gen = sgaGenotype(
[
range(dxy, 200 - dxy),
range(dxy, 200 - dxy) # polygon center x,y coordinates
,
range(-dxy, dxy + 1),
range(-dxy, dxy + 1) # polygon 1 point dx,dy \
,
range(-dxy, dxy + 1),
range(-dxy, dxy + 1) # polygon 2 point dx,dy \
,
range(-dxy, dxy + 1),
range(-dxy, dxy + 1) # polygon 3 point dx,dy \
,
range(-dxy, dxy + 1),
range(-dxy, dxy + 1) # polygon 4 point dx,dy \
,
[x
for x in range(220) if x % 51 == 0] # polygon fill color R component\
,
[x
for x in range(220) if x % 51 == 0] # polygon fill color G component\
,
[x for x in range(220) if x % 51 == 0]
] # polygon fill color B component\
,
100 # Number of polygons\
,
MeasureColorDifference # Fitness function, for determining picture quality\
,
BetterFoundNotify # Notify function, informs when better solution is found\
,
Minimize=True) # Do we need to minimize or maximize fitness function
gr[0].Genes[n] + gr[2].Genes[n], \
gr[1].Genes[n] + gr[3].Genes[n]], fill = gr[4].Genes[n], width = gr[5].Genes[n] )
return im
def MeasureColorDifference(gen,n):
global data
dif = 0
for co,cg in zip(data,list(ConvertToImage(gen,n).getdata())):
dif+=abs(co-cg)
return dif
def BetterFoundNotify(gen, it):
global cycles
print "Iteration",it,"of",cycles,';','Fitness',gen.Fitness[2]
ConvertToImage(gen,2).save('/tmp/0000'+str(it)+'.png')
data = list(Image.open("LenaG.png").getdata()) # 200x200 pixels Lena image
cycles = 100000 # How much iterations do we try
dxy = 20
gen = sgaGenotype([range(dxy,200-dxy),range(dxy,200-dxy) # line start x,y \
,range(-dxy,dxy+1),range(-dxy,dxy+1) # line dx,dy \
, [x for x in range(220) if x%5 == 0] # line grayscale color\
, range(1,8) ] # line width in pixels\
, 200 # Number of lines\
, MeasureColorDifference # Fitness function, for determining picture quality\
, BetterFoundNotify # Notify function, informs when better solution is found\
, Minimize = True) # Do we need to minimize or maximize fitness function
gen.Evolve(cycles)
print "Done. Now you can go to drink coffee."
global data
dif = 0
for co,cg in zip(data,list(ConvertToImage(gen,n).getdata())):
for x,y in zip(co,cg):
dif+=abs(x-y)
return dif
def BetterFoundNotify(gen, it):
global cycles
print "Iteration",it,"of",cycles,';','Fitness',gen.Fitness[2]
ConvertToImage(gen,2).save('/tmp/0000'+str(it)+'.png')
data = list(Image.open("LenaC.png").getdata()) # 200x200 pixels Lena image
cycles = 100000 # How much iterations do we try
dxy = 50
gen = sgaGenotype([range(dxy,200-dxy),range(dxy,200-dxy) # polygon center x,y coordinates
,range(-dxy,dxy+1),range(-dxy,dxy+1) # polygon 1 point dx,dy \
,range(-dxy,dxy+1),range(-dxy,dxy+1) # polygon 2 point dx,dy \
,range(-dxy,dxy+1),range(-dxy,dxy+1) # polygon 3 point dx,dy \
,range(-dxy,dxy+1),range(-dxy,dxy+1) # polygon 4 point dx,dy \
, [x for x in range(220) if x%51 == 0] # polygon fill color R component\
, [x for x in range(220) if x%51 == 0] # polygon fill color G component\
, [x for x in range(220) if x%51 == 0] ] # polygon fill color B component\
, 100 # Number of polygons\
, MeasureColorDifference # Fitness function, for determining picture quality\
, BetterFoundNotify # Notify function, informs when better solution is found\
, Minimize = True) # Do we need to minimize or maximize fitness function
gen.Evolve(cycles)
print "Done. Now you can go to drink coffee."
