I am trying to use the OpenCVs Background Subtractor class MOG2 to seperate a person moving infront of a camera. I got everything set up and working nicely. But the resulting mask I am getting looks something like this:
(default settings)
Now what I would like to get is something like this:
(bad gimp skills :D)
I have already tryed to mess around with the parameter described in the docu, but all I managed to accomplish was something the looked like a motion blur effect...
So I was hopeing somebody with a better understanding of the algorithm or somebody who has already done something similar might be able to help me!
Thanks in advance, Foaly
I am working also with that and what I've seen is that this algorithm needs a good calibration to accomplish that goal, because you should be aware that this algorithm try to put in the background some pixels that don't show changes, e.g. in your skin major part of the pixels have the same color maybe this is the reason. I recommend to you that use other kind of methods (using zncc) if you want to use an application like the one that is showed in your question.
So I guess this were our image processing skills come into play. The first thing I would do is make the lines on the image thicker and join it it. We can use the following:
1) I would want the lines thicker. Use Morphological operators tutorial on Morphological operators with Otsu's method. This paper worked for me when I did my ear biometrics http://www4.comp.polyu.edu.hk/~csajaykr/myhome/papers/PR2011.pdf
2) Fill in connected components using opencv and clean the image
3) Segment human profile
Related
I'm trying to create 360 panorama using OpenCV. I found Stitcher class and now I try to use it. It is working good with normal panorama like this:
This panorama is created using 22 images. Stitcher class is good in that case. It's not fast but result is acceptable.
I have problem when I try to add ground and sky to it. I know it is very hard to this class to connect all this pictures, because ground and sky pictures are very similar to each other. I made some pictures in my house, with items on the floor and it's still not working so I think outside it's even more complicated for this algoritm.
Do you have any ideas how to do it using OpenCV? Maybe not OpenCV, something other, giving better results? Other languages are acceptable too. I know that there are applications doing this, but I don't want to use them. I'm looking for solution to write this myself (using libraries etc).
I'm stuck trying to get perspective transformation to work.
I want to draw an image so it fits 4 given points. It's something like you have in Photoshop and it's used to change the perspective of image (see image below).
I have an image in byte array and I'm not using any additional libraries.
Everything I've found so far was either for OpenCV or didn't do what I wanted.
I have found some open-source program PhotoDemon and it does exactly what I want, and there is a code for that. I was trying to get it to work for many hours but it gives me completly weird results (second line on the image below).
Could someone provide me with some code or step-by-step math of what and how to do or even just a pseudo-code. I'm a little bit sick of it. It seems easy but I need some help.
currently i am having much difficulty thinking of a good method of removing the gradient from a image i received.
The image is a picture taken by a microscope camera that has a light glare in the middle. The image has a pattern that goes throughout the image. However i am supposed to remove the light glare on the image created by the camera light.
Unfortunately due to the nature of the camera it is not possible to take a picture on black background with the light to find the gradient distribution. Nor do i have a comparison image that is without the gradient. (note- the location of the light glare will always be consistant when the picture is taken)
In easier terms its like having a photo with a flash in it but i want to get rid of the flash. The only problem is i have no way to obtaining the image without flash to compare to or even obtaining a black image with just the flash on it.
My current thought is conduct edge detection and obtain samples in specific locations away from the edges (due to color difference) and use that to gauge the distribution of gradient since those areas are supposed to have relatively identical colors. However i was wondering if there was a easier and better way to do this.
If needed i will post a example of the image later.
At the moment i have a preferrence of solving this in c++ using opencv if that makes it easier.
thanks in advance for any possible ideas for this problem. If there is another link, tutorial, or post that may solve my problem i would greatly appreciate the post.
as you can tell there is a light thats being shinned on the img as you can tell from the white spot. and the top is lighter than the bottome due to the light the color inside the oval is actually different when the picture is taken in color. However the color between the box and the oval should be consistant. My original idea was to perhaps sample only those areas some how and build a profile that i can utilize to remove the light but i am unsure how effective that would be or if there is a better way
EDIT :
Well i tried out Roger's suggestion and the results were suprisngly good. Using 110 kernel gaussian blurr to find illumination and conducting CLAHE on top of that. (both done in opencv)
However my colleage told me that the image doesn't look perfectly uniform and pointed out that around the area where the light used to be is slightly brighter. He suggested trying a selective gaussian blur where the areas above certain threshold pixel values are not blurred while the rest of the image is blurred.
