def add(self, fn):
"""Given a filename for a linegraph file this loads it, extracts all chunks and stores them in the db."""
if fn in self.fnl: return 0
self.fnl.append(fn)
self.kdtree = None
# Load the LineGraph from the given filename...
data = ply2.read(fn)
lg = LineGraph()
lg.from_dict(data)
texture = os.path.normpath(os.path.join(os.path.dirname(fn), data['meta']['image']))
# 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
# Chop it up into chains, extract chunks and store them in the database...
ret = 0
for raw_chain in lg.chains():
for chain in filter(lambda c: len(c)>1, [raw_chain, raw_chain[::-1]]):
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)
# Create the chunk...
chunk = LineGraph()
chunk.from_vertices(lg, chain[head:tail+1])
# Tag it...
chunk.add_tag(0, 0.1, 'file:%s'%fn)
chunk.add_tag(0, 0.2, 'texture:%s'%texture)
# Store it...
self.chunks.append((chunk, median_radius))
ret += 1
# 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 ret
def add(self, fn):
"""Given a filename for a linegraph file this loads it, extracts all chunks and stores them in the db."""
if fn in self.fnl: return 0
self.fnl.append(fn)
self.kdtree = None
# Load the LineGraph from the given filename...
data = ply2.read(fn)
lg = LineGraph()
lg.from_dict(data)
texture = os.path.normpath(
os.path.join(os.path.dirname(fn), data['meta']['image']))
# 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
# Chop it up into chains, extract chunks and store them in the database...
ret = 0
for raw_chain in lg.chains():
for chain in filter(lambda c: len(c) > 1,
[raw_chain, raw_chain[::-1]]):
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)
# Create the chunk...
chunk = LineGraph()
chunk.from_vertices(lg, chain[head:tail + 1])
# Tag it...
chunk.add_tag(0, 0.1, 'file:%s' % fn)
chunk.add_tag(0, 0.2, 'texture:%s' % texture)
# Store it...
self.chunks.append((chunk, median_radius))
ret += 1
# 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 ret
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 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