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'.)
Related
I'm trying to create a geochoropleth that maps subregions, but also includes outlines of larger regions. (You can think of it like mapping counties, but then wanting to include thicker outlines of states). Not all subregions are part of larger regions that need to be outlined. (Most aren't.) You can see an example of what I'm trying to replicate here:
What's the best way to add this regional outline to my map? I've tried keeping the regions and subregions as two separate files, with two overlaygeojsons calls in my geochoropleth call (with added d3 styling to change the fill and stroke to just be an outline). But when I do - the projection of the regional outline layer is strangely offset from the lower one.
I've also considered having both sets of boundaries in just the one geojson. However, I wasn't sure how to work with this.
While it would be nice to be able to mouseover the boundaries of the larger regions and get a tooltip before crossing over into the individual subregions and getting their tooltips, this isn't a must. I could live with just outlines around the regions. Please advise on the best way to do this. Happy to provide more detail, and thanks so much!
EDIT: I discovered that I had a misplaced transform tag which is what offset the second layer. Fixed now!
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 am trying to count the number of hairs transplanted in the following image. So practically, I have to count the number of spots I can find in the center of image.
(I've uploaded the inverted image of a bald scalp on which new hairs have been transplanted because the original image is bloody and absolutely disgusting! To see the original non-inverted image click here. To see the larger version of the inverted image just click on it). Is there any known image processing algorithm to detect these spots? I've found out that the Circle Hough Transform algorithm can be used to find circles in an image, I'm not sure if it's the best algorithm that can be applied to find the small spots in the following image though.
P.S. According to one of the answers, I tried to extract the spots using ImageJ, but the outcome was not satisfactory enough:
I opened the original non-inverted image (Warning! it's bloody and disgusting to see!).
Splited the channels (Image > Color > Split Channels). And selected the blue channel to continue with.
Applied Closing filter (Plugins > Fast Morphology > Morphological Filters) with these values: Operation: Closing, Element: Square, Radius: 2px
Applied White Top Hat filter (Plugins > Fast Morphology > Morphological Filters) with these values: Operation: White Top Hat, Element: Square, Radius: 17px
However I don't know what to do exactly after this step to count the transplanted spots as accurately as possible. I tried to use (Process > Find Maxima), but the result does not seem accurate enough to me (with these settings: Noise tolerance: 10, Output: Single Points, Excluding Edge Maxima, Light Background):
As you can see, some white spots have been ignored and some white areas which are not actually hair transplant spots, have been marked.
What set of filters do you advise to accurately find the spots? Using ImageJ seems a good option since it provides most of the filters we need. Feel free however, to advise what to do using other tools, libraries (like OpenCV), etc. Any help would be highly appreciated!
I do think you are trying to solve the problem in a bit wrong way. It might sound groundless, so I'd better show my results first.
Below I have a crop of you image on the left and discovered transplants on the right. Green color is used to highlight areas with more than one transplant.
The overall approach is very basic (will describe it later), but still it provides close to be accurate results. Please note, it was a first try, so there is a lot of room for enhancements.
Anyway, let's get back to the initial statement saying you approach is wrong. There are several major issues:
the quality of your image is awful
you say you want to find spots, but actually you are looking for hair transplant objects
you completely ignores the fact average head is far from being flat
it does look like you think filters will add some important details to your initial image
you expect algorithms to do magic for you
Let's review all these items one by one.
1. Image quality
It might be very obvious statement, but before the actual processing you need to make sure you have best possible initial data. You might spend weeks trying to find a way to process photos you have without any significant achievements. Here are some problematic areas:
I bet it is hard for you to "read" those crops, despite the fact you have the most advanced object recognition algorithms in your brain.
Also, your time is expensive and you still need best possible accuracy and stability. So, for any reasonable price try to get: proper contrast, sharp edges, better colors and color separation.
2. Better understanding of the objects to be identified
Generally speaking, you have a 3D objects to be identified. So you can analyze shadows in order to improve accuracy. BTW, it is almost like a Mars surface analysis :)
3. The form of the head should not be ignored
Because of the form of the head you have distortions. Again, in order to get proper accuracy those distortions should be corrected before the actual analysis. Basically, you need to flatten analyzed area.
3D model source
4. Filters might not help
Filters do not add information, but they can easily remove some important details. You've mentioned Hough transform, so here is interesting question: Find lines in shape
I will use this question as an example. Basically, you need to extract a geometry from a given picture. Lines in shape looks a bit complex, so you might decide to use skeletonization
All of a sadden, you have more complex geometry to deal with and virtually no chances to understand what actually was on the original picture.
5. Sorry, no magic here
Please be aware of the following:
You must try to get better data in order to achieve better accuracy and stability. The model itself is also very important.
Results explained
As I said, my approach is very simple: image was posterized and then I used very basic algorithm to identify areas with a specific color.
