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I need to draw a circle in a 2D numpy array given [i,j] as indexes of the array, and r as the radius of the circle. Each time a condition is met at index [i,j], a circle should be drawn with that as the center point, increasing all values inside the circle by +1. I want to avoid the for-loops at the end where I draw the circle (where I use p,q to index) because I have to draw possibly millions of circles. Is there a way without for loops? I also don't want to import another library for just a single task.
Here is my current implementation:
for i in range(array_shape[0]):
for j in range(array_shape[1]):
if (condition): # Draw circle if condition is fulfilled
# Create a square of pixels with side lengths equal to radius of circle
x_square_min = i-r
x_square_max = i+r+1
y_square_min = j-r
y_square_max = j+r+1
# Clamp this square to the edges of the array so circles near edges don't wrap around
if x_square_min < 0:
x_square_min = 0
if y_square_min < 0:
y_square_min = 0
if x_square_max > array_shape[0]:
x_square_max = array_shape[0]
if y_square_max > array_shape[1]:
y_square_max = array_shape[1]
# Now loop over the box and draw circle inside of it
for p in range(x_square_min , x_square_max):
for q in range(y_square_min , y_square_max):
if (p - i) ** 2 + (q - j) ** 2 <= r ** 2:
new_array[p,q] += 1 # Incrementing because need to have possibility of
# overlapping circles
If you're using the same radius for every single circle, you can simplify things significantly by only calculating the circle coordinates once and then adding the center coordinates to the circle points when needed. Here's the code:
# The main array of values is called array.
shape = array.shape
row_indices = np.arange(0, shape[0], 1)
col_indices = np.arange(0, shape[1], 1)
# Returns xy coordinates for a circle with a given radius, centered at (0,0).
def points_in_circle(radius):
a = np.arange(radius + 1)
for x, y in zip(*np.where(a[:,np.newaxis]**2 + a**2 <= radius**2)):
yield from set(((x, y), (x, -y), (-x, y), (-x, -y),))
# Set the radius value before running code.
radius = RADIUS
circle_r = np.array(list(points_in_circle(radius)))
# Note that I'm using x as the row number and y as the column number.
# Center of circle is at (x_center, y_center). shape_0 and shape_1 refer to the main array
# so we can get rid of coordinates outside the bounds of array.
def add_center_to_circle(circle_points, x_center, y_center, shape_0, shape_1):
circle = np.copy(circle_points)
circle[:, 0] += x_center
circle[:, 1] += y_center
# Get rid of rows where coordinates are below 0 (can't be indexed)
bad_rows = np.array(np.where(circle < 0)).T[:, 0]
circle = np.delete(circle, bad_rows, axis=0)
# Get rid of rows that are outside the upper bounds of the array.
circle = circle[circle[:, 0] < shape_0, :]
circle = circle[circle[:, 1] < shape_1, :]
return circle
for x in row_indices:
for y in col_indices:
# You need to set CONDITION before running the code.
if CONDITION:
# Because circle_r is the same for all circles, it doesn't need to be recalculated all the time. All you need to do is add x and y to circle_r each time CONDITION is met.
circle_coords = add_center_to_circle(circle_r, x, y, shape[0], shape[1])
array[tuple(circle_coords.T)] += 1
When I set radius = 10, array = np.random.rand(1200).reshape(40, 30) and replaced if CONDITION with if (x == 20 and y == 20) or (x == 25 and y == 20), I got this, which seems to be what you want:
Let me know if you have any questions.
Adding each circle can be vectorized. This solution iterates over the coordinates where the condition is met. On a 2-core colab instance ~60k circles with radius 30 can be added per second.
