So I am fairly inexperienced with After Effects. But I am making a title for a video project and am having a hard time figuring something out. I have some text that slides over, and appears once it is in the masked area. However this is a very hard edge and doesn't look the best. How do I soften that edge? I tried making another shape and blurring it and then masking that over the other shape but that didn't work. I tried Googling but it was full of people saying you cannot feather a shapes edges in AE.
I would use the mask in my image directly with the feather option but then that mask moves with my image, and that's not what I want.
So I learned that you can just make an image in photoshop that does whatever you want it to do and just use that.
The masks feathering moves your image? Can you put up a screen recording of the problem?
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I am working on a project in which my task is to find malfunctioning detector-pixels. I thought that this problem is really similar to the problems people facing, when trying to detect bad pixels on an image. Right now I have maps, that have good and bad detector pixels. The way to find out if a detector part is bad is the following: if collects different data then the other non-malfunctioning pixels around it, then it probably is malfunctioning. However, in my case, the bad pixels tend to be next to each other clumping up, and I don't really know how I should interpret this. Can someone help me out with a good algorithm, or a book that is helpful?
This is how a map looks:
These should be found:
If you have multiple images from the same sensor, and there are bad pixels at the same place, you can detect them by comparing images pixel by pixel. This will allow you to detect places that does not change (probably bad pixel).
Other idea may be using something like Gauss-Filter and then compare this blurred image with original one.
Good Idea will be loading some images to Gimp or Photoshop and try some filters and then if you will find good way to spot the bad pixels - implement it by yourself. I would recommend OpenCV for this task.
OpenCV has lots of build-in mechanism. Some of them (maybe edge-detection? blurring?) may be interesting for you.
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.
Basicly i need to cut foreground object from green screen video. I need to make green transparent or directly cut the foreground object. I need to use OpenCv and C++. I find couple of methods but doesnt work. What i need to do it?
there isn't a magical way to do so. You need to programatically select the roi applying effects on each frame (i.e. on the Mat object). You may need to reduce noise, apply blur, extract each channels and do much more. So be patient and start experimenting.
Some background:
Hi all! I have a project which involves cloud imaging. I take pictures of the sky using a camera mounted on a rotating platform. I then need to compute the amount of cloud present based on some color threshold. I am able to this individually for each picture. To completely achieve my goal, I need to do the computation on the whole image of the sky. So my problem lies with stitching several images (about 44-56 images). I've tried using the stitch function on all and some subsets of image set but it returns an incomplete image (some images were not stitched). This could be because of a lack of overlap of something, I dunno. Also the output image has been distorted weirdly (I am actually expecting the output to be something similar to a picture taken by a fish-eye lense).
The actual problem:
So now I'm trying to figure out the opencv stitching pipeline. Here is a link:
http://docs.opencv.org/modules/stitching/doc/introduction.html
Based on what I have researched I think this is what I want to do. I want to map all the images to a circular shape, mainly because of the way how my camera rotates, or something else that has uses a fairly simple coordinate transformation. So I think I need get some sort of fixed coordinate transform thing for the images. Is this what they call the homography? If so, does anyone have any idea how I can go about my problem? After this, I believe I need to get a mask for blending the images. Will I need to get a fixed mask like the one I want for my homography?
Am I going through a possible path? I have some background in programming but almost none in image processing. I'm basically lost. T.T
"So I think I need get some sort of fixed coordinate transform thing for the images. Is this what they call the homography?"
Yes, the homography matrix is the transformation matrix between an original image and the ideal result. It warps an image in perspective so it can fit in stitching to the other image.
"If so, does anyone have any idea how I can go about my problem?"
Not with the limited information you provided. It would ease the problem a lot if you know the order of pictures (which borders which.. row, column position)
If you have no experience in image processing, I would recommend you use a tutorial covering stitching using more basic functions in detail. There is some important work behind the scenes, and it's not THAT harder to actually do it yourself.
Start with this example. It stitches two pictures.
http://ramsrigoutham.com/2012/11/22/panorama-image-stitching-in-opencv/
After spending on a while on this, I finally managed to detect the hands through thresholding. The only problem is that VERY FEW pixels in the background remain, which will mess up the next step. Any suggestions on how to get rid of the few background pixels? Because I don't want to go through the whole background subtraction thing for just a few pixels. Background Subtraction is not an option for the program, so please don't suggest it
Thanks
It's hard to be sure without a more detailed description of your hand detection algorithm. If you have a few background pixels that are isolated from the hands you have detected, I would suggest morphological operation like opening to eliminate single pixel detections in your binary mask. In openCV, I think you need to erode and then dilate