def exhaustive(points, show=True, save=False, detailed=False):
"""
Returns the vertices comprising the boundaries of convex hull containing all points in the input set.
The input 'points' is a list of [x,y] coordinates.
Uses a very naive method: iterates over the whole set of convex polygons from size 3 to n
:param points: the points from which to find the convex hull
:param show: if True, the progress in constructing the hull will be plotted on each iteration in a window
:param save: if True, the progress in constructing the hull will be saved on each iteration in a .png file
:param detailed: if True, even non convex explored polygons are plotted
:return: the convex hull
"""
i = 3
while i <= len(points):
# iterates over the whole set of subset of points
for subset in permutations(points, i):
#if (show or save) and detailed:
#scatter_plot(points, [subset], title="exhaustive search", show=show, save=save)
# only consider convex subsets
if is_convex(subset):
#if (show or save) and not detailed:
#scatter_plot(points, [subset], title="exhaustive search", show=show, save=save)
one_out = False
j = 0
# iterates until a point is found outside the polygon
while not one_out and j < len(points):
point = points[j]
if point not in list(subset) and not point_in_polygon(
point, list(subset)):
one_out = True
j = j + 1
if not one_out:
return subset
i = i + 1
return points
def import_dxf_example():
dxf_filepath = os.path.join(os.getcwd(), 'example_files',
'section_holes_complex.dxf')
my_dxf = dxf.DxfImporter(dxf_filepath)
my_dxf.process()
my_dxf.cleanup()
polygons = my_dxf.polygons
for p in polygons:
x, y = p.exterior.xy
new = utils.find_holes(polygons)
x, y = new.exterior.xy
plt.plot(x, y)
for hole in new.interiors:
x, y = hole.xy
plt.plot(x, y)
for i in range(100):
p = utils.point_in_polygon(new)
x, y = p.xy
plt.plot(x, y, marker='o', markersize=3, color="red")
plt.show()
def __fitness_obstacles(self, subject, mapa):
# Prioriza rotas que não ultrapassem obstáculos
# Não bater em obstáculos
count = 0
for gene_decoded_t1, gene_decoded_t2 in pairwise_circle(subject.dna_decoded):
for area_n in mapa.areas_n:
wp1 = CartesianPoint(gene_decoded_t1.x, gene_decoded_t1.y)
if point_in_polygon(wp1, area_n):
count += 1
wp2 = CartesianPoint(gene_decoded_t2.x, gene_decoded_t2.y)
if segment_in_polygon(wp1, wp2, area_n):
count += 1
return count
def __fitness_obstacles(self, subject, mapa):
# Prioriza rotas que não ultrapassem obstáculos
count = 0
for gene_decoded_t1, gene_decoded_t2 in pairwise(subject.dna_decoded):
# Utiliza somente as áreas infladas para cálculo
for area_n in mapa.areas_n_inf:
wp1 = CartesianPoint(gene_decoded_t1.x, gene_decoded_t1.y)
wp2 = CartesianPoint(gene_decoded_t2.x, gene_decoded_t2.y)
# Calcula se algum waypoint está dentro de algum obstáculo
if point_in_polygon(wp1, area_n):
count += 1
# Calcula se alguma conexão entre os waypoints intersecciona algum obstáculo
if segment_in_polygon(wp1, wp2, area_n):
count += 1
return count