def generate_texture(self):
text = np.zeros((self.size[0], self.size[1]))
if self.texture_type == BGTextureType.Plain:
for i in xrange(self.size[0]):
for j in xrange(self.size[1]):
text[i][j] = 0
elif self.texture_type == BGTextureType.Perlin:
perlin = PerlinNoiseGenerator(self.texture.shape[0],
self.texture.shape[1])
text = perlin.get_background_noise()
elif self.texture_type == BGTextureType.HilbertCurve:
hcurves = HilbertCurves(self.size[0], self.size[1],
self.hilbert_level)
self.texture = hcurves.gen_hilbert_curve()
if self.corruptor is not None:
text = self.corruptor(text)
return text
class SpritePlacer(object):
"""
Defines a dataset composed of a sequence of images of the
specified dimensions (``gen.w`` by ``gen.h`` pixels). Each image
contains a certain number of binary sprites or "sprites" scattered
about. The ``gen`` parameter must be an iterator or an iterable
such that:
iter(gen) -> iterator of
((((x1, y1), sprite1), ((x2, y2), sprite2), ...), target)
Where ``spriten`` is a :class:`Sprite` object, ``0 <= xn < w``, ``0 <=
yn < h``. ``SpritePlacer`` will generate a scene where the top left
pixel of the nth sprite is positioned at ``(xn, yn)``
coordinates. ``target`` is a tuple, list or vector of numbers
representing the class(es) or target(s) associated to the
scene. Additionally, ``gen`` must have the following attributes:
- ``gen.w`` must be the width of the scene in pixels.
- ``gen.h`` must be the height of the scene in pixels.
- ``gen.nout`` must be the length of the target
- ``gen.nclasses`` must be either ``None`` or a tuple, list or
vectors of integers or ``None``, an integer at the nth position
meaning that the nth target is a class identifier from 0 included
to ``nclasses[n]`` excluded, ``None`` meaning that the nth target
- ``gen.out_format`` must be 'array'
- ``gen.out_dtype`` must be the dtype of the targets generated by
iterating over ``iter(gen)`` (typically 'uint8', if the
target(s) is(are) classes).
The ``spriteland.gen`` module contains generators corresponding to
this interface.
If ``collision_check`` is set to ``True``, no sprites' masks will
overlap. This is done by rejection sampling: gen will be iterated
over until it produces sprites at positions that do not lead to
overlaps. For small images with large sprites or a lot of sprites, the
rejection rate might be high. SpritePlacer provides a method,
``self.rejection_rate()`` to have an estimate of the rejection
rate for the samples generated up to the point the method is
called. A warning will be issued during the generation of the very
first sample if it takes more than 1000 tries, to help identify
problems with placement.
Another issue to be aware of with ``collision_check`` and
rejection sampling is that the distribution of scenes might be
biased as a result. For instance, if some sprites are larger than
some others, scenes with them might be rejected at a greater
frequency. Assymetrical sprites might be found more often at one side
or corner than at the others, and so on. These biases might make
learning easier. For the time being SpritePlacer does not give
feedback to the generator that might help it doing a counter-bias
to guarantee certain important statistics. It is recommended to at
least verify the distribution of classes.
The dataset can be iterated over and yields (input, target) where
input and target are vectors.
"""
def __init__(self, gen, collision_check=False, enable_perlin=False):
self.gen = gen
self.collision_check = collision_check
self.use_patch_centers = gen.use_patch_centers
self.enable_perlin = enable_perlin
self.w = gen.w
self.h = gen.h
self.nin = gen.w * gen.h
assert gen.out_format == 'array'
self.out_format = gen.out_format
self.out_dtype = gen.out_dtype
self.nout = gen.nout
self.nclasses = gen.nclasses
self.n_successes = 0
self.n_rejections = 0
if enable_perlin:
self.perlin_gen = PerlinNoiseGenerator(self.w, self.h, 32)
def rejection_rate(self):
"""
If collision_check is True, returns the rejection rate of the
generator, which is the number of scenes that were rejected
because of overlaps divided by the total number of scenes
considered.