import numpy as np
np.random.seed(42)
arr = np.random.rand(400,300)
r = 30
xx, yy = np.mgrid[-r:r+1, -r:r+1]
circle = xx**2 + yy**2 <= r**2
condition = np.where(arr > .999) # np.where(arr > .5) to benchmark 60k circles
for x,y in zip(*condition):
# valid indices of the array
i = slice(max(x-r,0), min(x+r+1, arr.shape[0]))
j = slice(max(y-r,0), min(y+r+1, arr.shape[1]))
# visible slice of the circle
ci = slice(abs(min(x-r, 0)), circle.shape[0] - abs(min(arr.shape[0]-(x+r+1), 0)))
cj = slice(abs(min(y-r, 0)), circle.shape[1] - abs(min(arr.shape[1]-(y+r+1), 0)))
arr[i, j] += circle[ci, cj]
Visualizing np.array arr
import matplotlib.pyplot as plt
plt.figure(figsize=(8,8))
plt.imshow(arr)
plt.show()
The Goal:
I would like to vectorize (or otherwise speed up) this code. It rotates a 3d numpy model around its center point (let x,y,z denote the dimensions; then we want to rotate around the z-axis). The np model is binary voxels that are either "on" or "off"
I bet some basic matrix operation could do it, like take a layer and apply the rotation matrix to each element. The only issue with that is decimals; where should I have the new value land since cos(pi / 6) == sqrt(3) / 2?
The Code:
def rotate_model(m, theta):
'''
theta in degrees
'''
n =np.zeros(m.shape)
for i,layer in enumerate(m):
rotated = rotate(layer,theta)
n[i] = rotated
return n
where rotate() is:
def rotate(arr, theta):
'''
Rotates theta clockwise
rotated.shape == arr.shape, unlike scipy.ndimage.rotate(), which inflates size and also does some strange mixing
'''
if theta == int(theta):
theta *= pi / 180
theta = -theta
# theta=-theta b/c clockwise. Otherwise would default to counterclockwise
rotated =np.zeros(arr.shape)
#print rotated.shape[0], rotated.shape[1]
y_mid = arr.shape[0]//2
x_mid = arr.shape[1]//2
val = 0
for x_new in range(rotated.shape[1]):
for y_new in range(rotated.shape[0]):
x_centered = x_new - x_mid
y_centered = y_new - y_mid
x = x_centered*cos(theta) - y_centered*sin(theta)
y = x_centered*sin(theta) + y_centered*cos(theta)
x += x_mid
y += y_mid
x = int(round(x)); y = int(round(y)) # cast so range() picks it up
# lossy rotation
if x in range(arr.shape[1]) and y in range(arr.shape[0]):
val = arr[y,x]
rotated[y_new,x_new] = val
#print val
#print x,y
return rotated
You have a couple of problems in your code. First, if you want to fit the original image onto a rotated grid then you need a larger grid (usually). Alternatively, imagine a regular grid but the shape of your object - a rectangle - is rotated, thus becoming a "rhomb". It is obvious if you want to fit the entire rhomb - you need a larger output grid (array). On the other hand, you say in the code "rotated.shape == arr.shape, unlike scipy.ndimage.rotate(), which inflates size". If that is the case, maybe you do not want to fit the entire object? So, maybe it is OK to do this: rotated=np.zeros(arr.shape). But in general, yeah, one has to have a larger grid in order to fit the entire input image after it is rotated.
Another issue is angle conversion that you are doing:
if theta == int(theta):
theta *= pi / 180
theta = -theta
Why??? What will happen when I want to rotate the image by 1 radian? Or 2 radians? Am I forbidden to use integer number of radians? I think you are trying to do too much in this function and therefore it will be very confusing to do use it. Just require the caller to convert angles to radians. Or, you can do it inside this function if input theta is always in degrees. Or, you can add another parameter called, e.g., units and caller could set it to radians or degrees. Don't try to guess it based on "integer-ness" of input!
Now, let's rewrite your code a little bit:
rotated = np.zeros_like(arr) # instead of np.zero(arr.shape)
y_mid = arr.shape[0] // 2
x_mid = arr.shape[1] // 2
# val = 0 <- this is unnecessary
# pre-compute cos(theta) and sin(theta):
cs = cos(theta)
sn = sin(theta)
for x_new in range(rotated.shape[1]):
for y_new in range(rotated.shape[0]):
x = int(round((x_new - x_mid) * cs - (y_new - y_mid) * sn + x_mid)
y = int(round((x_new - x_mid) * sn - (y_new - y_mid) * cs + y_mid)
# just use comparisons, don't search through many values!
if 0 <= x < arr.shape[1] and 0 <= y < arr.shape[0]:
rotated[y_new, x_new] = arr[y, x]
So, now I can see (more easily) that for each pixel from the output array is mapped to a location in the input array. Yes, you can vectorize this.
