def convert(self, lg, choices = 1, adv_match = False, textures = TextureCache(), memory = 0):
"""Given a line graph this chops it into chunks, matches each chunk to the database of chunks and returns a new line graph with these chunks instead of the original. Output will involve heavy overlap requiring clever blending. choices is the number of options it select from the db - it grabs this many closest to the requirements and then randomly selects from them. If adv_match is True then instead of random selection from the choices it does a more advanced match, and select the best match in terms of colour distance from already-rendered chunks. This option is reasonably expensive. memory is how many recently use chunks to remember, to avoid repetition."""
if memory > (choices - 1):
memory = choices - 1
# If we have no data just return the input...
if self.empty(): return lg
# Check if the indexing structure is valid - if not create it...
if self.kdtree==None:
data = numpy.array(map(lambda p: self.feature_vect(p[0], p[1]), self.chunks), dtype=numpy.float)
self.kdtree = scipy.spatial.cKDTree(data, 4)
# Calculate the radius scaler and distance for this line graph, by calculating the median radius...
rads = map(lambda i: lg.get_vertex(i)[5], xrange(lg.vertex_count))
rads.sort()
median_radius = rads[len(rads)//2]
radius_mult = 1.0 / median_radius
dist = self.dist * median_radius
# Create the list into which we dump all the chunks that will make up the return...
chunks = []
temp = LineGraph()
# List of recently used chunks, to avoid obvious patterns...
recent = []
# If advanced match we need a Composite of the image thus far, to compare against...
if adv_match:
canvas = Composite()
min_x, max_x, min_y, max_y = lg.get_bounds()
canvas.set_size(int(max_x+8), int(max_y+8))
# Iterate the line graph, choping it into chunks and matching a chunk to each chop...
for chain in lg.chains():
head = 0
tail = 0
length = 0.0
while True:
# Move tail so its long enough, or has reached the end...
while length<dist and tail+1<len(chain):
tail += 1
v1 = lg.get_vertex(chain[tail-1])
v2 = lg.get_vertex(chain[tail])
length += numpy.sqrt((v1[0]-v2[0])**2 + (v1[1]-v2[1])**2)
# Extract a feature vector for this chunk...
temp.from_vertices(lg, chain[head:tail+1])
fv = self.feature_vect(temp, median_radius)
# Select a chunk from the database...
if choices==1:
selected = self.kdtree.query(fv)[1]
orig_chunk = self.chunks[selected]
else:
options = list(self.kdtree.query(fv, choices)[1])
options = filter(lambda v: v not in recent, options)
if not adv_match:
selected = random.choice(options)
orig_chunk = self.chunks[selected]
else:
cost = 1e64 * numpy.ones(len(options))
for i, option in enumerate(options):
fn = filter(lambda t: t[0].startswith('texture:'), self.chunks[option][0].get_tags())
if len(fn)!=0:
fn = fn[0][0][len('texture:'):]
tex = textures[fn]
chunk = LineGraph()
chunk.from_many(self.chunks[option][0])
chunk.morph_to(lg, chain[head:tail+1])
part = canvas.draw_line_graph(chunk)
cost[i] = canvas.cost_texture_nearest(tex, part)
selected = options[numpy.argmin(cost)]
orig_chunk = self.chunks[selected]
# Update recent list...
recent.append(selected)
if len(recent)>memory:
recent.pop(0)
# Distort it to match the source line graph...
chunk = LineGraph()
chunk.from_many(orig_chunk[0])
chunk.morph_to(lg, chain[head:tail+1])
# Record it for output...
chunks.append(chunk)
# If advanced matching is on write it out to canvas, so future choices will take it into account...
if adv_match:
fn = filter(lambda t: t[0].startswith('texture:'), chunk.get_tags())
if len(fn)!=0:
fn = fn[0][0][len('texture:'):]
tex = textures[fn]
part = canvas.draw_line_graph(chunk)
canvas.paint_texture_nearest(tex, part)
# If tail is at the end exit the loop...
if tail+1 >= len(chain): break
# Move head along for the next chunk...
to_move = dist * self.factor
while to_move>0.0 and head+2<len(chain):
head += 1
v1 = lg.get_vertex(chain[head-1])
v2 = lg.get_vertex(chain[head])
offset = numpy.sqrt((v1[0]-v2[0])**2 + (v1[1]-v2[1])**2)
length -= offset
to_move -= offset
# Return the final line graph...
ret = LineGraph()
ret.from_many(*chunks)
return ret
def render(lg,
border=8,
textures=TextureCache(),
cleverness=0,
radius_growth=3.0,
stretch_weight=0.5,
edge_weight=0.5,
smooth_weight=2.0,
alpha_weight=1.0,
unary_mult=1.0,
overlap_weight=0.0,
use_linear=True):
"""Given a line_graph this will render it, returning a numpy array that represents an image (As the first element in a tuple - second element is how many graph cut problems it solved.). It will transform the entire linegraph to obtain a suitable border. The cleverness parameter indicates how it merges the many bits - 0 means last layer (stupid), 1 means averaging; 2 selecting a border using max flow; 3 using graph cuts to take into account weight as well."""