"""
total = self.n_successes + self.n_rejections
if total == 0:
return None
else:
return float(self.n_rejections) / total
def __iter__(self):
return copy.copy(self)
def next(self):
nrows_patches = self.gen.nrows_patches
ncols_patches = self.gen.ncols_patches
nelems = nrows_patches * ncols_patches
gen = self.gen
if self.collision_check:
collider = numpy.zeros((self.h, self.w))
#while True:
if self.enable_perlin:
data = self.perlin_gen.get_background_noise()
else:
data = numpy.zeros((self.h, self.w))
targets = numpy.zeros((self.nout, ), dtype=self.out_dtype)
if self.use_patch_centers:
object_presences = numpy.zeros(nelems)
object_presences.fill(-1)
if not self.collision_check:
description, target = gen.next()
for (x, y), sprite in description:
if self.use_patch_centers:
#Center the object in the patch
ystart = y - (sprite.h / 2)
yend = y + (sprite.h / 2)
xstart = x - (sprite.w / 2)
xend = x + (sprite.w / 2)
if sprite.h % 2 == 1:
ystart = y - (sprite.h / 2) - 1
yend = y + (sprite.h / 2)
if sprite.w % 2 == 1:
xstart = x - (sprite.w / 2) - 1
xend = x + (sprite.w / 2)
data[ystart:yend, xstart:xend] = sprite.textured_patch
idx = 0
for center in self.gen.patch_centers:
if numpy.array_equal(center, [x, y]):
break
idx += 1
object_presences[idx] = self.gen.spritenames.index(
sprite.name)
else:
data[y:y + sprite.h,
x:x + sprite.w] = sprite.textured_patch
targets[:] = target
self.n_successes += 1
else:
ntrials = 0
while True:
if self.n_successes == 0 and ntrials and ntrials % 1000 == 0:
print >> sys.stderr, "WARNING: Rejected %i candidates, could not yet fit a single scene together. You might want to generate larger images." % ntrials
ntrials += 1
description, target = gen.next()
collider.fill(0)
for (x, y), sprite in description:
a, b = y - sprite.marginh, y + sprite.h + sprite.marginh
c, d = x - sprite.marginw, x + sprite.w + sprite.marginw
if a < 0 or b >= self.h or c < 0 or d >= self.w:
ba, bb = (a < 0) * -a, sprite.mh - (b >= self.h) * (
b - self.h + 1)
bc, bd = (c < 0) * -c, sprite.mw - (d >= self.w) * (
d - self.w + 1)
a, b = max(0, a), min(b, self.h - 1)
c, d = max(0, c), min(d, self.w - 1)
collider[a:b, c:d] += sprite.mask[ba:bb, bc:bd]
else:
collider[a:b, c:d] += sprite.mask
if numpy.any(collider > 255) or numpy.any(
collider == 2) or numpy.any(collider == 3):
self.n_rejections += 1
continue
else:
for (x, y), sprite in description:
if self.use_patch_centers:
# the x,y is the
# upper left corner of
# the object.
ystart = y
yend = y + (sprite.h)
xstart = x
xend = x + (sprite.w)
#Center the object in the patch
if self.gen.center_objects:
ystart = y - \
int(math.ceil(sprite.h / 2))
yend = y + \
int(math.ceil(sprite.h / 2))
xstart = x - \
int(math.ceil(sprite.w / 2))
xend = x + \
int(math.ceil(sprite.w / 2))
data[ystart:yend,
xstart:xend] = sprite.textured_patch
idx = 0
for center in self.gen.patch_centers:
if numpy.array_equal(center, [x, y]):
break
idx += 1
object_presences[idx] = self.gen.spritenames.index(
sprite.name)
else:
data[y:y + sprite.h,
x:x + sprite.w] = sprite.textured_patch
targets[:] = target
self.n_successes += 1
break
if self.use_patch_centers:
return data.reshape(self.nin), targets, object_presences
else:
return data.reshape(self.nin), targets
class SpritePlacer(object):
"""
Defines a dataset composed of a sequence of images of the
specified dimensions (``gen.w`` by ``gen.h`` pixels). Each image
contains a certain number of binary sprites or "sprites" scattered
about. The ``gen`` parameter must be an iterator or an iterable
such that:
iter(gen) -> iterator of
((((x1, y1), sprite1), ((x2, y2), sprite2), ...), target)
Where ``spriten`` is a :class:`Sprite` object, ``0 <= xn < w``, ``0 <=
yn < h``. ``SpritePlacer`` will generate a scene where the top left
pixel of the nth sprite is positioned at ``(xn, yn)``
coordinates. ``target`` is a tuple, list or vector of numbers
representing the class(es) or target(s) associated to the
scene. Additionally, ``gen`` must have the following attributes:
- ``gen.w`` must be the width of the scene in pixels.
- ``gen.h`` must be the height of the scene in pixels.
- ``gen.nout`` must be the length of the target
- ``gen.nclasses`` must be either ``None`` or a tuple, list or
vectors of integers or ``None``, an integer at the nth position
meaning that the nth target is a class identifier from 0 included
to ``nclasses[n]`` excluded, ``None`` meaning that the nth target
- ``gen.out_format`` must be 'array'
- ``gen.out_dtype`` must be the dtype of the targets generated by
iterating over ``iter(gen)`` (typically 'uint8', if the
target(s) is(are) classes).
The ``spriteland.gen`` module contains generators corresponding to
this interface.
If ``collision_check`` is set to ``True``, no sprites' masks will
overlap. This is done by rejection sampling: gen will be iterated
over until it produces sprites at positions that do not lead to
overlaps. For small images with large sprites or a lot of sprites, the
rejection rate might be high. SpritePlacer provides a method,
``self.rejection_rate()`` to have an estimate of the rejection
rate for the samples generated up to the point the method is
called. A warning will be issued during the generation of the very
first sample if it takes more than 1000 tries, to help identify
problems with placement.