import numpy as np
def rotate(arr, theta, unit='rad'):
# deal with theta units:
if unit.startswith('deg'):
theta = np.deg2rad(theta)
# for convenience, store array size:
ny, nx = arr.shape
# generate arrays of indices and flatten them:
y_new, x_new = np.indices(arr.shape)
x_new = x_new.ravel()
y_new = y_new.ravel()
# compute center of the array:
x0 = nx // 2
y0 = ny // 2
# compute old coordinates
xc = x_new - x0
yc = y_new - y0
x = np.round(np.cos(theta) * xc - np.sin(theta) * yc + x0).astype(np.int)
y = np.round(np.sin(theta) * xc - np.cos(theta) * yc + y0).astype(np.int)
# main idea to deal with indices is to create a mask:
mask = (x >= 0) & (x < nx) & (y >= 0) & (y < ny)
# ... and then select only those coordinates (both in
# input and "new" coordinates) that satisfy the above condition:
x = x[mask]
y = y[mask]
x_new = x_new[mask]
y_new = y_new[mask]
# map input values to output pixels *only* for selected "good" pixels:
rotated = np.zeros_like(arr)
rotated[y_new, x_new] = arr[y, x]
return rotated
Here is some code for anyone also doing 3d modeling. It solved my specific use-case pretty well. Still figuring out how to rotate in the proper plane. Hope it's helpful to you as well:
def rotate_model(m, theta):
'''
Redefines the prev 'rotate_model()' method
theta has to be in degrees
'''
rotated = scipy.ndimage.rotate(m, theta, axes=(1,2))
# have tried (1,0), (2,0), and now (1,2)
# ^ z is "up" and "2"
# scipy.ndimage.rotate() shrinks the model
# TODO: regrow it back
x_r = rotated.shape[1]
y_r = rotated.shape[0]
x_m = m.shape[1]
y_m = m.shape[0]
x_diff = abs(x_r - x_m)
y_diff = abs(y_r - y_m)
if x_diff%2==0 and y_diff%2==0:
return rotated[
x_diff//2 : x_r-x_diff//2,
y_diff//2 : y_r-y_diff//2,
:
]
elif x_diff%2==0 and y_diff%2==1:
# if this shift ends up turning the model to shit in a few iterations,
# change the following lines to include a flag that alternates cutting off the top and bottom bits of the array
return rotated[
x_diff//2 : x_r-x_diff//2,
y_diff//2+1 : y_r-y_diff//2,
:
]
elif x_diff%2==1 and y_diff%2==0:
return rotated[
x_diff//2+1 : x_r-x_diff//2,
y_diff//2 : y_r-y_diff//2,
:
]
else:
# x_diff%2==1 and y_diff%2==1:
return rotated[
x_diff//2+1 : x_r-x_diff//2,
y_diff//2+1 : y_r-y_diff//2,
:
]
I have code to expand the polygon, it works by multiplying the xs and ys by a factor then re centering the resultant polyon at the center of the original.
I also have code to find the value for the expansion factor, given a point that the polygon needs to reach:
import numpy as np
import itertools as IT
import copy
from shapely.geometry import LineString, Point
def getPolyCenter(points):
"""
http://stackoverflow.com/a/14115494/190597 (mgamba)
"""
area = area_of_polygon(*zip(*points))
result_x = 0
result_y = 0
N = len(points)
points = IT.cycle(points)
x1, y1 = next(points)
for i in range(N):
x0, y0 = x1, y1
x1, y1 = next(points)
cross = (x0 * y1) - (x1 * y0)
result_x += (x0 + x1) * cross
result_y += (y0 + y1) * cross
result_x /= (area * 6.0)
result_y /= (area * 6.0)
return (result_x, result_y)
def expandPoly(points, factor):
points = np.array(points, dtype=np.float64)
expandedPoly = points*factor
expandedPoly -= getPolyCenter(expandedPoly)
expandedPoly += getPolyCenter(points)
return np.array(expandedPoly, dtype=np.int64)
def distanceLine2Point(points, point):
points = np.array(points, dtype=np.float64)
point = np.array(point, dtype=np.float64)
points = LineString(points)
point = Point(point)
return points.distance(point)
def distancePolygon2Point(points, point):
distances = []
for i in range(len(points)):
if i==len(points)-1:
j = 0
else:
j = i+1
line = [points[i], points[j]]
distances.append(distanceLine2Point(line, point))
minDistance = np.min(distances)
#index = np.where(distances==minDistance)[0][0]
return minDistance
"""
Returns the distance from a point to the nearest line of the polygon,
AND the distance from where the normal to the line (to reach the point)
intersets the line to the center of the polygon.
