Quick question: At this website here http://www.vischeck.com/examples/ there are a few pictures of numbers hidden within another color to test for color blindness. Is there any way that these images can be generated algorithmically?
They are based on ready-made dot fields, and you overlay a number on them, and do the coloring on the whole dot each time it is partially filled. If You know the correct colors - that will do ;)
What language are you coding in? It's impossible to give any definitive answer without knowing your problem well.
If you're in .NET, GDI is your best bet for generating such a dot field, but it is not simple to do algorithmically, and it's possible that these were hand-drawn.
One easier possibility you have is to use an evenly-spaced circles field, even if it's not as elegant.
Then, you'd pick two colors that aren't supposed to be (easily) distinguishable by (certain?) color-blind people.
Now, you draw a number in the square field (using one of 10 matrices for the numbers 0-9 that represent each number character with sizes compatible with the circle field) using (limited) random variations of the two colors that the person shouldn't distinguish.
In other words, if the person isn't supposed to distinguish red and green, you'd make a character using shades of red on top of a shades of green background.
You'd possibly need HUE>RGB functions, for .NET you'd have to look for a library (I remember using one from codeproject).
Related
I was looking on stackoverflow for an answer, but wasn't really satisfied with what I found.
I need several different colors. I only find out the number of different colors I need at runtime.
I created the colors here now with 3 random numbers each. But as you can see, the two light green / brown colors are already quite difficult to distinguish from each other.
In the worst case, it can happen that the values are completely the same or differ only in one number.
So I wanted to ask, how do you create colors in a colorspace or in a color scheme, so that the generated colors can be distinguished?
And as an additional question, how can you create them so that they are softened, because you don't want an object to be immediately eye-catching?
I wrote this in python but any answer would help me so I can write it then by my own in python.
I don't know much about image processing so please bear with me if this is not possible to implement.
I have several sets of aerial images of the same area originating from different sources. The pictures have been taken during different seasons, under different lighting conditions etc. Unfortunately some images look patchy and suffer from discolorations or are partially obstructed by clouds or pix-elated, as par example picture1 and picture2
I would like to take as an input several images of the same area and (by some kind of averaging them) produce 1 picture of improved quality. I know some C/C++ so I could use some image processing library.
Can anybody propose any image processing algorithm to achieve it or knows any research done in this field?
I would try with a "color twist" transform, i.e. a 3x3 matrix applied to the RGB components. To implement it, you need to pick color samples in areas that are split by a border, on both sides. You should fing three significantly different reference colors (hence six samples). This will allow you to write the nine linear equations to determine the matrix coefficients.
Then you will correct the altered areas by means of this color twist. As the geometry of these areas is intertwined with the field patches, I don't see a better way than contouring the regions by hand.
In the case of the second picture, the limits of the regions are blurred so that you will need to blur the region mask as well and perform blending.
In any case, don't expect a perfect repair of those problems as the transform might be nonlinear, and completely erasing the edges will be difficult. I also think that colors are so washed out at places that restoring them might create ugly artifacts.
For the sake of illustration, a quick attempt with PhotoShop using manual HLS adjustment (less powerful than color twist).
The first thing I thought of was a kernel matrix of sorts.
Do a first pass of the photo and use an edge detection algorithm to determine the borders between the photos - this should be fairly trivial, however you will need to eliminate any overlap/fading (looks like there's a bit in picture 2), you'll see why in a minute.
Do a second pass right along each border you've detected, and assume that the pixel on either side of the border should be the same color. Determine the difference between the red, green and blue values and average them along the entire length of the line, then divide it by two. The image with the lower red, green or blue value gets this new value added. The one with the higher red, green or blue value gets this value subtracted.
On either side of this line, every pixel should now be the exact same. You can remove one of these rows if you'd like, but if the lines don't run the length of the image this could cause size issues, and the line will likely not be very noticeable.
This could be made far more complicated by generating a filter by passing along this line - I'll leave that to you.
