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maptrace.py
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maptrace.py
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# -*- encoding: utf-8 -*-
import sys, re, os, argparse, heapq
from datetime import datetime
from collections import namedtuple, defaultdict
import numpy as np
from PIL import Image
from scipy import ndimage
######################################################################
DIR_RIGHT = 0
DIR_DOWN = 1
DIR_LEFT = 2
DIR_UP = 3
NEIGHBOR_OFFSET = np.array([
[ 0, 1 ],
[ 1, 0 ],
[ 0, -1 ],
[ -1, 0 ]
])
TURN_RIGHT = np.array([ DIR_DOWN, DIR_LEFT, DIR_UP, DIR_RIGHT ])
TURN_LEFT = np.array([ DIR_UP, DIR_RIGHT, DIR_DOWN, DIR_LEFT ])
VMAP_OFFSET = np.array([
[ -1, 0, 0 ],
[ 0, 0, 1 ],
[ 0, 0, 0 ],
[ 0, -1, 1 ]
])
DIAG_OFFSET = NEIGHBOR_OFFSET + NEIGHBOR_OFFSET[TURN_LEFT]
OPP_OFFSET = NEIGHBOR_OFFSET[TURN_LEFT]
CROSS_ELEMENT = np.array([[0,1,0],[1,1,1],[0,1,0]],dtype=np.bool)
BOX_ELEMENT = np.ones((3,3), dtype=np.bool)
######################################################################
# Some helper classes
EdgeInfo = namedtuple('EdgeInfo', ['node0', 'node1', 'label0', 'label1'])
EdgeRef = namedtuple('EdgeRef', ['edge_index', 'opp_label', 'step'])
######################################################################
# Class to store boundary representation for our map
class BoundaryRepresentation(object):
def __init__(self):
# list of nodes (points) or None for deleted
self.node_list = []
# list of sets of edge indices
self.node_edges = []
# list of point arrays (or empty for deleted edges)
self.edge_list = []
# list of EdgeInfo (or None for deleted edges)
self.edge_infolist = []
# map from point to node index
self.node_lookup = dict()
# map from EdgeInfo to edge index
self.edge_lookup = dict()
# map from label to list of list of EdgeRef
self.label_lookup = defaultdict(list)
def lookup_node(self, point, insert=False):
key = tuple(map(float, point))
if insert and key not in self.node_lookup:
node_idx = len(self.node_list)
self.node_list.append(point.copy())
self.node_edges.append(set())
self.node_lookup[key] = node_idx
else:
node_idx = self.node_lookup[key]
return node_idx
def add_edges(self, cur_label, contour_edges):
edge_refs = []
for opp_label, edge in contour_edges:
assert cur_label != opp_label
assert cur_label != 0
label0 = min(cur_label, opp_label)
label1 = max(cur_label, opp_label)
if label0 == cur_label:
step = 1
else:
step = -1
edge_to_add = edge[::step]
node0 = self.lookup_node(edge_to_add[0], insert=True)
node1 = self.lookup_node(edge_to_add[-1], insert=True)
edge_info = EdgeInfo(node0, node1, label0, label1)
if edge_info in self.edge_lookup:
edge_idx = self.edge_lookup[edge_info]
stored_edge = self.edge_list[edge_idx]
assert self.edge_infolist[edge_idx] == edge_info
assert np.all(stored_edge == edge_to_add)
assert edge_idx in self.node_edges[node0]
assert edge_idx in self.node_edges[node1]
else:
edge_idx = len(self.edge_list)
self.edge_list.append( edge_to_add )
self.edge_infolist.append( edge_info )
self.edge_lookup[edge_info] = edge_idx
self.node_edges[node0].add( edge_idx )
self.node_edges[node1].add( edge_idx )
edge_refs.append(EdgeRef(edge_idx, opp_label, step))
self.label_lookup[cur_label].append( edge_refs)