"""
def distancePolygon2PointAndCenter(points, point):
distances = []
for i in range(len(points)):
if i==len(points)-1:
j = 0
else:
j = i+1
line = [points[i], points[j]]
distances.append(distanceLine2Point(line, point))
minDistance = np.min(distances)
i = np.where(distances==minDistance)[0][0]
if i==len(points)-1:
j = 0
else:
j = i+1
line = copy.deepcopy([points[i], points[j]])
centerDistance = distanceLine2Point(line, getPolyCenter(points))
return minDistance, centerDistance
minDistance, centerDistance = distancePolygon2PointAndCenter(points, point)
expandedPoly = expandPoly(points, 1+minDistance/centerDistance)
This code only works when the point is directly opposing one of the polygons lines.
Modify your method distancePolygon2PointAndCenter to instead of
Returns the distance from a point to the nearest line of the polygon
To return the distance from a point to the segment intersected by a ray from the center to the point. This is the line that will intersect the point once the polygon is fully expanded. To get this segment, take both endpoints of each segment of your polygon, and plug them into the equation for the line parallel & intersecting the ray mentioned earlier. That is y = ((centerY-pointY)/(centerX-pointX)) * (x - centerX) + centerY. You want to want to find endpoints where either one of them intersect the line, or the two are on opposite sides of the line.
Then, the only thing left to do is make sure that we pick the segment intersecting the right "side" of the line. To do this, there are a few options. The fail-safe method would be to use the formula cos(theta) = sqrt((centerX**2 + centerY**2)*(pointX**2 + pointY**2)) / (centerX * pointX + centerY * pointY) however, you could use methods such as comparing x and y values, taking the arctan2(), and such to figure out which segment is on the correct "side" of center. You'll just have lots of edge cases to cover. After all this is said and done, your two (unless its not convex, in which case take the segment farthest from you center) endpoints makeup the segment to expand off of.
Determine what is "polygon center" as central point C of expanding. Perhaps it is centroid (or some point with another properties?).
Make a segment from your point P to C. Find intersection point I between PC and polygon edges. If polygon is concave and there are some intersection points, choose the closest one to P.
Calculate coefficient of expanding:
E = Length(PC) / Length(CI)
Calculate new vertex coordinates. For i-th vertex of polygon:
V'[i].X = C.X + (V[i].X - C.X) * E
V'[i].Y = C.Y + (V[i].Y - C.Y) * E
Decide which point you want to reach, then calculate how much % your polygon needs to expand to reach that point and use the shapely.affinity.scale function. For example, in my case I just needed to make the polygon 5% bigger:
region = shapely.affinity.scale(myPolygon,
xfact=1.05, yfact=1.05 )
I want to use matplotlib.pyplot.pcolormesh to plot a depth plot.
What I have is a xyz file
Three columns i.e. x(lat), y(lon), z(dep).
All columns are of equal length
pcolormesh require matrices as input.
So using numpy.meshgrid I can transform the x and y into matrices:
xx,yy = numpy.meshgrid(x_data,y_data)
This works great...However, I don't know how to create Matrix of my depth (z) data...
How do I create a matrix for my z_data that corresponds to my x_data and y_data matrices?
Depending on whether you're generating z or not, you have at least two different options.
If you're generating z (e.g. you know the formula for it) it's very easy (see method_1() below).
If you just have just a list of (x,y,z) tuples, it's harder (see method_2() below, and maybe method_3()).
Constants
# min_? is minimum bound, max_? is maximum bound,
# dim_? is the granularity in that direction
min_x, max_x, dim_x = (-10, 10, 100)
min_y, max_y, dim_y = (-10, 10, 100)
Method 1: Generating z
# Method 1:
# This works if you are generating z, given (x,y)
def method_1():
x = np.linspace(min_x, max_x, dim_x)
y = np.linspace(min_y, max_y, dim_y)
X,Y = np.meshgrid(x,y)
def z_function(x,y):
return math.sqrt(x**2 + y**2)
z = np.array([z_function(x,y) for (x,y) in zip(np.ravel(X), np.ravel(Y))])
Z = z.reshape(X.shape)
plt.pcolormesh(X,Y,Z)
plt.show()
Which generates the following graph:
This is relatively easy, since you can generate z at whatever points you want.
