I'm trying to generate a scrolling starfield for a game with C++ and SDL. I'm using a simple, naive algorithm that just creates a lot of white pixels on black backround. However, this "starfield" looks too unnatural - probably because of the random number generator's poor quality (I use the rand() function).
Are there any special algorithms for generating starfields that look more or less realistic?
Thanks.
There's always this classic. Highlights:
[...] imagine the stars to be points in 3D space, all of them moving towards the viewer, along the Z-axis. At each time step, the 3D coordinates of the stars will be projected onto the screen, and displayed.
For a smoother effect, we can make the stars black when they first appear (so you don't notice them) then get brighter as they get closer.
There are two ways the sense of vastness can be modeled. The first is simply to model a huge area of space, which is impractical to say the least. The second is to make the stars move with a range of velocities.
I found this useful tutorial a while ago on creating a 'realistic' star field. It's not C++, but it should be easily adaptable once you get the idea.
You could use Lloyd's algorithm to relax the random points and make them semi-random. I read this idea in a map generator but it probably can be used do create an eventually distributed star field too.
You probably don't want it to be truly random. You will end up with blobs of pixels in some places when you really want individual pixels scattered around. Your best bet would probably be to code a smaller section and then just repeat it over and over to get the full starfield look.
Related
I'm thinking of making an online version of the game Sprouts, possibly using the JavaScript web browser graphics library p5.js
You can read more about it but basically there are 2 players that draw lines with their mouse between points. The lines can be straight or curved in any way. One of the rules is that no 2 lines can cross.
I haven't started making the game yet, but planning it ahead, everything seems relatively simple to accomplish except for one problem:
When a user draws a line, I need to figure out if this line intersects with another line. However, since these lines aren't linear or exactly mathematical in any way I'm used to, there doesn't seem to be a simple mathematical way I can check for intersection.
How would I check if two such lines, given that I know the location of every pixel on the lines, cross?
I apologize for no code, I haven't yet started it. If you wish to include code in your answer, you can use psuedocode or any gui programming that you might be familiar with. However, I would prefer a purely hypothetical answer, since everything is at this stage.
Here are some ideas I have:
For each pixel on the line, I could check if any of the other lines has a pixel of the same position, in which case they overlap. This seems inefficient, so the below point is something else I came up with that is more efficient but less rigid and reliable.
Before drawing the line, If you make sure all the lines are one color, for every pixel on the line, you could check if this pixel is already colored in the same color as the color of the lines, using something like getPixel() If so, abort drawing the line. This solutions seems prone to many problems and a bit fragile.
These two solutions either trade efficiency for reliability or vice-versa. Are there any other solutions you know? Keep in mind this will run in a browser, so efficiency is important.
Keep in mind this will run in a browser, so efficiency is important.
You need to give yourself a better idea of what kind of "efficiency" is important to you and your users. Both approaches you outlined seem reasonable to me. I wouldn't assume that a solution is inefficient before you try it and measure its performance.
Taking a step back, in general you're going to need to store the lines in some kind of data structure. You said the lines are not mathematical, but you can break the lines down into individual line segments or points, which are mathematical. That could be an array of line segments, or a 2D array of boolean values, or a map of points, or a quadtree. There are many options. Then you need to check for collisions between those lines or points and the new lines or points added by the other player.
Another option to consider is decreasing the resolution of your input space. For example, maybe your game window is 500x500 pixels, but you really only need the game board to be 100x100 possible point positions. You could scale that 100x100 game board up so it's displayed at 500x500. This would improve the "efficiency" of whatever solution you come up with.
But again, I wouldn't worry about "efficiency" at this point. Either solution you mentioned seems fine. Get that working and then iterate on it if you notice a problem. Good luck.
May be this article from Mike Bostock about the Sutherland-Hodgman algorythm can interest you. It is more related to the intersection of 2 polygons rather than 2 lines but may be it can be adapted to your problem.
I am trying to count the number of hairs transplanted in the following image. So practically, I have to count the number of spots I can find in the center of image.
