I try to identify changes on an object. Therefore I take a picture before and after using the object. At the moment I'm working with the absolute Difference of the two pictures and taking the contours of the resulting difference image. That works fine as long as the object is positioned perfectly and captured like in the image before. Only small differences in its position make my method useless.
Has anybody a different solution approach with OpenCV oder EmguCV? I was thinking about checking if one of the neighbor pixels is identical then there should be no change detected, but I don't know of an existing performant algorithm.
Example Images (Pictures don't match my usecase, but they should be helpful to illustrate my problem):
Before
After
Yes there are many way to do this. I like the following:
Histogram match. Get a histogram before and after and check for differences. Is sensitive to changes in lighting. Very good method if you are in a controlled lighting setting
Correlation match. If you use MatchTemplate you can get the “quality” of the match. This can be made to be less sensitive to light. But is sensitive to rotation changes between the two images.
Try to implement some and let’s see your code.
Related
I'm following a unity tutorial and made a very basic top-down 2D scene using tilemaps. Starting up the game the rendering seems fine, but as soon the camera moves along the Y axis, the tiles seems to "move apart". Also, the sprites I use seems to get an offset for the tile-map, as if they were cut incorrectly. (1 pixel dilation on the y axis.)
Before moving:
After moving:
Any ideas why this happens and how to deal with it?
My issue seems to be something like this one, but there were no answers here. Another thread found a solution to a similar issue, however I couldn't make this work, as Unity does not allow me to directly change the Pixel Snap property of the shader, but says "MaterialPropertyBlock is used to modify these values".
I have some ideas here:
I found this video which uses a SpriteAtlas to alleviate a similar problem. The person mentions also setting the Filter Mode to Point for the Sprite Atlas, along with Compression set to High Quality.
Also double check that your sprites are setup the same way... Point (no filter) and High Quality Compression. I think I remember there being an option for NO compression... that might be worth a shot as well, since I think that most 2d images are already compressed.
I also remember there being a "Pixel Perfect Camera", as documented here:
It specifically mentions being a good solution "which ensures your pixel art remains crisp and clear at different resolutions, and stable in motion.". The basic idea is that you use the Pixel Perfect Camera Component, and use settings similar to what was mentioned above.
For your sprites:
Filter Mode: Point (no filter)
Compression: None
Use the same Pixels Per Unit for each
With the Sprite Editor, set all the sprites Pivot Unit Mode to "Pixels".
Set your snap settings to 1 / Asset Pixels Per Unit (and apply to existing Game Object)
There are also specific settings for the Pixel Perfect Camera component.
This is possibly just a floating point precision error caused by your tile calculation somewhere.
There are a number of complex fixes that are the "right" way to do it, but a quick an easy fix is to move the tiles closer together by a very small amount (0.0001f). If this doesn't fix it, its probably not a precision error.
I'm looking for a good way to isolate an air bubble from the following image. I'm using Visual Studio 2015 and C#.
I've heard of the watershed method and believe it may be a good solution.
I tried implementing the code solution found here: watershed image segmentation
I haven't had much success. The solution has trouble finding functions, for example: FilterGrayToGray.
Does anyone know of a good way to do this?
You should just train a Neural network to recognize parts of image when there are no bubbles (in example groups of 16x16 pixels). Then when recognizing a square is not successfull you do a burst of horizontal scanlines and you register where the edge starts and ends. You can determine pretty precisely the section of a bubble (however determine its volume needs to keep into account surface curvature, wich is possible but more hard) on the image. If you have the possibility to use more cameras you can triangulate more sections of a bubble and get a precise idea of real volume. As another euristic to know bubble size you can also use the known volume throughput, so you know that if in a time interval you emitted X liters of air, and the bubbles have sections given in a certain proportion you can redistribute total volume across bubbles and further increase precision (of course you have to keep in mind pressure since bubbles on bottom of the pool will be more little).
As you see you can play with simple algorithms like gaussian difference and contrast to achieve different quality results.
In the left picture you can easily remove all background noise, however you have lost now part of the bubbles. It is possible you can re-gain the missed bubbles edge by using a different illumination on the pool
In the right picture you have the whole bubbles edges, but now you also have more areas that you need to manually discard from picture.
As for edge detections algorithm you should use an algorithm that do not add a fixed offset to edges (like convolution matrix or laplace), for this I think gaussian difference would work best.