Does anyone have opinions regarding this and perhaps a link, tutorial, or an example of something like this being done? Most of the things i find tend to be selective blur for programs like photoshop and gimp
EDIT2 :
it is difficult to tell with just eyes but i believe i have achieved relatively close uniformization by using a simple plane fitting algorithm.((-A * x - B * y) / C) (x,y,z) where z is the pixel value. I think that this can be improved by utilizing perhaps a sine fitting function? i am unsure. But I am relatively happy with the results. Many thanks to Roger for the great ideas.
I believe using a bunch of pictures and getting the avg would've been another good method (suggested by roger) but Unofruntely i was not able to implement this since i was not supplied with various pictures and the machine is under modification so i was unable to use it.
I have done some work in this area previously and found that a large Gaussian blur kernel can produce a reasonable approximation to the background illumination. I will try to get something working on your example image but, in the meantime, here is an example of your image after Gaussian blur with radius 50 pixels, which may help you decide if it's worth progressing.
UPDATE
Just playing with this image, you can actually get a reasonable improvement using adaptive histogram equalisation (I used CLAHE) - see comparison below - any use?
I will update this answer with more details as I progress.
I would like to point you to this paper: http://www.cs.berkeley.edu/~ravir/dirtylens.pdf, but, in my opinion, without any sort of calibration/comparison image taken apriori, it is difficult to mine out the ground truth from the flared image.
However, if you are trying to just present the image minus the lens flare, disregarding the actual scientific data behind the flared part, then you switch into the domain of image inpainting. Criminsi's algorithm, as described in this paper: http://research.microsoft.com/pubs/67276/criminisi_tip2004.pdf and explained/simplified in these two links: http://cs.brown.edu/courses/csci1950-g/results/final/eboswort/ http://www.cc.gatech.edu/~sooraj/inpainting/, will do a very good job in restoring texture information to the flared up regions. (If you'd really like to pursue this approach, do mention that. More comprehensive help can be provided for this).
However, given the fact that we're dealing with microscopic data, I doubt if you'd like to lose the scientific data contained in a particular region of an image. In that case, I really think you need to find a workaround to determine the flare model of the flash/light source w.r.t the lens you're using.
I hope someone else can shed more light on this.
I'm developing application that uses DirectShow combined with C++.
Its main goal is to capture users' faces.
I have reached the phase when I capture a image from my webcam.
The problem is I need an intelligent render. In fact, I need that render to be able to detect a face inside a rectangle.
I'm wondring if there is a filter that I can use for this purpose,
or if I need to create my own custmized filter.
If so enlighten my mind.
It would look like this:
I need to understand how I can draw a recangle in my render in the first place. Because otherwise, even if I know the algorithm, I will not be able to apply it. This is my main goal now.
I have some idea but I don't know if they are correct. I think I need to grab each frame separately and apply some modification in some pixels, like what's drawn in the live render.
Have a look at OpenCV
Quick look inside and I found this.
Making your own "filter" that works well is no easy job.
Are you talking about automatic detection of where there is something like a human face in the shot you have taken with the webcam? In this case object detection algorithms like Viola-Jones might be interesting for you.
If a commercial package is an option, you can use the Montivision Filter SDK which includes filters that should do the job out of the box. They offer a free eval which is perfect for experimentation.
Im looking for a way to warp an image similar to how the liquify/IWarp tool works in Photoshop/Gimp.
I would like to use it to move a few points on an image to make it look wider than it was originally.
Anyone have any ideas on libraries that could be used to do this? I'm currently using OpenCV in the same project so if theres a way using that it would be easiest but I'm open to anything really
Thanks.
EDIT: Heres an example of what im looking to do http://i.imgur.com/wMOzq.png
All I've done there is pulled a few points out sideways and thats what im looking to do from inside my application
From this search 'image warp operator source c++' i get:
..... Added function 'CImg ::[get_]warp()' that can warp an image using a deformation .... Added function 'CImg ::save_cpp()' allowing to save an image directly as a C/C++ source code. ...
then CImg could do well for you.
OpenCV's remap can accomplish this. You only have to provide x and y displacement maps. I would suggest you can create the displacement map directly if you are clever, and this would be good for brush-stroke manipulation similar to Photoshop's liquify. The mesh warp and sparse point map approach is another option, but essentially computes the displacement map based on interpolation.
You may want to take a look at http://code.google.com/p/imgwarp-opencv/. This library seems to be exactly what you need: image warping based on a sparse grid.
Another option is, of course, generate displacements yourself and use cv::Remap() function of OpenCV.