I write an svg parser.
I have an svg file with the following string:
<polygon fill="#969696" stroke="#323232" stroke-width="0.5" stroke-miterlimit="10" points="555.583,394.805 564.085,394.805 564.02,394.817 568.896,399.655"/>
And this picture looks different in various applications. Possible cases:
1)linejoin = bevel
2)linejoin = miter (Corel Draw)
3)two nearest points are not connected (Inkscape, adobe illustrator)
The second and the third case look look like:
It seems that svg documentation advices to draw the same picture as corel draw draws (right picture). But it is not very pretty.
So what rule will allow to achieve the left picture?
You seem to have a bogus co-ordinate in your polygon. The polygon has 3 vertices but you're supplying 4 and the bogus one is confusing things. Try this instead...
<polygon fill="#969696" stroke="#323232" stroke-width="0.5" stroke-miterlimit="10" points="555.583,394.805 564.085,394.805 568.896,399.655"/>
Related
I wrote a gimp python plugin to create an image, add a text layer, and write some text into it. Similar to this one.
I want to apply a different formatting to a portion of this text but I can't find the function to do it when I browse Filters > Script-Fu > Console > Browser.
This gif illustrates what I want. It was done manually:
Very complicated. You can get the info in the "parasite" attached to the text layer:
data=layer.parasite_find('gimp-text-layer').data
So for instance, for an image like this:
Data looks like this:
(markup "<markup>SO<span font=\"URW Bookman L Bold\">ME</span> <span size=\"15728\">TE</span>XT</markup>")
(font "Bungee")
(font-size 80)
(font-size-unit pixels)
(antialias yes)
(language "en")
(base-direction ltr)
(color (color-rgb 0 0 0))
(justify fill)
(box-mode dynamic)
(box-unit pixels)
(hinting yes)
Markup is possibly related to Cairo (which is the library used by Gimp to draw text and curves). However,
The parasite is only available once the image has been saved to disk
I have never checked what happens if you update the parasite.
Edited:
It seems creating or updating the parasite doesn't work and Gimp seems to ignored the parasite. It only considers it when loading the file, and when the file is saved, it generates a new parasite from the actual layer contents.
It may be easier for you to format several individual text layers side-by-side. To align them to the same baseline: if you use pdb.gimp_text_get_extents_fontname() with a character that has a flat bottom (I typically use "X") the "ascent" is the distance of the baseline from the top of the layer (round character such as "O" can extent slightly under the baseline).
I am currently working on an algorithm to detect the playing area of a pool table. For this purpose, I captured an image, transformed it to grayscale, and used a Sobel operator on it. Now I want to detect the playing area as a box with 4 corners located in the 4 corners of the table.
Detecting the edges of the table is quite straightforward, however, it turns out that detecting the 4 corners is not so easy, as there are pockets in the pool table. Now I just want to fit a line to each of the side edges, and from those lines, I can compute the intersects, which are the corners for my table.
I am stuck here, because I could not yet come up with a good solution to find these lines in my image. I can see it very easily when I used the Sobel operator. But what would be a good way of detecting it and computing the position of the corners?
EDIT: I added some sample Images
Basic Image:
Grayscale Image
Sobel Filter (horizontal only)
For a general solution, there will be many sources of noise: problems with cloth around the rails, wood texture (or no texture) on the rails, varying lighting, shadows, stains on the cloth, chalk on the rails, and so on.
When color and lighting aren't dependable, and when you want to find the edges of geometric objects, then it's best to think in terms of edge pixels rather than gray/color pixels.
A while back I was thinking of making a phone-based app to save ball positions for later review, including online, so I've though a bit about this problem. Although I can provide some guidance for your current question, it occurs to me you'll run into new problems each step of the way, so I'll try to provide a more complete answer.
Convert the image to grayscale. If we can't get an algorithm to work in grayscale, we'll inevitably run into problems with color. (See below)
[TBD] Do some preprocessing to reduce noise.
Find edge points using Sobel or (if you must) Canny.
Run Hough lines detection, but with a few caveats and parameterizations as described below.
Find the lines described a keystone-shaped quadrilateral. (This will likely be the inner quadrilateral of two: one inside the rail on the bed, and the other slightly larger quadrilateral at the cloth/wood rail edge at top.)
(Optional) Use the side pockets to help determine the orientation of the quadrilateral.
Use an affine transform to map the perspective-distorted table bed to a rectangle of [thankfully] known relative dimensions. We know the bed sizes in advance, so you can remap the distorted rectangle to a proper rectangle. (We'll ignore some optical effects for now.)
Remap the color image to the perspective-corrected rectangle. You'll probably need to tweak the positions of some balls.
