def extend_boundary(self, r):
"""
Extends a 2d contour out from points in self.xycoords by a distance
<r> (radius) in all directions.
"""
s = "::: extending boundary by %i meters :::" % r
print_text(s, self.color)
xycoords = self.xycoords
polygons = []
for i, v in enumerate(xycoords):
polygons.append(shapelyPoint(v[0], v[1]).buffer(r))
# union of our original polygon and convex hull
p1 = cascaded_union(polygons)
p2 = Polygon(zip(xycoords[:, 0], xycoords[:, 1]))
p3 = cascaded_union([p1, p2])
xycoords_buf = array(zip(p3.exterior.xy[:][0], p3.exterior.xy[:][1]))
self.plot_coords["xycoords_buf"] = xycoords_buf
self.xycoords = xycoords_buf
s = "::: extended contour created of length %i :::" % len(
self.xycoords)
print_text(s, self.color)
def __create_grid(self): """ Create uniform grid over region. """ polygon_coords = self.__resample_polygon() # define polygon from polygon coords polygon = Polygon(polygon_coords) # get bounding box coordinates bounding_box = polygon.bounds min_lon = bounding_box[0] max_lon = bounding_box[2] min_lat = bounding_box[1] max_lat = bounding_box[3] # compute number of nodes along lat and lon n_lat = int(round((max_lat - min_lat) / self.grid_spacing)) + 1 n_lon = int(round((max_lon - min_lon) / self.grid_spacing)) + 1 # create grid of points inside polygon grid = [] for i in range(n_lat): for j in range(n_lon): lat = min_lat + i * self.grid_spacing lon = min_lon + j * self.grid_spacing p = shapelyPoint(lon,lat) if polygon.contains(p) or polygon.touches(p): grid.append((lon,lat)) return grid
def extend_edge(self, r):
"""
Extends a 2d contour out from points labeled in self.edge by a distance
<r> (radius) in all directions.
NOTE: this only works for greenland.
"""
s = "::: extending edge by %i meters :::" % r
print_text(s, self.color)
xycoords = self.xycoords
edge = self.edge
polygons = []
for i, v in enumerate(xycoords):
if edge[i]:
polygons.append(shapelyPoint(v[0], v[1]).buffer(r))
# union of our original polygon and convex hull
p1 = cascaded_union(polygons)
p2 = Polygon(zip(xycoords[:, 0], xycoords[:, 1]))
p3 = cascaded_union([p1, p2])
xycoords_buf = array(zip(p3.exterior.xy[:][0], p3.exterior.xy[:][1]))
self.plot_coords["xycoords_buf"] = xycoords_buf
self.xycoords = xycoords_buf
def in_fence(geo, latitude, longitude):
dataset = Point.objects.filter(fence_id__id=geo.id).order_by('order')
lats_longs_vect = dataset.values_list('x', 'y')
# print(lats_longs_vect)
polygon = Polygon(lats_longs_vect)
point = shapelyPoint(latitude, longitude)
_in = polygon.contains(point)
return _in
def contains_point(self, point):
if self.geometry.is_empty:
return False
sh_point = shapelyPoint(get_coords(point))
return (
point_in_rectangle(point, self.geometry.bounds)
and self.geometry.intersects(sh_point)
)
def extend_edge(self, r):
"""
Extends a 2d contour out from points labeled in self.edge by a distance
<r> (radius) in all directions.
"""
xycoords = self.longest_cont
polygons = []
for i, v in enumerate(xycoords):
polygons.append(shapelyPoint(v[0], v[1]).buffer(r))
# union of our original polygon and convex hull
p1 = cascaded_union(polygons)
p3 = cascaded_union(p1)
xycoords_buf = array(zip(p3.exterior.xy[:][0], p3.exterior.xy[:][1]))
self.longest_cont = xycoords_buf
def get_approx_tumor_mask(polygons, thumbnail_nrow, thumbnail_ncol, patch_size=256):
"""Indicate whether a particular tile overlaps with tumor annotation.
