I have an RGB image.Suppose say I have two circles in it.I want to know which one is filled and which one is not.I followed the following steps:
-imported the image as Bitmap
-converted into grayscale and used CannyEdgeDetector filter to find the edges. I get the following image
circle containing the letter "D" gives two edges, where as I need only one circle edge. Secondly, how do I find out which circle is filled and which one is not.
For the MCQ answers sheet, it might be easier to go by positions of the circles.
Just compare stock images of the clear, circled A, B, C and D's to the scanned images and see which differ the most. A simple darkness summation might be enough.
Also: Comparing the circles against each other might be useful too to compensate for a bad/dark/light scan or smudgy paper.
For special cases if the test isn't absolutely sure I'd probably pass it to a human being for further investigation. E.g., when you allow students to undo an answer by circling their second guess or when a student decides to make a real mess by using a bad eraser.
Final tip: make sure you do not accept answers that have the correct circle filled; make sure the other circles are clear at the same time so students can't cheat by filling all circles. (Some professors use a paper mask that overlays all bad answers so they can quickly spot unmarked correct answers. But if the student just marks all answers this fails big time.)
I would not know any "formal" algorithm matching your criteria. I would also doubt that you would find any.
When you say the circles are "darkened", this can be interpreted as this: "Many pixels (above a population threshold) would be black, or at least dark (above a colour threshold).". Based on known circle and radius, I would follow this approach (written in pseudocode but I suppose you get the meaning):
//define thresholds
colourThreshold=...
populationThreshold=...
centerPoint = getCircleCenter();
radius = getCircleRadius();
darkPixelsCount = 0;
for(x=centerPoint.X-radius;x<centerPoint.X+radius;x++){
for(y=centerPoint.Y-sqrt(abs(radius^2-(x - centerPoint.X)^2));centerPoint.Y+sqrt(abs(radius^2-(x - centerPoint.X)^2));y++){
if (image(x, y) > colourThreshold){
darkPixelsCounter++;
if(darkPixelsCounter > populationThreshold){
//If you reach this point, circle has been darkened
return;
}
}
}
}
//If you reach this point, circle has not been darkened
return;
I don't know if it is efficient, or if there is a more elegant way to do it. I suppose you could play with performance and accuracy by moderating the thresholds.
Hope I helped!
Perform blob detection, with a pixel count
or even simpler count the black pixels
or even simpler and faster, take middle line and count how often it switches from white to black
Related
I want to slice a 3D model relative to an infinite plane(In WPF). I'm checking if edges intersect with the infinite plane. If true, I'll create a new point at the intersection position, so I'm getting a couple of points that I want to generate a cap on so that the model is closed after slicing. For example, if this is the cross section, the result would be as follows:
Note: The triangulation ain't important. I just need triangles.
I also need to detect the holes as follows(holes are marked in red):
If it is impossible to do it the way I think(It seems to be so), the how should I do it? How do developers cap an object after being sliced?
There is also too much confusion. For example, The first picture's result may be:
What am I missing??
EDIT:
After some research, I knew one thing that I am missing:
The input is now robust, and I need the exact same output. How do I accomplish that??
In the past, I have done this kind of thing using a BSP.
Sorry to be so vague, but its not a a trivial problem!
Basically you convert your triangle mesh into the BSP representation, add your clipping plane to the BSP, and then convert it back into triangles.
As code11 said already you have too few data to solve this, the points are not enough.
Instead of clipping edges to produce new points you should clip entire triangles, which would give you new edges. This way, instead of a bunch of points you'd have a bunch of connected edges.
In your example with holes, with this single modification you'd get a 3 polygons - which is almost what you need. Then you will need to compute only the correct triangulation.
Look for CSG term or Constructive Solid Geometry.
EDIT:
If the generic CSG is too slow for you and you have clipped edges already then I'd suggest to try an 'Ear Clipping' algorithm.
Here's some description with support for holes:
https://www.geometrictools.com/Documentation/TriangulationByEarClipping.pdf
You may try also a 'Sweep Line' approach:
http://sites-final.uclouvain.be/mema/Poly2Tri/
And similar question on SO, with many ideas:
Polygon Triangulation with Holes
I hope it helps.
Building off of what zwcloud said, your point representation is ambiguous. You simply don't have enough points to determine where any concavities/notches actually are.
However, if you can solve that by obtaining additional points (you need midpoints of segments I think), you just need to throw the points into a shrinkwrap algorithm. Then at least you will have a cap.
The holes are a bit more tricky. Perhaps you can get away with just looking at the excluded points from the output of the shrinkwrap calculation and trying to find additional shapes in that, heuristically favoring points located near the centroid of your newly created polygon.
Additional thought: If you can limit yourself to convex polygons with only one similarly convex hole, the problem will be much easier to solve.
I have a series of Lat/Long points in a SQL Server database. I would like to be able to find shapes. By that I mean if in the mess of coordinates there are 8 coordinates making a perfect circle, or 7 coordinates making a triangle I would like to know.