Keep all intermediate data so one can easily verify and tweak the algorithm and increase its precision.
EDIT:
The code depends on wich library you use, you can easily implement Gaussian Blur and Horizontal Scanline, for Neural Networks there are already c# solutions out there.
// Do gaussian difference
Image ComputeGaussianDifference (Image img, float r1, float r2){
Image img = img.GaussianBlur( r1);
Image img2 = img.GaussianBlur( r2);
return (img-img2).Normalize(); // make values more noticeable
}
more edits pending.. try do document yourself in the meantime, I already given enough trace to let you do the job, you just need basic understanding of simple image processing algorithms and usage of ready neural networks.
Just in case if you are looking for some fun - you could investigate Application Example: Photo OCR. Basically you train one NN to detect bubble, and try it on a sliding window across the image. When you capture one - you use another NN, which is trained to estimate bubble size or volume (you probably can measure your air stream to train the NN). It is not so difficult as it sounds, and provides very high precision and adaptability.
P.S. Azure ML looks good as a free source of all the bells and whistles without need to go deep.
To solutions come to mind:
Solution 1:
Use the Hough transform for circles.
Solution 2:
In the past I also had a lot of trouble with similar image segmentation tasks. Basically I ended up with a flood fill, which is similar to the watershed algorithm you programmed.
A few hat tricks that I would try here:
Shrink the image.
Use colors. I notice you're just making everything gray; that makes little sense if you have a dark-blue background and black boundaries.
Do you wish to isolate the air bubble in a single image, or track the same air bubble from an image stream?
To isolate a 'bubble' try using a convolution matrix on the image to detect the edges. You should pick the edge detection convolution based on the nature of the image. Here is an example of a laplace edge detection done in gimp, however it is faily straight forward to implement in code.
This can help in isolating the edges of the bubbles.
If you are tracking the same bubble from a stream, this is more difficult due to as the way bubbles distort when flowing through liquid. If the frame rate is high enough it would be easy to see difference from frame to frame and you can judge which bubble it is likely to be (based on positional difference). i.e you would have to compare current frame to previous frame and use some intelligence to attempt to work out which bubble is the same from frame to frame. Using a fiducial to help give a point of reference would be useful too. The nozzle at the bottom of the image might make a good one, as you can generate a signature for it (nozzle won't change shape!) and check that each time. Signatures for the bubbles aren't going to help much since they could change drastically from one image to the next, so instead you would be processing blobs and their likely location in the image from one frame to the next.
For more information on how convolution matrices work see here.
For more information on edge detection see here.
Hope this helps, good luck.
In my application,I want to find the exact coordinates of the objects detected in a laser scanner placed in a moving vehicle in real time. Till now I have found out the local minima of the points in the graph and is giving all local minimas including the unwanted 2 show in the fig .But I want only the main object locations like the one indicated as 1 in the figure.
I tried these methods in C# after searching in google and stackoverflow
I did moving average for the curve and found out the local minima.the result is okay.But Since it is real time,I worry that it may take some processing time.
I also tried finding out the slopes of the different points in the curve and tried to mark the positions with the maximum and minimum slopes. It works but not exactly finding the correct position.
I tried marking the points which satisfy both criteria i.e local minima having high slopes.but it is not working as intended.
The last option I have is to have a reference in the first scan and subtracting the other object graphs from the reference.Then I can compare the subtracted range and the local minima to find the exact position. i.e the part 1 and the black curve at the bottom.
the scanning frequency is 50Hz and if the moving average does not much time. I will go with the first option. Finally I am going to code the algorithm in c++. I am trying different things in c# since it is easier to view and analyse the graphs.
I have finally found out a solution. I used the foreground segmentation and blob detection algorithm. I referred this http://www.v2.nl/lab/projects/laser-measurement-system-object-for-max
Here's the scenario:
I am using Visual Studio 2008 with .NET framework 3.5. I am using C#. And for database I am using MySQL. I have a picturebox on a form and 10-12 buttons (each with some image manipulation function). On clicking one of the buttons openfiledialog box is shown up where the user can select the specific file to provide to the program. On clicking another button the program should perform the actions as explained below.
I have an image of a circuit. Suppose this is the image which is provided to the program. e.g.
What I intend to do is that - the program should hypothetically label the circuit as follows:
and then it should separate the image and store the information in a database.
