I'm trying to find a way to implement fill holes algorithm for binary image in VB 6.0. I found these sites so far
http://answers.opencv.org/question/9863/fill-holes-of-a-binary-image/
http://www.mathworks.com/help/images/ref/imfill.html
I'm totally confused by the algorithm to find holes. Can someone please refer me to a better site or simple code so can assist me to implement the algorithm
It is really easy to fill holes in the binary image with a morphological closing operation that typically consists of a dilation followed by an erosion. A side effect of these operations is a small distortion of the shape. morhplogyEx() with parameter MORPH_CLOSE will do it for you in one operation.
A more sophisticated algorithm to use is flood fill which will return the area of the hole as well as will mark it with a specified color. It starts at a seed area specified with a Point seedPoint.
I personally prefer to invert the image contrast and find holes as small blobs or connected components. I then erase blobs with a certain size and invert the image again. OpenCV has a simple blob detector.
Related
I am trying to create an algorithm that can take a set of objects and organize them in a given area such that a box bounding all of the shapes is optimized (either by area used, or by maximizing the span along one of the dimensions, etc.). All of the shapes are closed and bounded.
The purpose of this is to try and minimize material waste from using a laser cutter. The shapes are generated in CAD and can read into this algorithm. The algorithm will then take arguments for the working area (effective laser cutting area) as well as the minimum separation between any two objects, then attempt to organize the objects within the specified dimensions while trying to minimize the area usage. Alternatively, the algorithm can also try to maximize the object locations along one axis while minimizing the span along the other dimension. This would be akin to cutting off a smaller workpiece to cut from.
Ideally, the algorithm would be able to make translations AND rotations, but rotations aren't necessary.
For example, this Picture depicts the required transformation.
It should work with an arbitrary, but small (<25) number of objects.
Lastly, I don't expect anyone to solve this for me, but I would appreciate help toward either finding an algorithm that can do this, or developing my own. Thank you.
I dont know to what extent you want to create said algorithm or how you want to implement it, But i know of a program called OptiNest that can do what you ask. It organizes geometric shapes to optimize the layout and minimize waste on a plane, i think in an autocad format.
I have SVG abirtrary paths which i need to pack as efficiently as possible within a given rectangle(as less waste of space as possible). After some research i found the bin packing algorithms which seems to be dealing with boxes and not curved random shapes(my SVG shapes are quite complex and include beziers etc.).
AFAIK, there is no deterministic algorithm for actually packing abstract shapes.
I wish to be proven wrong here which would be ideal(having a mathematical deterministic method for packing them). In case I am right however and there is not, what would be the best approach to this problem
The subject name is Shape Nesting, Nesting Problem or Nesting Process.
In Shape Nesting there is no single/uniform algorithm or mathematical method for nesting shapes and getting the least space waste possible.
The 1st method is the packing algorithm(creates an imaginary bounding
box for each shape and uses a rectangular 2D algorithm to pack the
bounding boxes).
This method is fast but the least efficient in regards to space
waste.
The 2nd method is some kind of incremental rotation. The algorithm
rotates the shape at incremental steps and checks if it fits in the
space. This is better than the packing method in regards to space
waste but it is painstakingly slow,
What are some other classroom examples for achieving a solution to this problem?
[Edit1] new answer
as mentioned before bin-packing is NP complete (hard) so forget about algebraic solution
known approaches are:
generate and test
either you test all possibility of the problem and remember the best solution or incrementally add items (not all at once) one by one with the same way. It is basically what you are doing now without proper heuristic is unusably slow. But has the best space efficiency (the first one is much better but much slower) O(N!)
take advantage of sorting items by size
something like this it is much faster almost O(N.log(N)) (according to used sorting algorithm). Space efficiency strongly depends on the items size range and count. For rectangular shapes is this the best approach (fastest and usable even for N>1000). For complex shapes is this not a good way but look at it anyway maybe you get some idea ...
use of Neural network
This is extremly vague approach without any warrant of solution but possible best space efficiency/runtime ratio
I think there could be some field approach out there
I sow a few for generating graph layouts. All items create fields (booth attractive and repulsive) so they are moving to semi-stable state.
At first all items are at random locations
When the movement stop remember best solution and shake all items a little or randomize their position again.