I'd be surprised if there is already something out there which does this already, especially in C# (the language I'm using). But My question is really, how should I approach this?
I probably have 200k, but their timestamped, so I should only be working with maybe 1k at a time...
What you're trying to do is called least squares fitting.
Basically, you pick a shape. Let's pick a straight line for now.
You calculate the sum of the squares of the offsets ("the residuals") of the points from the line. You do this with different lines until you've minimized the sum of the squares.
I have no idea how you would automate this for several types of shapes.
You need to find a library, or develop your self, a way to calculate Least Squares over shapes.
If the error margin is over a threshold level of R2 then you do not have that "Shape". You will need to define a formula for the shape you test against (For example a circle: x2+y2=r2).
For things that do not have curves (triangle,square, ect.) it will be harder to do as they do not have a "Formula". You can use the least square for finding each side of the shape for a line (y=mX+b) and then building those lines together to make shapes.
I've got two polygons defined as a list of Vectors, I've managed to write routines to transform and intersect these two polygons (seen below Frame 1). Using line-intersection I can figure out whether these collide, and have written a working Collide() function.
This is to be used in a variable step timed game, and therefore (as shown below) in Frame 1 the right polygon is not colliding, it's perfectly normal for on Frame 2 for the polygons to be right inside each other, with the right polygon having moved to the left.
My question is, what is the best way to figure out the moment of intersection? In the example, let's assume in Frame 1 the right polygon is at X = 300, Frame 2 it moved -100 and is now at 200, and that's all I know by the time Frame 2 comes about, it was at 300, now it's at 200. What I want to know is when did it actually collide, at what X value, here it was probably about 250.
I'm preferably looking for a C# source code solution to this problem.
Maybe there's a better way of approaching this for games?
I would use the separating axis theorem, as outlined here:
Metanet tutorial
Wikipedia
Then I would sweep test or use multisampling if needed.
GMan here on StackOverflow wrote a sample implementation over at gpwiki.org.
This may all be overkill for your use-case, but it handles polygons of any order. Of course, for simple bounding boxes it can be done much more efficiently through other means.
I'm no mathematician either, but one possible though crude solution would be to run a mini simulation.
Let us call the moving polygon M and the stationary polygon S (though there is no requirement for S to actually be stationary, the approach should work just the same regardless). Let us also call the two frames you have F1 for the earlier and F2 for the later, as per your diagram.
If you were to translate polygon M back towards its position in F1 in very small increments until such time that they are no longer intersecting, then you would have a location for M at which it 'just' intersects, i.e. the previous location before they stop intersecting in this simulation. The intersection in this 'just' intersecting location should be very small — small enough that you could treat it as a point. Let us call this polygon of intersection I.
To treat I as a point you could choose the vertex of it that is nearest the centre point of M in F1: that vertex has the best chance of being outside of S at time of collision. (There are lots of other possibilities for interpreting I as a point that you could experiment with too that may have better results.)
Obviously this approach has some drawbacks:
The simulation will be slower for greater speeds of M as the distance between its locations in F1 and F2 will be greater, more simulation steps will need to be run. (You could address this by having a fixed number of simulation cycles irrespective of speed of M but that would mean the accuracy of the result would be different for faster and slower moving bodies.)
The 'step' size in the simulation will have to be sufficiently small to get the accuracy you require but smaller step sizes will obviously have a larger calculation cost.
Personally, without the necessary mathematical intuition, I would go with this simple approach first and try to find a mathematical solution as an optimization later.
If you have the ability to determine whether the two polygons overlap, one idea might be to use a modified binary search to detect where the two hit. Start by subdividing the time interval in half and seeing if the two polygons intersected at the midpoint. If so, recursively search the first half of the range; if not, search the second half. If you specify some tolerance level at which you no longer care about small distances (for example, at the level of a pixel), then the runtime of this approach is O(log D / K), where D is the distance between the polygons and K is the cutoff threshold. If you know what point is going to ultimately enter the second polygon, you should be able to detect the collision very quickly this way.
Hope this helps!
For a rather generic solution, and assuming ...
no polygons are intersecting at time = 0
at least one polygon is intersecting another polygon at time = t
and you're happy to use a C# clipping library (eg Clipper)
then use a binary approach to deriving the time of intersection by...
double tInterval = t;
double tCurrent = 0;
int direction = +1;
while (tInterval > MinInterval)
{
tInterval = tInterval/2;
tCurrent += (tInterval * direction);
MovePolygons(tCurrent);
if (PolygonsIntersect)
direction = +1;
else
direction = -1;
}
Well - you may see that it's allways a point of one of the polygons that hits the side of the other first (or another point - but thats after all almost the same) - a possible solution would be to calculate the distance of the points from the other lines in the move-direction. But I think this would end beeing rather slow.