If you don't have that ability, and are given a fixed (x,y,z). You could do the following. First, I define a function that generates fake data:
def gen_fake_data():
# First we generate the (x,y,z) tuples to imitate "real" data
# Half of this will be in the + direction, half will be in the - dir.
xy_max_error = 0.2
# Generate the "real" x,y vectors
x = np.linspace(min_x, max_x, dim_x)
y = np.linspace(min_y, max_y, dim_y)
# Apply an error to x,y
x_err = (np.random.rand(*x.shape) - 0.5) * xy_max_error
y_err = (np.random.rand(*y.shape) - 0.5) * xy_max_error
x *= (1 + x_err)
y *= (1 + y_err)
# Generate fake z
rows = []
for ix in x:
for iy in y:
z = math.sqrt(ix**2 + iy**2)
rows.append([ix,iy,z])
mat = np.array(rows)
return mat
Here, the returned matrix looks like:
mat = [[x_0, y_0, z_0],
[x_1, y_1, z_1],
[x_2, y_2, z_2],
...
[x_n, y_n, z_n]]
Method 2: Interpolating given z points over a regular grid
# Method 2:
# This works if you have (x,y,z) tuples that you're *not* generating, and (x,y) points
# may not fall evenly on a grid.
def method_2():
mat = gen_fake_data()
x = np.linspace(min_x, max_x, dim_x)
y = np.linspace(min_y, max_y, dim_y)
X,Y = np.meshgrid(x, y)
# Interpolate (x,y,z) points [mat] over a normal (x,y) grid [X,Y]
# Depending on your "error", you may be able to use other methods
Z = interpolate.griddata((mat[:,0], mat[:,1]), mat[:,2], (X,Y), method='nearest')
plt.pcolormesh(X,Y,Z)
plt.show()
This method produces the following graphs:
error = 0.2
error = 0.8
Method 3: No Interpolation (constraints on sampled data)
There's a third option, depending on how your (x,y,z) is set up. This option requires two things:
The number of different x sample positions equals the number of different y sample positions.
For every possible unique (x,y) pair, there is a corresponding (x,y,z) in your data.
From this, it follows that the number of (x,y,z) pairs must be equal to the square of the number of unique x points (where the number of unique x positions equals the number of unique y positions).
In general, with sampled data, this will not be true. But if it is, you can avoid having to interpolate:
def method_3():
mat = gen_fake_data()
x = np.unique(mat[:,0])
y = np.unique(mat[:,1])
X,Y = np.meshgrid(x, y)
# I'm fairly sure there's a more efficient way of doing this...
def get_z(mat, x, y):
ind = (mat[:,(0,1)] == (x,y)).all(axis=1)
row = mat[ind,:]
return row[0,2]
z = np.array([get_z(mat,x,y) for (x,y) in zip(np.ravel(X), np.ravel(Y))])
Z = z.reshape(X.shape)
plt.pcolormesh(X,Y,Z)
plt.xlim(min(x), max(x))
plt.ylim(min(y), max(y))
plt.show()
error = 0.2
error = 0.8
I have a point-i and i wish to create a function to know if this point lies on the border of a polygon.
using:
def point_inside_polygon(x, y, poly):
"""Deciding if a point is inside (True, False otherwise) a polygon,
where poly is a list of pairs (x,y) containing the coordinates
of the polygon's vertices. The algorithm is called the 'Ray Casting Method'"""
n = len(poly)
inside = False
p1x, p1y = poly[0]
for i in range(n):
p2x, p2y = poly[i % n]
if y > min(p1y, p2y):
if y <= max(p1y, p2y):
if x <= max(p1x, p2x):
if p1y != p2y:
xinters = (y-p1y) * (p2x-p1x) / (p2y-p1y) + p1x
if p1x == p2x or x <= xinters:
inside = not inside
p1x, p1y = p2x, p2y
return inside
I am able to know only if the points lies within the polygon.
poly = [(0,0), (2,0), (2,2), (0,2)]
point_inside_polygon(1,1, poly)
True
point_inside_polygon(0,0, poly)
false
point_inside_polygon(2,0, poly)
False
point_inside_polygon(2,2, poly)
True
point_inside_polygon(0,2, poly)
True
How can I write a function to find if a point lay on the border of a polygon instead?