Cycle this few times
This approach is much faster then genere and test and can provide very close solution to it but it can hang in local min/max or oscillate if the fields are not optimally choosed. For example all items can have constant attractive force to each other and repulsive force getting stronger only when the items are very close. You have to prevent overlapping of items (either by stronger repulsion or by collision tests). You have also to create some rotation moment for example with that repulsive force. It differs on any vertex so it creates a rotation moment (that can automatically align similar sides closer together). Also you can have semi-stable state with big distances between items and after finding best solution just turn off repulsion fields so they stick together. Sometimes it can have better results some times not ... here is nice example for graph layout computation
Logic to strategically place items in a container with minimum overlapping connections
Demo from the same QA
And here solver for placing sliders in 2D:
How to implement a constraint solver for 2-D geometry?
[Edit0] old answer before reformulating the question
I am not clear what you want to achieve.
have SVG picture and want to separate its parts to rectangular regions
as filled as can be
least empty space in them
no shape change in picture
have svg picture and want to change its shapes according to some purpose
if this is the case some additional info is needed
[solution for 1]
create a list of points for whole SVG in global SVG space (all points are transformed)
for line you need add 2 points
for rectangles 4 points
circle/elipse/bezier/eliptic arc 8 points
find local centres of mass
use classical approach
or can speed things up by computing the average density of points per x,y axis separately and after that just check all combinations of found positions of local max of densities if they really are sub cluster center or not.
all sub cluster center is the center of your region
now find the most far points which are still part of your cluster (the are close enough to neighbour points)
create rectangular area that cover all points from sub cluster.
you also can remove all used points from list
repeat fro all valid sub clusters
until all points are used
another not precise but simpler approach is:
find SVG size
create planar map of svg with some precision for example int map[256][256].
size of map can be constant or with the same aspect as SVG
clear map with 0
for any point of SVG set related map point to 1 (or inc or whatever)
now just segmentate map and you will have find your objects
after segmentation you have position and size of all objects
so finding of bounding boxes should be easy
You can start with a variant of the rectangle bin-packing algorithm and add rotation. There is a method "Guillotine bin packer" and you can download a paper and a library at github.
Currently I'm working on a little project just for a bit of fun. It is a C++, WinAPI application using OpenGL.
I hope it will turn into a RTS Game played on a hexagon grid and when I get the basic game engine done, I have plans to expand it further.
At the moment my application consists of a VBO that holds vertex and heightmap information. The heightmap is generated using a midpoint displacement algorithm (diamond-square).
In order to implement a hexagon grid I went with the idea explained here. It shifts down odd rows of a normal grid to allow relatively easy rendering of hexagons without too many further complications (I hope).
After a few days it is beginning to come together and I've added mouse picking, which is implemented by rendering each hex in the grid in a unique colour, and then sampling a given mouse position within this FBO to identify the ID of the selected cell (visible in the top right of the screenshot below).
In the next stage of my project I would like to look at generating more 'playable' terrains. To me this means that the shape of each hexagon should be more regular than those seen in the image above.
So finally coming to my point, is there:
A way of smoothing or adjusting the vertices in my current method
that would bring all point of a hexagon onto one plane (coplanar).
EDIT:
For anyone looking for information on how to make points coplanar here is a great explination.
A better approach to procedural terrain generation that would allow
for better control of this sort of thing.
A way to represent my vertex information in a different way that allows for this.
To be clear, I am not trying to achieve a flat hex grid with raised edges or platforms (as seen below).
)
I would like all the geometry to join and lead into the next bit.
I'm hope to achieve something similar to what I have now (relatively nice undulating hills & terrain) but with more controllable plateaus. This gives me the flexibility of cording off areas (unplayable tiles) later on, where I can add higher detail meshes if needed.
Any feedback is welcome, I'm using this as a learning exercise so please - all comments welcome!
It depends on what you actually want and what you mean by "more controlled".
Do you want to be able to say "there will be a mountain on coordinates [11, -127] with radius 20"? Complexity of this this depends on how far you want to go. If you want just mountains, then radial gradients are enough (just add the gradient values to the noise values). But if you want some more complex shapes, you are in for a treat.
I explore this idea to great depth in my project (please consider that the published version is just a prototype, which is currently undergoing major redesign, it is completely usable a map generator though).
Another way is to make the generation much more procedural - you just specify a sequence of mathematical functions, which you apply on the terrain. Even a simple value transformation can get you very far.