I guess normaly the distances between frames are so small that it's not importand to really know excactly where it hit first - some small intersections will not be visible and after all the things will rebound or explode anyway - don't they? :)
I have a List of 2D points. What's an efficient way of iterating through the points in order to determine whether the list of points are in a straight line, or curved (and to what degree). I'd like to avoid simply getting slopes between smaller subsets. How would I go about doing this?
Thanks for any help
Edit: Thanks for the response. To clarify, I don't need it to be numerically accurate, but I'd like to determine if the user has created a curved shape with their mouse and, if so, how sharp the curve is. The values are not too important, as long as it's possible to determine the difference between a sharp curve and a slightly softer one.
If you simply want to know if all your points fit more or less on a curve of degree d, simply apply Lagrange interpolation on the endpoints and d-2 equally spaced points from inside your array. This will give you a polynomial of degree d.
Once you have your curve, simply iterate over the array and see how far away from the curve each point is. If they're farther than a threshold, your data doesn't fit your degree d polynomial.
Edit: I should mention that iterating through values of d is a finite process. Once d reaches the number of points you have, you'll get a perfect fit because of how Lagrange interpolation works.
To test if it's a straight line, compute the correlation coefficient. I'm sure that's covered on wikipedia.
To test if it's curved is more involved. You need to know what kind of curves you expect, and fit against those.
Here is a method to calculate angle: Calculate Angle between 2 points using C#
Simply calculate angle between each and every point in your list and create list of angles, then compare if angles list values are different. If they are not different then it means it's straight line, otherwise it's curve...
If it's a straight line then angle between all points has to be a same.
The question is really hazy here: "I'd like to avoid simply getting slopes between smaller substes"
You probably want interpolation a-la B-splines. They use two points and two extra control points if memory serves me. Implementations are ubiquitous since way back (at least 1980's). This should get you underway
Remember that you'll probably need to add control points to make the curve meet the endpoints. One trick to make sure those are reached is to simply duplicate the endpoints as extra controlpoints.
Cheers
Update Added link to codeproject
it would appear that what I remember from back in the 80's could have been Bezier curves - a predecessor of sorts.
I am working on a project with a robot that has to find its way to an object and avoid some obstacles when going to that object it has to pick up.
The problem lies in that the robot and the object the robot needs to pick up are both one pixel wide in the pathfinder. In reality they are a lot bigger. Often the A* pathfinder chooses to place the route along the edges of the obstacles, sometimes making it collide with them, which we do not wish to have to do.
I have tried to add some more non-walkable fields to the obstacles, but it does not always work out very well. It still collides with the obstacles, also adding too many points where it is not allowed to walk, results in that there is no path it can run on.
Do you have any suggestions on what to do about this problem?
Edit:
So I did as Justin L suggested by adding a lot of cost around the obstacles which results in the folling:
Grid with no path http://sogaard.us/uploades/1_grid_no_path.png
Here you can see the cost around the obstacles, initially the middle two obstacles should look just like the ones in the corners, but after running our pathfinder it seems like the costs are overridden:
Grid with path http://sogaard.us/uploades/1_map_grid.png
Picture that shows things found on the picture http://sogaard.us/uploades/2_complete_map.png
Picture above shows what things are found on the picture.
Path found http://sogaard.us/uploades/3_path.png
This is the path found which as our problem also was before is hugging the obstacle.
The grid from before with the path on http://sogaard.us/uploades/4_mg_path.png
And another picture with the cost map with the path on.
So what I find strange is why the A* pathfinder is overriding these field costs, which are VERY high.
Would it be when it evaluates the nodes inside the open list with the current field to see whether the current fields path is shorter than the one inside the open list?
And here is the code I am using for the pathfinder:
Pathfinder.cs: http://pastebin.org/343774
Field.cs and Grid.cs: http://pastebin.org/343775
Have you considered adding a gradient cost to pixels near objects?
Perhaps one as simple as a linear gradient:
C = -mx + b
Where x is the distance to the nearest object, b is the cost right outside the boundary, and m is the rate at which the cost dies off. Of course, if C is negative, it should be set to 0.
Perhaps a simple hyperbolic decay
C = b/x
where b is the desired cost right outside the boundary, again. Have a cut-off to 0 once it reaches a certain low point.
Alternatively, you could use exponential decay
C = k e^(-hx)
Where k is a scaling constant, and h is the rate of decay. Again, having a cut-off is smart.
Second suggestion
I've never applied A* to a pixel-mapped map; nearly always, tiles.
You could try massively decreasing the "resolution" of your tiles? Maybe one tile per ten-by-ten or twenty-by-twenty set of pixels; the tile's cost being the highest cost of a pixel in the tile.
Also, you could try de-valuing the shortest-distance heuristic you are using for A*.
You might try to enlarge the obstacles taking size of the robot into account. You could round the corners of the obstacles to address the blocking problem. Then the gaps that are filled are too small for the robot to squeeze through anyway.
I've done one such physical robot. My solution was to move one step backward whenever there is a left and right turn to do.
The red line is as I understand your problem. The Black line is what I did to resolve the issue. The robot can move straight backward for a step then turn right.