def replace_endpoints(self, edge_idx, na, nb, nc):
edge = self.edge_list[edge_idx]
edge_info = self.edge_infolist[edge_idx]
assert (edge_info.node0 == na or edge_info.node0 == nb or
edge_info.node1 == na or edge_info.node1 == nb)
n0 = None
n1 = None
if edge_info.node0 == na:
n0 = na
new_n0 = nc
elif edge_info.node0 == nb:
n0 = nb
new_n0 = nc
else:
new_n0 = edge_info.node0
if edge_info.node1 == na:
n1 = na
new_n1 = nc
elif edge_info.node1 == nb:
n1 = nb
new_n1 = nc
else:
new_n1 = edge_info.node1
if n0 is not None and n1 is not None:
self.edge_list[edge_idx] = edge[:0]
self.edge_infolist[edge_idx] = None
# NB we will rebuild label_lookup after all merges
return
self.node_edges[nc].add(edge_idx)
pc = self.node_list[nc]
for node_idx, which_end, lo, hi in [(n0, 0, 1, 0), (n1, -1, 0, 1)]:
if node_idx is None:
continue
p = self.node_list[node_idx]
delta = (pc - p).reshape(1, 2)
u = np.linspace(lo, hi, len(edge)).reshape(-1, 1)
edge = edge + delta * u
edge[which_end] = pc
edge_info = EdgeInfo(new_n0, new_n1, edge_info.label0, edge_info.label1)
self.edge_list[edge_idx] = edge
self.edge_infolist[edge_idx] = edge_info
assert np.all(edge[0] == self.node_list[edge_info.node0])
assert np.all(edge[-1] == self.node_list[edge_info.node1])
def merge_nodes(self, tol):
node_points = np.array(self.node_list)
rng = range(len(node_points))
i, j = np.meshgrid(rng, rng)
use = i > j
i = i[use]
j = j[use]
ni = node_points[i]
nj = node_points[j]
dists = np.linalg.norm(ni - nj, axis=1)
heap = list(zip(dists, i, j))
heapq.heapify(heap)
retired_nodes = set()
active_nodes = set(rng)
while len(heap):
dmin, na, nb = heapq.heappop(heap)
assert na > nb
if dmin > tol:
break
if na in retired_nodes or nb in retired_nodes:
continue
print(' merge nodes {} and {} with distance {}'.format(
na, nb, dmin))
pa = self.node_list[na]
pb = self.node_list[nb]
pc = 0.5*(pa + pb)
nc = len(self.node_list)
nkey = tuple(map(float, pc))
self.node_list.append(pc.copy())
self.node_edges.append(set())
self.node_lookup[nkey] = nc
assert self.lookup_node(pc) == nc
for node_idx in (na, nb):
for edge_idx in self.node_edges[node_idx]:
if self.edge_infolist[edge_idx] is not None:
self.replace_endpoints(edge_idx, na, nb, nc)
for node_idx in (na, nb):
p = self.node_list[node_idx]
pkey = tuple(map(float, p))
del self.node_lookup[pkey]
self.node_list[node_idx] = None
self.node_edges[node_idx] = set()
retired_nodes.add(node_idx)
active_nodes.remove(node_idx)
for nj in active_nodes:
pj = self.node_list[nj]
dcj = np.linalg.norm(pc - pj)
hkey = (dcj, nc, nj)
heapq.heappush(heap, hkey)
active_nodes.add(nc)
# rebuild label lookup
new_label_lookup = dict()
for label, contours in self.label_lookup.items():
new_contours = []
for contour in contours:
new_contour = []
for edge_ref in contour:
idx, _, _ = edge_ref
if self.edge_infolist[idx] is not None:
new_contour.append(edge_ref)
if len(new_contour):
new_contours.append(new_contour)
if len(new_contours):
new_label_lookup[label] = new_contours
else:
print('totally deleted label {}!'.format(label))
self.label_lookup = new_label_lookup
def save_debug_image(self, opts, orig_shape, colors, name):
filename = opts.basename + '_debug_' + name + '.svg'
with open(filename, 'w') as svg:
svg.write('<svg width="{}" height="{}" '
'xmlns="http://www.w3.org/2000/svg">\n'.
