I am fairly new to openCV and am not sure how to proceed.
I have this thresholded image:
And using this image, I need to calculate the distance between two points. The points are also unknown. Illustrated here:
I need to calculate 'd' value. It is the distance from the midpoint of the middle line to where the top line would have been. I am not sure how to proceed with identifying the points and getting the distance, any help would be appreciated!
While I'm not sure about the selecting points part of your problem, calculating the distance between two points is trivial. You can imagine your two points as the beginning and end of the hypotenuse of a right triangle, and use Pythagoreans Theorem to find the length of that hypotenuse.
The math module has a distance method which takes two points, or you can just write the function yourself very simply.
Related
Hello,
in my 2d software i have two inputs available:
an array of XY points
[(x,y),(1,1),(2,2),(2,3),(-1,3),...]
and another matrix representing the closed 2D bezier curve handles
[((x,y),(x,y),(x,y)),
((-1,-1),(1,1),(1,2)),
((1,1),(2,2),(2,3)),
...]
How can i check if a point is inside or outside the given curve using python ? using preferably numpy maybe
I don't know how the theory of Bezier curves, so if your second list of points is a kind of compressed way to represent a Bezier curve, first try to sample some points of the curve with the precision you want.
So you have n points of your curve, and then you can apply a simple PIP algorithm : https://en.wikipedia.org/wiki/Point_in_polygon
I can explain in details later if you want to know how to do it programmatically.
I cant write code right here, because I need the entire program to understand properly, however I may provide two approaches how to do that.
The hardest way is to approximate each Bézier curve by a polyline. And then, according to the wiki you can use two techniques:
Ray casting algorithm: the shorthand of the algorithm: You put a ray, which starting from a point and goes through the entire polygon to an another point. Some lines lies inside a polygon, some outside. And then you check to which line belongs a specific point Looks like this:
Winding number algorithm: A little bit about winding numbers. So if a winding number is non-zero, the point lies inside the polygon
The huge drawback of this approach is that the accuracy depends on how close you approximated a curve to a polyline.
The second way is to use a bitmap. For example, you set your points to the white then render the area under the curve to the black and see if your points remain white. This method is more accurate and the fastest one, because you can use the GPU for the render.
And some links related to the first a approach:
https://pomax.github.io/bezierinfo/#intersections
http://web.mit.edu/hyperbook/Patrikalakis-Maekawa-Cho/node80.html
I have a rather complicated problem. Suppose I have the shape below. You can think of the red dot as a person and the pointy shape inside the big polygon as an obstacle. My goal is to compute the total unblocked vision of the person inside the big polygon, which is the area of the polygon minus the red shaded area.
I want to write a function that takes in the coordinates of the person, the coordinates of the ordered vertices of the obstacle(s) and those of the ordered vertices of the big polygon, and returns the area of the unblocked vision.
I have tried multiple things, and I'm aware of the shoelace algorithm, but the only way that I can come up with is through monte carlo. Can I get a hint on a more intelligent and efficient way to compute the area in a closed-form way?
I think your problem is solved by finding the "Visibility polygon".
https://en.wikipedia.org/wiki/Visibility_polygon
You can use https://karlobermeyer.github.io/VisiLibity1/ library to compute the visibility area.
The first task is to get the two extreme lines of sight from the person.
A simple brute-force checking. I doubt there's a better method, unless you need this calculation at each frame. (See (a) below).
Calculate the angle (relative to X-axis, or whatever) of the line person-to-obstacle_vertex for every vertex.
Find the lowest and highest values. This can be tricky if the obstacle may somehow warp around the person.
So yo can calculate the angle of each pair of sight lines (combinatory issue), an get that with maximum angle. For this job use the Dot-Product.
The second task is to get the area of the shaded region.
You need to get the two intersections of the sight lines and the outer polygon. And then build a list of vertices of the polygon between the two intersections. Add these intersections to that list.
The area can be calculated as the sum of area of triangles, those from the person to each edge (two points in that list). While you have all coordinates, an easier way is to use the Shoelace Algorithm.