It might help to break down the problem into three steps:
Write a function that can determine if a point is on a line segment.
Compute all of the line segments that make up the border of the polygon.
The point is on the border if it is on any of the line segments.
Here's some python code, assuming you've written or found a suitable candidate for isPointOnLineSegmentBetweenPoints:
def pointOnPolygon(point, polygonVertices):
n = len(polygonVertices)
for i in range(n):
p1 = polygonVertices[i]
p2 = polygonVertices[-n+i+1]
if isPointOnLineSegmentBetweenPoints(point, p1, p2):
return true
return false
I haven't tested this, but the general idea is:
def pointOnBorder(x, y, poly):
n = len(poly)
for(i in range(n)):
p1x, p1y = poly[i]
p2x, p2y = poly[(i + 1) % n]
v1x = p2x - p1x
v1y = p2y - p1y #vector for the edge between p1 and p2
v2x = x - p1x
v2y = y - p1y #vector from p1 to the point in question
if(v1x * v2y - v1y * v2x == 0): #if vectors are parallel
if(v2x / v1x > 0): #if vectors are pointing in the same direction
if(v1x * v1x + v1y * v1y >= v2x * v2x + v2y * v2y): #if v2 is shorter than v1
return true
return false
For each pair of adjacent vertices A,B:
construct a vector from A to B, call it p
now construct a vector from A to your test point X call it q
the dot product formula for a pair of vectors is p.q = |p||q|cosC
where C is the angle between the vectors.
so if p.q/|p||q| == 1 then the points AX and AB are co-linear. Working on a computer, you will want 1 - p.q/|p||q| < some_small_value depending on how accurate you want to be.
also need to check that |q| < |p| (ie X is closer to A than B)
if 4&5 are true your point is on the border.
Edit
The other way I think I've seen this done is to take your test point X, and construct a line through X perpendicular to the line between A and B. Find where this line and the line A->B cross. Work out the distance from X to this crossing point, if that is sufficiently small you consider the point as being on the line.
Edit -- fun little exercise!
Posted some code that was wrong earlier due to me misremembering some maths.
Had a play in Pythonista on the train home and came up with this which seems to basically work. Have left the maths proof out as editing posts on iPad is painful!
Not much testing done, no testing for division by zero etc, caveat user.
# we determine the point of intersection X between
# the line between A and B and a line through T
# that is perpendicular to the line AB (can't draw perpendicular
# in ascii, you'll have to imagine that angle between AB and XT is 90
# degrees.
#
# B
# /
#. X
# / \
# / T
# A
# once we know X we can work out the closest the line AB
# comes to T, if that distance is 0 (or small enough)
# we can consider T to be on the line
import math
# work out where the line through test point t
# that is perpendicular to ab crosses ab
#
# inputs must be 2-tuples or 2-element lists of floats (x,y)
# returns (x,y) of point of intersection
def intersection_of_perpendicular(a,b,t):
if a[0] == b[0]:
return (a[0],t[1])
if a[1] == b[1]:
return (t[0],a[1])
m = (a[1] - b[1])/(a[0] - b[0]) #slope of ab
x_inter = (t[1] - a[1] + m*a[0] + (1/m)*t[0])*m/(m**2 + 1)
y_inter = m*(x_inter - a[0]) + a[1]
y_inter2 = -(1/m)*(x_inter - t[0]) + t[1]
#print '...computed ',m,(x_inter, y_inter), y_inter2
return (x_inter, y_inter)
# basic Pythagorean formula for distance between two points
def distance(a,b):
return math.sqrt( (a[0]-b[0])**2 + (a[1]-b[1])**2 )
# check if a point is within the box defined by a,b at
# diagonally opposite corners
def point_in_box(a,b,t):
xmin = min(a[0],b[0])
xmax = max(a[0],b[0])
ymin = min(a[1],b[1])
ymax = max(a[1],b[1])
x_in_bounds = True
if xmax != xmin:
x_in_bounds = xmin <= t[0] <= xmax
y_in_bounds = True
if ymax != ymin:
y_in_bounds = ymin <= t[1] <= ymax
return x_in_bounds and y_in_bounds
# determine if point t is within 'tolerance' distance
# of the line between a and b
# returns Boolean
def is_on_line_between(a,b,t,tolerance=0.01):
intersect = intersection_of_perpendicular(a,b,t)
dist = distance(intersect, t)
in_bounds = point_in_box(a,b,t)
return in_bounds and (dist < tolerance)
a = (0,0)
b = (2,2)
t = (0,2)
p = intersection_of_perpendicular(a,b,t)
bounded = point_in_box(a,b,t)
print 'd ',distance(p,t), ' p ',p, bounded
a = (0,2)
b = (2,2)
t = (1,3)
p = intersection_of_perpendicular(a,b,t)
bounded = point_in_box(a,b,t)
print 'd ',distance(p,t),' p ',p, bounded
a = (0.0,2.0)
b = (2.0,7.0)
t = (1.7,6.5)
p = intersection_of_perpendicular(a,b,t)
bounded = point_in_box(a,b,t)
on = is_on_line_between(a,b,t,0.2)
print 'd ',distance(p,t),' p ',p, bounded,on
Everyone is overcomplicating things. Here is a short point on polygon, assuming you have a distance function and a small EPSILON.
