I use the Perl Imager library to process images.
I am trying to do something similar to the trim operation in ImageMagick or autocrop in GIMP: eliminate empty white space at the borders of the image.
Is there such a thing? I've searched the documentation but found nothing.
I think you are out of luck regarding a prewritten Imager method to do what you want. But I have looked at what the C API has to offer and I think it should be reasonably simple to write an XS extension to do the job.
My approach would be to pick apart the ImageMagick code for the Trim function and implement it in terms of the Imager API, which defines an i_gpix function to determine the colour of individual pixels.
Rather than duplicate the function of the Imager crop function, I would choose that the XS code returned left, right, top, and bottom margin sizes that could be passed directly to the existing crop.
Not what you were hoping for, I know, but well within the bounds of possibility.
Related
I want to remove the regular strips of the image as shown as follow. I try many methods, and they do not work, such as image media filter and FFT filter.
Could you tell me how to remove the strips?
All that black is removing a ton of information from the image. You have two options available - either re-capture that missing information in a new shot, or attempt to invent / synthesize / extrapolate the missing information with software.
If you can re-shoot, get your camera as close to the mesh fence as you can, use the largest aperture your lens supports to have the shallowest possible depth of field, and set your focus point as deep as possible - this will minimize the appearance of the mesh.
If that is the only still you have to work with, you've got a few dozen hours of playing with the clone and blur tools in front of you in just about any image editing software package you like.
Photoshop would be my go to tool of choice for this. In Photoshop CS5 they introduced something called content aware fill. I'm not sure if it will help you in this specific case because there is SO MUCH black that Adobe's algorithm may think other parts of the mesh are valid sources for filling in the mesh you're trying to clear out.
I have two binary image like this. I have a data set with lots of picture like at the bottom but with differents signs.
and
I would like to compare them in order to know if it's the same figure or not (especially inside the triangle). I took a look in Sift and Surf feature but it's doesn't work well on this type of picture (it find matchning point whereas the two picture are different,especially inside).
I also hear about SVM but i don't know if i have to implement it for this type of problem.
Do you have an idea ?
Thank you
I think you should not use SURF features on the binary image as you have already discarded a lot of information at that stage with your edge detector.
You could also use the Linear or Circle Hough Transform that in this case could tell you a lot about image differences.
If you wat to find 2 exactly identical images, simply use hash functions like md5.
But if you want to find related ( not exatcly identical) images, you are running in trouble ;). look for artificial neural network libs...
Is there any option to tell gs to convert strokes to fills? Something similar to the "Expand" feature of the Adobe Illustrator? Thanks in advance.
You can't convert strokes to fills, no.
You can use the strokepath PostScript operator, this will replace the current path with a path which encloses the shape that would be drawn if 'stroke' was applied to that path.
The result of strokepath can be used for fill, clip and pathbbox operations, but is not itself suitable for stroking.
Its completely unclear to me why you would want to replace a stroke with a fill, since the two will cover exactly the same area. I'm not at all familiar with Adobe Illustrator so using it as an example doesn't help me. Perhaps you could explain what it is you really want to do, and why you can't do it in the application producing the PostScript, which is almost always a better solution.
I need to remove the blur this image:
Image source: http://www.flickr.com/photos/63036721#N02/5733034767/
Any Ideas?
Although previous answers are right when they say that you can't recover lost information, you could investigate a little and make a few guesses.
I downloaded your image in what seems to be the original size (75x75) and you can see here a zoomed segment (one little square = one pixel)
It seems a pretty linear grayscale! Let's verify it by plotting the intensities of the central row. In Mathematica:
ListLinePlot[First /# ImageData[i][[38]][[1 ;; 15]]]
So, it is effectively linear, starting at zero and ending at one.
So you may guess it was originally a B&W image, linearly blurred.
The easiest way to deblur that (not always giving good results, but enough in your case) is to binarize the image with a 0.5 threshold. Like this:
And this is a possible way. Just remember we are guessing a lot here!
HTH!
You cannot generally retrieve missing information.
If you know what it is an image of, in this case a Gaussian or Airy profile then it's probably an out of focus image of a point source - you can determine the characteristics of the point.
Another technique is to try and determine the character tics of the blurring - especially if you have many images form the same blurred system. Then iteratively create a possible source image, blur it by that convolution and compare it to the blurred image.
This is the general technique used to make radio astronomy source maps (images) and was used for the flawed Hubble Space Telescope images
When working with images one of the most common things is to use a convolution filter. There is a "sharpen" filter that does what it can to remove blur from an image. An example of a sharpen filter can be found here:
http://www.panoramafactory.com/sharpness/sharpness.html
Some programs like matlab make convolution really easy: conv2(A,B)
And most nice photo editing have the filters under some name or another (sharpen usually).
But keep in mind that filters can only do so much. In theory, the actual information has been lost by the blurring process and it is impossible to perfectly reconstruct the initial image (no matter what TV will lead you to believe).
In this case it seems like you have a very simple image with only black and white. Knowing this about your image you could always use a simple threshold. Set everything above a certain threshold to white, and everything below to black. Once again most photo editing software makes this really easy.
You cannot retrieve missing information, but under certain assumptions you can sharpen.
Try unsharp masking.
Basically, suppose that I have a fingerprint. I know the dimension of my image, and I know that the fingerprint is black on a white background or that it is green on a black background or something like that.
Is there a way to process only the parts that delimit the image, in this case, the fingerprint? What I'm trying to do is basically this:
1) Delimit fingerprint
2) Extract the important points to compare to other fingerprints
3) Find best match on a database of other fingerprints that had their points previously extracted
I already have methods for 2 and 3, so now I just would have to delimit the image.
Programming language would have to be Ruby, Java or C++. Ruby preferred, then Java, and God help me if I have to use C++. I don't have any experience with image processing, but I'd like to do this with multiple common formats such as jpg, gif, png, if possible.
I think that the best way to do it is applying a edge detection filter to your image.
There are may approaches as suggested by wikipedia (article), but noone of them is trivial because they work on gradients or kernels. You should check Canny Edge Detection that should be enough straight-forward to implement: tutorial.
In any case if you want to avoid going deep into implementation details you should use OpenCV that is a computer vision library able to do these things in a simple way. You can use it for sure in C++ and Java but I think that a wrapper for Ruby is offered too. This is a simple example using that library with Canny algorithm.
EDIT: actually my answer covers point 2-3, so I'm wondering what you mean by delimiting the image? Think about the fact that scaling or rotating must be considered too if you want to compare different fingerprints: you need a fuzzy comparator.. maybe you should work on the Fast Fouried Transform version of the image that can handle such things in a better way.
An easy approach could be using threshold, like:
Convert your image to grayscale - so you have fingerprint in white on black.
Find a threshold value that gets most of the fingerprint.
Use open operation (http://en.wikipedia.org/wiki/Mathematical_morphology) to remove noise.
(experiment with dilate a few times)
Find the center of gravity (x,y) of the image and the standard deviation (vx, vy).
In the box:
[x-2vx,y-2vy],
[x-2vx,y+2vy],
[x+2vx,y+2vy],
[x+2vx,y-2vy]
You will find 95.4% of the pixels
You could narrow the box down to find the actual max and min pixels in it, if you have many outliers.
Use the box to clip from the original image.
It is simple method that might work well for your situation :)