How to specify the thickness of edge in dotfile? - graphviz

I'd like set the thickness of edge in dot file, but could not find the corresponding attribute, anyone know that ? Thanks

You are probably looking for penwidth:
Specifies the width of the pen, in points, used to draw lines and
curves, including the boundaries of edges and clusters. The value is
inherited by subclusters. It has no effect on text.
Previous to 31 January 2008, the effect of penwidth=W was achieved by
including setlinewidth(W) as part of a style specification. If both
are used, penwidth will be used.

Related

How to increase the coordinate resolution of a d3-geo chart

I have a GeoJSON file with small details and features that I want to render using D3. Unfortunately, important details are lost because D3
removes polygon coordinate pairs that are closely spaced.
I've set up a small example to show this. Both links use the exact same GeoJSON data, rendered with both D3-geo and mapbox through github.
Specifically, notice the two areas marked by the red circles.
https://bl.ocks.org/alvra/eebb06be793bc06ff3ae01e6945298b6
https://gist.github.com/alvra/eebb06be793bc06ff3ae01e6945298b6
The top one one marks a part of polygon that is rounded using many closely spaced coordinate pairs, but D3 removes most points and just draws a rough square end.
The lower red circle marks a tiny triangle that is removed altogether. The adjacent polygons should touch exactly, but are also affected by D3's loss of precision.
I haven't found any documentation about D3's coordinate precision or a (configurable) feature size limit.
I've tried decreasing D3-geo's EPSILON and related EPSILON2 values and that removes this problem (for me), although I'm sure even smaller features will still be affected.
Assuming this is related to the fact that D3 uses proper geodesics for polygon segments, while the other mapping libraries just draw straight lines (in the output coordinate space),
I was hoping that this process can only introduce new points.
I haven't been able to find other users experiencing similar problems with small features, although I'm surprised this has never come up before.
Does anyone have an idea about the proper way to deal with this?
Through epsilon, I've narrowed the problem down to this use of pointEqual(). This indicates the problem is with clipCircle considering closely spaced coordinates equal and removes them.
Indeed, if I disable circular clipping projection.clipAngle(null), the problem disappears.

Increasing rank spacing in graphviz

I have a graph laid out using D3 graphviz. It's laying out in a pretty dense way, and I'd like it to use more of the width of the screen.
I have tried adjusing the ranksep and the ratio, but neither seem to have any effect. I've also tried changing the node border, to no effect. Changing the edge minlength just makes the edges look floppy. I can try giving every node an invisible child, but that seems like a horrible hack.
For clarity, by rank, I mean the horizontal spacing between the columns of nodes. (The graph is laid out left to right.)
Ideally I'd be able to give it an aspect ratio and it'd make the most of that space to lay out the graph in a way that doesn't need too much acrobatics.
Am I missing something obvious?
I may well be using the options incorrectly
or the D3 graphviz implementation may not have those features?
Is there no good way to do this?
I have added :
size = "16.66,8.33!"; // 1200x600 at 72px/in, "!" to force
ratio = "fill"; // see https://graphviz.gitlab.io/_pages/doc/info/attrs.html#d:ratio
at the beginning of the graph declaration (just after digraph G {).
It renders a wider output.
Have a try and tell.

GraphViz Dot. How to distribute elements around a boxed size?

I have a dot graphviz file that geneates the following output:
http://www.qlands.com/other_files/test.png
However the output is organized vertically. If I setup the size to be for example 8.27 inches; How can I distribute the elements around a box of 8.27 x 8.27 inches?
Your graph is a directed graph layed out with dot from left to right, and the first rank contains many nodes which results in a very high image.
The main tool to break up graphs with this problem is unflatten:
unflatten is a preprocessor to dot that is used to improve the aspect
ratio of graphs having many leaves or disconnected nodes. The usual
layout for such a graph is generally very wide or tall. unflatten
inserts invisible edges or adjusts the minlen on edges to improve
layout compaction.
You may combine this with other tools and techniques to get the result you want:
Use the unflatten utility - please see this answer for a detailed example using unflatten.
Use invisible edges to introduce new ranks (basically what unflatten does automatically, but with human inspiration... example also here)
If you need the output to be of this exact size, be sure to understand graphviz's various attributes which have an impact on it, such as size, margin, ratio... (see also this and yet another answer providing details)
Finally, you could simply use a different layout (neato for example)

