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starrynight.py
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starrynight.py
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# -*- coding: utf-8 -*-
"""
Created for code golf starry night
http://codegolf.stackexchange.com/questions/69930/paint-starry-night-objectively-in-1kb-of-code
Some really interesting ideas from other posts taken here
- use of a blur to push the final score up a bit
- use of binary code directly in the final python output
- use of a 2D subspace of the color space
"""
import cv2
import matplotlib.pyplot as plt
import numpy as np
from scipy import optimize
from skimage import filters
from skimage.morphology import disk as mcircle
from skimage.filters import rank
import bz2
import zlib
import base64
import sys
import os
import shutil
import time
def score(img,orig):
z=(img.astype(np.float32)-orig.astype(np.float32)).ravel()**2
return np.sum(z)/255./255.
def bound(a,low,high):
return max(min(a,high),low)
def distanceFromBoxSquared(p,b1,b2):
dx=0
dy=0
if p[0]<b1[0]:
dx=b1[0]-p[0]
if p[0]>b2[0]:
dx=max(dx,p[0]-b2[0])
if p[1]<b1[1]:
dy=b1[1]-p[1]
if p[1]>b2[1]:
dy=max(dy,p[1]-b2[1])
return dx*dx+dy*dy
def regularizer(params,orig):
if (len(params)-7)%8 == 0:
circles=params[7:]
blurSize=None
else:
circles=params[8:]
blurSize=params[7]
#print "circles",circles
assert len(circles)%8 == 0
for i in range(0,len(circles),8):
c1=(circles[0+i],circles[1+i])
c2=(c1[0]+circles[2+i],c1[1]+circles[3+i])
d=max(distanceFromBoxSquared(c1,(0,0),(orig.shape[1]-1,orig.shape[0]-1)),
distanceFromBoxSquared(c2,(0,0),(orig.shape[1]-1,orig.shape[0]-1)))
r=circles[6]
if d>r*r:
return 1000
return 0
class artist:
def __init__(self,img):
self.shape=img.shape
self.dtype=img.dtype
self.cache=dict()
# create color subspace
C=np.cov(orig.reshape(orig.shape[0]*orig.shape[1],3).T)
eigenvalues,eigenvectors=np.linalg.eig(C)
repack=sorted([(eigenvalues[i],eigenvectors[:,i]) for i in range(3) ],key=lambda x:-x[0],)
R=np.array([v[1] for v in repack])
self.mtx=R[0:2,:]
self.meancolor=np.round(np.mean(img,(0,1)))
meanoutimage=img.astype(np.int)-self.meancolor
testimage=np.dot(self.mtx,(meanoutimage.reshape(img.shape[0]*img.shape[1],3).T*1.)).T
self.org=np.array([[np.min(testimage[:,0]),np.min(testimage[:,1])]])
self.scl=np.array([99./(np.max(testimage[:,0])-np.min(testimage[:,0])),99./(np.max(testimage[:,1])-np.min(testimage[:,1]))])
scaledimage=np.round((testimage-self.org)*self.scl)
assert(np.max(scaledimage)<100)
assert(np.min(scaledimage)>=0)
self.roff=np.round(np.dot(R.T[:,0:2],self.org.reshape(2,1))+self.meancolor.reshape(3,1)).astype(np.int).reshape(3)
tmp=R.T[:,0:2].copy()
tmp[:,0]/=self.scl[0]
tmp[:,1]/=self.scl[1]
self.rmtx=np.round(tmp,1) #).astype(np.int)
def toColorSubspace(self,color):
assert(len(color)==3)
z=np.round((np.dot(self.mtx,(color-self.meancolor).reshape(3,1))-self.org)*self.scl)
return (z[0,0],z[1,0])
def fromColorSubspace(self,color):
