I am trying to move Pepper from one point to another which involves a turn of 90 degrees and it is around 40 metres distance. I can make it work when there are no obstacles but when there are obstacles the Pepper stops and then we don't know his location to move again. I am using ALNavigation navigateTo method.
I have tried getting the position by using ALMotion getRobotPosition but it is very error prone and I don't know if we can use that while using ALNavigation API.
Please suggest any solution for this.
Thanks
Your best bet is to use ALNavigationProxy::navigateToInMap.
You can play around with this example: https://github.com/aldebaran/naoqi_navigation_samples
Get the code from github or http://doc.aldebaran.com/2-5/naoqi/motion/exploration-api.html#exploration-api
-see the sample code at the bottom of the page.
Map your space
ALNavigationProxy::navigateToInMap
Related
So I'm making a 2D platformer called "agent 404". I'm right now making the enemy but can't seem to make it. So I looked for a tutorial but couldn't find any tutorials related to a 2D platformer enemy that follows the player I tried all but most of them but all end up wrong. I want to ask if any of you know a way or a tutorial that could help me?
If you're using a tilemap you can use a Navigation2D to do nearly "out-of-the-box" pathfinding.
GDQuest explains some of it in this tutorial:
https://www.youtube.com/watch?v=0fPOt0Jw52s
Since you're making a platformer however, you might run into the same problem where the only tiles with navigation on them are blank and so aren't filled in by the autotiler. If that's the case you can find a workaround here (something I was just stuck on myself :-) ):
navigation tilemaps without placing walkable tiles manually
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.
You can see the code there: http://jsfiddle.net/jocose/CkL5F/901/
(double click on the box and move your mouse)
NOTE: This is a simplified example that is part of a larger system. My ultimate goal is to manipulate individual vertices of a path.
Update: I crunched the numbers and the math actually apears to be correct. What I want to do is calculate the offset from each point to the mouse, and then move that point to the mouses position + the offset.
So if I have a mouse of 224 then 224-103 = 121 then I add: 121+224=345
These creates a cycle of ups and downs that I am seeing. I don't know why these is stumping me so badly, any help would be much appreciated.
I need to manually update a Raphael path element.
To do this I convert an absolute path into an array using Raphael great built in function "parsePathString"
I then loop through that array and modify the values based off the mouse position.
The update is done to the X values only, and is in real time; called each time the mouse moves.
When the element moves it flickers back and forth between the correct position and some anomalous one.
I have no clue why its doing this. I have spent almost 5 hours trying to figure this out and I'm officially stuck.
Here is a sample of the result where you can see the values jumping around:
MOUSE224
M,103.676287
MOUSE225
M,346.323713
MOUSE227
M,107.676287
MOUSE228
M,348.323713 12
MOUSE228
M,107.676287
MOUSE229
M,350.323713
MOUSE231
M,111.67S287
MOUSE232
M,3S2.323713
MOUSE233
M,113.676287
MOUSE233
M,3S2.323713
Here's my version of your fiddle modified to do what I think you need. At least, it seems to work. It's the same type of problem I had to fix for the Raphael 2 transformations here.
Basically, in your mousemove, I've changed mx to be a calculation of the offset between where your mouse is now and where it was the last time mousemove was called. Your move() function now only has to add this value to the x-coords.
Hope this helps you out somewhat
I am now working on an eye tracking project. In this project I am tracking eyes in a webcam video (resolution if 640X480).
I can locate and track the eye in every frame, but I need to locate the pupil. I read a lot of papers and most of them refer to Alan Yuille's deformable template method to extract and track the eye features. Can anyone help me with the code of this method in any languages (matlab/OpenCV)?
I have tried with different thresholds, but due to the low resolution in the eye regions, it does not work very well. I will really appreciate any kind of help regarding finding pupil or even iris in the video.
What you need to do is to convert your webcam to a Near-Infrared Cam. There are plenty of tutorials online for that. Try this.
A Image taken from an NIR cam will look something like this -
You can use OpenCV then to threshold.
Then use the Erode function.
After this fill the image with some color takeing a corner as the seed point.
Eliminate the holes and invert the image.
Use the distance transform to the nearest non-zero value.
Find the max-value's coordinate and draw a circle.
If you're still working on this, check out my OptimEyes project: https://github.com/LukeAllen/optimeyes
It uses Python with OpenCV, and works fairly well with images from a 640x480 webcam. You can check out the "Theory Paper" and demo video on that page also. (It was a class project at Stanford earlier this year; it's not very polished but we made some attempts to comment the code.)
Depending on the application for tracking the pupil I would find a bounding box for the eyes and then find the darkest pixel within that box.
Some psuedocode:
box left_location = findlefteye()
box right_location = findrighteye()
image_matrix left = image[left_location]
image_matrix right = image[right_location]
image_matrix average = left + right
pixel min = min(average)
pixel left_pupil = left_location.corner + min
pixel right_pupil = right_location.corner + min
In the first answer suggested by Anirudth...
Just apply the HoughCirles function after thresholding function (2nd step).
Then you can directly draw the circles around the pupil and using radius(r) and center of eye(x,y) you can easily find out the Center of Eye..
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.