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I am trying to import R plots for editing in inkscape (tried bot svg and pdf). Once I do so inkscape becomes too slow. Is there any solution to this problem? I tried to simplify the paths through PATH/simplify but there was no significant improvement
Thanks
Unfortunately, probably not. My guess is that you are trying to edit R plots with many data points, which then translates to many SVG elements in Inkscape.
Inkscape handles well relatively small to moderately complex/involved graphics, but consumes large amounts of RAM once many SVG elements are rendered to the canvas, even if each element is small.
You can try a couple of things: (a) try opening Inkscape on a more powerful computer with lots of RAM; (b) see if you can create an R plot which conveys the same meaning, but has fewer data points/elements; (c) try to dig around the web to find an unofficial 64-bit version of Inkscape. Alternatively, if editing the plots is a one-off, consider trialing Adobe Illustrator 64-bit and creating SVG plots this way.
Sorry I could not be of more help.
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How can I detect QR codes when processing the pixels of an image?
The image might be pixelated (low-fidelity), noisy (missing or extra pixels in a line), blurry (antialiasing), or be at an angle (image skew). Simply looping through the pixels seems like it would require some sort of line detection and then you could figure out a good calculate of the number of expected blocks between the three (or four) different corners.
You will need to use an image processing library to detect QRCodes. I’ve used go-zxing in the past which is 100% native go.
You will need to be familiar with the go image package that’s part of the standard library.
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I have recently taken up film photography. Part of the workflow is to scan the images using a flatbed scanner. Unfortunately this process is very slow. Using some software (Silverfast) you make a prescan, zoom in make a more detailed pre scan, click ad drag around a rectangle which highlights the frame, do this for 12 frames, then set the software to do the full res scans.
I want to automate this process. Rather than layout where each frame is, I want to scan the whole film strip, and then use ML.Net to find each frame (X,Y coordinates of the top left corner) which I will then pass to ImageMagick to extract the actual image.
I want to use ML.Net because I am a .Net developer and may have the opportunity to use this experience later. So although example using OpenCV would be welcome, ML.Net would be preferable.
I am a bit of noob when it comes to ML stuff. My first thought is to try train a neural net, inputting the scan image and outputting the X and Y values. However that seems naive (as the image is 100s of MB in size). I imagine the there are better tool then just a raw neural net.
My searching on 'ML object recognition' didn't seem to help as the examples I found were about finding the Dog or Person in an image not a 'frame'; which could be a dog or a person.
Even a pointer in the right direction, of the correct name for this problem would be a great help.
So, what are the type of tool/functions I should I be using to try and solve this type of problem using ML.net?
This is not so much a machine learning problem as it is an image processing problem. I would think ML.Net is quite overkill.
What you probably want is an image processing library and utilize some form of edge detection or "region of interest" detection.
For example, look at this question:
Detect display corners with Emgu
Maybe I misunderstand what you want to do and you actually would benefit from machine learning; then you probably should pre process your images with an image processing library before feeding them to your model.
Hope it helps.
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I have recently came across a great image processing algorithm called Seam Carving.
It uses graphs (Shortest Path algorithm) to do its job.
What other amazing algorithms are out there worth looking at?
I'm not talking about the majority of boring matrix based ones, rather something you could never think about before you came across it, just like this one.
Even though this is offtopic I'm going to answer ;)
In this page you can find algorithms which:
Depixelize an image
Color a black and white image
Restore a picture or make a HDR (whatever that means...)
Turn a photo into an hand-drawn like image
Seam carving
Remove objects
Compose a picture
Beautify a face
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I have questions about both sift and phash
First of all, I'm using SIFT to identify similar image in real-time service.
Like pictures by phone-camera, small amount of rotation and blurred effect could be.
And I found Phash. So, I test phash on its demo page. But result made me to sigh.
This is result of above test:
In this test, two images are fixed on x-axis. So they don'
t have rotation. But right images' logo were removed and person was moved to left side. In my eye, This is 'Very Similar'. In addition, SIFT catch this completely.
Now, This is question.
pHash is faster than SIFT?
Is pHash's accuracy reliable?
SIFT's output was too big to use in real-time service. So I must use hash to make output smaller size like LSH(Locality-sensitive hashing). Any other way to I try?
Ok, I got it.
pHash can't recognize rotation and critical movement as same thing.
In case of data space, pHash was dramatically good for using. It is very small size: one image to one hash. SIFT, however, need 128 bytes to get feature point. And there are many feature points in one image.
Eventually, SIFT can identify similar image well than pHash. But more and more size was needed.
In speed bench, I can't test yet. But I think, pHash was faster than SIFT because SIFT have to operate for many features on one images.
If you have another answers for above question, tell me please.
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Let's say I have a very simple image with a few colours (white, black, grey). And I have a set of tiny images (lots of them). Is there any library that can help me rebuild the big image using all of the tiny images as building blocks? Like a mosaic.
Here's an example: http://d.pr/i/LJ0Z
P.S. I've tried to google a solution, but it seems I can't create correct search query because of my bad english. I only get software solutions for creating mosaics.
Just posting a solution to my problem here in case anyone will need in the future.
Let's say you have a simple image with a few colours. You need to re-create it with a set of smaller images (like a mosaic).
Split your image into cells (4x4, 8x8 or any other number) and assign corresponding colour for each cells (in other words "pixelate" your image), create array of cells for each color you have.
Implement listing #4 for Maximal Rectangle Problem from this article: http://www.drdobbs.com/database/the-maximal-rectangle-problem/184410529 (huge thanks to David Vandevoorde for such a great explanation!)
Now, for each small image you have (pieces of mosaic), determine its size as a rectangle. Then, using rectangle packing algorithm of your choice fill the max rectangles with these small images (I've been using this one for C#: http://kossovsky.net/index.php/2009/07/cshar-rectangle-packing/ again, huge thanks to the author for sharing!)
That's it. It's not very fast, but it gets the job done.
this search turns up various results:
andreamosaic
pixisnap
etc.
adding "python library" found osaic. repeat for whatever language you are using...