How to convert modern day photos to the look and feel of those Polaroid photos ? References and/or sample codes are welcome. Thanks!
Convert the images to HSV (cv::cvtColor) then look at adjusting the hue/saturation values
see http://en.wikipedia.org/wiki/HSL_and_HSV for a rather too technical article
Here is a video showing how to do it in GIMP: http://www.youtube.com/watch?v=1LAUm-SrWJA and here is the tutorial: http://howto.nicubunu.ro/gimp_polaroid_photo/
You can look at various steps (each one of them would be some basic image processing operation) and glue together to make your own code. I think each GIMP operation is in turn available as a script-fu script/code.
I would suggest using blend modes along with the HSV conversion.
This website below has been of tremendous help to me while processing images to give them an 'old' look.
http://www.simplefilter.de/en/basics/mixmods.html
Do note that you need to mix and match different blend modes with color tints and blur algorithms to achieve the various Polaroid effects.
A good starting point would be look at ImagemMagick. It already does have cmdline options to change the hue and saturation of a photo. Find a parameter set that gives you the result that you want and look at the source code to see what it is doing behind the scenes..
Programmatically, you'd want to use an image processing library such as OpenCV.
A large part of the effect (besides adding the white frame) is a change in the image color balance and histogram. This is due to the degradation of the chemical elements in the Polaroid film.
The types of operations you would need to apply to the image:
Changing color spaces such as HSV;
Desaturation;
Blending with color filters (this is the suggested way here);
Changing the brightness and contrast of the image channels for the chosen color space.
Obviously, most tutorials about how to do this in Photoshop (or other photo editing apps), can be converted into programs using OpenCV.
Related
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.
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/
I want to blend multiple photo shots of same scene but only one object is in different position on every shot. I want to know what kind of algorithm would give desired results. Here is an example
Well, what you are looking for is called Image Fusion. There are many methods that do this, but it is still a fairly active research idea. Based on the images you have, you should select the one that performs the best. Because your images will have imperfections and lighting, shadowing differences this is way beyond than a simple cut and paste.
Here is a little more information and some algorithm explanations: Image Fusion by Image Blending.
I am working on a project with gesture recognition. Now I want to prepare a presentation in which I can only show images. I have a series of images defining a gesture, and I want to show them in a single image just like motion history images are shown in literature.
My question is simple, which functions in opencv can I use to make a motion history image using lets say 10 or more images defining the motion of hand.
As an example I have the following image, and I want to show hand's location (opacity directly dependent on time reference).
I tried using GIMP to merge layers with different opacity to do the same thing, however the output is not good.
You could use cv::updateMotionHistory
Actually OpenCV also demonstrates the usage in samples/c/motempl.c
How can I get most clear iplimage among rest , using OpenCV.? When there is no training image to compare.
As a example there is a web cam input which move a hand. But when I stream this video ,I get 10 iplimages. But only 5th one is more clear. I want to filter that 5th one using openCV.
Its good to evaluate each and every iplimage (10 images) and assign rank using clearness of the images. Is there any way to do it so..?
I hope kindness support from your all.
Thank you.
Several measures of 'clearness' or blur in pictures have been quantified in research papers. So what you're looking for probably is to estimate the amount of blur present in an image and then use it to decide whether it is to be discarded or not. I found this study to be quite helpful, gives a comparative overview of the different metrics of blurriness. Google Scholar's always there with a search string of estimating blur in images if you want to check other resources.
You could choose whichever method gives you the best results with your set of images, experimentally determining which threshold of the measure will be 'clear' enough. Or of course you could run the blur estimation on every frame and choose the one with the lowest blur.