Develop Reference

Displaying a route on a floor plan image

I have a floor plan on which the walls are black, the doors are orange and the target is red. What I want is to make an app where given a specific point on the image, the route to the target is calculated and displayed. I already have a routing method, but it is in matlab and each position and object is defined in the code and it doesn't use an image. What I would like to know is how to scan the image to identify the walls, the doors and the target by color in order to apply the routing method and then display the route over the image of the map (I guess I should use drawable for that).

This are some steps to implement a pathfinding algorithmm from an image.
Upload your image
Apply a color detection HSV(in the real life is most easy control the
light changes with this format) algorithm to obtain the objects
separately.
Make a new binary Matrix with 1 for your floor and 0 to the
obstacles.
Apply to that binary Matrix an Occupancy grid algorithm(this reduce
your matrix because in the pathfinding algorithm you need
processing).
and now ur path finding algorithm. I recommend use the diijistrak or A star algorithm, in this two cases
you need construct an adjacency matrix.
The graph theory will help you to understand better.Good Luck!!
You can work in processing IDE for rapid prototipyng and migrate all the processing IDE core to eclipse, you need implement the PApplet class in your eclipse project, and can compile your app to Android.

I would use somekind of occupancy grid/map where each grid cell = one pixel (or possibly a small collection of pixels like 2x2 3x3, etc) And just do k-means clustering on the image. There are a few choices for k
k=2
you have walls is one group (the black lines)
everything else is considered opened space (this assumes doors can be opened).
You will need to know where the red point is located, but it doens't need to be visible in your map. It is just another open space in your map. that your program internally knows is the endpoint.
k=4
a group for everything black=walls(occupied), orange=doors(may or may not look like occupied cells depending on whether or not they can be opened),red=target(unoccupied), white=open space(unoccupied).
In both cases you can generate labels for your clusters and use those in your map. I'm not sure what exactly your path finding algorithm is, but typically the goal is to minimize some cost function, and as such you assign a extremely high cost to walls (so they will never be crossed), possibly assign a medium cost to doors (in case they can't be opened). Just some ideas, good luck

Android: How to detect these objects in images? (Image included). Tried OpenCV and metaioSDK, but both are not working good enough

i have been working with object detection / recognition in images captured from an android device camera recently.
the object i am trying to detect are all kinds of buttons that look like this:
Picture of buttons
so far i have been trying with OpenCV and also with the metaio SDK. results:
OpenCV was always detecting something, but gave lots of false hits. also it is too much work to collect all the pictures for what i have in mind. i have tried three ways with OpenCV:
FeatureDetection (SURF, ORB and so on) -> was way too slow and not enough features on my objects.
Template Matching -> seems to only work when the template is exactly a part out of the scene image
Training classifiers -> this worked the best so far, but is too much work for my goal, and still gives too many false detections.
metaioSDK was working ok when i took my reference images (the icon part of each button) out of a picture like shown above, then printed the full image and pointed my android device camera at the printed picture. but when i tried with the real buttons (not a picture of them) then almost nothing got detected anymore. in the metaio documentation it is said that the reference images need to have lots of features and color differences and also should not only consist of white text. well, as you see my reference images are exactly the opposite from what they should be. but thats just how the buttons look ;)
so, my question would be: does any of you have a suggestion about what else i could try to detect and recognize each of those buttons when i point my android camera at them?

As a suggestion can you try the following approach:
Class-Specific Hough Forest for Object Detection
they provide a C code implementation. Compile and run it and see the results, then replace positive and negative training images with the ones you have according the following rules:
In a car you will need to define the following 3 areas:
target region (the image you provided is a good representation of a target region)
nearby working area (this area have information regarding you target relative location) I would recommend: area 3-5 times the target regions, around the target, can be a good working area
everything outside the above can be used as negative images
then,
Use "many" positive images (100-1000) at different viewing angles (-30 - +30 degrees) and various distances.
You will have to make assumptions at which viewing angles and distances your users will use the application. The more strict they are the better performance you will get. A simple "hint" camera overlay can give a good idea to people what you expect the working area to be.
Use few times (3-5) more different negative image set which includes pictures of things that might be in the camera but should not contribute any target position information.
Do not use big images, somewhere around 100-300px in width should be enough
Assemble the database, and modify the configuration file that the code comes with. Run the program, see if performance is OK for your needs.
The program will return a voting map cloud of the object you are looking fore. Add gaussian blur to it, and apply some threshold to it (you will have to make another assumption for this threshold value).
Extracted mask will define the area you are looking for. The size of the masked region can give you good estimate of the object scale. Given this information it will be much easier to select proper template and perform template matching.
(Also some thoughts) You can also try to do a small trick by using goodFeaturesToTrack function with the mask you got, to get a set of locations and compare them with the corresponding locations on a template. Constuct an SSD and solve it for rotation, scale and transition parameters, by mimizing alignment error (but not sure if this approach will work)

Motion detection using OpenCV

I see queries related to opencv motion detection, but my requirement is much simpler , so i am asking the question again .
I would like to analyse video frames and see if something has changed in the frame. Any kind of motion occurring in the frame has be recognized. I just want to get notified if something happens. I don't need to track/ draw contours.
Attempts made :
1) Template matching using OpenCV ( TM_CCORR_NORMED ).
I get the similarity index using cvMinMaxLoc &
if( sim_index > threshold )
"Nothing chnged"
else
"Changed
Problem faced :
I couldn't find a way to decide on how to set thresholds. The values of false match and perfect were very close.
2) Method 2
a) Make running average
b) Take abs difference between current frame and moving average.
c) Threshold it and made it binary
d) Count the number of non zero values
Again am stuck with how to threshold it, because i am getting a large number of non zero values even for very similar frames.
Please advice me on what approach i should take. Am i going in the right direction with the above two methods, or is there a simple method which can work in all most generic scenarios.

