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Break up raster() into sub-functions. Documentation to follow...

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jan 2017-09-06 01:17:38 -07:00
parent 6dbdd63df4
commit 206d3885b6
1 changed files with 143 additions and 36 deletions
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float_raster.py
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@ -12,7 +12,7 @@ from scipy import sparse
__author__ = 'Jan Petykiewicz'
def raster(poly_xy: numpy.ndarray,
def raster(vertices: numpy.ndarray,
grid_x: numpy.ndarray,
grid_y: numpy.ndarray
) -> numpy.ndarray:
@ -23,25 +23,21 @@ def raster(poly_xy: numpy.ndarray,
usually only allow for 256 (and often fewer) possible pixel values without performing (very
slow) super-sampling.
:param poly_xy: 2xN ndarray containing x,y coordinates for each point in the polygon
Polygons are assumed to have clockwise vertex order; reversing the vertex order is equivalent
to multiplying the result by -1.
:param vertices: 2xN ndarray containing x,y coordinates for each vertex of the polygon
:param grid_x: x-coordinates for the edges of each pixel (ie, the leftmost two columns span
x=grid_x[0] to x=grid_x[1] and x=grid_x[1] to x=grid_x[2])
:param grid_y: y-coordinates for the edges of each pixel (see grid_x)
:return: 2D ndarray with pixel values in the range [0, 1] containing the anti-aliased polygon
"""
poly_xy = numpy.array(poly_xy)
vertices = numpy.array(vertices)
grid_x = numpy.array(grid_x)
grid_y = numpy.array(grid_y)
if poly_xy.shape[0] != 2:
raise Exception('poly_xy must be 2xN')
if grid_x.size < 1 or grid_y.size < 1:
raise Exception('Grid must contain at least one full pixel')
num_xy_px = numpy.array([grid_x.size, grid_y.size]) - 1
min_bounds = floor(poly_xy.min(axis=1))
max_bounds = ceil(poly_xy.max(axis=1))
min_bounds = floor(vertices.min(axis=1))
max_bounds = ceil(vertices.max(axis=1))
keep_x = logical_and(grid_x >= min_bounds[0],
grid_x <= max_bounds[0])
@ -49,21 +45,47 @@ def raster(poly_xy: numpy.ndarray,
grid_y <= max_bounds[1])
if not (keep_x.any() and keep_y.any()): # polygon doesn't overlap grid
return zeros(num_xy_px)
return zeros((grid_x.size - 1, grid_y.size - 1))
vertices = create_vertices(vertices, grid_x, grid_y)
parts_grid = get_raster_parts(vertices, grid_x, grid_y).toarray()
result_grid = numpy.real(parts_grid) + numpy.imag(parts_grid).cumsum(axis=0)
return result_grid
def calculate_intersection_vertices(
vertices: numpy.ndarray,
grid_x: numpy.ndarray,
grid_y: numpy.ndarray
):
"""
"""
if vertices.shape[0] != 2:
raise Exception('vertices must be 2xN')
min_bounds = floor(vertices.min(axis=1))
max_bounds = ceil(vertices.max(axis=1))
keep_x = logical_and(grid_x >= min_bounds[0],
grid_x <= max_bounds[0])
keep_y = logical_and(grid_y >= min_bounds[1],
grid_y <= max_bounds[1])
if not (keep_x.any() or keep_y.any()): # polygon doesn't overlap grid
mat_shape = (vertices.shape[1], grid_x.size + grid_y.size)
return zeros(mat_shape), zeros(mat_shape), zeros(mat_shape, dtype=bool)
y_seg_xs = hstack((min_bounds[0], grid_x[keep_x], max_bounds[0])).T
x_seg_ys = hstack((min_bounds[1], grid_y[keep_y], max_bounds[1])).T
num_poly_vertices = poly_xy.shape[1]
'''
Calculate intersections between polygon and grid line segments
'''
xy1b = numpy.roll(poly_xy, -1, axis=1)
xy1b = numpy.roll(vertices, -1, axis=1)
# Lists of initial/final coordinates for polygon segments
xi1 = poly_xy[0, :, newaxis]
yi1 = poly_xy[1, :, newaxis]
xi1 = vertices[0, :, newaxis]
yi1 = vertices[1, :, newaxis]
xf1 = xy1b[0, :, newaxis]
yf1 = xy1b[1, :, newaxis]
@ -105,6 +127,27 @@ def raster(poly_xy: numpy.ndarray,
int_normalized_distance_1to2 = u_a
# print('sparsity', int_adjacency_matrix.astype(int).sum() / int_adjacency_matrix.size)
return int_normalized_distance_1to2, int_xy_matrix, int_adjacency_matrix
def create_vertices(
vertices: numpy.ndarray,
grid_x: numpy.ndarray,
grid_y: numpy.ndarray,
new_vertex_data=None
) -> sparse.coo_matrix:
"""
"""
if vertices.shape[0] != 2:
raise Exception('vertices must be 2xN')
if grid_x.size < 1 or grid_y.size < 1:
raise Exception('Grid must contain at least one line in each direction?')