(I've uploaded the inverted image of a bald scalp on which new hairs have been transplanted because the original image is bloody and absolutely disgusting! To see the original non-inverted image click here. To see the larger version of the inverted image just click on it). Is there any known image processing algorithm to detect these spots? I've found out that the Circle Hough Transform algorithm can be used to find circles in an image, I'm not sure if it's the best algorithm that can be applied to find the small spots in the following image though.
P.S. According to one of the answers, I tried to extract the spots using ImageJ, but the outcome was not satisfactory enough:
I opened the original non-inverted image (Warning! it's bloody and disgusting to see!).
Splited the channels (Image > Color > Split Channels). And selected the blue channel to continue with.
Applied Closing filter (Plugins > Fast Morphology > Morphological Filters) with these values: Operation: Closing, Element: Square, Radius: 2px
Applied White Top Hat filter (Plugins > Fast Morphology > Morphological Filters) with these values: Operation: White Top Hat, Element: Square, Radius: 17px
However I don't know what to do exactly after this step to count the transplanted spots as accurately as possible. I tried to use (Process > Find Maxima), but the result does not seem accurate enough to me (with these settings: Noise tolerance: 10, Output: Single Points, Excluding Edge Maxima, Light Background):
As you can see, some white spots have been ignored and some white areas which are not actually hair transplant spots, have been marked.
What set of filters do you advise to accurately find the spots? Using ImageJ seems a good option since it provides most of the filters we need. Feel free however, to advise what to do using other tools, libraries (like OpenCV), etc. Any help would be highly appreciated!
I do think you are trying to solve the problem in a bit wrong way. It might sound groundless, so I'd better show my results first.
Below I have a crop of you image on the left and discovered transplants on the right. Green color is used to highlight areas with more than one transplant.
The overall approach is very basic (will describe it later), but still it provides close to be accurate results. Please note, it was a first try, so there is a lot of room for enhancements.
Anyway, let's get back to the initial statement saying you approach is wrong. There are several major issues:
the quality of your image is awful
you say you want to find spots, but actually you are looking for hair transplant objects
you completely ignores the fact average head is far from being flat
it does look like you think filters will add some important details to your initial image
you expect algorithms to do magic for you
Let's review all these items one by one.
1. Image quality
It might be very obvious statement, but before the actual processing you need to make sure you have best possible initial data. You might spend weeks trying to find a way to process photos you have without any significant achievements. Here are some problematic areas:
I bet it is hard for you to "read" those crops, despite the fact you have the most advanced object recognition algorithms in your brain.
Also, your time is expensive and you still need best possible accuracy and stability. So, for any reasonable price try to get: proper contrast, sharp edges, better colors and color separation.
2. Better understanding of the objects to be identified
Generally speaking, you have a 3D objects to be identified. So you can analyze shadows in order to improve accuracy. BTW, it is almost like a Mars surface analysis :)
3. The form of the head should not be ignored
Because of the form of the head you have distortions. Again, in order to get proper accuracy those distortions should be corrected before the actual analysis. Basically, you need to flatten analyzed area.
3D model source
4. Filters might not help
Filters do not add information, but they can easily remove some important details. You've mentioned Hough transform, so here is interesting question: Find lines in shape
I will use this question as an example. Basically, you need to extract a geometry from a given picture. Lines in shape looks a bit complex, so you might decide to use skeletonization
All of a sadden, you have more complex geometry to deal with and virtually no chances to understand what actually was on the original picture.
5. Sorry, no magic here
Please be aware of the following:
You must try to get better data in order to achieve better accuracy and stability. The model itself is also very important.
Results explained
As I said, my approach is very simple: image was posterized and then I used very basic algorithm to identify areas with a specific color.
Posterization can be done in a more clever way, areas detection can be improved, etc. For this PoC I just have a simple rule to highlight areas with more than one implant. Having areas identified a bit more advanced analysis can be performed.
Anyway, better image quality will let you use even simple method and get proper results.
