I'm looking for a way to 'draw' a filled polygon into a numpy array based upon a set of polygon vertices. I'd prefer to use as few external libraries as possible.
For example: I have a 20x20 numpy array and I'd like the region bounded by points (3,12), (8,18), (13,14), (11,6) and (4,6) to be filled with 1.0 while the rest of the array contains 0.0
The following solution requires only numpy. It works for vertices (defined in clockwise order in [Row, Column] coordinate system) for convex polygons. Concave polygons will work, but will end up cutting off the protruding points.
import numpy as np
def check(p1, p2, base_array):
"""
Uses the line defined by p1 and p2 to check array of
input indices against interpolated value
Returns boolean array, with True inside and False outside of shape
"""
idxs = np.indices(base_array.shape) # Create 3D array of indices
p1 = p1.astype(float)
p2 = p2.astype(float)
# Calculate max column idx for each row idx based on interpolated line between two points
max_col_idx = (idxs[0] - p1[0]) / (p2[0] - p1[0]) * (p2[1] - p1[1]) + p1[1]
sign = np.sign(p2[0] - p1[0])
return idxs[1] * sign <= max_col_idx * sign
def create_polygon(shape, vertices):
"""
Creates np.array with dimensions defined by shape
Fills polygon defined by vertices with ones, all other values zero"""
base_array = np.zeros(shape, dtype=float) # Initialize your array of zeros
fill = np.ones(base_array.shape) * True # Initialize boolean array defining shape fill
# Create check array for each edge segment, combine into fill array
for k in range(vertices.shape[0]):
fill = np.all([fill, check(vertices[k-1], vertices[k], base_array)], axis=0)
# Set all values inside polygon to one
base_array[fill] = 1
return base_array
# (Row, Col) Vertices of Polygon (Defined Clockwise)
vertices = np.array([
[5,12],
[8,18],
[13,14],
[11,6],
[4,6],
])
polygon_array = create_polygon([20,20], vertices)
# This section prints numbers at each vertex for visual check, just comment out
# to print an array of only zeros and ones
for n, vertex in enumerate(vertices):
polygon_array[vertex[0],vertex[1]] = 10*(n+1)
# Simple routine to print the final array
for row in polygon_array.tolist():
for c in row:
print '{:4.1f}'.format(c),
print ''
I found one bug in @schoolie response and stackoverflow do not allow add multi line code snippets in comments the problem is when p1[0] == p2[0]. My suggestion of update in function check:
if p1[0] == p2[0]:
max_col_idx = (idxs[0] - p1[0]) * idxs.shape[1]
sign = np.sign(p2[1] - p1[1])
else:
max_col_idx = (idxs[0] - p1[0]) / (p2[0] - p1[0]) * (p2[1] - p1[1]) + p1[1]
sign = np.sign(p2[0] - p1[0])