General notes:
Filtering by color in the general sense can be difficult. It's tempting to think of the cloth as being simply green, blue, or red (or some other color), but when you look at the actual RGB values and try to separate colors you'll begin to appreciate what a nightmare working in color can be.
Optical distortion might throw off some edges.
The far short rail may be difficult to detect, BUT you do this: find the inside lines for the two long rails, then search vertically between the two rails for the first strong horizontal-ish edge at the far side of the image. That'll be the far short rail.
Although you probably want to use your phone camera for convenience, using a Kinect camera or similar (preferably smaller) device would make the problem easier. Not only would you have both color data and 3D data, but you would eliminate some problems with lighting since the depth data wouldn't depend on visible lighting.
For your app, consider limiting the search region for rail edges to a perspective-distorted rectangle. The user might be able to adjust the search region. This could greatly simplify the processing, and could help you work around problems if the table isn't lit well (as can be the case).
If color segmentation (as suggested by #Dima) works, get the outline of the blob using contour following. Then simplify the outline to a quadrilateral (or a polygon of few sides) by the Douglas-Peucker algorithm. You should find the four table edges this way.
For more accuracy, you can refine the edge location by local search of transitions across it and perform line fitting. Then intersect the lines to get the corners.
The following answer assumes you have already found the positions of the lines in the image. This however can be done "easily" by directly looking at the pixels and seeing if they are in a "line". Usually it is easier to detect this if the image has been deskewed first as well, i.e. Rotated so the rectangle (pool table) is more like this: [] than like /=/. Then it is just a case of scanning the pixels and if there are ones of similar colour alongside it assuming a line is between them.
The code works by looping over the lines found in the image. Whenever the end points of each line falls within a tolerance on within the x and y coordinates it is marked as a corner. Once the corners are found I take the average value between them to find where the corner lies. For example:
A horizontal line ending at 10, 10 and a vertical line starting at 12, 12 will be found to be a corner if there is a tolerance of 2 or more. The corner found will be at: 11, 11
NOTE: This is just to find Top Left corners but can easily be adapted to find all of them. The reason it has been done like this is because in the application where I use it, it is faster to sort each array first into an order where relevant values will be found first, see: Why is processing a sorted array faster than an unsorted array?.
Also note that my code finds the first corner for each line which might not be applicable for you, this is mainly for performance reasons. However the code can easily be adapted to find all the corners with all the lines then either select the "more likely" corner or average through them all.
Also note my answer is written in C#.
private IEnumerable<Point> FindTopLeftCorners(IEnumerable<Line> horizontalLines, IEnumerable<Line> verticalLines)
{
List<Point> TopLeftCorners = new List<Point>();
Line[] laHorizontalLines = horizontalLines.OrderBy(l => l.StartPoint.X).ThenBy(l => l.StartPoint.Y).ToArray();
Line[] laVerticalLines = verticalLines.OrderBy(l => l.StartPoint.X).ThenBy(l => l.StartPoint.Y).ToArray();
foreach (Line verticalLine in laVerticalLines)
{
foreach (Line horizontalLine in laHorizontalLines)
{
if (verticalLine.StartPoint.X <= (horizontalLine.StartPoint.X + _nCornerTolerance) && verticalLine.StartPoint.X >= (horizontalLine.StartPoint.X - _nCornerTolerance))
{
if (horizontalLine.StartPoint.Y <= (verticalLine.StartPoint.Y + _nCornerTolerance) && horizontalLine.StartPoint.Y >= (verticalLine.StartPoint.Y - _nCornerTolerance))
{
int nX = (verticalLine.StartPoint.X + horizontalLine.StartPoint.X) / 2;
int nY = (verticalLine.StartPoint.Y + horizontalLine.StartPoint.Y) / 2;
TopLeftCorners.Add(new Point(nX, nY));
break;
}
}
}
}
return TopLeftCorners;
}
Where Line is the following class:
public class Line
{
public Point StartPoint { get; private set; }
public Point EndPoint { get; private set; }
public Line(Point startPoint, Point endPoint)
{
this.StartPoint = startPoint;
this.EndPoint = endPoint;
}
}
And _nCornerTolerance is an int of a configurable amount.
A playing area of a pool table typically has a distinctive color, like green or blue. I would try a color-based segmentation approach first. The Color Thresholder app in MATLAB gives you an easy way to try different color spaces and thresholds.
I have a slide with some hand-drawn circles on it. I'd like to get a list of the coordinates and radii (sizes) of them. Attached is an image and link. Anyone have an idea how?
I started looking into computer vision techniques, but it seems like there should be a much more direct way.
