I'm working on an application for generating a path for custom-made CNC machine. It is based on a PLC controller which does not support G-code, therefore I need to define the whole path as a list of commands.
I'm having a trouble with defining the toolpath for pocket milling. As an input, I use DXF files with different kind of shapes in it. Each shape is located on different layer and built of simple elements such as LINE, ARC etc. What I need is to analyze these simple elements as a closed contour and generate toolpath for milling the whole material inside this contour. Do you know of any library or simple algorithm where I can define the shape (in this case, based on the DXF data) and the lib/algorithm would generate the whole toolpath, taking the tool diameter into consideration?
For simple shapes like circles or rectangles, I'm able to generate such toolpath manually but when the shape is more complex (e.g. like below) I'm running out of ideas how to do so.
There is a lot of freeware CAM software in the internet and each of them generates the toolpath in form of G-Code, so I assume such kind of algorithm is implemented there somehow. I thought about using such CAM software but the G-code output is not usable for me, besides I do not need any GUI. Most of them is also written in higher-level languages whilst I'm writing my app in JavaScript running under node.js.
Do you mean you know how to process each entity individually and don't know how to combine them together? Since they touch you just need to find the next entity according to its starting/ending point (1), from the current entity's ending point. And if the point (1) was an ending point of that entity, you will need to process the found entity in reverse, or process it in normal order and reverse the resulting line. Of course taking care to offset it in the correct direction.
For faster neighbor search sort them first by either X or Y coordinate of both their starting and ending points.
Related
I have a problem which in my eyes is on the rather simple side of image recognition. I am trying to check if in a video a certain picture is shown. I only want to provide some kind of simple Picture, e.g a logo of a company or a simple shape. If this Picture is found in the video feed an action should be started.
I have no experience in image recognition but I find many libraries detecting whole objects and faces. Finding a given image should be done way more easily, or should it? I am trying to solve this problem in JS but any starting point would be helpful.
Regards
This problem requires specific object detection whose feature may vary a lot in natural scenes because of different view point, intensity, So you can try using SIFT, SURF for key point matching and object recognition. If the image features do not vary a lot you can achieve this feat with simple temple matching using opencv.js(JavaScript port of OpenCV library).
Also if the object of interest contains certain fixed color, you can filter the object using a predefined color range in HSV color model image as explained here
More robust solution: If you have good data-set of your object of interest, you can build a deep learning model for object detection using tensorflow and use it in JavaScript using Tensorflow.js, though i have not tried this approach.
I am currently trying to write a program using three.js that will generate novel 3D blocks such as this. As you can see, the model is made from relatively very simple geometric figures. I've considered everything from randomly appending primitives to each other to using machine learning data (just a thought). What are your thoughts on this? So far, I am leaning towards the former.
On another note, do you think it would be more wise to use geometries from three.js's stock, or to generate points and connect them with lines?
I have a SVG generated map for the game I am developing. I have no problems with the game being open-source and it uses open web technologies such as HTML and SVG. No problems there.
But at the same time I want the players not to be able to see or reverse engineer a map of the whole world (to retain true exploration). For now I generate map using a seed that is secret and not version controlled. So even though the algorithm is known curious players can use open-sourced code to generate "game-like worlds" but not that exact one. This solves the "global" problem.
But since SVG is rendered on a page as a single Voronoi diagram all the data (I don't mind the coordinates of points) would be extractable. Data like resources, land types, biomes, climate etc. could be fetched from SVG to gain an upper hand in finding good locations for settlements.
Any idea how to prevent that? Players have limited vision so I thought about either:
not rendering the whole Voronoi diagram at all (just the visible part), but that could be potentially tricky to do (maybe, haven't looked into it yet),
inserting the resource/land tile data into SVG graph only to visible locations
I can see the benefits of both approaches and if done correctly it could even boost the performance (not rendering the whole thing/rendering with less data) and lead to bigger worlds without impacting performance.
Any other ideas/programming/architectural approaches to help with the issue?