format(orig_shape[1], orig_shape[0]))
svg.write(' <rect width="100%" height="100%" fill="#eee" />\n')
for ilabel in range(2):
if ilabel == 0:
svg.write(' <g stroke-linejoin="miter" stroke-width="4" fill="none">\n')
else:
svg.write(' <g stroke-linejoin="miter" stroke-width="4" fill="none" stroke-dasharray="8, 8" >\n')
for edge, einfo in zip(self.edge_list, self.edge_infolist):
svg.write(' <path d="')
last = np.array([0,0])
for i, pt in enumerate(edge):
pt = pt.astype(int)
if i == 0:
svg.write('M{},{}'.format(pt[0], pt[1]))
else:
diff = pt - last
if diff[1] == 0:
svg.write('h{}'.format(diff[0]))
elif diff[0] == 0:
svg.write('v{}'.format(diff[1]))
else:
svg.write('l{},{}'.format(*diff))
last = pt
color = colors[einfo.label0 if ilabel == 0 else einfo.label1]
svg.write('" stroke="#{:02x}{:02x}{:02x}" />\n'.format(*color))
svg.write(' </g>\n')
svg.write(' <g stroke="none" fill="#000">\n')
for pt in self.node_list:
svg.write(' <circle cx="{}" cy="{}" r="4" />\n'.format(*pt))
svg.write(' </g>\n')
svg.write('</svg>\n')
print('wrote', filename)
######################################################################
# Input is string, output is pair (string, lambda image -> image)
def filter_type(fstr):
m = re.match(r'^\s*([a-z]+)\s*:\s*([a-z]+)\s*,\s*([1-9][0-9]*)\s*$', fstr)
if m is None:
raise argparse.ArgumentTypeError('invalid filter string')
operation = m.group(1)
element = m.group(2)
iterations = int(m.group(3))
fnmap = dict(
open=ndimage.binary_opening,
close=ndimage.binary_closing,
dilate=ndimage.binary_dilation,
erode=ndimage.binary_erosion)
if operation not in fnmap.keys():
raise argparse.ArgumentTypeError('invalid operation ' + operation)
if element == 'box':
element = BOX_ELEMENT
elif element == 'cross':
element = CROSS_ELEMENT
else:
raise argparse.ArgumentTypeError('invalid element ' + element)
f = lambda img: fnmap[operation](img, element, iterations=iterations)
return fstr, f
######################################################################
# Confirm with [y/n]
def confirm(prompt):
while True:
print(prompt + ' [y/n]: ', end='')
sys.stdout.flush()
choice = input().lower()
if choice in ['y', 'yes']:
return True
elif choice in ['n', 'no']:
return False
else:
print('invalid choice')
######################################################################
# Parse command-line options, return namespace containing results
def get_options():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('image', type=argparse.FileType('rb'),
metavar='IMAGE.png', nargs='?',
help='image to approximate')
parser.add_argument('-z', '--zoom', type=float, metavar='ZOOM',
default=1.0,
help='amount to resize image on load')
parser.add_argument('-t', '--threshold', type=int, metavar='T',
default=64,
help='intensity threshold for outlines')
parser.add_argument('-a', '--alpha-threshold', type=int, metavar='T',
default=127,
help='threshold for alpha channel')
parser.add_argument('-C', '--connectivity', choices=('4','8'),
default='4',
help='connectivity of non-outline regions')
parser.add_argument('-f', '--filter', type=filter_type, default=None,
help='filter for preprocessing outline map '
'after thresholding but before connected '
'component analysis; must be of the format '
'(erode|dilate|open|close):(box|cross),ITERATIONS '
'e.g., erode:cross,1')
parser.add_argument('-e', '--edge-tol', type=float, metavar='E',
default='1.42',
help='tolerance in px for simplifying edges')
parser.add_argument('-n', '--node-tol', type=float, metavar='N',
default=0,
help='tolerance in px for merging nodes')
parser.add_argument('-o', '--output-file', type=str, metavar='FILENAME.svg',
default=None,
help='output SVG file name')
parser.add_argument('-s', '--stroke-width', type=float, metavar='S',
default=1.0,
help='output SVG stroke width')
parser.add_argument('-b', '--bg-stroke-width', type=float, metavar='S',
default=None,
help='output SVG stroke width for largest region')
parser.add_argument('-d', '--debug-images', action='store_true',
help='generate debug images')
parser.add_argument('-D', '--allow-dark-colors', action='store_true',
help='flag to prevent applying grayscale threshold '
'to image supplied with -c')
parser.add_argument('-m', '--min-area', type=int, metavar='A',
default=1, help='minimum region area in pixels')
parser.add_argument('-c', '--color-image', type=argparse.FileType('rb'),
default=None, help='image to supply color for output map')
parser.add_argument('-q', '--color-quantize-bits', type=int,
default=8, help='quantization for finding region '
'colors with -c')
parser.add_argument('-r', '--random-colors', action='store_true',
help='color regions randomly')
parser.add_argument('-R', '--random-seed', type=int,
help='random seed for colors')
parser.add_argument('-y', '--overwrite', action='store_true',
help='overwrite output')
parser.add_argument('-S', '--solid-colors', action='store_true',
help='input image is solid colors with no outlines')
opts = parser.parse_args()
if opts.image is None:
if opts.color_image is None:
print('error: must provide image filename or set color image with -c')
sys.exit(1)
else:
opts.image = open(opts.color_image.name, 'rb')
basename = os.path.basename(opts.image.name)
opts.basename, _ = os.path.splitext(basename)
if opts.bg_stroke_width is None:
opts.bg_stroke_width = opts.stroke_width
if opts.output_file is None:
opts.output_file = opts.basename + '.svg'
if os.path.exists(opts.output_file) and not opts.overwrite:
if not confirm(opts.output_file + ' exists. Overwrite?'):
print('will not overwite output, exiting')
sys.exit(1)