Finally
How did the clinic manage to get Yondu as client? :)
Update (tools and techniques)
Posterization - GIMP (default settings,min colors)
Transplant identification and visualization - Java program, no libraries or other dependencies
Having areas identified it is easy to find average size, then compare to other areas and mark significantly bigger areas as multiple transplants.
Basically, everything is done "by hand". Horizontal and vertical scan, intersections give areas. Vertical lines are sorted and used to restore the actual shape. Solution is homegrown, code is a bit ugly, so do not want to share it, sorry.
The idea is pretty obvious and well explained (at least I think so). Here is an additional example with different scan step used:
Yet another update
A small piece of code, developed to verify a very basic idea, evolved a bit, so now it can handle 4K video segmentation in real-time. The idea is the same: horizontal and vertical scans, areas defined by intersected lines, etc. Still no external libraries, just a lot of fun and a bit more optimized code.
Additional examples can be found on YouTube: RobotsCanSee
or follow the progress in Telegram: RobotsCanSee
I've just tested this solution using ImageJ, and it gave good preliminary result:
On the original image, for each channel
Small (radius 1 or 2) closing in order to get rid of the hairs (black part in the middle of the white one)
White top-hat of radius 5 in order to detect the white part around each black hair.
Small closing/opening in order to clean a little bit the image (you can also use a median filter)
Ultimate erode in order to count the number of white blob remaining. You can also certainly use a LoG (Laplacian of Gaussian) or a distance map.
[EDIT]
You don't detect all the white spots using the maxima function, because after the closing, some zones are flat, so the maxima is not a point, but a zone. At this point, I think that an ultimate opening or an ultimate eroded would give you the center or each white spot. But I am not sure that there is a function/pluggin doing it in ImageJ. You can take a look to Mamba or SMIL.
A H-maxima (after white top-hat) may also clean a little bit more your results and improve the contrast between the white spots.
As Renat mentioned, you should not expect algorithms to do magic for you, however I'm hopeful to come up with a reasonable estimate of the number of spots. Here, I'm going to give you some hints and resources, check them out and call me back if you need more information.
First, I'm kind of hopeful to morphological operations, but I think a perfect pre-processing step may push the accuracy yielded by them dramatically. I want you put my finger on the pre-processing step. Thus I'm going ti work with this image:
That's the idea:
Collect and concentrate the mass around the spot locations. What do I mean my concentrating the masses? Let's open the book from the other side: As you see, the provided image contains some salient spots surrounded by some noisy gray-level dots.
By dots, I mean the pixels that are not part of a spot, but their gray-value are larger than zero (pure black) - which are available around the spots. It is clear that if you clear these noisy dots, you surely will come up with a good estimate of spots using other processing tools such as morphological operations.
Now, how to make the image more sharp? What if we could make the dots to move forward to their nearest spots? This is what I mean by concentrating the masses over the spots. Doing so, only the prominent spots will be present in the image and hence we have made a significant step toward counting the prominent spots.
How to do the concentrating thing? Well, the idea that I just explained is available in this paper, which its code is luckily available. See the section 2.2. The main idea is to use a random walker to walk on the image for ever. The formulations is stated such that the walker will visit the prominent spots far more times and that can lead to identifying the prominent spots. The algorithm is modeled Markov chain and The equilibrium hitting times of the ergodic Markov chain holds the key for identifying the most salient spots.
What I described above is just a hint and you should read that short paper to get the detailed version of the idea. Let me know if you need more info or resources.
That is a pleasure to think on such interesting problems. Hope it helps.
You could do the following:
Threshold the image using cv::threshold
Find connected components using cv::findcontour
Reject the connected components of size larger than a certain size as you seem to be concerned about small circular regions only.
Count all the valid connected components.
Hopefully, you have a descent approximation of the actual number of spots.
To be statistically more accurate, you could repeat 1-4 for a range of thresholds and take the average.
This is what you get after applying unsharpen radius 22, amount 5, threshold 2 to your image.