(I am using Vue.js, d3.js, svg-pan-zoom and Laravel backend just in case it helps.)
The ideas that you gave are perfect, but for implementing them, you need to make hard work, and spend much time.
I have a suggestion. Is will work for most of the users. Maybe some users will "hack" it. But I believe it will work for 95% of the times.
You can create a very big rectangle, from the top left point 0,0 until the right bottom point. The rectangle will be white, and it will be over all other shapes.
This way if someone will download the SVG, we will see nothing. Just a big white rectangle.
In you game HTML, you can add a CSS selector, to hide this rectangle.
If you following this method, most of the users (who don't have a photo editing software) will not be able to see the map.
Users who knows how to inspect elements in HTML may see the map. But I believe that most of them who will see a white box, will not believe that there is something behind.
I think that this is a simple temporary approach that you can do, before doing other more defensive ways.
I have a dataset which is best represented by a graph. It consists of nodes of 6 or 7 different "types" with directed edges (dependencies on one another, guaranteed not to have cyclic dependencies). The dataset is essentially a template of a layered configuration, and the user needs to be able to select bits and pieces of the configuration from different layers which are desired, and have the dependent bits be brought in automatically.
The general UI need is for a user to select or un-select items from multi-select boxes (one such box for each node type), and have "depended-on" items in the other boxes become selected or unselected as needed. I need to be able to pull down the dataset from the server, let the user select the desired bits (with the dependency processing being done in javascript on the client side for responsiveness), and then submit the result back when they are finished.
The dataset is large and complex enough that actually showing it as a graph would be overwhelming and confusing to the user. Only basic graph traversal operations are needed, since all that is required is to cascade selections out the dependencies. (For example, a user un-selecting a node would result in that nodes dependencies becoming unselected if there were no other selected node which still depended on them. A user selecting a node would result in all of that node's dependencies becoming selected.) A simple depth or breadth first search following directed edges from the start node will suffice to visit all affected nodes. If I can follow edges either direction, bonus. (If not I can easily generate an edge-reversed graph and use that when needed.)
I have dug around on here and found references to a number of javascript graph visualization libraries, but most of these discussions seem to interpret "graph" as "chart" and I have no charting needs here. My digging has led me to this list: Raphael, protovis, flare, D3, jsVis, Dracula, and prefuse. From this list it looks like jsVis or Dracula might have the underlying graph constructs I need if I just ignore the visualization side, but it isn't clear to me from the documentation if that is the case. I have to rule out a few others because I cannot bring in any flash dependencies. Unfortunately I don't have time to prototype things with this many libraries. (I will be digging into jsVis and dracula more though, barring some handy input here.)
If anyone has experience with something from that list and believes that the graph portion of it can be used independently of the visualization portion, that will certainly meet my needs. If there is some other library I could use that meets my needs, that would be great too. One final requirement regarding licensing: the library needs to be "free" in a non-copyleft way - So ideally Apache v2.0, BSD, MIT, or something like that.
I haven't used it, but you might want to check out data.js. It's an MIT-licensed library with a range of data-structure utilities. In particular, it includes Data.Node and Data.Graph:
A Data.Graph can be used for representing arbitrary complex object graphs. Relations between objects are expressed through links that point to referred objects. Data.Graphs can be traversed in various ways.
Suppose I have a large list of objects (thousands or tens of thousands), each of which is tagged with a handful of tags.
There are dozens or hundreds of possible tags and their usage follows a typical power law:
some tags are used extremely often but most are rare.
All but the most frequent couple dozen tags could typically be ignored, in fact.
Now the problem is how to visualize the relationship between these tags.
A tag cloud is a nice visualization of just their frequencies but it ignores which tags occur with which other tags.
Suppose tag :bar only occurs on objects also tagged :foo.
That should be visually apparent.
Similarly for three tags that tend to occur together.
You could make each tag a bubble and let them partially overlap with each other.
Technically that's a Venn diagram but treating it that way might be unwieldy.