return opts
######################################################################
# Downsample pixel values, rounding to center of bins.
def quantize(image, bits_per_channel=None):
if bits_per_channel is None:
bits_per_channel = 8
assert image.dtype == np.uint8
shift = 8-bits_per_channel
halfbin = (1 << shift) >> 1
return ((image.astype(int) >> shift) << shift) + halfbin
######################################################################
# Pack RGB triplets into ints
def pack_rgb(rgb):
orig_shape = None
if isinstance(rgb, np.ndarray):
assert rgb.shape[-1] == 3
orig_shape = rgb.shape[:-1]
else:
assert len(rgb) == 3
rgb = np.array(rgb)
rgb = rgb.astype(int).reshape((-1, 3))
packed = (rgb[:, 0] << 16 |
rgb[:, 1] << 8 |
rgb[:, 2])
if orig_shape is None:
return packed
else:
return packed.reshape(orig_shape)
######################################################################
# Unpack ints to RGB triplets
def unpack_rgb(packed):
orig_shape = None
if isinstance(packed, np.ndarray):
assert packed.dtype == int
orig_shape = packed.shape
packed = packed.reshape((-1, 1))
rgb = ((packed >> 16) & 0xff,
(packed >> 8) & 0xff,
(packed) & 0xff)
if orig_shape is None:
return rgb
else:
return np.hstack(rgb).reshape(orig_shape + (3,))
######################################################################
# Get the dominant color in a list of colors (with optional
# quantization)
def get_dominant_color(colors, bits_per_channel=None):
assert colors.shape[-1] == 3
quantized = quantize(colors, bits_per_channel).astype(int)
packed = pack_rgb(quantized)
unique, counts = np.unique(packed, return_counts=True)
packed_mode = unique[counts.argmax()]
return unpack_rgb(packed_mode)
######################################################################
# Save a debug image if allowed
def save_debug_image(opts, name, image):
if not opts.debug_images:
return
if isinstance(image, np.ndarray):
if image.dtype == np.bool:
image = (image.astype(np.uint8) * 255)
if len(image.shape) == 2:
mode = 'L'
else:
mode = 'RGB'
image = Image.fromarray(image, mode)
filename = opts.basename + '_debug_' + name + '.png'
image.save(filename)
print('wrote', filename)
######################################################################
# Open an input image and get the RGB colors as well as the mask
def get_mask(input_image, opts):
rgb = input_image
alpha = None
if (rgb.mode == 'LA' or
(rgb.mode == 'P' and 'transparency' in rgb.info)):
rgb = rgb.convert('RGBA')
if rgb.mode == 'RGBA':
alpha = np.array(rgb.split()[-1])
rgb = rgb.convert('RGB')
rgb = np.array(rgb)
gray = rgb.max(axis=2)
mask = (gray > opts.threshold)
if alpha is not None:
mask = mask | (alpha < opts.alpha_threshold)
save_debug_image(opts, 'mask', mask)
if opts.filter is not None:
print('applying filter:', opts.filter[0])
mask = opts.filter[1](mask)
save_debug_image(opts, 'mask_filtered', mask)
return mask
######################################################################
def printp(*args):
print(*args, end='')
sys.stdout.flush()
######################################################################
def get_labels_and_colors_outlined(mask, opts):
if opts.connectivity == '8':
structure = BOX_ELEMENT
else:
structure = CROSS_ELEMENT
labels, num_labels = ndimage.label(mask, structure=structure)
print('found {} labels'.format(num_labels))
unlabeled = ~mask
printp('computing areas... ')
start = datetime.now()
areas, bins = np.histogram(labels.flatten(),
bins=num_labels,
range=(1, num_labels+1))
elapsed = (datetime.now() - start).total_seconds()
print('finished computing areas in {} seconds.'.format(elapsed))
idx = np.hstack( ([0], np.argsort(-areas)+1) )
replace = np.zeros_like(idx)
replace[idx] = range(len(idx))
labels = replace[labels]
