I have a chart that has several existing curves on it that I have tried to interpolate new curves in between. I have used linear interpolation in the form of y = ((x - x1)(y2 - y1) / (x2 - x1)) + y1, however the new curves look out of place.
You can see in the picture that every second line (from the bottom) is the interpolated line. While the very second line data points are exactly centered between the first and third data points in the y axis, the third line data points are not centered between the second and fourth y data points, making the graph look skew.
So I am thinking linear interpolation may not be what I am after here. Can someone recommend another method that would create curves between the existing ones, but replicates the same form?
Sudden changes in gradient are hard to interpolate. When you're at the point where you want an interpolated line to suddenly change gradient, there is no information from the points in close proximity that give information as to where the sudden change in gradient should occur.
To replicate the pattern, you actually need to copy the gradient of the line below then smoothly transition to the gradient of the line above. Visually we can see that it should occur half way between the change in gradients for the lines above and below on either side, but detecting the locations of those changes is not trivial.
The points where the sudden change in gradient are occurring are separated by a large change in the x-axis by only a small change in the y-axis. When calculating y-values for x-values in between the the changes in gradient you get the aberrations. I suggest trying to interpolate x-values based on y-values instead. For each curve, for each small arbitrary step in the y-axis, find/calculate the closest x-values from the curve on either side and take the average to plot your interpolation.
An unconventional approach may be a piece-meal style of interpolation. It may be possible to model the 3 regions of different gradients separately.
Start by identifying the 2 lines that would be drawn through the 2 sets of kinks, creating 3 regions of space. The vertical line would stop at the horizontal line near the bottom right corner of the graph.
For each region (and potentially for each value of x in each region) determine the gradient of the lines. When you're doing your interpolation of a new line, for each starting point (x1, y1), look at which region it falls in. Use the gradient of that region as a significant factor when determining the next point. Keep doing this until you reach a region boundary. When the interpolated point crosses into a different region, then use the gradient of that region as a significant factor to interpolate the next point.
It will be quite pointy if you did this strictly, so graph with some smoothing (or incorporate a smoothing factor using weighted averages of the gradients as you transition between regions, but this could be a whole lot of effort without necessarily closer results!)
Related
The Xiaolin Wu algorithm draws an anti-aliased line between two points. The points can be at sub-pixel, i.e. non-integer coordinates. I'll assume the reader is familiar with the algorithm and just recall the important features. We loop across the major (longer) axis of the line, let's say it's the x-axis, basically proceeding column-by-column. In each column we color two pixels. The computation is equivalent to this: place a 1x1 square centered on the line, at the point whose x coordinate is the center of the the given column of pixels. Let's call it S. If we think of each pixel as a 1x1 square in the plane, we now calculate the area of intersection between S and each of the two pixels it straddles, and use those areas as the intensities with which to color each pixel.
That's nice and clear, but what is going on with the calculations for the endpoints? Because the endpoints can be at non-integer positions, they have to be treated as a special case. Here's the pseudocode from the linked Wikipedia article for handling the first endpoint x0, y0:
// handle first endpoint
xend := round(x0)
yend := y0 + gradient * (xend - x0)
xgap := rfpart(x0 + 0.5)
xpxl1 := xend // this will be used in the main loop
ypxl1 := ipart(yend)
plot(ypxl1, xpxl1, rfpart(yend) * xgap)
plot(ypxl1+1, xpxl1, fpart(yend) * xgap)
I edited out the if (steep) condition, so this is the code for the case when the slope of the line is less than 1. rfpart is 1-fpart, and fpart is the fractional part. ipart is the integer part.
I just have no idea what this calculation is supposed to be doing, and I can't find any explanations online. I can see that yend is the y-coordinate of the line above xend, and xend is the x coordinate of the pixel that the starting point (x0, y0) is inside of. Why are we even bothering to calculate yend? It's as if we're extending the line until the nearest integer x-coordinate.
I realize that we're coloring both the pixel that the endpoint is in, and the pixel either immediately above or below it, using certain intensities. I just don't understand the logic behind where those intensities come from.