The issue with this could be where there was development/ fall colors etc, this might mess with your algorithm, but there's only one way to find out!
I need some advice about how to improve the visualization of cartographic information.
User can select different species and the webmapping app shows its geographical distribution (polygonal degree cells), each specie with a range of color (e.g darker orange color where we find more info, lighter orange where less info).
The problem is when more than one specie overlaps. What I am currently doing is just to calculate the additive color mix of two colors using http://www.xarg.org/project/jquery-color-plugin-xcolor/
As you can see in the image below, the resulting color where two species overlap (mixed blue and yellow) is not intuitive at all.
Someone has any idea or knows similar tools where to get inspiration? for creating the polygons I use d3.js, so if more complex SVG features have to be created I can give a try.
Some ideas I had are...
1) The more data on a polygon, the thicker the border (or each part of the border with its corresponding color)
2) add a label at the center of polygon saying how many species overlap.
3) Divide polygon in different parts, each one with corresponding species color.
thanks in advance,
Pere
My suggestion is something along the lines of option #3 that you listed, with a twist. Rather painting the entire cell with species colors, place a dot in each cell, one for each species. You can vary the color of each dot in the same way that you currently are: darker for more, ligher for less. This doesn't require you to blend colors, and it will expose more of your map to provide more context to the data. I'd try this approach with the border of the cell and without, and see which one works best.
Your visualization might also benefit from some interactivity. A tooltip providing more detailed information and perhaps a further breakdown of information could be displayed when the user hovers his mouse over each cell.
All of this is very subjective. However one thing's for sure: when you're dealing with multi-dimensional data as you are, the less you project dimensions down onto the same visual/perceptual axis, the better. I've seen some examples of "4-dimensional heatmaps" succeed in doing this (here's an example of visualizing latency on a heatmap, identifying different sources with different colors), but I don't think any attempt's made to combine colors.
My initial thoughts about what you are trying to create (a customized variant of a heat map for a slightly crowded data set, I believe:
One strategy is to employ a formula suggested for
n + 1
with regards to breaks in bin spacing. This causes me some concern regarding how many outliers your set has.
Equally-spaced breaks are ideal for compact data sets without
outliers. In many real data sets, especially proteomics data sets,
outliers can make this representation less effective.
One suggestion I have would be to consider the idea of adding some filters to your categories if you have not yet. This would allow slimming down the rendered data for faster reading by the user.
another solution would be to use something like (Comprehensive) R
or maybe even DanteR
Tutorial in displaying mass spectrometry-based proteomic data using heat maps
(Particularly worth noting I felt, was 'Color mapping'.)
I need to draw n different objects on a chart. I want to pick a different color for each of them to make them distinguishable. The objects will be moved around, so I cannot count on ideas like "four color theorem" to assign same color to non-adjacent items. So far my problem call for up to 20 different items.
Is there a good way to pick n different colors to make them as distinguishable from each other as possible?
First of all, I have since changed the design so that it is not important to use 20 distinct colors. The default palette of 10 colors show up quite well.
Secondly, I've found an answer to my own question. The thing I want to do is called Color scale for categorical coding. Here is a paper that propose a method to do it
An algorithm for generating color scales for both categorical and ordinal coding - Breslow - 2009 - Color Research & Application - Wiley Online Library
http://onlinelibrary.wiley.com/doi/10.1002/col.20559/full
I'm going to give the paper a glance. It is probably too technical than what I prepare to do.
I'd say colour distinction is a very subjective matter and you're probably better off looking for an existing colour palette and working your way from there. The higher your n, the higher your chance of two automatically generated colours being indistinguishable by your users even though by some colour-theoretic criterion they are very different.
