Each red circle in this map is a point but due to the density of them, there isn't enough space to show the text labels for all of them. So I want to filter them down to just show those which can show a text label.
How can I do this? marker-spacing seemed to be promising but makes no difference. I see no "marker-min-distance" as there is with "text-min-distance".
Basically there's no point in showing a marker at all if it can't be identified with text. This is for a non-interactive offline map.
There is marker-spacing.
If you want to draw only labelled markers, use shields instead (answered here).
Related
I've a sprite sheet containing a set of icons as shown here:
I'd like to get the bounding box (at pixel precision) of all icons inside it, some cases like list, grid have to be considered as only one icons. Any ideas are more than welcome.
I think the main issue in your problem is that some icons contain disjoint parts.
If all the icons were in only one part, you could just find the "connected components" (groups of white pixels) in your image and isolate them.
I don't know your level in image processing but to connect the parts of one icons, I would probably use dilation, which is a morphological method to expand (under constraints) the areas of maximum intensity in an image.
If you need any clarification, please let me know !
In general, it is not possible: only the humans have enough context to determine which of the disjoint parts belong together. You can approximate it using various ways, but it's a lost cause - and IMHO completely unnecessary. Imagine writing a test for this functionality - it's impossible, it requires a human in the loop, since the results for any particular icon sheet don't generalize. Knowing that the algorithm works for some sheet tells you nothing about whether it will work for some other sheet that you know nothing about a-priori.
It'd be simpler to manually colorize each sprite to have a color different than that of its neighbors. Then a greedy algorithm could find the bounding boxes easily without having to approximate anything.
(a) what I have, (b) what I get, (c) what I want
I have a simple vector graphic in Inkscape, which consists of a rectangle, filled points and stars. Since the axis ranges are not really nice (the height equals approximatly 3 times the width of the picture) for a publication, I want to rescale the picture. However, I do not have the raw data, such that I can plot it again. How can I rescale my graphic (see figure (a)), such that the x-range is more wide (see figure (c)) without getting distortions (see figure (b))? In the end I want to create a PDF file out of it.
Any ideas on that?
Thanks for your help.
You can try to do it in 2 steps, using the Object -> Transform tool (Shift-Ctrl-M).
First, select everything, and with the transform tool select the Scale tab, and scale horizontally by, say, 300%. All figures will be distorted.
Now, unselect the rectangle, and scale horizontally again by 33.3%, but first click on Apply to each object separately. This will undo the distortion (but not the translation) of each object.
Note that 300% followed by 33.3% should leave the individual objects with the same size.
Documentation here.
I have a list of bounding boxes, I was wondering how I could calculate which ones were redundant / duplicates.
The reason being is I have 2 million of these I send to a API and I want to know which are overlapping others so I can reduce them down so each box only covers a unique area of land, so no two bounding boxes cover the same piece of geo space.
How would I calculate it so that these bounding boxes were each covering their own unique space of geo land ?
I am writing this program in C++ btw.
I think that this task is more complex then you think.
You would have to split existing boxes, untill no overlapping exists, and then remove the boxes totally contained in another.
Instead giving you a solution to that, I recomend to check if you can live with:
1) remove the boxes that are totally contained in another box.
2) leave (partly-)overlapping boxes as they are.
For 2 millions you need a spatial index (QuadTree), to get a list of all boxes nearby one box.
If you have to avoid any overlappings, then you must continue to think what should be the result?
A) A union of overlapping rectangles that therfore is not an rectangle anymore, but a polygon.
or B) The result should be rectangles.
You could check if X% of a box's vertices are inside another box to find if it's overlapped but I suppose this isn't the optimal solution.
My program is supposed to position a set of text boxes with lines in a proper manner in documents. You can find an example below:
Here's the information my program has available:
Sizes of the text boxes
Target area to which their arrow is supposed to point to.
To which point exactly in that area the arrow is pointing is not critical (center, closest border)
Now I'm looking for a layouting algorithm providing me the following information:
Where to place the text boxes
Where to attach the lines on the text boxes
Optional: Where to attach the lines on the target boxes (i.e. which exact point to point to)
Optional: Where to bend the lines
In addition to that, the following conditions should be adhered to:
Text boxes should not overlap each other or target boxes
Text boxes may overlap any other document content
Optional: minimum line length
Optional: lines should not intersect
Is there a suitable layouting algorithm for this kind of problem that I could use as a starting point?
Thanks in advance for your ideas here!
i want to detect text area from image as a preprocessing step for tesseract OCR engine, the engine works well when the input is text only but when the input image contains Nontext content it falls, so i want to detect only text content in image,any idea of how to do that will be helpful,thanks.
Take a look at this bounding box technique demonstrated with OpenCV code:
Input:
Eroded:
Result:
Well, I'm not well-experienced in image processing, but I hope I could help you with my theoretical approach.
In most cases, text is forming parallel, horisontal rows, where the space between rows will contail lots of background pixels. This could be utilized to solve this problem.
So... if you compose every pixel columns in the image, you'll get a 1 pixel wide image as output. When the input image contains text, the output will be very likely to a periodic pattern, where dark areas are followed by brighter areas repeatedly. These "groups" of darker pixels will indicate the position of the text content, while the brighter "groups" will indicate the gaps between the individual rows.
You'll probably find that the brighter areas will be much smaller that the others. Text is much more generic than any other picture element, so it should be easy to separate.
You have to implement a procedure to detect these periodic recurrences. Once the script can determine that the input picture has these characteristics, there's a high chance that it contains text. (However, this approach can't distinguish between actual text and simple horisontal stripes...)
For the next step, you must find a way to determine the borderies of the paragraphs, using the above mentioned method. I'm thinking about a pretty dummy algorithm, witch would divide the input image into smaller, narrow stripes (50-100 px), and it'd check these areas separately. Then, it would compare these results to build a map of the possible areas filled with text. This method wouldn't be so accurate, but it probably doesn't bother the OCR system.
And finally, you need to use the text-map to run the OCR on the desired locations only.
On the other side, this method would fail if the input text is rotated more than ~3-5 degrees. There's another backdraw, beacuse if you have only a few rows, then your pattern-search will be very unreliable. More rows, more accuracy...
Regards, G.
I am new to stackoverflow.com, but I wrote an answer to a question similar to this one which may be useful to any readers who share this question. Whether or not the question is actually a duplicate, since this one was first, I'll leave up to others. If I should copy and paste that answer here, let me know. I also found this question first on google rather than the one i answered so this may benefit more people with a link. Especially since it provides different ways of going about getting text areas. For me, when I looked up this question, it did not fit my problem case.
Detect text area in an image using python and opencv
In the Current time, the best way to detect the text is by using EAST (An Efficient and Accurate Scene Text Detector)
The EAST pipeline is capable of predicting words and lines of text at arbitrary orientations on 720p images, and furthermore, can run at 13 FPS, according to the authors.
EAST quick start tutorial can be found here
EAST paper can be found here