With the Xiaolin Wu algorithm (and sub-pixel rendering techniques in general) we imagine that the screen is a continuous geometric plane, and each pixel is a 1x1 square region of that plane. We identify the centers of the pixels as being the points with integer coordinates.
First, we find the so-called "major axis" of the line, the axis along which the line is longest. Let's say that it's the x axis. We now loop across each one-pixel-wide column that the line passes through. For each column, we find the point on the line which is at the center of that column, i.e. such that the x-axis is an integer. We imagine there's a 1x1 square centered at that point. That square will completely fill the width of that column and will overlap two different pixels. We color each of those pixels according to the area of the overlap between the square and the pixel.
For the endpoints, we do things slightly differently: we still draw a square centered at the place where the line crosses the centerline of the column, but we cut that square off in the horizontal direction at the endpoint of the line. This is illustrated below.
This is a zoomed-in view of four pixels. The black crosses represent the centers of those pixels, and the red line is the line we want to draw. The red circle (x0, y0) is the starting point for the line, the line should extend from that point off to the right.
You can see the grey squares centered on the red crosses. Each pixel is going to be colored according to the area of overlap with those squares. However, in the left-hand column, we cut-off the square at x-coordinate x0. In light grey you can see the entire square, but only the part in dark grey is used for the area calculation. There are probably other ways we could have handled the endpoints, for instance we could have shifted the dark grey region up a bit so it's vertically centered at the y-coordinate y0. Presumably it doesn't make much visible difference, and this is computationally efficient.
I've annotated the drawing using the names of variables from the pseudocode on Wikipedia.
The algorithm is approximate at endpoints. This is justified because exact computation would be fairly complex (and depend on the type of endpoint), for a result barely perceivable. What matters is aliasing along the segment.
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.
In an infinite 2D space there are a set of lines, each line having a start and end point, and a time of creation: Line(p0, p1, t).
I want to find the lines that should be rendered in a top-down view of this 2D space (higher values of t show up closer to the viewport, not that it should be relevant.)
The intuitive answer is "check if either point is within the viewport coordinates," but this falls down when the points are further apart than the viewport area covers.
The other idea I had was using something like geohash, this would limit precision i.e. maximum zoom level of the viewport. The idea is enumerating the hashes of the cells intersected and storing them. This way querying is a matter of asking the right question.
Are there any ideal solutions? Has this been solved before?
I think you need to check two conditions: one that the rectangle of viewport overlaps the rectangle with corners (p0,p1) and the second that some corners of viewport rectangle are on the different sides of the whole line which contains line segment (p0,p1).
The task of finding rectangle overlap can be solved very effectively for very large number of rectangles using R-trees (http://en.wikipedia.org/wiki/R-tree).
The second task can be reduced to checking signs of the cross product of (p1-p0) x (corner_coordinate-p0)
(all three quantities taken as 3-d vectors with third coordinate equal to zero, the result will be vector along the perpendicular direction). There should be corners with the opposite sign of this cross product.
I have stress strain data from tensile test. I have drawn a stress vs strain graph in excel.I need to find the yield point.For that, I need to draw a line parallel to the linear(straight) portion of the curve with 0.2% offset in x axis and see where it intersects with the original curve.
So, I tried to keep only the linear portion data and drew a trendline, which gives me straight line equation y=mx. Now, if I want 0.2% offset, equation of line is y=mx +c.
I have the equation, how to draw this line in excel with the equation? And how to get the intersection point? Is my approach right? Please help.
A Stress-strain curve with data courtesy of YouTube:
It can be observed that this is linear (elastic deformation) for about the first nine data points. Hide the other data points and add a liner trend line to the chart, with Display Equation on chart. This shows y=4232.x + 0.701.
In say N1 =4232*D2+.701 and copied down to N10 provides the data for an additional data series. Copy N2:N10, select the chart, Paste and format the added series to see the match:
For a 0.2% offset x is .002 when y=0. For a parallel line the slope (determined by 4232 in the formula) cannot be changed so the constant must be, from +0.701 to -7.763. A further data set can be created with the altered constant and added to the chart as before:
By observation the offset yield strength (0.2% proof strength) can be seen to be around 80 MPa in this example (where the green line intersects the blue curve).
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?