assert(len(color)==2)
return np.clip(np.dot(self.rmtx,np.array(color).reshape(2,1)).reshape(3)+self.roff,0,255)
def doit(self,params):
#print params
# params 0, split level
# params 1,2,3 top color
# params 4,5,6 bottom color
#s=bound(params[0],1,img.shape[0]-2)
if -1 in self.cache and self.cache[-1][0] == params[0:8]:
img=self.cache[-1][1]
else:
self.cache.clear()
img=np.zeros(self.shape,self.dtype)
for c in range(3):
si=params[0] #np.floor(s)
#mantissa=s-si
img[:si,:,c]=params[c+1]
img[si:,:,c]=params[c+4]
self.cache[-1]=(params[0:8],img.copy())
#img[si+1:,:,c]=c2
#img[si,:,c]=c1*mantissa+c2*(1.-mantissa)
# a circle is x, y, radius, r,g,b
if (len(params)-7)%8 == 0:
circles=params[7:]
blurSize=None
else:
circles=params[8:]
blurSize=params[7]
#print "circles",circles
assert len(circles)%8 == 0
for i in range(0,len(circles),8):
circ=circles[i:i+8]
if i in self.cache and self.cache[i][0]==circ:
img=self.cache[i][1]
#print "Usingn cache for",circ
else:
#print "circ",circ
for k in self.cache.keys():
if k>=i:
del self.cache[k]
img=img.copy()
cv2.circle(img,(circ[0],circ[1]),circ[7],self.fromColorSubspace(self.toColorSubspace(circ[4:7])),-1)
for j in range(31):
r=circ[0]+circ[2]*j//30
c=circ[1]+circ[3]*j//30
cv2.circle(img,(r,c),circ[7],self.fromColorSubspace(self.toColorSubspace(circ[4:7])),-1)
self.cache[i]=(circ,img.copy())
# for row in range(max(0,int(circles[0+i]-circles[2+i])-3),min(img.shape[0],int(circles[0+i]+circles[2+i])+3)):
# for col in range(max(0,int(circles[1+i]-circles[2+i])-3),min(img.shape[1],int(circles[1+i]+circles[2+i])+3)):
# dr=circles[0+i]-row
# dc=circles[1+i]-col
# radius=np.sqrt(dr*dr+dc*dc)
# mantissa=bound( (circles[2+i]-radius)/2.+1,0,1)
# oneminus=1.-mantissa
# if mantissa>0:
# for c in range(3):
# img[row,col,c]=np.uint8(oneminus*img[row,col,c]+mantissa*circles[3+i+c])
if not blurSize is None:
return cv2.blur(img,(blurSize,blurSize))
return img.copy()
def makeProgram(self,xopt):
s=""
if (len(xopt)-7)%8 == 0:
circles=xopt[7:]
blurSize=None
else:
circles=xopt[8:]
blurSize=xopt[7]
for i in range(0,len(circles),8):
if i > 1:
s+=","
#print "s:",s
#print tuple(xopt[i:i+6])
c=self.toColorSubspace(circles[i+4:i+7])
s+="(%d,%d,%d,%d,%d,%d,%d)"%(circles[i],circles[i+1],circles[i+2],circles[i+3],c[0],c[1],circles[i+7])
prg ="import cv2,numpy as n\n"
prg+="z=n.ones((320,386,3),n.uint8)\n"
prg+="z[:,:,:]=(%d,%d,%d)\n"%(xopt[1],xopt[2],xopt[3])
prg+="z[%d:,:,:]=(%d,%d,%d)\n"%(xopt[0],xopt[4],xopt[5],xopt[6])
prg+="for p,q,x,y,c,d,r in [%s]:\n"%s
prg+=" for k in range(31):\n"
prg+=" cv2.circle(z,(p+x*k/30,q+y*k/30),r,n.clip((%.1f*c%+.1f*d%+d,%.1f*c%+.1f*d%+d,%.1f*c%+.1f*d%+d),0,255),-1)\n"%(self.rmtx[0,0],self.rmtx[0,1],self.roff[0],self.rmtx[1,0],self.rmtx[1,1],self.roff[1],self.rmtx[2,0],self.rmtx[2,1],self.roff[2])
if not blurSize is None:
prg+="cv2.imwrite('a.png',cv2.blur(z,(%d,%d)))\n"%(blurSize,blurSize)
else:
prg+="cv2.imwrite('a.png',z)\n"
return prg
def integerOptimize1D(f,low,high,step):