Another issue to be aware of with ``collision_check`` and
rejection sampling is that the distribution of scenes might be
biased as a result. For instance, if some sprites are larger than
some others, scenes with them might be rejected at a greater
frequency. Assymetrical sprites might be found more often at one side
or corner than at the others, and so on. These biases might make
learning easier. For the time being SpritePlacer does not give
feedback to the generator that might help it doing a counter-bias
to guarantee certain important statistics. It is recommended to at
least verify the distribution of classes.
The dataset can be iterated over and yields (input, target) where
input and target are vectors.
"""
def __init__(self, gen, collision_check = False, enable_perlin = False):
self.gen = gen
self.collision_check = collision_check
self.use_patch_centers = gen.use_patch_centers
self.enable_perlin = enable_perlin
self.w = gen.w
self.h = gen.h
self.nin = gen.w * gen.h
assert gen.out_format == 'array'
self.out_format = gen.out_format
self.out_dtype = gen.out_dtype
self.nout = gen.nout
self.nclasses = gen.nclasses
self.n_successes = 0
self.n_rejections = 0
if enable_perlin:
self.perlin_gen = PerlinNoiseGenerator(self.w, self.h, 32)
def rejection_rate(self):
"""
If collision_check is True, returns the rejection rate of the
generator, which is the number of scenes that were rejected
because of overlaps divided by the total number of scenes
considered.
"""
total = self.n_successes + self.n_rejections
if total == 0:
return None
else:
return float(self.n_rejections) / total
def __iter__(self):
nrows_patches = self.gen.nrows_patches
ncols_patches = self.gen.ncols_patches
nelems = nrows_patches * ncols_patches
gen = iter(self.gen)
if self.collision_check:
collider = numpy.zeros((self.h, self.w))
while True:
if self.enable_perlin:
data = self.perlin_gen.get_background_noise()
else:
data = numpy.zeros((self.h, self.w))
targets = numpy.zeros((self.nout,), dtype = self.out_dtype)
if self.use_patch_centers:
object_presences = numpy.zeros(nelems)
object_presences.fill(-1)
if not self.collision_check:
description, target = gen.next()
for (x, y), sprite in description:
if self.use_patch_centers:
#Center the object in the patch
ystart = y - (sprite.h / 2)
yend = y + (sprite.h / 2)
xstart = x - (sprite.w / 2)
xend = x + (sprite.w / 2)
if sprite.h % 2 == 1:
ystart = y - (sprite.h / 2) - 1
yend = y + (sprite.h / 2)
if sprite.w % 2 == 1:
xstart = x - (sprite.w / 2) - 1
xend = x + (sprite.w / 2)
data[ystart: yend, xstart: xend] = sprite.textured_patch
idx = 0
for center in self.gen.patch_centers:
if numpy.array_equal(center, [x, y]):
break
idx +=1
object_presences[idx] = self.gen.spritenames.index(sprite.name)
else:
data[y:y + sprite.h, x:x + sprite.w] = sprite.textured_patch
targets[:] = target
self.n_successes += 1
else:
ntrials = 0
while True:
if self.n_successes == 0 and ntrials and ntrials % 1000 == 0:
print >> sys.stderr, "WARNING: Rejected %i candidates, could not yet fit a single scene together. You might want to generate larger images." % ntrials
ntrials += 1
description, target = gen.next()
collider.fill(0)
for (x, y), sprite in description:
a, b = y - sprite.marginh, y + sprite.h + sprite.marginh
c, d = x - sprite.marginw, x + sprite.w + sprite.marginw
if a < 0 or b >= self.h or c < 0 or d >= self.w:
ba, bb = (a < 0)*-a, sprite.mh - (b >= self.h) * (b - self.h + 1)
bc, bd = (c < 0)*-c, sprite.mw - (d >= self.w) * (d - self.w + 1)
a, b = max(0, a), min(b, self.h - 1)
c, d = max(0, c), min(d, self.w - 1)
collider[a:b, c:d] += sprite.mask[ba:bb, bc:bd]
else:
collider[a:b, c:d] += sprite.mask
if numpy.any(collider > 255) or numpy.any(collider == 2) or numpy.any(collider == 3):
self.n_rejections += 1
continue
else:
for (x, y), sprite in description:
if self.use_patch_centers:
# the x,y is the
# upper left corner of
# the object.
ystart = y
yend = y + (sprite.h)
xstart = x
xend = x + (sprite.w)
#Center the object in the patch
if self.gen.center_objects:
ystart = y - \
int(math.ceil(sprite.h / 2))
yend = y + \
int(math.ceil(sprite.h / 2))
xstart = x - \
int(math.ceil(sprite.w / 2))
xend = x + \
int(math.ceil(sprite.w / 2))
data[ystart: yend, xstart: xend] = sprite.textured_patch
idx = 0
for center in self.gen.patch_centers:
if numpy.array_equal(center, [x, y]):
break
idx +=1
object_presences[idx] = self.gen.spritenames.index(sprite.name)
else:
data[y: y + sprite.h, x: x + sprite.w] = sprite.textured_patch
targets[:] = target
self.n_successes += 1
break
if self.use_patch_centers:
yield data.reshape(self.nin), targets, object_presences
else:
yield data.reshape(self.nin), targets