num_poly_vertices = vertices.shape[1]
if new_vertex_data is None:
new_vertex_data = calculate_intersection_vertices(vertices, grid_x, grid_y)
int_normalized_distance_1to2, int_xy_matrix, int_adjacency_matrix = new_vertex_data
'''
Insert any polygon-grid intersections as new polygon vertices
@ -116,39 +159,104 @@ def raster(poly_xy: numpy.ndarray,
sortix_paired = (numpy.arange(num_poly_vertices)[:, newaxis], sortix)
assert(int_normalized_distance_1to2.shape[0] == num_poly_vertices)
# If any new points fall outside the window, shrink them back onto it
# if any new points fall outside the window, shrink them back onto it
xy_shrunken = (numpy.real(int_xy_matrix).clip(grid_x[0], grid_x[-1]) + 1j *
numpy.imag(int_xy_matrix).clip(grid_y[0], grid_y[-1]))
# Use sortix to sort adjacency matrix and the intersection (x, y) coordinates,
# and hstack the original points to the left of the new ones
xy_with_original = hstack((poly_xy[0, :, newaxis] + 1j * poly_xy[1, :, newaxis],
xy_with_original = hstack((vertices[0, :, newaxis] + 1j * vertices[1, :, newaxis],
xy_shrunken[sortix_paired]))
has_intersection = hstack((ones((poly_xy.shape[1], 1), dtype=bool),
has_intersection = hstack((ones((vertices.shape[1], 1), dtype=bool),
int_adjacency_matrix[sortix_paired]))
# Now remove all extra entries which don't correspond to new vertices
# (ie, no intersection happened), and then flatten, creating our
# polygon-with-extra-vertices, though some extra vertices are included,
# which we must remove manually.
# polygon-with-extra-vertices, though some redundant vertices are included,
# which we must later remove manually.
vertices = xy_with_original[has_intersection]
return vertices
def clip_vertices_to_window(
vertices: numpy.ndarray,
min_x: float = -numpy.inf,
max_x: float = numpy.inf,
min_y: float = -numpy.inf,
max_y: float = numpy.inf
) -> numpy.ndarray:
"""
"""
# xy_shrunken = (numpy.real(vertices).clip(min_x, max_x) + 1j *
# numpy.imag(vertices).clip(max_y, min_y))
#
# # forward_difference: 1 if either coordinate changed from the previous point
# forward_diff = (numpy.roll(xy_shrunken, 1) - xy_shrunken) != 0
# xys = xy_shrunken[forward_diff]
#
# forward_diff = numpy.roll(xys, 1) - xys
# roll_diff = numpy.roll(forward_diff, -1)
# keep = numpy.logical_or(
# numpy.logical_and(numpy.real(forward_diff),
# numpy.real(roll_diff)),
# numpy.logical_and(numpy.imag(forward_diff),
# numpy.imag(roll_diff)))
# nondup = xys[keep]
# return nondup
# Remove points outside the window (these will only be original points)
# Since the boundaries of the window are also pixel boundaries, this just
# makes the polygon boundary proceed along the window edge
inside = logical_and.reduce((numpy.real(vertices) <= grid_x[-1],
numpy.real(vertices) >= grid_x[0],
numpy.imag(vertices) <= grid_y[-1],
numpy.imag(vertices) >= grid_y[0]))
inside = logical_and.reduce((numpy.real(vertices) <= max_x,
numpy.real(vertices) >= min_x,
numpy.imag(vertices) <= max_y,
numpy.imag(vertices) >= min_y))
vertices = vertices[inside]
# Remove consecutive duplicate vertices
consecutive = numpy.ediff1d(vertices, to_begin=[1 + 1j]).astype(bool)
vertices = vertices[consecutive]
return vertices
def get_raster_parts(
vertices: numpy.ndarray,
grid_x: numpy.ndarray,
grid_y: numpy.ndarray
) -> sparse.coo_matrix:
"""
This function performs the same task as raster(...), but instead of returning a dense array
of pixel values, it returns a sparse array containing the value
(-area + 1j * cover)
for each pixel which contains a line segment, where
cover is the fraction of the pixel's y-length that is traversed by the segment,
multiplied by the sign of (y_final - y_initial)
area is the fraction of the pixel's area covered by the trapezoid formed by
the line segment's endpoints (clipped to the cell edges) and their projections
onto the pixel's left (i.e., lowest-x) edge, again multiplied by
the sign of (y_final - y_initial)
Note that polygons are assumed to be wound clockwise.