Is there any way to do that. Can anyway tell me the approach to do that? Any help or suggestions please.
Thanks.
In image processing, the problem of finding the 'parts' of the circuit is known as connected component labeling. If you are using C#, I believe that you can use EmguCV (a wrapper to the OpenCV library) to solve the first part of the problem. To do that, you have to consider that the white pixels are the background and that the black pixels are objects.
Now that you have the separated traces, the problem is reduced to finding and labeling the white dots. Again, you can solve it by connected component labeling, but now the objects are represented by white pixels and the background are the black pixels.
At least for your example case, a very simple algorithm would work.
Find a black pixel from the image
Using a flood-fill algorithm, find all the pixels connected to it, and separate it. That's one of your traces.
Working with the separated trace, find a white pixel and use a flood-fill algorithm to find all the pixels connected to it. If you run to the edge of the image, it's not a hole. If you don't, it might be a hole, or a loop in the trace. Use a threshold for the hole size to determine if it's a terminal hole or a loop.
Label the hole and remove it from consideration. Repeat until there are no more unprocessed white pixels.
Remove the whole trace from consideration, and jump to 1.
When there are no more black pixels in consideration in step 1, you're done.
You should probably get pretty far with a basic image editing library that has a flood-fill function, a function to separate a certain color into a new image, and a function to replace colors (the last two are trivial to implement, and there are plenty of flood-fill algorithms available online). You can use different colors to mark different things, for instance, color everything "no in consideration" red. It also makes for an interesting visualization if you look at it in real time!
As a pet project/learning experience (no this is not homework) I'm working on software to recognize barcodes from a photograph. I'm not looking for software or a library that does it - instead I'm using this as a learning exercise that I'm blogging about and will post up on Codeplex.
I have code that successfully recognizes EAN13 barcodes (which I published on CodePlex) and UPC version A/E should follow shortly. I have two areas that I'm concerned about, though. First is in decoding barcodes that are in a picture that is bit blurry or with poor contrast, etc. Second is in simply finding the actual barcode in a larger picture (right now you have to give me a photo of just the barcode).
I have the gut feeling that some form of AI is going to help me out here. I played a bit in the past with genetic algorithms and I took a course ages ago on AI so it's not totally foreign to me, but I'm not quite sure where to start.
What type of algorithm is best suited to this type of problem? Any recommended reading or code for the AI grunt work? Yes, I want to understand what's happening, but I don't necessarily want to go down to the level of coding the sorts, etc myself.
I would suggest to search for properties that a barcode has. Some that I have in mind are:
Histogram of colors shows two distinct colors in about even distribution
Doing a hough transformation finds many parallel lines
The thickness of the lines have two distinct dimensions.
Some other?
Having this I would split the image into pieces and do a classification with these features then cobine the results to calculate a liklyhood if the piece contains an barcode or not.
For your second problem (blurry image) I would suggest to calculate the 1st order derivative of the grayvalues and then detect the edges of the lines in this space. The maximum of the derivative is lower if the image is blurred but it should be detectable to a certain blurring factor.
Does this help you?
As mp already noted you don't necessary need any real AI technique for it. Have a look at chapter 12 of Real World Haskell. It implements an almost complete barcode recognizer. Sample code is in Haskell, but there is plenty of explanation, so you can probably understand the ideas and tricks even without Haskell experience.
If you want to solve it with AI then the best bet is probably using ANNs. For the given problem I would recommend to use a quite advanced technique called HyperNEAT. See my explanation (and links) as the first answer to the SO question Neural Network Size...
I would probably use two or three different networks,
The first one to find the barcode on the bigger picture. One output neuron per pixel/set of pixels, output value is the confidence if that pixel seems to be a part of a barcode. Based on the result I would use some image transformation to convert it to a "standard" format (x*y rectangle)
If you have difficulties with finding the location of the barcode use a second one. Feed the result of the first one, and ask it to give the coordinates of two corners. However, I'm not quite sure that it will be very easy to evolve this one.
Last one would work on the standardized format, output neurons for each line (or square, if you work with a possibly 2D barcode), saying if the given area should be considered black or white.
Probably it would also help to do some pre-processing of the image, e.g. those that are described in RWH.
You don't need any specific AI or softcomputing technique. You need to apply image processing technique to improve the quality of the image or to isolate the barcode from a larger image.
You could use Matlab for prototyping and learnig more about image processing.