This increases the contrast between the dots and the surrounding areas. I used the ballpark assumption that the dots are somewhere between 18 and 25 pixels in diameter.
Now you can take the local maxima of white as a "dot" and fill it in with a black circle until the circular neighborhood of the dot (a circle of radius 10-12) erases the dot. This should let you "pick off" the dots joined to each other in clusters more than 2. Then look for local maxima again. Rinse and repeat.
The actual "dot" areas are in stark contrast to the surrounding areas, so this should let you pick them off as well as you would by eyeballing it.
I 'm trying to find an efficient way of acceptable complexity to
detect an object in an image so I can isolate it from its surroundings
segment that object to its sub-parts and label them so I can then fetch them at will
It's been 3 weeks since I entered the image processing world and I've read about so many algorithms (sift, snakes, more snakes, fourier-related, etc.), and heuristics that I don't know where to start and which one is "best" for what I'm trying to achieve. Having in mind that the image dataset in interest is a pretty large one, I don't even know if I should use some algorithm implemented in OpenCV or if I should implement one my own.
Summarize:
Which methodology should I focus on? Why?
Should I use OpenCV for that kind of stuff or is there some other 'better' alternative?
Thank you in advance.
EDIT -- More info regarding the datasets
Each dataset consists of 80K images of products sharing the same
concept e.g. t-shirts, watches, shoes
size
orientation (90% of them)
background (95% of them)
All pictures in each datasets look almost identical apart from the product itself, apparently. To make things a little more clear, let's consider only the 'watch dataset':
All the pictures in the set look almost exactly like this:
(again, apart form the watch itself). I want to extract the strap and the dial. The thing is that there are lots of different watch styles and therefore shapes. From what I've read so far, I think I need a template algorithm that allows bending and stretching so as to be able to match straps and dials of different styles.
Instead of creating three distinct templates (upper part of strap, lower part of strap, dial), it would be reasonable to create only one and segment it into 3 parts. That way, I would be confident enough that each part was detected with respect to each other as intended to e.g. the dial would not be detected below the lower part of the strap.
From all the algorithms/methodologies I've encountered, active shape|appearance model seem to be the most promising ones. Unfortunately, I haven't managed to find a descent implementation and I'm not confident enough that that's the best approach so as to go ahead and write one myself.
If anyone could point out what I should be really looking for (algorithm/heuristic/library/etc.), I would be more than grateful. If again you think my description was a bit vague, feel free to ask for a more detailed one.
From what you've said, here are a few things that pop up at first glance:
Simplest thing to do it binarize the image and do Connected Components using OpenCV or CvBlob library. For simple images with non-complex background this usually yeilds objects
HOwever, looking at your sample image, texture-based segmentation techniques may work better - the watch dial, the straps and the background are wisely variant in texture/roughness, and this could be an ideal way to separate them.
The roughness of a portion can be easily found by the Eigen transform (explained a bit on SO, check the link to the research paper provided there), then the Mean Shift filter can be applied on the output of the Eigen transform. This will give regions clearly separated according to texture. Both the pyramidal Mean Shift and finding eigenvalues by SVD are implemented in OpenCV, so unless you can optimize your own code its better (and easier) to use inbuilt functions (if present) as far as speed and efficiency is concerned.
I think I would turn the problem around. Instead of hunting for the dial, I would use a set of robust features from the watch to 'stitch' the target image onto a template. The first watch has a set of squares in the dial that are white, the second watch has a number of white circles. I would per type of watch:
Segment out the squares or circles in the dial. Segmentation steps can be tricky as they are usually both scale and light dependent
Estimate the centers or corners of the above found feature areas. These are the new feature points.
Use the Hungarian algorithm to match features between the template watch and the target watch. Alternatively, one can take the surroundings of each feature point in the original image and match these using cross correlation
Use matching features between the template and the target to estimate scaling, rotation and translation
Stitch the image
As the image is now in a known form, one can extract the regions simply via pre set coordinates
I am thinking of implement a image processing based solution for industrial problem.