If you are familiar with openCV the method HoughCircles() will do the job:
http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.html
Are you familiar with Matlab? imfindcircles() will do it:
http://www.mathworks.com/help/images/ref/imfindcircles.html
If this is a one time job you can post it as a job for someone else to do it for you for a small fee. Example: https://www.mturk.com/mturk/welcome
If you don't know any programming language and this is a one time job, you can do it manually. You can select each circle in photoshop, count the amount of pixels (and using the formulae of circumference = 2*pi*radius) find the radius. The center of mass of all the pixels will be the center of the circle.
It is a bit tricky to separate overlapping circles but you can do it by hand
I found a suitable method using vector graphics.
Select all the circles in powerpoint, right click and 'save as a picture'. Use .emf (windows metafile) format (this option was only available on my windows machine, not mac).
Open the emf file in inkscape, and save it to an 'svg' format, which is ascii and human readable.
Extract the information from the path commands.
E.g.: Each circle is represented as a path object, with a line:
d="m 36.527169,36.434607 c 0,-9.696733 9.075703,-17.551993 20.274845,-17.551993 11.194626,0 20.270329,7.85526 20.270329,17.551993 0,9.69264 -9.075703,17.552246 -20.270329,17.552246 -11.199142,0 -20.274845,-7.859606 -20.274845,-17.552246"
Here, the (x,y) following the 'm' character is the center of the circle, and the 12 (x,y) pairs following 'c' denote a 4-segment polybezier curve in which pairs 3,6,9,12 are the four compass points. Therefore in the above object, this is not a circle but an ellipse with axes ~ 20.27 and 17.55.
I have a bunch of uncompressed bitonal TIF document images. All of them have a watermark in the middle. When I run them through OCR, the text that overlaps with the watermark does not get recognized. I am trying to see if I can apply some type of cleanup to remove those watermarks to be able to recognize the missing text.
Again, the images are black and white, but when you look at the watermark it appears grey since it has a pattern of black and white pixels that makes the letters in the watermark less "dense" than regular text. At the same time, the watermark letters are very big, much bigger than the regular text.
An example of a somewhat similar image is this (except this one is color and the watermark characters in my case are a lot thicker and bigger; my watermarks are also a lot shorter: only 3 to 4 letters long)
It seems that there might be some sort of clean up filter that would be similar to removing large black borders from an image except borders are ually "denser" than a watermark so they appear "more black".
I have 3 tools at my disposal: GIMP, ImageMagick and IrfanView. Can you recommend any specific features of any subset of these tools that might help me?
Playing with contrast etc did not help, but I found a different way. As stated above, the regular text is a lot "denser" than the watermark text meaning that a regular black pixel has more surrounding black pixels than a watermark black pixel. So I devised a simple window-based filtering and thresholding algorithm.
Here's how I did it in Matlab, using a 5X5 window:
im=imread('imageWithWmark.tif');
imInv = ~im;
nr=size(imInv,1);
nc=size(imInv,2);
d = 2; % for 5X5 window
counts = zeros(nr,nc);
for rr = d+1 : nr-d-1
for cc = d+1 : nc-d-1
counts(rr,cc) = nnz(imInv(rr-d:rr+d,cc-d:cc+d));
end
end
thresh=10; % 10 out of 25 -- the larger the thresh the thinner the resulting letters are
imThresh = (counts>=thresh) & imInv;
imwrite(~imThresh,sprintf('Thresh_%d.tif',thresh),'Compression','none','Resolution',300);
Of course, the size of the window, the threshold and other parameters depend on the parameters of the regular text on the page (letter bigger/smaller, thicker/thinner etc) but even this initial version worked pretty well
I'm trying to draw a label on a polygon of an svg file. The problem I'm facing is to find out roughly the center of this polygon to place the label, as the path's coordinates are in svg format and need to be parsed. Is there an easier way to determine the center of an svg polygon (maybe someone can point out a javascript library or a snippet)? I'm using Raphael javascript library to manipulate the svg, but it doesn't seem to go beyond the standard svg functionality.
You could try the following approximation for doing something similar to the polygon suggestion, based on SVG DOM methods:
var totalPathLength = pathelm.getTotalLength();
var step = totalPathLength / 100;
for(var dist=0; dist < totalPathLength; dist+=step)
{
var pt = pathelm.getPointAtLength(dist);
addToAverage(pt.x, pt.y);
}
I think the simplest approach is to use the center of the path element's boundingbox (pathelm.getBBox()), that's simpler than the polygon suggestion.
The simplest thing you could try doing is to calculate the center by taking the average of all the points in the polygon. It should work for all but the most irregular of polygons. I've used the same algorithm to good effect in my programs.
Best of luck.
Insert a text tag inside the svg and position it by calculating the width and hight
<svg width="447pt" height="559pt" viewBox="0 0 894 1118" version="1.1" xmlns="http://www.w3.org/2000/svg">
............
............
<text x="450" y="300" font-family="Verdana" font-size="15" fill="red" >
Text To Show
</text>
</svg>