def pointOnPolygon(point, poly):
for i in range(len(poly)):
a, b = poly[i - 1], poly[i]
if abs(dist(a, point) + dist(b, point) - dist(a, b)) < EPSILON:
return true
return false
For a convex polygon, you can solve the problem in O(log n) time by ordering vertices clock-wise and storing for each vertex the angle between the vertex and a point c in the interior of the polygon. Then for a query point x, you get the angle from c to x and binary search to find the unique pair of adjacent vertices (v1,v2) such that the angle to x is between the angles to v1 and to v2. Then x is either on the edge (v1,v2), or x is not on the boundary.
If you have a more complicated polygon, then you can try decomposing the polygon into a union of convex polygons by adding some interior edges (e.g. first triangulate and then remove edges to get bigger convex polygons); if the number of convex polygons you get is small (say k), then you can test each convex polygon to see if a point is on an edge, and the overall running time is O(k lg n) where n is the total number of vertices in your polygon.
Alternatively, if you are not worried about using extra space, and you really want to quickly determine if you're on an edge, then you can break up each edge into equally spaced segments by adding extra "vertices" along each edge; it's hard to say how many is enough (sounds like an interesting math problem), but clearly if you add enough extra vertices along each edge then you can tell which edge a point must lie on simply by finding the nearest neighbor out of your set of vertices (original vertices and the ones you added), and then just test the one or two edges that the nearest neighbor vertex lies on. You can very quickly find k-nearest neighbors in 2-d if you use a 2-dimensional kd-tree (you build the tree as a pre-processing step, and then the tree supports fast k-nearest neighbor queries), and the kd-tree tree only uses linear space.
Found the solution to this at: http://geospatialpython.com/2011/08/point-in-polygon-2-on-line.html
Here the code:
# Improved point in polygon test which includes edge
# and vertex points
def point_in_poly(x,y,poly):
# check if point is a vertex
if (x,y) in poly: return "IN"
# check if point is on a boundary
for i in range(len(poly)):
p1 = None
p2 = None
if i==0:
p1 = poly[0]
p2 = poly[1]
else:
p1 = poly[i-1]
p2 = poly[i]
if p1[1] == p2[1] and p1[1] == y and x > min(p1[0], p2[0]) and x < max(p1[0], p2[0]):
return "IN"
n = len(poly)
inside = False
p1x,p1y = poly[0]
for i in range(n+1):
p2x,p2y = poly[i % n]
if y > min(p1y,p2y):
if y <= max(p1y,p2y):
if x <= max(p1x,p2x):
if p1y != p2y:
xints = (y-p1y)*(p2x-p1x)/(p2y-p1y)+p1x
if p1x == p2x or x <= xints:
inside = not inside
p1x,p1y = p2x,p2y
if inside: return "IN"
else: return "OUT"
# Test a vertex for inclusion
polygon = [(-33.416032,-70.593016), (-33.415370,-70.589604),
(-33.417340,-70.589046), (-33.417949,-70.592351),
(-33.416032,-70.593016)]
lat= -33.416032
lon= -70.593016
print point_in_poly(lat, lon, polygon)
# test a boundary point for inclusion
poly2 = [(1,1), (5,1), (5,5), (1,5), (1,1)]
x = 3
y = 1
print point_in_poly(x, y, poly2)