Posterization can be done in a more clever way, areas detection can be improved, etc. For this PoC I just have a simple rule to highlight areas with more than one implant. Having areas identified a bit more advanced analysis can be performed.
Anyway, better image quality will let you use even simple method and get proper results.
Finally
How did the clinic manage to get Yondu as client? :)
Update (tools and techniques)
Posterization - GIMP (default settings,min colors)
Transplant identification and visualization - Java program, no libraries or other dependencies
Having areas identified it is easy to find average size, then compare to other areas and mark significantly bigger areas as multiple transplants.
Basically, everything is done "by hand". Horizontal and vertical scan, intersections give areas. Vertical lines are sorted and used to restore the actual shape. Solution is homegrown, code is a bit ugly, so do not want to share it, sorry.
The idea is pretty obvious and well explained (at least I think so). Here is an additional example with different scan step used:
Yet another update
A small piece of code, developed to verify a very basic idea, evolved a bit, so now it can handle 4K video segmentation in real-time. The idea is the same: horizontal and vertical scans, areas defined by intersected lines, etc. Still no external libraries, just a lot of fun and a bit more optimized code.
Additional examples can be found on YouTube: RobotsCanSee
or follow the progress in Telegram: RobotsCanSee
I've just tested this solution using ImageJ, and it gave good preliminary result:
On the original image, for each channel
Small (radius 1 or 2) closing in order to get rid of the hairs (black part in the middle of the white one)
White top-hat of radius 5 in order to detect the white part around each black hair.
Small closing/opening in order to clean a little bit the image (you can also use a median filter)
Ultimate erode in order to count the number of white blob remaining. You can also certainly use a LoG (Laplacian of Gaussian) or a distance map.
[EDIT]
You don't detect all the white spots using the maxima function, because after the closing, some zones are flat, so the maxima is not a point, but a zone. At this point, I think that an ultimate opening or an ultimate eroded would give you the center or each white spot. But I am not sure that there is a function/pluggin doing it in ImageJ. You can take a look to Mamba or SMIL.
A H-maxima (after white top-hat) may also clean a little bit more your results and improve the contrast between the white spots.
As Renat mentioned, you should not expect algorithms to do magic for you, however I'm hopeful to come up with a reasonable estimate of the number of spots. Here, I'm going to give you some hints and resources, check them out and call me back if you need more information.
First, I'm kind of hopeful to morphological operations, but I think a perfect pre-processing step may push the accuracy yielded by them dramatically. I want you put my finger on the pre-processing step. Thus I'm going ti work with this image:
That's the idea:
Collect and concentrate the mass around the spot locations. What do I mean my concentrating the masses? Let's open the book from the other side: As you see, the provided image contains some salient spots surrounded by some noisy gray-level dots.
By dots, I mean the pixels that are not part of a spot, but their gray-value are larger than zero (pure black) - which are available around the spots. It is clear that if you clear these noisy dots, you surely will come up with a good estimate of spots using other processing tools such as morphological operations.
Now, how to make the image more sharp? What if we could make the dots to move forward to their nearest spots? This is what I mean by concentrating the masses over the spots. Doing so, only the prominent spots will be present in the image and hence we have made a significant step toward counting the prominent spots.
How to do the concentrating thing? Well, the idea that I just explained is available in this paper, which its code is luckily available. See the section 2.2. The main idea is to use a random walker to walk on the image for ever. The formulations is stated such that the walker will visit the prominent spots far more times and that can lead to identifying the prominent spots. The algorithm is modeled Markov chain and The equilibrium hitting times of the ergodic Markov chain holds the key for identifying the most salient spots.
What I described above is just a hint and you should read that short paper to get the detailed version of the idea. Let me know if you need more info or resources.
That is a pleasure to think on such interesting problems. Hope it helps.
You could do the following:
Threshold the image using cv::threshold
Find connected components using cv::findcontour
Reject the connected components of size larger than a certain size as you seem to be concerned about small circular regions only.
Count all the valid connected components.
Hopefully, you have a descent approximation of the actual number of spots.
To be statistically more accurate, you could repeat 1-4 for a range of thresholds and take the average.
This is what you get after applying unsharpen radius 22, amount 5, threshold 2 to your image.
This increases the contrast between the dots and the surrounding areas. I used the ballpark assumption that the dots are somewhere between 18 and 25 pixels in diameter.
Now you can take the local maxima of white as a "dot" and fill it in with a black circle until the circular neighborhood of the dot (a circle of radius 10-12) erases the dot. This should let you "pick off" the dots joined to each other in clusters more than 2. Then look for local maxima again. Rinse and repeat.
The actual "dot" areas are in stark contrast to the surrounding areas, so this should let you pick them off as well as you would by eyeballing it.
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.