In such cases, only blurring the image is enough to use watershed segmentation. Sometimes the objects are located closely and touch each other, thus the boundaries of objects are not clear like in this example. In such cases, using distance transform-->blur-->watershed helps. In this question, the logical approach should be using distance transform. However, this time the boundaries are not clear due to shadows on and nearby the trees. In such cases, it is good to use any information that helps to separate the objects as in here or emphasise objects itself.
In this question, I suggest using colour information to emphasise tree pixels.
Here are the MATLAB codes and results.
im=imread('https://i.stack.imgur.com/aBHUL.jpg');
im=im(58:500,86:585,:);
imOrig=im;
%% Emphasize trees
im=double(im);
r=im(:,:,1);
g=im(:,:,2);
b=im(:,:,3);
tmp=((g-r)./(r-b));
figure
subplot(121);imagesc(tmp),axis image;colorbar
subplot(122);imagesc(tmp>0),axis image;colorbar
%% Transforms
% Distance transform
im_dist=bwdist(tmp<0);
% Blur
sigma=10;
kernel = fspecial('gaussian',4*sigma+1,sigma);
im_blured=imfilter(im_dist,kernel,'symmetric');
figure
subplot(121);imagesc(im_dist),axis image;colorbar
subplot(122);imagesc(im_blured),axis image;colorbar
% Watershed
L = watershed(max(im_blured(:))-im_blured);
[x,y]=find(L==0);
figure
subplot(121);
imagesc(imOrig),axis image
hold on, plot(y,x,'r.','MarkerSize',3)
%% Local thresholding
trees=zeros(size(im_dist));
centers= [];
for i=1:max(L(:))
ind=find(L==i & im_blured>1);
mask=L==i;
[thr,metric] =multithresh(g(ind),1);
trees(ind)=g(ind)>thr*1;
trees_individual=trees*0;
trees_individual(ind)=g(ind)>thr*1;
s=regionprops(trees_individual,'Centroid');
centers=[centers; cat(1,[],s.Centroid)];
end
subplot(122);
imagesc(trees),axis image
hold on, plot(y,x,'r.','MarkerSize',3)
subplot(121);
hold on, plot(centers(:,1),centers(:,2),'k^','MarkerFaceColor','r','MarkerSize',8)
You could try out a marker-based watershed. Vanilla watershed transforms never work out of the box in my experience. One way to perform one would be to first create a distance map of the segmented area by using imdist(). Then you could suppress local maxima by calling imhmax(). Then calling watershed() will usually perform noticeably better.
Here's a sample script on how to do it:
bwTrees = imopen(bwTrees, strel('disk', 10));
%stabilize the borders to lessen oversegmentation
distTrees = -bwDist(~bwTrees); %Distance transform
distTrees(~bwTrees) = -Inf; %set background to -Inf
distTrees = imhmin(distTrees, 3); %suppress local minima
basins = watershed(distTrees);
ridges = basins == 0;
segmentedTrees = bwTrees & ~ridges; %segment
segmentedTrees = imopen(segmentedTrees, strel('disk', 2));
%remove 'segmentation trash' caused by oversegmentation near the borders.
I fiddled around with the parameters for ~10min but got fairly poor results:
You'd need to pour work into this. Mostly in pre- and post-processing via the morphology. More curvature would help the segmentation, if you could lower the sensitivity of the segmentation in the first part. The size of the h-minima transform is also an paramater of interest. You can probably get adequate results this way.
Probably a better approach would come from the world of clustering techniques. If you have or can find a way to estimate the number of trees in the forest you should be able to use traditional clustering methods to segment out the trees. A Gaussian mixture model or a k-means with k-trees would probably work much better than a marker based watershed if you get even nearly the right amount of trees. Normally I'd estimate the number of trees based on the number of suppressed maxima on a h-maxima transform, but your labels might be a bit too sausagey for that. It's worth a try though.
I am trying to detect corners (x/y coordinates) in 2D scatter vectors of data.
The data is from a laser rangefinder and our current platform uses Matlab (though standalone programs/libs are an option, but the Nav/Control code is on Matlab so it must have an interface).
Corner detection is part of a SLAM algorithm and the corners will serve as the landmarks.
I am also looking to achieve something close to 100Hz in terms of speed if possible (I know its Matlab, but my data set is pretty small.)
Sample Data:
[Blue is the raw data, red is what I need to detect. (This view is effectively top down.)]
[Actual vector data from above shots]
Thus far I've tried many different approaches, some more successful than others.
I've never formally studied machine vision of any kind.
My first approach was a homebrew least squares line fitter, that would split lines in half resurivly until they met some r^2 value and then try to merge ones with similar slope/intercepts. It would then calculate the intersections of these lines. It wasn't very good, but did work around 70% of the time with decent accuracy, though it had some bad issues with missing certain features completely.
My current approach uses the clusterdata function to segment my data based on mahalanobis distance, and then does basically the same thing (least squares line fitting / merging). It works ok, but I'm assuming there are better methods.
[Source Code to Current Method] [cnrs, dat, ~, ~] = CornerDetect(data, 4, 1) using the above data will produce the locations I am getting.
I do not need to write this from scratch, it just seemed like most of the higher-class methods are meant for 2D images or 3D point clouds, not 2D scatter data. I've read a lot about Hough transforms and all sorts of data clustering methods (k-Means etc). I also tried a few canned line detectors without much success. I tried to play around with Line Segment Detector but it needs a greyscale image as an input and I figured it would be prohibitivly slow to convert my vector into a full 2D image to feed it into something like LSD.
Any help is greatly appreciated!
I'd approach it as a problem of finding extrema of curvature that are stable at multiple scales - and the split-and-merge method you have tried with lines hints at that.
You could use harris corner detector for detecting corners.