The method has an approx in its name, as the entire tile
might not be coming from a tumor annotated region as parts of
it might actually be normal. We just report
here if the patch overlaps with a tumor annotation. It might have
an overlap with a normal region as well, but that is filtered
in a later method so we don't worry about it here.
Parameters
----------
polygons: dict
dict with keys ['normal', 'tumor']
as obtained from `get_annotation_polygons`
thumbnail_nrow: int
Number of rows in the thumbnail image
thumbnail_ncol: int
Number of columns in the thumbnail
Returns
-------
mask: array
tumor mask
"""
scaled_tumor_polygons = []
for tpol in polygons["tumor"]:
scaled = translate_and_scale_polygon(tpol, 0, 0, 1 / 256)
scaled_tumor_polygons.append(scaled)
polymasked = poly2mask(scaled_tumor_polygons, (thumbnail_nrow, thumbnail_ncol))
# Is any of the masked out points inside a normal annotated region?
poly_x, poly_y = np.where(polymasked > 0)
set_to_zero = []
for px, py in zip(poly_x, poly_y):
point = shapelyPoint(px, py)
for npol in polygons["normal"]:
scaled = translate_and_scale_polygon(npol, 0, 0, 1 / 256)
pol = shapelyPolygon(scaled.get_xy())
if pol.contains(point):
set_to_zero.append((px, py))
if len(set_to_zero):
set_to_zero = np.array(set_to_zero)
polymasked[set_to_zero] = 0
return polymasked
import numpy
from shapely.geometry import Point as shapelyPoint
from shapely.geometry.polygon import Polygon
v0 = [7.115125222, -2.5]
v1 = [2, 3.5]
v2 = [-2, 4]
v3 = [-3, -3]
v4 = [0, -10]
np = numpy
lats_vect = np.array([v0[0], v1[0], v2[0], v3[0], v4[0]])
lons_vect = np.array([v0[1], v1[1], v2[1], v3[1], v4[1]])
x, y = 1.123654, 1
lats_longs_vect = np.column_stack((lats_vect, lons_vect))
polygon = Polygon(lats_longs_vect)
point = shapelyPoint(x, y)
_in = polygon.contains(point)
if _in:
print('Entering the fence')
else:
print('leaving from fence')
def contains_point(self, point):
if self.geometry.is_empty:
return False
pure_point = feature_to_point(featurize(point))
shPoint = shapelyPoint(pure_point)
return point_in_rectangle(pure_point, self.geometry.bounds) and self.geometry.contains(shPoint)
if x['properties']['ZONECLASS'] == 'OVERLAY':
continue
tmp = np.array(coords)
if tmp.ndim == 1: # Shape is a MultiLineString
poly = shapelyPolygon(coords[0])
mls.append(poly)
mlsz.append(x['properties']['ZONECLASS'])
elif tmp.ndim == 2: # Shape is a LineString
poly = shapelyPolygon(coords)
ls.append(poly)
lsz.append(x['properties']['ZONECLASS'])
n = len(x_coordinates)
i = 0
while i < n:
pnts.append(shapelyPoint(x_coordinates[i], y_coordinates[i]))
i += 1
output_file = open('output.csv', mode='w')
out_write = csv.writer(output_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
c_res_1, c_res_2, c_res_3, c_res_4, c_res_5, c_res_6, c_res_7, c_res_8, c_res_9 = 0, 0, 0, 0, 0, 0, 0, 0, 0
c_ofc_1, c_ofc_2, c_ofc_3 = 0, 0, 0
c_prk_1 = 0
c_ind_1, c_ind_2 = 0, 0
c_com_1, c_com_2, c_com_3, c_com_4, c_com_5 = 0, 0, 0, 0, 0
c_nzn = 0
n = len(pnts) #something