Method 2 is generally regarded as the most simple method for motion detection, and is very effective if you have no water, swaying trees or highly variable lighting conditions in your video.
Normally you implement it like this:
motion_frame=abs(newframe-running_avg);
running_avg=(1-alpha)*running_avg+alpha*newframe;
You can threshold the motion_frame if you want, then count the nonzeroes. But you could also just sum the elements of the motion_frame and threshold that instead (be sure to work with floating point numbers). Optimizing the parameters for this is pretty easy, just make two trackbars and play around with it. Typically alpha is around [0.1; 0.3].
Lastly, it is probably overkill to do this on entire frames, you could just use subsampled versions and the result will be very similar.

Collision detection for rotated bitmaps on Android

I need pixel-perfect collision detection for my Android game. I've written some code to detect collision with "normal" bitmaps (not rotated); works fine. However, I don’t get it for rotated bitmaps. Unfortunately, Java doesn’t have a class for rotated rectangles, so I implemented one myself. It holds the position of the four corners in relation to the screen and describes the exact location/layer of its bitmap; called "itemSurface". My plan for solving the detection was to:
Detect intersection of the different itemSurfaces
Calculating the overlapping area
Set these areas in relation to its superior itemSurface/bitmap
Compare each single pixel with the corresponding pixel of the other bitmap
Well, I’m having trouble with the first one and the second one. Does anybody has an idea or got some code? Maybe there is already code in Java/Android libs and I just didn’t find it.

I understand that you want a collision detection between rectangles (rotated in different way). You don't need to calculate the overlapping area. Moreover, comparing every pixel will be ineffective.
Implement a static boolean isCollision function which will tell you is there a collision between one rectangle and another. Before you should take a piece of paper do some geometry to find out the exact formulas. For performance reasons do not wrap a rectangle in some Rectangle class, just use primitive types like doubles etc.
Then (pseudo code):
for (every rectangle a)
for (every rectangle b)
if (a != b && isCollision(a, b))
bounce(a, b)
This is O(n^2), where n is number of rectangles. There are better algorithms if you need more performance. bounce function changes vectors of moving rectangles so that imitates a collision. If the weight of objects was the same (you can aproximate weight with size of the rectangles), you just need to swap two speed vectors.
To bounce elements correctly you could need to store auxiliary table boolean alreadyBounced[][] to determine which rectangles do not need a change of their vectors after bounce (collision), because they were already bounced.
One more tip:
If you are making a game under Android you have to watch out to not allocate memory during gameplay, because it will faster invoke GC, which takes a long time and slow downs your game. I recommend you watching this video and related. Good luck.

Using OpenCV to analyze data from protein gels

So what I want to do is write an application that, at least in the future, could be ported to mobile platforms(such as android) that can scan an image of a protein gel and return data such as the number of bands(ie weights) in a column, relative concentration(thickness of the band), and the weights of each in each column.
For those who aren't familiar, mixtures of denatured proteins(basically, molecules made completed straight) are loaded into each column, and with the use of electricity the proteins are pulled through a gel(because the proteins are polar molecules). The end columns of each side of this image http://i52.tinypic.com/205cyrl.gif are where you place a mixture of proteins of known weights(so if you have 4 different weights, the band on top is the largest weight, and the weight/size of the protein decreases the further it travels down). Is something like this possible to analyze using OpenCV? The given image is a really clean looking gel, they can often get really messy(see google images). I figured if I allowed a user to enter the number of columns, which columns contain known weight markers and their actual weights, as well as provide an adjustable rectangle to size around the edges of the gel, that maybe it would be possible to scan and extract data from the images of these gels? I skimmed through a textbook on OpenCV but I didn't see any obvious and reliable way I could approach this. Any ideas? Maybe a different library would be better suited?

I believe you can do this using OpenCV
My approach would be a color based separation. And then counting the separate different components.
In big steps your app would do the following steps:
Load the image, rotate it scale manually through the GUI of your app, to match your needs
Create a second grayscale image in which each pixel contains a value between [0,255], that represents how good the color of the original point matches the target color (in the case of this image the shade of blue)
In one of my experiments I've used the concept of fuzzy sets and alpha cuts to extract objects of a certain color. The triangular membership function gave me pretty good results. This simply meant that I've defined triangular functions for all three color channels RGB, and summed their result for each color given as input. If the values of the color were close to the centers of the triangles, then I had a strong similarity. Plus, by controlling the width of the triangles you can define the tolerance of the matches. (another option would be to use trapezoidal membership functions)
At this point you have a grayscale image, where the background (gel) is black and the proteins are gray/white. If you wish to clear up some noise use the morphological operators (page 127) erode and dilate (cvErode and cvDelate in openCV).
After it, can use this great openCV based blob extraction library to extract the bounding boxes of the remaining gray areas - representing the proteins
Having all the coordinates of the bounding boxes you can apply your own algorithms, to extract whatever data you wish
In my opinion OpenCV gives you all the necesarry tools. However a fully automated solution might be hard to obtain. But I'm sure you can easily build a GUI where you can set the parameters of the operators you apply during the above described steps
As for Android: I didn't develop for mobile platforms, but I know that you can create C++ apps for these devices - have read several questions regarding iPhone & openCV -, so I think that your app would be portable, or at least the image processing part of it (the GUI might be too platform specific).