All of these methods should work just fine for hex grid. If artefacts occur because of the odd-row shift, then you could interpolate the odd rows instead (just calculate the height value for the vertex from the two vertices between which it is located with simple linear interpolation formula).
Consider a function, which maps the purple line into the blue curve - it emphasizes lower located heights as well as very high located heights, but makes the transition between them steeper (this example is just a cosine function, making the curve less smooth would make the transformation more prominent).
You could also only use bottom half of the curve, making peaks sharper and lower located areas flatter (thus more playable).
"sharpness" of the curve can be easily modulated with power (making the effect much more dramatic) or square root (decreasing the effect).
Implementation of this is actually extremely simple (especially if you use the cosine function) - just apply the function on each pixel in the map. If the function isn't so mathematically trivial, lookup tables work just fine (with cubic interpolation between the table values, linear interpolation creates artefacts).
Several more simple methods of "gamification" of random noise terrain can be found in this paper: "Realtime Synthesis of Eroded Fractal Terrain for Use in Computer Games".
Good luck with your project
Im trying to transform a path along an arc.
My project is running on osX 10.8.2 and the painting is done via CoreAnimation in CALayers.
There is a waveform in my project which will be painted by a path. There are about 200 sample points which are mirrored to the bottom side. These are painted 60 times per second and updated to a song postion.
Please ignore the white line, it is just a rotation indicator.
What i am trying to achieve is drawing a waveform along an arc. "Up" should point to the middle. It does not need to go all the way around. The waveform should be painted along the green circle. Please take a look at the sketch provided below.
Im not sure how to achieve this in a performant manner. There are many points per second that need coordinate correction.
I tried coming up with some ideas of my own:
1) There is the possibility to add linear transformations to paths, which, i think, will not help me here. The only thing i can think of is adding a point, rotating the path with a transformation, adding another point, rotating and so on. But this would be very slow i think
2) Drawing the path into an image and bending it would surely lead to image-artifacts.
3) Maybe the best idea would be to precompute sample points on an arc, then save save a vector to the center. Taking the y-coordinates of the waveform, placing them on the sample points and moving them along the vector to the center.
But maybe i am just not seeing some kind of easy solution to this problem. Help is really appreciated and fresh ideas very welcome. Thank you in advance!
IMHO, the most efficient way to go (in terms of CPU usage) would be to use some form of pre-computed approach that would take into account the resolution of the display.
Cleverly precomputed values
I would go for the mathematical transformation (from linear to polar) and combine two facts:
There is no need to perform expansive mathematical computation
There is no need to render two points that are too close from each other
I have no ready-made algorithm for you, but you could use a pre-computed sin or cos table, and match the data range to the display size in order to work with integers.
For instance imagine we have some data ranging from 0 to 1E6 and we need to display the sin value of each point in a 100 pix height rectangle. We can use a pre-computed sin table and work with integers. This way displaying the sin value of a point would be much quicker. This concept can be refined to get a nicer result.
Also, there are some ways to retain only significant points of a curve so that the displayed curve actually looks like the original (see the Ramer–Douglas–Peucker algorithm on wikipedia). But I found it to be inefficient for quickly displaying ever-changing data.
Using multicore rendering
You could compute different areas of the curve using multiple cores (can be tricky)
Or you could use pre-computing using several cores, and one core to do finish the job.
Can somebody help me, to find a library, or a detailed description of algorithm, that could embed a Digital watermark(invisible watermark, just a kind of steganography) to a jpeg/png file. But the quality of algorithm, should be great. It should be possible to extract this mark after rotation and expansion(if possible) of image.
Mark is just a key 32bytes.
I found a good site, but the algorithm are made for the NetPBM format, that is dead...
I know that there is a LSB method, but it is not stable to the expansion. Are there something better?
Changing metadata, is not suitable, because it is visible changes.
This maybe won't really be an answer, as I don't think it would be easy to give a magical, precise answer on this question.Watermarking is complex, and the best way to do it is by yourself : this will make things more hard for an attacker trying to reverse engineer your code. One could even read your question here, guess what library you used, and attack your system more easily.
Making Steganography resist to expansion in JPEG images is also very hard, because the JPEG compression is reapplied after the expansion. There are in fact a bunch of JPEG steganography algorithms. Which one you should use, depends on what exactly do you require :
Data confidentiality ?
Message presence confidentiality ?
Message coherence after JPEG changes ?