(a) If the obstacle has thousands of vertices and the person moves continuosly I'd try to reduce the number of pairs to check. You can mantain a list of shown/hidden vertices, and when the person moves check the last two used vertices and their neighbours, until you get a new couple of ending vertices.
I want to find the highest distance between two pixels in any labeled area or find the top and bottom pixels.How can do this? Can you explain algorithm that related this topic ,please ?Thank you...
let's say that you are going to find the area in a rectangle, or a a square spaced area. In order to find the longest distance between the pixels, you are willing to use the simple math here. As you know the longest straight line distance between 2 points is called as the diagonal of that figure, hence, you will need to use the following formula: https://gyazo.com/c2bd3e0342008642bfde579816cbfd5e
As a result, by just putting in the coordinates of each point, you can achieve the longest distance!
I am using Ipython Notebook. I am working on a project where I need to look at about 100 data points in 3D space and figure out the distance between each and the angle from one another. I want to see correlations of the data points and ultimately see if there is any structure to the data (a straight line hidden somewhere). I have looked into clustering techniques and hough transforms, but they seem not to give me the result I need. Any ideas are much appreciated.. thanks!
For the first issue of determining the pairwise distance between three dimensional points, you can use scipy.spatial.distance.pdist(). This will generate n(n-1)/2 distances for n points. For the second issue finding the angle between points, that's trickier. It seems so tricky that I don't even really want to think about it; however, to that end, you can use scipy.spatial.distance.cosine(), which will determine the cosine distance between two vectors.
Have you looked at scikits? I've found them very helpful in my work. http://scikit-learn.org/stable/
The distance is best found using scipy.spatial.distance.pdist() as mentioned in cjohnson318's answer. For a small array of points 'a' defined as:
import numpy as np
a=np.array([[0,0,0],[1,1,1],[4,2,-2],[3,-1,2]])
The distance euclidean distance 'D' between the points can be found as:
from scipy.spatial.distance import pdist, squareform
D = squareform(pdist(a))
In 3d polar notation, you would need 2 angles to define the direction from one point to another. It seems like a Cartesian unit vector giving the direction would likely serve your purpose just as well. These can be found as:
(a-a[:,np.newaxis,:]) / D[...,np.newaxis]
This will include NaN's in the diagonal elements, as there is no vector from a point to itself. If necessary, these can be changed to zeros using np.nan_to_num
If you actually do need the angles, you could get them by applying np.arctan to the components of the unit vector.
So I'm working on a piece of code to take positional data for a RC Plane Crop Duster and compute the total surface area transversed (without double counting any area). I cannot figure out how to calculate the area for a given period of operation.
Given the following Table Calculate the area the points cover.
x,y
1,2
1,5
4,3
6,6
3,4
3,1
Any Ideas? I've browsed Greens Theorem and I'm left without a practical concept in which to code.
Thanks for any advise
Build the convex hull from the given points
Algorithms are described here
See a very nice python demo + src
Calculate its area
Python code is here
Someone mathier than me may have to verify the information here. But it looks legit: http://www.wikihow.com/Calculate-the-Area-of-a-Polygon and fairly easy to apply in code.
I'm not entirely sure that you're looking for "Surface area" as much as you're looking for Distance. It seems like you want to calculate the distance between one point and the next for that list. If that's the case, simply use the Distance Formula.
If the plane drops a constant width of dust while flying between those points, then the area is simply the distance between those points times the width of the spray.
If your points are guaranteed to be on an integer grid - as they are in your example - (and you really are looking for enclosed area) would Pick's Theorem help?
You will have to divide the complex polygon approximately into standard polygons (triangles, rectangles etc) and then find area of all of them. This is just like regular integration (only difference is that you are yet to find a formula to approximate your data).
The above points are when you assume that you are forming a closed polygon with your data.
Use to QHull to triangulate the region, then sum the areas of the resulting triangles.
Python now conveniently has a library that implements the method Lior provided. https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.ConvexHull.html will calculate the convex hull for any N dimensional space and calculate the area/volume for you as well. See the example and return value attributes towards the bottom of the page for details.