pyopengl How to color concave polygons，graphics like this，When filling the color, I always fill the concave place flat. I hope I can fill the color according to the outline of the figure
When filling the color, I always fill the concave place flat. I hope I can fill the color according to the outline of the figure
Don't draw a polygon. Draw many triangles, which combine to make the polygon.
Of course, you can generate the triangles in any way you like, such as with pencil and paper and a ruler and typing in the coordinates.
For this polygon, you might find GL_TRIANGLE_FAN useful, since not much change is needed: add one new vertex at the beginning of the vertices, in the middle of the polygon, so that every other vertex can see the one you added. OpenGL will generate triangles radiating outwards from the central point to all the edge vertices. This is convenient for polygons like yours which are "mostly convex". The triangles will look like this:
If the polygon wasn't "mostly convex", you would most likely need to use GL_TRIANGLES which allows you to split it up however you like.
Another option is to draw an alpha-textured quad, with alpha test turned on. The GPU will draw a square and ignore the pixels in the corners where the alpha is 0. Since your polygon is "almost a square" the wasted work to calculate those pixels and ignoring them could be less than the extra work to process a bunch more triangles. There's no way to perfectly predict that. With this approach, the corner shape would be pixel-based instead of triangle-based, so it would get blocky if you zoomed in.
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I have been looking for a Python library that would allow me, from a set of points in 3D and RGB colors associated to each point, to get a transparent surface (i. e., with some degree of transparency). The idea would be to be able to display (and manipulate/rotate) things similar to the image below:
The atoms and bonds are inside a 3D surface that is smooth and constructed from a series of points each with a RGB color.
I could get some rough Poisson reconstruction running with Mayavi but the colors appeared very pixelized and I couldn't find a way to make the surface transparent. I could obtain a lot of features I wanted for this work with Open3D (I actually place these objects inside crystal structures so I need to represent bonds, atoms, crystal edges, axes and so on), but here again I couldn't find a Poisson reconstruction algorithm to recreate the smooth surface from points nor any functionality to make a surface transparent. Any suggestion would be appreciated.
I'm trying to remove uneven white borders from different set of pictures. They all look like these:
What I'm doing right now is just drawing a rectangle around the picture in hope that it covers the white area:
h, w = img.shape
cv2.rectangle(img, (0,0), (w,h), (0,0,0), 2)
Depending on the picture it might work or not. As there are a variety number of pictures which are in similar situation I'm looking for a more logical solution which is applicable to all pictures with this kind of issue.
I think your way is right, but it's unaware whether it overlays figures (you may increase the thickness if you know there won't be figures with that margin) and the desired thickness is unknown.
You may use findContour. Find the "thick" figures (if you expect particular metrics as in the picture). Sort their extreme coordinates, add some margin and that would set the max depth of the border.
However then not a rectangle, but a line would be better drawn per each side, in case there are figures very close to the border.
Another scenario: first draw concentrating black rectangles (or lines per side) in order to clear the unevenness, then draw the white lines/rectangle with the desired thickness.
I'm looking for some solutions that, given a set S of circles with 2D-center points and radii, returns a minimal sub-set M in S that covers entirely a specific circle with 2d-center point and radius. This last circle is not in S.
I've chosen circles, but it doesn't matter if we change them to squares, hexagons, etc.
You have two distinct problems: you need to turn the geometric problem into a combinatoric problem, and then you need to solve the combinatoric problem. For the latter, you are looking at a minimum set cover problem, and there should be plenty of literature on that. Personally I like Knuth's Dancing Links approach to enumerate all solutions of a set cover, but I guess for a single minimal solution you can do better. A CPLEX formulation (to match your tag) would use a binary variable for each row, and a ≥1 constraint for each column.
So now about turning geometry into combinatorics. All the lines of all your circles divide the plane into a bunch of areas. The areas are delimited by lines. Of particular relevance are the points where two or more circles meet. The exact shape of the line between these points is less relevant, and you might imagine pulling those arcs straight to come up with a more classical planar graph representation. So compute all the pair-wise intersections between all your circles. Order all intersections of a single circle by angle and connect them with graph edges in that order. Do so for all circles. Then you can do a kind of bucket fill to determine for each circle which graph faces are within and which are outside.
Now you have your matrix for the set cover: every graph face which is inside the big circle is a column you need to cover. Every circle is a row and covers some of these faces, and you know which.
Need to get rectangular shapes from a noisy color segmented image.
The problem is that sometimes the object isn't uniformly the correct color causing holes in the image, or sometimes reflection of the object in the background cause noise/false positive for the color segmentation.
The object could be in any position of the image and of any unknown rectangular size, the holes can occur anywhere inside the object and the noise could occur on any side of the object.
The only known constant is that the object is rectangular in shape.
Whats the best way to filter out that noise to the left of the object and get a bounding box around the object?
Using erosion would remove the detail from the bottom of the object and would cause the size of the bounding box to be wrong
I can't comment because of my rep, but I think you could try to analyse the colored image using other color spaces. Create a upper and a lower bound of the color you want until it selects the object, leaving you with less noise, which you can filter with erode/dilate/opening/closing.
For example, in my project I wanted to found a bounding box of a color-changing green rectangle, so I went and tried a lot of diferent color spaces with a lot of diferent upper/lower bounds until I finally got something worthy. Here is a nice read of what I'm talking about : Docs
You can also try filtering the object by área, after dilating it (you dilate first so the closer points connect to one another, while the more distant ones, which are the noise, don't, creating a big rectangle with lots of noise, but then you filter by a big área).
One method is to take histogram projection on both the horizontal and vertical axes, and select the intersection of ranges that have high projections.
The projections are just totals of object pixels in each row and each column. When you are looking for only one rectangle, the values indicated the probablity of the row/column belonging to the rectangle.
I have a data set of different images. After drawing ellipses, I want to crop them.
I drew ellipses on them. The actual ellipses are the blue ones. The green ones are just how they look like with their angle 0 degrees.
I am using numpy array slicing to get the cropped image:
output_image[y-(minor_axis/2):y+(minor_axis/2), x-(major_axis/2):x+(major_axis/2)]
However the result isn't as it should be. For example the result of the large ellipse looking like an oval is as follows:
I first thought that it is happening because I am using angle during numpy slicing. Therefore, I drew the green circles of angle 0 degree having the same other major axis, minor axis etc values but the result also doesn't correspond to the green circle.
What's going on here?
As far as I can tell from the images and from the single line of code you posted, the problem is that you are not taking the rotation of the ellipse into account.
y + (minor_axis / 2) would only correspond to the top of the cropped region if the minor axis of the ellipse was exactly aligned with the x-axis. However, since your ellipse is rotated by 45 degrees, this is not the case.
You could try to work out the bounding box yourself, based on the parametric equations for an ellipse (see this post for an example). A much simpler option is to let OpenCV do the work, and get the bounding box using cv2.boundingRect, as Miki mentioned in the comments above.