def reframe(self):
TwoDoubles = ctypes.c_double * 2
b_min = TwoDoubles()
b_max = TwoDoubles()
b_eps = 0.01
nsamples = 10000
genome = Genome.from_string(self.to_string(False))[0]
flam3_estimate_bounding_box(genome, b_eps, nsamples, b_min, b_max, RandomContext())
#print "%f %f" % ((b_min[0]+b_max[0])/2,(b_min[1]+b_max[1])/2)
bxoff = (b_min[0]+b_max[0])/2
if abs(bxoff)<5:
self.x_offset = bxoff
byoff = (b_min[1]+b_max[1])/2
if abs(byoff)<5:
self.y_offset = byoff
denom = min(b_max[1]-b_min[1],b_max[0]-b_min[0])
if denom==0:
tmpscale = 0.0
else:
tmpscale = 0.7 * 100.0/min(b_max[1]-b_min[1],b_max[0]-b_min[0])
if tmpscale<10:
self.scale = 10
elif tmpscale>100:
self.scale = 100
else:
self.scale = tmpscale
def reframe(self):
TwoDoubles = ctypes.c_double * 2
b_min = TwoDoubles()
b_max = TwoDoubles()
b_eps = 0.1
nsamples = 10000
genome = Genome.from_string(self.to_string(False))[0]
flam3_estimate_bounding_box(genome, b_eps, nsamples, b_min, b_max,
RandomContext())
bxoff = (b_min[0] + b_max[0]) / 2
if abs(bxoff) < 5:
self.x_offset = bxoff
byoff = (b_min[1] + b_max[1]) / 2
if abs(byoff) < 5:
self.y_offset = byoff
denom = min(b_max[1] - b_min[1], b_max[0] - b_min[0])
if denom == 0:
tmpscale = 0.0
else:
tmpscale = 0.4 * 100.0 / min(b_max[1] - b_min[1],
b_max[0] - b_min[0])
if tmpscale < 10:
self.scale = 10
elif tmpscale > 100:
self.scale = 100
else:
self.scale = tmpscale
def DrawHistogram(self, dc=None):
""" Create and draw the color histogram."""
dc = dc or wx.ClientDC(self)
genome = Genome.from_string(self.parent.flame.to_string(True))[0]
array = (c_double *256)()
flam3_colorhist(genome, 1, array)
dc.DrawLines([(i*1.5, 30-j*500) for i,j in enumerate(array)], 2, 2)
def load_flame(self, flame_origin, flame_target=None, lerp=0.0):
loaded_flame = self.flame
try:
if (lerp == 0.0 or flame_target is None):
loaded_flame = flame_origin
elif (lerp >= 1.0 or flame_origin is None):
loaded_flame = flame_target
else:
# interpolation:
flame_origin.time = 0
flame_target.time = 1
flames_lerp = [flame_origin, flame_target]
flames_str = "<flames>%s</flames>" % "".join(
map(flame_to_string, flames_lerp))
genomes, ngenomes = Genome.from_string(flames_str)
targetflame = Genome()
flam3_interpolate(genomes, ngenomes, lerp, 0,
byref(targetflame))
loaded_flame = Flame(targetflame.to_string())
except Exception as ex:
print('[!] error during interpolation at %s: %s' % (lerp, str(ex)))
traceback.print_exc()
return None
return loaded_flame
def var_preview(self, xform, range, numvals, depth):
result = (c_double * (2* (2*numvals+1)**2))()
genome = Genome.from_string(xform._parent.to_string(True))[0]
index = xform.index
if index is None:
index = genome.final_xform_index
flam3_xform_preview(genome, index, range, numvals, depth,
result, RandomContext())
return [(x,-y) for x,y in zip(*[iter(result)]*2)]
def interpolate(flames):
s = "<flames>%s</flames>" % "".join(map(to_string, flames))
genomes, ngenomes = Genome.from_string(s)
target = Genome()
for i in range(int(flames[0].time), int(flames[-1].time + 1)):
flam3_interpolate(genomes, ngenomes, i, 0, byref(target))
flame = Flame(target.to_string())
flame.name = "morphed_%04d" % i
yield flame
def __init__(self, xform, Color):
self.genome = Genome.from_string(xform._parent.to_string(True))[0][0]
xform = xform._parent if xform.ispost() else xform
self.index = xform.index
if self.index is None:
self.index = self.genome.final_xform_index
kwds = config["Xform-Preview-Settings"].copy()
depth = kwds.pop("depth")
self.objects = [FC.PointSet(self.var_preview(depth=i + 1, **kwds),
Color=tuple(c/(depth - i) for c in Color))
for i in range(depth)]
def Update(self, flame=None):
flame = flame or self.parent.flame
# Make the gradient image
img = wx.ImageFromBuffer(256, 1, flame.gradient.to_buffer())
img.Rescale(384, 50)
self.bmp = wx.BitmapFromImage(img)
# Calculate the color histogram
genome = Genome.from_string(flame.to_string(omit_details=True))[0]
flam3_colorhist(genome, 3, RandomContext(), self.colorhist_array)
self.Refresh()
def Update(self, flame=None):
flame = flame or self.parent.flame
# Make the gradient image
img = wx.ImageFromBuffer(256, 1, flame.gradient.to_buffer())
img.Rescale(384, 50)
self.bmp = wx.BitmapFromImage(img)
# Calculate the color histogram
genome = Genome.from_string(flame.to_string(omit_details=True))[0]
flam3_colorhist(genome, 3, RandomContext(), self.colorhist_array)
self.Refresh()
def __init__(self, xform, Color):
self.genome = Genome.from_string(xform._parent.to_string(True))[0][0]
xform = xform._parent if xform.ispost() else xform
self.index = xform.index
if self.index is None:
self.index = self.genome.final_xform_index
kwds = config["Xform-Preview-Settings"].copy()
depth = kwds.pop("depth")
self.objects = [
FC.PointSet(self.var_preview(depth=i + 1, **kwds),
Color=tuple(c / (depth - i) for c in Color))
for i in range(depth)
]
def flam3_render(flame, size, quality, **kwds):
"""Passes render requests on to flam3."""
flame = flame if type(flame) is Flame else Flame(flame)
# try:
genome = Genome.from_string(flame.to_string())[0]
# except Exception:
# raise ValueError("Error while parsing flame string.")
width,height = size
try:
genome.pixels_per_unit /= genome.width/float(width) # Adjusts scale
except ZeroDivisionError:
raise ZeroDivisionError("Size passed to render function is 0.")
genome.width = width
genome.height = height
genome.sample_density = quality
output_buffer, stats = genome.render(**kwds)
return output_buffer
from fr0stlib.pyflam3 import flam3_colorhist, Genome, flam3_xform_preview, RandomContext from ctypes import c_double genome = Genome.from_string(self.flame.to_string())[0] #array = (c_double *256)() #flam3_colorhist(genome, 1, array) array = (c_double *100)() flam3_xform_preview(genome, 0, 1, 3, 1, array, RandomContext()) print array[:]