# Setup the compositor...
comp = Composite()
min_x, max_x, min_y, max_y = lg.get_bounds()
do_transform = False
offset_x = 0.0
offset_y = 0.0
if min_x < border:
do_transform = True
offset_x = border - min_x
if min_y < border:
do_transform = True
offset_y = border - min_y
if do_transform:
hg = numpy.eye(3, dtype=numpy.float32)
hg[0, 2] = offset_x
hg[1, 2] = offset_y
lg.transform(hg)
max_x += offset_x
max_y += offset_y
comp.set_size(int(max_x + border), int(max_y + border))
# Break the lg into segments, as each can have its own image - draw & paint each in turn...
lg.segment()
duplicate_sets = dict()
for s in xrange(lg.segments):
slg = LineGraph()
slg.from_segment(lg, s)
part = comp.draw_line_graph(slg, radius_growth, stretch_weight)
done = False
fn = filter(lambda t: t[0].startswith('texture:'), slg.get_tags())
if len(fn) != 0: fn = fn[0][0][len('texture:'):]
else: fn = None
for pair in filter(lambda t: t[0].startswith('duplicate:'),
slg.get_tags()):
key = pair[0][len('duplicate:'):]
if key in duplicate_sets: duplicate_sets[key].append(part)
else: duplicate_sets[key] = [part]
tex = textures[fn]
if tex is not None:
if use_linear:
comp.paint_texture_linear(tex, part)
else:
comp.paint_texture_nearest(tex, part)
done = True
if not done:
comp.paint_test_pattern(part)
# Bias towards pixels that are opaque...
comp.inc_weight_alpha(alpha_weight)
# Arrange for duplicate pairs to have complete overlap, by adding transparent pixels, so graph cuts doesn't create a feather effect...
if overlap_weight > 1e-6:
for values in duplicate_sets.itervalues():
for i, part1 in enumerate(values):
for part2 in values[i:]:
comp.draw_pair(part1, part2, overlap_weight)
# If requested use maxflow to find optimal cuts, to avoid any real blending...
count = 0
if cleverness == 2:
count = comp.maxflow_select(edge_weight, smooth_weight, maxflow)
elif cleverness == 3:
count = comp.graphcut_select(edge_weight, smooth_weight, unary_mult,
maxflow)
if cleverness == 0:
render = comp.render_last()
else:
render = comp.render_average()
# Return the rendered image (If cleverness==0 this will actually do some averaging, otherwise it will just create an image)...
return render, count
def convert(self,
lg,
choices=1,
adv_match=False,
textures=TextureCache(),
memory=0):
"""Given a line graph this chops it into chunks, matches each chunk to the database of chunks and returns a new line graph with these chunks instead of the original. Output will involve heavy overlap requiring clever blending. choices is the number of options it select from the db - it grabs this many closest to the requirements and then randomly selects from them. If adv_match is True then instead of random selection from the choices it does a more advanced match, and select the best match in terms of colour distance from already-rendered chunks. This option is reasonably expensive. memory is how many recently use chunks to remember, to avoid repetition."""
if memory > (choices - 1):
memory = choices - 1
# If we have no data just return the input...
if self.empty(): return lg
# Check if the indexing structure is valid - if not create it...
if self.kdtree == None:
data = numpy.array(map(lambda p: self.feature_vect(p[0], p[1]),
self.chunks),
dtype=numpy.float)
self.kdtree = scipy.spatial.cKDTree(data, 4)
# Calculate the radius scaler and distance for this line graph, by calculating the median radius...
rads = map(lambda i: lg.get_vertex(i)[5], xrange(lg.vertex_count))
rads.sort()
median_radius = rads[len(rads) // 2]
radius_mult = 1.0 / median_radius
dist = self.dist * median_radius
# Create the list into which we dump all the chunks that will make up the return...
chunks = []
temp = LineGraph()
# List of recently used chunks, to avoid obvious patterns...
recent = []
# If advanced match we need a Composite of the image thus far, to compare against...
if adv_match:
canvas = Composite()
min_x, max_x, min_y, max_y = lg.get_bounds()
canvas.set_size(int(max_x + 8), int(max_y + 8))
# Iterate the line graph, choping it into chunks and matching a chunk to each chop...
for chain in lg.chains():
head = 0
tail = 0
length = 0.0
while True:
# Move tail so its long enough, or has reached the end...
while length < dist and tail + 1 < len(chain):
tail += 1
v1 = lg.get_vertex(chain[tail - 1])
v2 = lg.get_vertex(chain[tail])
length += numpy.sqrt((v1[0] - v2[0])**2 +
(v1[1] - v2[1])**2)
# Extract a feature vector for this chunk...
temp.from_vertices(lg, chain[head:tail + 1])
fv = self.feature_vect(temp, median_radius)
