I am using VTK to build meshes from CT images. I find myself stuck trying to understand the cryptic vtkMarchingCubes::SetValue(). As per the documentation The first parameter is said to be the contour number, the second one is the "contour value", my question here is what exactly is "contour value"? is that an intensity value?
If it is indeed an intensity value, does VTK just look for that exact value or does it look around? is there any way I can specify a range rather than a single number? My last question is, how do I extract multiple contours from the image using vtkMarchingCubes in one pass?
Yes, it is the image intensity, I.e. the level for the level-set.
Image intensities are interpolated, so if you have a voxel with intensity 0 and a neighboring voxel with intensity 1, and set the value to 0.5, the generated surface will be half-way in-between. If you set the value to 0.9, the surface will be closer to the 1 voxel.
To extract multiple contours, you'd specify multiple values, I.e
mc->SetValue(0, 60);
mc->SetValue(1, 600);
I am not sure what you would want to achieve by specifying a range?
Related
I like to get a specific contour from image data.
My main goal is to remesh a polydata in grid form. So I followed below pipeline:
convert polydata to image using PolyDataToImageData
convert above image output to vtkImageDataGeometryFilter
use vtkImplicitPolyDataDistance to compute the distance from the original polydata
copy the distance values to image output scalars in step 2
The result is below:
I then tried to use vtkContourFilter to get polydata with SetValue(0, 0.0). And as you can see the result is not entirely correct:
The value of distance array is https://pastebin.ubuntu.com/p/2mZsgdrcmX/ and it is never 0 so I think I am doing it wrong in SetValue but I am also not sure how to get that specific green contour.
Is there any way to get those green points contour?
I am not completely sure to understand your pipeline.
In the vtkContourFilter, the SetValue takes two parameters. The first one is the id of the contour (as the filter can extract several contours at once, see the SetNumberOfContours). The second is the isovalue of the contour.
Here, you set an isovalue of 0.0. Which means you want the points at a distance 0 of the original data set. Looking at the first image, it seems these are the red points. If you want a contour at the green points, you may want to specify a higher scalar value.
PS: If the goal of your pipeline is to have a "larger version" of your shape, you may also have a look at the vtkWarpVector (and give it the normals of your polydata).
I have a 2d grid where pixel centers are at the intersection of two half-grid lines, as shown below.
I also have a shape that is drawn on this grid. In my case the shape is a glyph, and is described by segments. Each segment has a start point, end point and a number of off-curve points. These segments can be quadratic curves or lines. What's important is that I can know the points and functions that make up the outline of the shape.
The rule for deciding which pixels should be turned on is simple: if the center of the pixel falls within the shape outline, turn that pixel on. The following image shows an example of applying this rule.
Now the problem I'm facing has to do with anti aliasing. What I'd like to do is to calculate what percentage of the area of a given pixel falls within the outline. As an example, in the image above, I've drawn a red square around a pixel that would be about 15% inside the shape.
The purpose of this would be so that I can then turn that pixel on only by 15% and thus get some cleaner edges for the final raster image.
While I was able to find algorithms for determining if a given point falls within a polygon (ray casting), I wasn't able to find anything about this type of problem.
Can someone can point me toward some algorithms to achieve this? Also let me know if I'm going about this problem in the wrong way!
This sounds like an X, Y problem.
You are asking for a way to calculate the perecentage of pixel coverage, but based on your question, it sounds that what you want to do is anti alias a polygon.
If you are working only with single color 2D shapes (i.e red, blue, magenta... squares, lines, curves...) A very simple solution is to create your image and blur the result afterwards.
This will automatically give you a smooth outline and is simple to implement in many languages.
I have an image and I am picking colors by RGB (data sampling). I select N points from a specific region in the image which has the "same" color. By "same" I mean, that part of the image belongs to an object, (let's say a yellow object). Each picked point in the RGB case has three values [R,G,B]. For example: [120,150,225]. And the maximum and minimum for each field are 255 and 0 respectively.
Let's assume that I picked N points from the region of the object in the image. The points obviously have different RGB values but from the same family (a gradient of the specific color).
Question:
I want to find a range for each RGB field that when I apply a color filter on the image the pixels related to that specific object remain (to be considered as inliers). Is it correct to find the maximum and minimum from the sampled points and consider them as the filter range? For example if the max and min of the field R are 120 ,170 respectively, can it be used as a the range that should be kept.
In my opinion, the idea is not true. Because when choosing the max and min of a set of sampled data some points will be out of that range and also there will be some point on the object that doesn't fit in this range.
What is a better solution to include more points as inliers?
If anybody needs to see collected data samples, please let me know.
