def concaveHull(dataset, k):
assert k >= 3, 'k has to be greater or equal to 3.'
points = dataset
# todo: remove duplicate points from dataset
# todo: check if dataset consists of only 3 or less points
# todo: make sure that enough points for a given k can be found
firstpoint = GetFirstPoint(points)
# init hull as list to easily append stuff
hull = []
# add first point to hull
hull.append(firstpoint)
# and remove it from dataset
points = removePoint(points, firstpoint)
currentPoint = firstpoint
# set prevPoint to a Point righ of currentpoint (angle=0)
prevPoint = (currentPoint[0] + 10, currentPoint[1])
step = 2
while ((not np.array_equal(firstpoint, currentPoint) or (step == 2))
and points.size > 0):
if (step == 5): # we're far enough to close too early
points = np.append(points, [firstpoint], axis=0)
kNearestPoints = GetNearestNeighbors(points, currentPoint, k)
cPoints = SortByAngle(kNearestPoints, currentPoint, prevPoint)
# avoid intersections: select first candidate that does not intersect any
# polygon edge
its = True
i = 0
while ((its == True) and (i < cPoints.shape[0])):
i = i + 1
if (np.array_equal(cPoints[i - 1], firstpoint)):
lastPoint = 1
else:
lastPoint = 0
j = 2
its = False
while ((its == False) and (j < np.shape(hull)[0] - lastPoint)):
its = li.doLinesIntersect(hull[step - 1 - 1], cPoints[i - 1],
hull[step - 1 - j - 1],
hull[step - j - 1])
j = j + 1
if (its == True):
print "all candidates intersect -- restarting with k = ", k + 1
return concaveHull(dataset, k + 1)
prevPoint = currentPoint
currentPoint = cPoints[i - 1]
# add current point to hull
hull.append(currentPoint)
points = removePoint(points, currentPoint)
step = step + 1
# check if all points are inside the hull
p = Path(hull)
pContained = p.contains_points(dataset, radius=0.0000000001)
if (not pContained.all()):
print "not all points of dataset contained in hull -- restarting with k = ", k + 1
return concaveHull(dataset, k + 1)
print "finished with k = ", k
return hull
def concaveHull(dataset, k, if_optimal=False):
#return list of hull [[x,y],[x2,y2]...]
assert k >= 3, 'k has to be greater or equal to 3.'
points = dataset
# todo: remove duplicate points from dataset
# todo: check if dataset consists of only 3 or less points
# todo: make sure that enough points for a given k can be found
firstpoint = GetFirstPoint(points)
# init hull as list to easily append stuff
hull = []
# add first point to hull
hull.append(firstpoint)
# and remove it from dataset
points = removePoint(points, firstpoint)
currentPoint = firstpoint
# set prevPoint to a Point right of currentpoint (angle=0)
prevPoint = (currentPoint[0] + 10, currentPoint[1])
step = 2
while ((not np.array_equal(firstpoint, currentPoint) or (step == 2))
and points.size > 0):
if (step == 5): # we're far enough to close too early
points = np.append(points, [firstpoint], axis=0)
kNearestPoints = GetNearestNeighbors(points, currentPoint, k)
cPoints = SortByAngle(kNearestPoints, currentPoint, prevPoint)
# avoid intersections: select first candidate that does not intersect any
# polygon edge
its = True
i = 0
while ((its == True) and (i < cPoints.shape[0])):
i = i + 1
if (np.array_equal(
cPoints[i - 1],
firstpoint)): # if next concave pt is the first pt
lastPoint = 1
else:
lastPoint = 0
j = 2
its = False
while ((its == False) and (j < np.shape(hull)[0] - lastPoint)
): #number of hull pts - lastpoint >2, j
its = li.doLinesIntersect(hull[step - 1 - 1], cPoints[i - 1],
hull[step - 1 - j - 1],
hull[step - j - 1])
j = j + 1
if (its == True):
# if k > 100:
# print("k exceed max value 100, concavehull fail, return hull k=100")
# return hull
if if_optimal:
# Todo a bug here # [-4.21707002 37.48664473] [-4.61409177 37.06382358] [-4.61262907 37.06245959] [-4.20343005 37.50127181] intersects
print("all candidates intersect -- restarting with k = ",
k + 1)
#print(hull)
# return hull
return concaveHull(dataset, k + 1, True)
prevPoint = currentPoint
currentPoint = cPoints[i - 1]
# add current point to hull
hull.append(currentPoint)
points = removePoint(points, currentPoint)
step = step + 1
# check if all points are inside the hull
p = Path(hull)
pContained = p.contains_points(dataset, radius=0.0000000001)
if if_optimal:
if (not pContained.all()):
# if k>100:
# print("k exceed max value 100, concavehull fail, return hull k=100")
# return hull
print(
"not all points of dataset contained in hull -- restarting with k = ",
k + 1)
return concaveHull(dataset, k + 1, True)
print("finished with k = ", k)
return hull
import sys
import polygonarea
import lineintersect
from lineintersect import Line
input = open(sys.argv[1], "r")
points = []
# assumes input ends with newline
for line in input:
points.append(line[:-1])
print polygonarea.calculateArea(points)
print lineintersect.doLinesIntersect(Line(points[0], points[1]),
Line(points[2], points[3]))