# Select a chunk from the database...
if choices == 1:
selected = self.kdtree.query(fv)[1]
orig_chunk = self.chunks[selected]
else:
options = list(self.kdtree.query(fv, choices)[1])
options = filter(lambda v: v not in recent, options)
if not adv_match:
selected = random.choice(options)
orig_chunk = self.chunks[selected]
else:
cost = 1e64 * numpy.ones(len(options))
for i, option in enumerate(options):
fn = filter(lambda t: t[0].startswith('texture:'),
self.chunks[option][0].get_tags())
if len(fn) != 0:
fn = fn[0][0][len('texture:'):]
tex = textures[fn]
chunk = LineGraph()
chunk.from_many(self.chunks[option][0])
chunk.morph_to(lg, chain[head:tail + 1])
part = canvas.draw_line_graph(chunk)
cost[i] = canvas.cost_texture_nearest(
tex, part)
selected = options[numpy.argmin(cost)]
orig_chunk = self.chunks[selected]
# Update recent list...
recent.append(selected)
if len(recent) > memory:
recent.pop(0)
# Distort it to match the source line graph...
chunk = LineGraph()
chunk.from_many(orig_chunk[0])
chunk.morph_to(lg, chain[head:tail + 1])
# Record it for output...
chunks.append(chunk)
# If advanced matching is on write it out to canvas, so future choices will take it into account...
if adv_match:
fn = filter(lambda t: t[0].startswith('texture:'),
chunk.get_tags())
if len(fn) != 0:
fn = fn[0][0][len('texture:'):]
tex = textures[fn]
part = canvas.draw_line_graph(chunk)
canvas.paint_texture_nearest(tex, part)
# If tail is at the end exit the loop...
if tail + 1 >= len(chain): break
# Move head along for the next chunk...
to_move = dist * self.factor
while to_move > 0.0 and head + 2 < len(chain):
head += 1
v1 = lg.get_vertex(chain[head - 1])
v2 = lg.get_vertex(chain[head])
offset = numpy.sqrt((v1[0] - v2[0])**2 +
(v1[1] - v2[1])**2)
length -= offset
to_move -= offset
# Return the final line graph...
ret = LineGraph()
ret.from_many(*chunks)
return ret
def render(lg, border = 8, textures = TextureCache(), cleverness = 0, radius_growth = 3.0, stretch_weight = 0.5, edge_weight = 0.5, smooth_weight = 2.0, alpha_weight = 1.0, unary_mult = 1.0, overlap_weight = 0.0, use_linear = True):
"""Given a line_graph this will render it, returning a numpy array that represents an image (As the first element in a tuple - second element is how many graph cut problems it solved.). It will transform the entire linegraph to obtain a suitable border. The cleverness parameter indicates how it merges the many bits - 0 means last layer (stupid), 1 means averaging; 2 selecting a border using max flow; 3 using graph cuts to take into account weight as well."""
# Setup the compositor...
comp = Composite()
min_x, max_x, min_y, max_y = lg.get_bounds()
do_transform = False
offset_x = 0.0
offset_y = 0.0
if min_x<border:
do_transform = True
offset_x = border-min_x
if min_y<border:
do_transform = True
offset_y = border-min_y
if do_transform:
hg = numpy.eye(3, dtype=numpy.float32)
hg[0,2] = offset_x
hg[1,2] = offset_y
lg.transform(hg)
max_x += offset_x
max_y += offset_y
comp.set_size(int(max_x+border), int(max_y+border))
# Break the lg into segments, as each can have its own image - draw & paint each in turn...
lg.segment()
duplicate_sets = dict()
for s in xrange(lg.segments):
slg = LineGraph()
slg.from_segment(lg, s)
part = comp.draw_line_graph(slg, radius_growth, stretch_weight)
done = False
fn = filter(lambda t: t[0].startswith('texture:'), slg.get_tags())
if len(fn)!=0: fn = fn[0][0][len('texture:'):]
else: fn = None
for pair in filter(lambda t: t[0].startswith('duplicate:'), slg.get_tags()):
key = pair[0][len('duplicate:'):]
if key in duplicate_sets: duplicate_sets[key].append(part)
else: duplicate_sets[key] = [part]
tex = textures[fn]
if tex!=None:
if use_linear:
comp.paint_texture_linear(tex, part)
else:
comp.paint_texture_nearest(tex, part)
done = True
if not done:
comp.paint_test_pattern(part)
# Bias towards pixels that are opaque...
comp.inc_weight_alpha(alpha_weight)
# Arrange for duplicate pairs to have complete overlap, by adding transparent pixels, so graph cuts doesn't create a feather effect...
if overlap_weight>1e-6:
for values in duplicate_sets.itervalues():
for i, part1 in enumerate(values):
for part2 in values[i:]:
comp.draw_pair(part1, part2, overlap_weight)
# If requested use maxflow to find optimal cuts, to avoid any real blending...
count = 0
if cleverness==2:
count = comp.maxflow_select(edge_weight, smooth_weight, maxflow)
elif cleverness==3:
count = comp.graphcut_select(edge_weight, smooth_weight, unary_mult, maxflow)
if cleverness==0:
render = comp.render_last()
else:
render = comp.render_average()
# Return the rendered image (If cleverness==0 this will actually do some averaging, otherwise it will just create an image)...
return render, count