I am not sure I fully grasp what you are asking for, but in my opinion filtering in RGB is not the way to go. You should use a different color space than RGB if you want to compare pixels of similar color. RGB is good for representing colors on a screen, but you actually want to look at the hue, saturation and intensity (lightness, or luminance) for analysing visible similarities in colors.
For example, you should convert your pixels to HSI or HSL color space first, then compare the different parameters you get. At that point, it is more natural to compare the resulting hue in a hue range, saturation in a saturation range, and so on.
Go here for further information on how to convert to and from RGB.
What happens here is that you implicitly try to reinvent either color indexing or histogram back-projection. You call it color filter but it is better to focus on probabilities than on colors and color spaces. Colors of course not super reliable and change with lighting (though hue tends to stay the same given non-colored illumination) that's why some color spaces are better than others. You can handle this separately but it seems that you are more interested in the principles of calculating "filtering operation" that will do segmentation of the foreground object from background. Hopefully.
In short, a histogram back-projection works by first creating a histogram for R, G, B within object area and then back-projecting them into the image in the following way. For each pixel in the image find its bin in the histogram, calculate its relative weight (probability) given overall sum of the bins and put this probability into the image. In such a way each pixel would have probability that it belongs to the object. You can improve it by dividing with probability of background if you want to model background too.
The result will be messy but somewhat resemble an object segment plus some background noise. It has to be cleaned and then reconnected into object using separate methods such as connected components, grab cut, morphological operation, blur, etc.
I have more then 1 week reading about selective color change of an image. It meand selcting a color from a color picker and then select a part of image in which I want to change the color and apply the changing of color form original color to color of color picker.
E.g. if I select a blue color in color picker and I also select a red part in the image I should be able to change red color to blue color in all the image.
Another example. If I have an image with red apples and oranges and if I select an apple on the image and a blue color in the color picket, then all apples should be changing the color from red to blue.
I have some ideas but of course I need something more concrete on how to do this
Thank you for reading
As a starting point, consider clustering the colors of your image. If you don't know how many clusters you want, then you will need methods to determine whether to merge or not two given clusters. For the moment, let us suppose that we know that number. For example, given the following image at left, I mapped its colors to 3 clusters, which have the mean colors as shown in the middle, and representing each cluster by its mean color gives the figure at right.
With the output at right, now what you need is a method to replace colors. Suppose the user clicks (a single point) somewhere in your image, then you know the positions in the original image that you will need to modify. For the next image, the user (me) clicked on a point that is contained by the "orange" cluster. Then he clicked on some blue hue. From that, you make a mask representing the points in the "orange" cluster and play with that. I considered a simple gaussian filter followed by a flat dilation 3x5. Then you replace the hues in the original image according to the produced mask (after the low pass filtering, the values on it are also considered as a alpha value for compositing the images).
Not perfect at all, but you could have a better clustering than me and also a much-less-primitive color replacement method. I intentionally skipped the details about clustering method, color space, and others, because I used only basic k-means on RGB without any pre-processing of the input. So you can consider the results above as a baseline for anything else you can do.
Given the image, a selected color, and a target new color - you can't do much that isn't ugly. You also need a range, some amount of variation in color, so you can say one pixel's color is "close enough" while another is clearly "different".
First step of processing: You create a mask image, which is grayscale and varying from 0.0 to 1.0 (or from zero to some maximum value we'll treat as 1.0), and the same size as the input image. For each input pixel, test if its color is sufficiently near the selected color. If it's "the same" or "close enough" put 1.0 in the mask. If it's different, put 0.0. If is sorta borderline, put an in-between value. Exactly how to do this depends on the details of the image.
This might work best in LAB space, and testing for sameness according to the angle of the A,B coordinates relative to their origin.
Once you have the mask, put it aside. Now color-transform the whole image. This might be best done in HSV space. Don't touch the V channel. Add a constant to S, modulo 360deg (or mod 256, if S is stored as bytes) and multiply S by a constant chosen so that the coordinates in HSV corresponding to the selected color is moved to the HSV coordinates for the target color. Convert the transformed S and H, with the unchanged L, back to RGB.
Finally, use the mask to blend the original image with the color-transformed one. Apply this to each channel - red, green, blue:
output = (1-mask)*original + mask*transformed
If you're doing it all in byte arrays, 0 is 0.0 and 255 is 1.0, and be careful of overflow and signed/unsigned problems.
I have an image with some curve draw in it and I want to extrapolate positions of next few pixels based on already drawn ones. An example is shown in figure 1. For this situation it is easy to fit curve (some parabola) using the least square fit (figure 2) and then based on this fit rasterize the curve to find next pixels (I also need the vector curve for some further calculations).
The problem is that there is often situations like one shown in figure 3, where I can't use curves like parabola, because I can't define such function. Have you any idea how to extrapolate curve/pixels which will work in both cases?