areas = areas[idx[1:]-1]
print('min area is {}, max is {}'.format(areas[-1], areas[0]))
if opts.min_area > areas[-1]:
print('killing all labels with area < {} px'.format(opts.min_area))
kill_labels = np.nonzero(areas < opts.min_area)[0]
num_labels = kill_labels.min()
kill_mask = (labels > num_labels)
save_debug_image(opts, 'kill_labels', kill_mask)
unlabeled = unlabeled | kill_mask
print('killed {} labels, now at {} total'.format(
len(kill_labels), num_labels))
colors = 255*np.ones((num_labels+1,3), dtype=np.uint8)
if opts.color_image is not None:
color_image = Image.open(opts.color_image)
labels_size = labels.shape[::-1]
if color_image.size != labels_size:
color_image = color_image.resize(labels_size, Image.NEAREST)
color_image = np.array(color_image.convert('RGB'))
print('assigning colors from {}...'.format(opts.color_image.name))
slices = ndimage.find_objects(labels, num_labels)
for label, (yslc, xslc) in zip(range(1, num_labels+1), slices):
print(' coloring label {}/{}'.format(label, num_labels))
lmask = (labels[yslc,xslc] == label)
crect = color_image[yslc,xslc]
if not opts.allow_dark_colors:
lmask = lmask & (crect.max(axis=2) > opts.threshold)
if not np.any(lmask):
print('no colors available for label {}, '
'try running with -D?'.format(label))
else:
colors[label] = get_dominant_color(crect[lmask],
opts.color_quantize_bits)
elif opts.random_colors:
if opts.random_seed is not None:
np.random.seed(opts.random_seed)
colors = np.random.randint(128, size=(num_labels+1,3),
dtype=np.uint8) + 128
colors[0,:] = 255
save_debug_image(opts, 'regions', colors[labels])
printp('running DT... ')
start = datetime.now()
result = ndimage.distance_transform_edt(unlabeled,
return_distances=opts.debug_images,
return_indices=True)
if opts.debug_images:
dist, idx = result
dist /= dist.max()
dist = (dist*255).astype(np.uint8)
save_debug_image(opts, 'dist', dist)
else:
idx = result
elapsed = (datetime.now() - start).total_seconds()
print('ran DT in {} seconds'.format(elapsed))
labels = labels[tuple(idx)]
assert not np.any(labels == 0)
labels_big = np.zeros((labels.shape[0]+2,labels.shape[1]+2),
dtype=labels.dtype)
labels_big[1:-1,1:-1] = labels
start = datetime.now()
printp('finding objects... ')
slices = ndimage.find_objects(labels, num_labels)
elapsed = (datetime.now() - start).total_seconds()
print('found all objects in {} seconds'.format(elapsed))
slices_big = []
for spair in slices:
spair_big = []
for s in spair:
spair_big.append(slice(s.start, s.stop+2))
slices_big.append( tuple(spair_big) )
assert labels_big.min() == 0 and labels_big.max() == num_labels
assert len(slices) == num_labels
save_debug_image(opts, 'regions_expanded', colors[labels_big[1:-1, 1:-1]])
return num_labels, labels_big, slices_big, colors
######################################################################
def get_labels_and_colors_solid(input_image, opts):
array = np.array(input_image)
print(array.shape, array.dtype)
if len(array.shape) == 2:
flattened = array.flatten()
axis = None
else:
assert len(array.shape) == 3
flattened = array.reshape(-1, array.shape[2])
axis = 0
unique, ulabels = np.unique(flattened,
axis=axis,
return_inverse=True)
ucount = len(unique)
# go from bright to dark
unique = unique[::-1]
ulabels = ucount - ulabels - 1
ulabels = ulabels.reshape(array.shape[:2])
print('unique:', unique)
print('ulabels:', ulabels)
rgb = np.array(input_image.convert('RGB'))
colors = []
labels = np.zeros(array.shape[:2], dtype=int)
max_label = 0
slices = []
for ulabel in range(ucount):
mask = (ulabels == ulabel)
yidx, xidx = np.nonzero(mask)
color = rgb[yidx[0], xidx[0]]
if ulabel == 0: # background
colors.append(color)
else:
sublabels, num_features = ndimage.label(mask)