The image is consists of a Red rectangle. Inside that I will see a matrix of circles. The requirement is to count the number of circles under following constraints. (Real application : Count the number of bottles in a bottle casing. Any missing bottles???)
The time taken for the operation should be very low.
I need to detect the red rectangle as well. My objective is to count the
items in package and there are no
mechanism (sensors) to trigger the
camera. So camera will need to capture
the photos continuously but the
program should have a way to discard
the unnecessary images.
Processing should be realtime.
There may be a "noise" in image capturing. You may see ovals instead of circles.
My questions are as follows,
What is the best edge detection algorithm that matches with the given
scenario?
Are there any other mechanisms that I can use other than the edge
detection?
Is there a big impact between the language I use and the performance of
the system?
AHH - YOU HAVE NOW TOLD US THE BOTTLES ARE IN FIXED LOCATIONS!
IT IS AN INCREDIBLY EASIER PROBLEM.
All you have to do is look at each of the 12 spots and see if there is a black area there or not. Nothing could be easier.
You do not have to do any edge or shape detection AT ALL.
It's that easy.
You then pointed out that the box might be rotatated, things could be jiggled. That the box might be rotated a little (or even a lot, 0 to 360 each time) is very easily dealt with. The fact that the bottles are in "slots" (even if jiggled) massively changes the nature of the problem. You're main problem (which is easy) is waiting until each new red square (crate) is centered under the camera. I just realised you meant "matrix" literally and specifically in the sentence in your original questions. That changes everything totally, compared to finding a disordered jumble of circles. Finding whether or not a blob is "on" at one of 12 points, is a wildly different problem to "identifying circles in an image". Perhaps you could post an image to wrap up the question.
Finally I believe Kenny below has identified the best solution: blob analysis.
"Count the number of bottles in a bottle casing"...
Do the individual bottles sit in "slots"? ie, there are 4x3 = 12 holes, one for each bottle.
In other words, you "only" have to determine if there is, or is not, a bottle in each of the 12 holes.
Is that correct?
If so, your problem is incredibly easier than the more general problem of a pile of bottles "anywhere".
Quite simply, where do we see the bottles from? The top, sides, bottom, or? Do we always see the tops/bottoms, or are they mixed (ie, packed top-to-tail). These issues make huge, huge differences.
Surf/Sift = overkill in this case you certainly don't need it.
If you want real time speed (about 20fps+ on a 800x600 image) I recommend using Cuda to implement edge detection using a standard filter scheme like sobel, then implement binarization + image closure to make sure the edges of circles are not segmented apart.
The hardest part will be fitting circles. This is assuming you already got to the step where you have taken edges and made sure they are connected using image closure (morphology.) At this point I would proceed as follows:
run blob analysis/connected components to segment out circles that do not touch. If circles can touch the next step will be trickier
for each connected componet/blob fit a circle or rectangle using RANSAC which can run in realtime (as opposed to Hough Transform which I believe is very hard to run in real time.)
Step 2 will be much harder if you can not segment the connected components that form circles seperately, so some additional thought should be invested on how to guarantee that condition.
Good luck.
Edit
Having thought about it some more, I feel like RANSAC is ideal for the case where the circle connected components do touch. RANSAC should hypothetically fit the circle to only a part of the connected component (due to its ability to perform well in the case of mostly outlier points.) This means that you could add an extra check to see if the fitted circle encompasses the entire connected component and if it does not then rerun RANSAC on the portion of the connected component that was left out. Rinse and repeat as many times as necessary.
Also I realize that I say circle but you could just as easily fit an ellipse instead of circles using RANSAC.
Also, I'd like to comment that when I say CUDA is a good choice I mean CUDA is a good choice to implement the sobel filter + binirization + image closing on. Connected components and RANSAC are probably best left to the CPU, but you can try pushing them onto CUDA though I don't know how much of an advantage a GPU will give you for those 2 over a CPU.
For the circles, try the Hough transform.
other mechanisms: dunno
Compiled languages will possibly be faster.