Resistance to "Known Cover" attacks (when attackers try to find the message, based on the steganographic system) ?
Resistance to "Known Message" attacks (when attackers try to find the steganographic system used, based on the message) ?
From what I know, usually, algorithm that resist to JPEG changes (picture recompression) are often really easier to attack, whereas algorithms that run the "encode" stage during the JPEG compression (after the DCT (lossy) transform, and before the Huffmann (non-lossy) transform) are more prone to resist.
Also, one key factor about steganography is scale : if you have only 32bytes of data to encode in a, say, 256*256px image, don't use an algo that can encode 512bytes of data in the same size. Either use a scalable algorithm, either use an algorithm at its efficient scale.
Also, the best way to do good steganography is to know its limitations,and to know how steganalyzers work. Try these tools, so you can understand what attackers will do to your picture.^
Now, I cannot tell you what steganographic system will be the best for you, but I can give you some indications :
jSteg - Quite old, I don't think it will resist to JPEG changes
OutGuess - Quite old too, but one of the best algorithms
F5 (and F3/F4) - More recent, good algorithm, scientifical research behind.
stegHide
I think all of these are LSB based : the encoding is done during the JPEG compression, after the DCT and Quantization. The only non LSB-based steganography system I heard of was mentionned in this research paper, however, I did not read it to the end yet, so I cannot tell if this will meet your needs.
However, I'm not sure there exists a real steganography algorithm resisting to JPEG compression, to JPEG resize and rotation, resisting to visual and statisticals attacks. Or I'm not aware of it.
Sorry for the lack of precise answer, I tried to give you what I know on the subject, as it's always better to be more informed. Sorry also for the lack of proper English, I'm French, nobody's perfect :)
Pistache is right in what he told you regarding the watermarking implementation algorithms. I will try to help you by showing one algorithm for the given requirements.
Before explaining you the algorithms first I guess that the distinction between the JPG and PNG formats should be done.
JPEG is a lossy format, i.e, the images are susceptible to compression that could remove the watermark. When you open an image for manipulation purposes and you save it, upon the writing procedure, a compression is made by using DCT filtering that removes some important components of the image.
On the other hand, PNG format is lossless, and that means that images are not susceptible to such kind of compression when stored after manipulation.
As a matter of fact, JPEG is used as a watermarking scheme attack due to its compressing characteristic that could remove the watermark if an attacker performed the compression.
Now that you know the difference between both formats, I can tell you a suitable algorithm resistant to the attacks that you mentioned.
Regarding methods to embed a watermark message for PNG files you can use the histogram embedding method. The histogram embedding method changes values on the histogram by changing the values of the neighbor bins. For example imagine that you have a PNG image in grayscale.
Therefore, you'll have only one channel for embedding and that means that you have one histogram with 256 bins. By selecting the neighbor bins x and x+1, you change the values of x and x+1 by moving the pixels with the bright x to x+1 or the other way around, so that (x/(x+1))>T for embedding a '1' or ((x+1)/x)>T for embedding a '0'.
You can repeat the same procedure for the whole histogram length and therefore you can embed in the best case up to 128bits. However this payload is less than what you asked. Therefore I suggest you to split the image into parts, for example blocks, and if you split one image into 4 components you'd be able to embed in the best case up to
512 bits which means 64 bytes.
This method although is very, susceptible to filtering and compression if applied straight in the space domain. Therefore, I suggest you to compute before the DWT of the image and use its low-frequency sub-band. This will provide you better transparency and robustness increased for the warping, resizing etc attacks and compression or filtering as well.
There are other approaches such as LPM (Log Polar Maps) but they are very complex to implement and I think for your case this approach would be fine.
I can suggest you two papers, the first is:
Watermarking digital image and video data. A state-of-the-art overview
This paper will give you some basic notions of watermarking and explain more in detail the LSB algorithm. And the second paper is:
Real-Time Compressed- Domain Video Watermarking Resistance to Geometric Distortions
This paper will explain the algorithm that I just explained now.
Cheers,
I do not know if you are considering approaches different to steganography. Instead of storing data hidden in the pixel data you could create a new data block in the JPEG file and store encripted data.
Take a look at the JPEG file structure on Wikipedia
You can create an application specific data block, using the marker 0xFF 0xEn. Doing so, any change in the image pixels do not change the information stored in the image. Moreover, many image editing software respect custom data blocks and will keep them even after image manipulation.