variable stroke width in NVD3 lineChart

i am trying to figure out if there is a reasonably easy way to extend NVD3's lineChart model to allow variable stroke widths along each line path in a chart.
specifically, i am dealing with a simple line chart where i need to show the year-on-year growth of employment in different sectors (for which NVD3's lineChart works perfectly), while also giving an idea of the relative weight of these sectors (i.e. agricolture might be growing while employing only a few hundred people overall, while retail might be struggling but still be employing a large percentage of the population) - this won't be a linear scale of course, but assuming that relative weight of each sector varies across time, a thicker line could represent a sector with more employees than one with a thin line.
obviously i could very easily change the stroke width for the whole line using i.e. an average weight of each sector across the whole timespan, but as far as i understand there is no way in SVG to specify a varying width of a single path element: would it make sense to create an NVD3 model that builds on top of lineChart but splits each line into discrete polygons (triangles?) for each year-on-year period?
Looking for an answer to this myself. It seems there is no easy way, but one possibility is to use the stroke-dasharray attribute.
http://owl3d.com/svg/vsw/articles/vsw_article.html
Essentially, you can create a series of cloned paths, covering a range of stroke widths. If you turn them into dash arrays, you can play with the spacing between dashes to control which paths are visible at each point.
Depending on the shape and width you are looking for, you may also be able to fudge it by adding a second path element with a varying offset from the first.
Perhaps generate a closed path and apply a pattern fill or regular fill on that path. The closed path is effectively a triangle shape, to mimic a line of varied width.

Best approach for specific Object/Image Recognition task?

I'm searching for an certain object in my photograph:
Object: Outline of a rectangle with an X in the middle. It looks like a rectangular checkbox. That's all. So, no fill, just lines. The rectangle will have the same ratios of length to width but it could be any size or any rotation in the photograph.
I've looked a whole bunch of image recognition approaches. But I'm trying to determine the best for this specific task. Most importantly, the object is made of lines and is not a filled shape. Also, there is no perspective distortion, so the rectangular object will always have right angles in the photograph.
Any ideas? I'm hoping for something that I can implement fairly easily.
Thanks all.
You could try using a corner detector (e.g. Harris) to find the corners of the box, the ends and the intersection of the X. That simplifies the problem to finding points in the right configuration.
Edit (response to comment):
I'm assuming you can find the corner points in your image, the 4 corners of the rectangle, the 4 line endings of the X and the center of the X, plus a few other corners in the image due to noise or objects in the background. That simplifies the problem to finding a set of 9 points in the right configuration, out of a given set of points.
My first try would be to look at each corner point A. Then I'd iterate over the points B close to A. Now if I assume that (e.g.) A is the upper left corner of the rectangle and B is the lower right corner, I can easily calculate, where I would expect the other corner points to be in the image. I'd use some nearest-neighbor search (or a library like FLANN) to see if there are corners where I'd expect them. If I can find a set of points that matches these expected positions, I know where the symbol would be, if it is present in the image.
You have to try if that is good enough for your application. If you have too many false positives (sets of corners of other objects that accidentially form a rectangle + X), you could check if there are lines (i.e. high contrast in the right direction) where you would expect them. And you could check if there is low contrast where there are no lines in the pattern. This should be relatively straightforward once you know the points in the image that correspond to the corners/line endings in the object you're looking for.
I'd suggest the Generalized Hough Transform. It seems you have a fairly simple, fixed shape. The generalized Hough transform should be able to detect that shape at any rotation or scale in the image. You many need to threshold the original image, or pre-process it in some way for this method to be useful though.
You can use local features to identify the object in image. Feature detection wiki
For example, you can calculate features on some referent image which contains only the object you're looking for and save the results, let's say, to a plain text file. After that you can search for the object just by comparing newly calculated features (on images with some complex scenes containing the object) with the referent ones.
Here's some good resource on local features:
Local Invariant Feature Detectors: A Survey

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