And don't forget to make sure you don't use colour as the only distinction between objects, or:
you'll be in for a lot of hate mail from colour blind people
you risk people mistaking objects of similar colours as having some sort of implicit grouping
Do you really need to use 20 different colors? That is a lot of colors if you still want people to be able to distinguish them. Also realize that people who are colorblind will be lost looking at your charts. 10% of males are color blind. It would be better if you could further break down your objects into two to five groups. Then you could use different shapes as well as color to distinguish objects. For instance, you might have crosses, circles, triangles, stars, and squares of four different colors as shown here:
For choice of colors, I would check out the color brewer. However, notice it doesn't go up to 20 colors.
I have nothing useful to do and was playing with jigsaw puzzle like this:
alt text http://manual.gimp.org/nl/images/filters/examples/render-taj-jigsaw.jpg
and I was wondering if it'd be possible to make a program that assists me in putting it together.
Imagine that I have a small puzzle, like 4x3 pieces, but the little tabs and blanks are non-uniform - different pieces have these tabs in different height, of different shape, of different size. What I'd do is to take pictures of all of these pieces, let a program analyze them and store their attributes somewhere. Then, when I pick up a piece, I could ask the program to tell me which pieces should be its 'neighbours' - or if I have to fill in a blank, it'd tell me how does the wanted puzzle piece(s) look.
Unfortunately I've never did anything with image processing and pattern recognition, so I'd like to ask you for some pointers - how do I recognize a jigsaw piece (basically a square with tabs and holes) in a picture?
Then I'd probably need to rotate it so it's in the right position, scale to some proportion and then measure tab/blank on each side, and also each side's slope, if present.
I know that it would be too time consuming to scan/photograph 1000 pieces of puzzle and use it, this would be just a pet project where I'd learn something new.
Data acquisition
(This is known as Chroma Key, Blue Screen or Background Color method)
Find a well-lit room, with the least lighting variation across the room.
Find a color (hue) that is rarely used in the entire puzzle / picture.
Get a color paper that has that exactly same color.
Place as many puzzle pieces on the color paper as it'll fit.
You can categorize the puzzles into batches and use it as a computer hint later on.
Make sure the pieces do not overlap or touch each other.
Do not worry about orientation yet.
Take picture and download to computer.
Color calibration may be needed because the Chroma Key background may have upset the built-in color balance of the digital camera.
Acquisition data processing
Get some computer vision software
OpenCV, MATLAB, C++, Java, Python Imaging Library, etc.
Perform connected-component on the chroma key color on the image.
Ask for the contours of the holes of the connected component, which are the puzzle pieces.
Fix errors in the detected list.
Choose the indexing vocabulary (cf. Ira Baxter's post) and measure the pieces.
If the pieces are rectangular, find the corners first.
If the pieces are silghtly-off quadrilateral, the side lengths (measured corner to corner) is also a valuable signature.
Search for "Shape Context" on SO or Google or here.
Finally, get the color histogram of the piece, so that you can query pieces by color later.
To make them searchable, put them in a database, so that you can query pieces with any combinations of indexing vocabulary.
A step back to the problem itself. The problem of building a puzzle can be easy (P) or hard (NP), depending of whether the pieces fit only one neighbour, or many. If there is only one fit for each edge, then you just find, for each piece/side its neighbour and you're done (O(#pieces*#sides)). If some pieces allow multiple fits into different neighbours, then, in order to complete the whole puzzle, you may need backtracking (because you made a wrong choice and you get stuck).
However, the first problem to solve is how to represent pieces. If you want to represent arbitrary shapes, then you can probably use transparency or masks to represent which areas of a tile are actually part of the piece. If you use square shapes then the problem may be easier. In the latter case, you can consider the last row of pixels on each side of the square and match it with the most similar row of pixels that you find across all other pieces.
You can use the second approach to actually help you solve a real puzzle, despite the fact that you use square tiles. Real puzzles are normally built upon a NxM grid of pieces. When scanning the image from the box, you split it into the same NxM grid of square tiles, and get the system to solve that. The problem is then to visually map the actual squiggly piece that you hold in your hand with a tile inside the system (when they are small and uniformly coloured). But you get the same problem if you represent arbitrary shapes internally.