print('found {} sublabels for {}'.format(
num_features, color))
subslices = ndimage.find_objects(sublabels,
num_features)
labels[mask] = sublabels[mask] + max_label
max_label += num_features
assert labels.max() == max_label
slices.extend(subslices)
colors.extend([color] * num_features)
colors = np.array(colors)
colors[0,:] = 255
randocolors = np.random.randint(128, size=(max_label+1, 3),
dtype=np.uint8) + 128
if opts.random_colors:
colors = randocolors
save_debug_image(opts, 'labels', randocolors[labels])
slices_big = []
for spair in slices:
spair_big = []
for s in spair:
spair_big.append(slice(s.start, s.stop+2))
slices_big.append( tuple(spair_big) )
return max_label, labels, slices_big, colors
######################################################################
def follow_contour(l_subrect, cur_label,
startpoints, pos):
start = pos
cur_dir = DIR_RIGHT
contour_info = []
while True:
ooffs = OPP_OFFSET[cur_dir]
noffs = NEIGHBOR_OFFSET[cur_dir]
doffs = DIAG_OFFSET[cur_dir]
neighbor = tuple(pos + noffs)
diag = tuple(pos + doffs)
opp = tuple(pos + ooffs)
assert l_subrect[pos] == cur_label
assert l_subrect[opp] != cur_label
contour_info.append( pos + (cur_dir, l_subrect[opp]) )
startpoints[pos] = False
if l_subrect[neighbor] != cur_label:
cur_dir = TURN_RIGHT[cur_dir]
elif l_subrect[diag] == cur_label:
pos = diag
cur_dir = TURN_LEFT[cur_dir]
else:
pos = neighbor
if pos == start and cur_dir == DIR_RIGHT:
break
n = len(contour_info)
contour_info = np.array(contour_info)
clabels = contour_info[:,3]
# set of unique labels for this contour
opp_label_set = set(clabels)
assert cur_label not in opp_label_set
# if multiple labels and one wraps around, correct this
if len(opp_label_set) > 1 and clabels[0] == clabels[-1]:
idx = np.nonzero(clabels != clabels[0])[0][0]
perm = np.hstack( (np.arange(idx, n),
np.arange(idx)) )
contour_info = contour_info[perm]
clabels = contour_info[:,3]
# make sure no wraparound
assert len(opp_label_set) == 1 or clabels[0] != clabels[-1]
# apply offset to get contour points
cpoints = contour_info[:,:2].astype(np.float32)
cdirs = contour_info[:,2]
cpoints += 0.5 * (OPP_OFFSET[cdirs] - NEIGHBOR_OFFSET[cdirs] + 1)
# put points in xy format
cpoints = cpoints[:,::-1]
if len(opp_label_set) == 1:
idx = np.arange(len(cpoints))
xyi = zip(cpoints[:,0], cpoints[:,1], idx)
imin = min(xyi)
i = imin[-1]
cpoints = np.vstack( ( cpoints[i:], cpoints[:i] ) )
assert np.all(clabels == clabels[0])
return cpoints, clabels
######################################################################
def split_contour(cpoints, clabels):
edges = []
shifted = np.hstack(( [-1], clabels[:-1] ))
istart = np.nonzero( clabels - shifted )[0]
iend = np.hstack( (istart[1:], len(clabels)) )
for start, end in zip(istart, iend):
assert start == 0 or clabels[start] != clabels[start-1]
assert clabels[end-1] == clabels[start]
opp_label = clabels[start]
if end < len(cpoints):
edge = cpoints[start:end+1]
else:
edge = np.vstack( (cpoints[start:end], cpoints[0]) )
edges.append( (opp_label, edge) )
start = end
return edges
######################################################################
def store_contour_edges(opts, labels,
edge_lookup, edge_list,
cur_label, contour_edges):
edge_refs = []
for opp_label, edge in contour_edges:
assert cur_label != opp_label
assert cur_label != 0
print(' storing contour edge with cur={}, opp={}'.format(
cur_label, opp_label))
lmin = min(cur_label, opp_label)
lmax = max(cur_label, opp_label)
if lmin == cur_label:
step = 1
else:
step = -1
edge_to_add = edge[::step]
p0 = tuple(map(float, edge_to_add[0]))
p1 = tuple(map(float, edge_to_add[1]))
key = (lmin, lmax, p0, p1)
if key in edge_lookup:
idx = edge_lookup[key]
if not np.all(edge_list[idx] == edge_to_add):