The result from raster(...) can be obtained with
raster_result = numpy.real(lines_result) + numpy.imag(lines_result).cumsum(axis=0)
:param vertices: 2xN ndarray containing x,y coordinates for each point in the polygon
:param grid_x: x-coordinates for the edges of each pixel (ie, the leftmost two columns span
x=grid_x[0] to x=grid_x[1] and x=grid_x[1] to x=grid_x[2])
:param grid_y: y-coordinates for the edges of each pixel (see grid_x)
:return: Complex sparse COO matrix containing area and cover information
"""
if grid_x.size < 2 or grid_y.size < 2:
raise Exception('Grid must contain at least one full pixel')
num_xy_px = numpy.array([grid_x.size, grid_y.size]) - 1
vertices = clip_vertices_to_window(vertices,
grid_x[0], grid_x[-1],
grid_y[0], grid_y[-1])
# If the shape fell completely outside our area, just return a blank grid
if vertices.size == 0:
return zeros(num_xy_px)
return sparse.coo_matrix(shape=num_xy_px)
'''
Calculate area, cover
@ -159,23 +267,22 @@ def raster(poly_xy: numpy.ndarray,
# Remove segments along the right,top edges
# (they correspond to outside pixels, but couldn't be removed until now
# because poly_xy stores points, not segments, and the edge points are needed
# because 'vertices' stored points, not segments, and the edge points are needed
# when creating endpoint_avg)
non_edge = numpy.logical_and(numpy.real(endpoint_avg) < grid_x[-1],
numpy.imag(endpoint_avg) < grid_y[-1])
endpoint_final = endpoint_avg[non_edge]
x_sub = numpy.digitize(numpy.real(endpoint_final), grid_x) - 1
y_sub = numpy.digitize(numpy.imag(endpoint_final), grid_y) - 1
endpoint_avg_final = endpoint_avg[non_edge]
x_sub = numpy.digitize(numpy.real(endpoint_avg_final), grid_x) - 1
y_sub = numpy.digitize(numpy.imag(endpoint_avg_final), grid_y) - 1
cover = diff(numpy.imag(poly), axis=0)[non_edge] / diff(grid_y)[y_sub]
area = (numpy.real(endpoint_final) - grid_x[x_sub]) * cover / diff(grid_x)[x_sub]
area = (numpy.real(endpoint_avg_final) - grid_x[x_sub]) * cover / diff(grid_x)[x_sub]
# Use coo_matrix(...).toarray() to efficiently convert from (x, y, v) pairs to ndarrays.
# We can use v = (-area + 1j * cover) followed with calls to numpy.real() and numpy.imag() to
# improve performance (Otherwise we'd have to call coo_matrix() twice. It's really inefficient
# because it involves lots of random memory access, unlike real() and imag()).
poly_grid = sparse.coo_matrix((-area + 1j * cover, (x_sub, y_sub)), shape=num_xy_px).toarray()
result_grid = numpy.real(poly_grid) + numpy.imag(poly_grid).cumsum(axis=0)
poly_grid = sparse.coo_matrix((-area + 1j * cover, (x_sub, y_sub)), shape=num_xy_px)
return poly_grid
return result_grid