SIFT should have a very good response to circular objects - it is patented, though. GLOHis a similar algorithm, but I do not know if there are any implementations readily available.
Actually, doing some more research, SURF is an improved version of SIFT with quite a few implementations available, check out the links on the wikipedia page.
Sum of colors + convex hull to detect boundary. You need, mostly, 4 corners of a rectangle, and not it's sides?
No motion, no second camera, a little choice - lot of math methods against a little input (color histograms, color distribution matrix). Dunno.
Java == high memory consumption, Lisp == high brain consumption, C++ == memory/cpu/speed/brain use optimum.
If the contrast is good, blob analysis is the algorithm for the job.
I'm trying to write a simple tracking routine to track some points on a movie.
Essentially I have a series of 100-frames-long movies, showing some bright spots on dark background.
I have ~100-150 spots per frame, and they move over the course of the movie. I would like to track them, so I'm looking for some efficient (but possibly not overkilling to implement) routine to do that.
A few more infos:
the spots are a few (es. 5x5) pixels in size
the movement are not big. A spot generally does not move more than 5-10 pixels from its original position. The movements are generally smooth.
the "shape" of these spots is generally fixed, they don't grow or shrink BUT they become less bright as the movie progresses.
the spots don't move in a particular direction. They can move right and then left and then right again
the user will select a region around each spot and then this region will be tracked, so I do not need to automatically find the points.
As the videos are b/w, I though I should rely on brigthness. For instance I thought I could move around the region and calculate the correlation of the region's area in the previous frame with that in the various positions in the next frame. I understand that this is a quite naïve solution, but do you think it may work? Does anyone know specific algorithms that do this? It doesn't need to be superfast, as long as it is accurate I'm happy.
Thank you
nico
Sounds like a job for Blob detection to me.
I would suggest the Pearson's product. Having a model (which could be any template image), you can measure the correlation of the template with any section of the frame.
The result is a probability factor which determine the correlation of the samples with the template one. It is especially applicable to 2D cases.
It has the advantage to be independent from the sample absolute value, since the result is dependent on the covariance related with the mean of the samples.
Once you detect an high probability, you can track the successive frames in the neightboor of the original position, and select the best correlation factor.
However, the size and the rotation of the template matter, but this is not the case as I can understand. You can customize the detection with any shape since the template image could represent any configuration.
Here is a single pass algorithm implementation , that I've used and works correctly.
This has got to be a well reasearched topic and I suspect there won't be any 100% accurate solution.
Some links which might be of use:
Learning patterns of activity using real-time tracking. A paper by two guys from MIT.
Kalman Filter. Especially the Computer Vision part.
Motion Tracker. A student project, which also has code and sample videos I believe.
Of course, this might be overkill for you, but hope it helps giving you other leads.
Simple is good. I'd start doing something like:
1) over a small rectangle, that surrounds a spot:
2) apply a weighted average of all the pixel coordinates in the area
3) call the averaged X and Y values the objects position
4) while scanning these pixels, do something to approximate the bounding box size
5) repeat next frame with a slightly enlarged bounding box so you don't clip spot that moves
The weight for the average should go to zero for pixels below some threshold. Number 4 can be as simple as tracking the min/max position of anything brighter than the same threshold.
This will of course have issues with spots that overlap or cross paths. But for some reason I keep thinking you're tracking stars with some unknown camera motion, in which case this should be fine.
I'm afraid that blob tracking is not simple, not if you want to do it well.
Start with blob detection as genpfault says.
Now you have spots on every frame and you need to link them up. If the blobs are moving independently, you can use some sort of correspondence algorithm to link them up. See for instance http://server.cs.ucf.edu/~vision/papers/01359751.pdf.
Now you may have collisions. You can use mixture of gaussians to try to separate them, give up and let the tracks cross, use any other before-and-after information to resolve the collisions (e.g. if A and B collide and A is brighter before and will be brighter after, you can keep track of A; if A and B move along predictable trajectories, you can use that also).
Or you can collaborate with a lab that does this sort of stuff all the time.