For example, Google charts can create Venn diagrams, but only for 3 or fewer sets (tags):
http://code.google.com/apis/chart/docs/gallery/venn_charts.html
The reason they limit it to 3 sets is that any more and it looks horrendous.
See "extentions to higher numbers of sets" on the Wikipedia page: http://en.wikipedia.org/wiki/Venn_diagrams
But that's only if every possible intersection is non-empty.
If no more than 3 tags ever co-occur (maybe after throwing out the rare tags) then a collection of Venn diagrams could work (with the sizes of the bubbles representing tag frequency).
Or perhaps a graph (as in vertices and edges) with visually thicker or thinner edges to represent frequency of co-occurrence.
Do you have any ideas, or pointers to tools or libraries?
Ideally I'd do this with javascript but I'm open to things like R and Mathematica or really anything else.
I'm happy to share some actual data (you'll laugh if I tell you what it represents) if anyone is curious.
Addendum: The application I originally had in mind was TagTime but it occurs to me that this also maps well to the problem of visualizing one's delicious bookmarks.
If i understand your question correctly, an image matrix should work nicely here. The implementation i have in mind would be an n x m matrix in which the tagged items are rows, and each tags type is a separate column. Every cell in the matrix would consist entirely of "1's" and "0's", i.e., a particular item either has a given tag or it doesn't.
In the matrix below (which i rotated 90 degrees so it would fit better in this window--so columns actually represent tagged items, and each row shows the presence or absence of a given tag across all items), i simulated the scenario in which there are 8 tags and 200 tagged items. , a "0" is blue and a "1" is light yellow.
All values in this matrix were randomly selected (each tagged item is eight draws from a box consisting of two tokens, one blue and one yellow (no tag and tag, respectively). So not surprisingly there's no visual evidence of a pattern here, but if there is one in your data, this technique, which is dead simple to implement, can help you find it.
I used R to generate and plot the simulated data, using only base graphics (no external packages or libraries):
# create the matrix
A = matrix(data=r1, nrow=1, ncol=8)
# populate it with random data
for (i in seq(0, 200, 1)){r1 = sample(0:1, 8, replace=TRUE); A = rbind(A, r1)}
# now plot it
image(z=A, ann=F, axes=F, col=topo.colors(12))
I would create something like this if you are targeting the web. Edges connecting the nodes could be thicker or darker in color, or perhaps a stronger force connecting them so they are close in distance. I would also add the tag name inside the circle.
Some libraries that would be very good for this include:
Protovis (Javascript)
Flare (Adobe Flash)
Some other fun javascript libraries worth looking into are:
Processing for Javascript
Raphael
Although this is an old thread, I just came across it today.
You may also want to consider using a Self-Organizing Map.
Here is an example of a self-organizing map for world poverty. It used 39 of what you call your "tags" to arrange what you call your "objects".
http://www.cis.hut.fi/research/som-research/povertymap.gif
Note sure it would work as I did not test that, but here is how I would start:
You can create a matrix as doug suggests in his answer, but instead of having documents as rows and tags as columns, you take a square matrix where tags are rows and columns. Value of the cell T1;T2 will be the number of documents tagged with both T1 and T2 (note that by doing that you'll get a symetric matrix because [T1;T2] will have the same value as [T2;T1]).
Once you have done that, each row (or column) is a vector locating the tag in a space with T dimensions. Tags near each others in this space often occur together. To visualize co-occurrence you can then use a method to reduce your space dimensionality or any clustering method. For example you can use a kohonen self organizing map to project your T-dimensions space to a 2D space, you'll then get a 2D matrix where each cell represents an abstract vector in the tag space (meaning the vector won't necessary exists in your data set). This vector reflect a topological constraint of your source space, and can be seen as a "model" vector reflecting a significant co-occurence of some tags. Moreover, cells near each others on this map will represent vectors close to each other in the source space, thus allowing you to map the tag space on a 2D matrix.
Final visualization of the matrix can be done in many ways but I cannot give you advice on that without first seeing the results of the previous processing.