currentVal=np.inf
currentReturn=low
for i in range(int(low),int(high),int(step)):
v=f(i)
if v<currentVal:
currentVal=v
currentReturn=i
return currentReturn,currentVal
def localopt(f,params,bounds,include,step):
assert(len(bounds)==len(params))
assert(len(include)==len(params))
lparams=params[:]
stepped=True
cv=np.inf
while stepped:
stepped=False
for i in range(len(lparams)):
if include[i]:
p=lparams[i]
def loc(z):
par=lparams[:]
par[i]=z
return f(par)
l=max(bounds[i][0],p-5*step)
h=min(bounds[i][1],p+5*step)
p2,v2=integerOptimize1D(loc,l,h,step)
if v2<cv:
cv=v2
stepped=True
lparams[i]=p2
if p2==l or p2==h:
params=lparams
break
return lparams
def compressProgram(prg):
j=bz2.compress(prg,9)
k=zlib.compress(prg,9)
def escape(b):
assert len(b)==1
if b == "\0":
return "\\0"
elif b=='\\':
return '\\\\'
elif b=='\'':
return "\\'"
elif b=='\n':
return "\\n"
elif b=='\r':
return "\\r"
elif b==b'\x8e':
return '\\x8e'
else:
return b
def packit(s):
return bytearray("".join([escape(b) for b in s]))
if len(j)<len(k):
cprg= bytearray(b'\xEF\xBB\xBF')
cprg+=bytearray(b"import bz2\n")
# obnoxious to debug
#z=packit(j)
#for i in range(10,len(z)+1):
# if z[i-1] != 92:
# cprg+="a='byte %d is %d,"%(i,z[i-1])+z[:i]+"'\n"
cprg+=bytearray(b"exec(bz2.decompress('")+packit(j)+bytearray(b"'))\n")
else:
cprg= bytearray(b'\xEF\xBB\xBF')
cprg+=bytearray(b"import zlib\n")
cprg+=bytearray(b"exec(zlib.decompress('")+packit(k)+bytearray(b"'))\n")
#print type(cprg)
#if 0 in cprg:
# print "zero in cprg"
#else:
# print "no zero in cprg"
#for i in cprg[:75]:
# if i>32 and i<127:
# print int(i),chr(i)
# else:
# print int(i)
return cprg
# currentbest=f(params)
# localp=params[:]
# while stepped:
# stepped=False
# for p in range(len(params)):
# localp=params[:]
def run(outdir,orig,params0,bounds,iterations):
assert len(params0)==len(bounds)
if os.path.exists(outdir):
shutil.rmtree(outdir)
os.mkdir(outdir)
anArtist=artist(orig)
#real image range
imgmax=np.max(orig,(0,1))
imgmin=np.min(orig,(0,1))
#z=anArtist.doit(params0)
#print z.shape
#plt.imshow(z)
#plt.title("first params")
#plt.show()
scorefile=open(outdir+"/scorefile.txt",'w')
def optimizeme(params):
img=anArtist.doit(params)
s=score(img,orig)+regularizer(params,orig)
return s
#xopt=optimize.fmin_powell(optimizeme,params0,xtol=0.5)
#res=optimize.differential_evolution(optimizeme,bounds)
#xopt=res.x
include=[True,]*len(params0)
xopt=localopt(optimizeme,params0,bounds,include,5)
xopt=localopt(optimizeme,xopt,bounds,include,1)
print "found",xopt
img=anArtist.doit(xopt)
cv2.imwrite(outdir+'/test_001.png',img)
s=score(img,orig)
print "At",time.asctime()
print "0 circles score: %f"%(s)
print >>scorefile,0,s
print "-------------------------------------"
for q in range(iterations):
lastscore=s
itercount=0
filtersize=5
while itercount<10:
img=anArtist.doit(xopt)
z=img.astype(np.float32)-orig.astype(np.float32)
shift=np.min(z)
scale=np.max(z)-shift
z=z-shift
z=z/scale
cv2.imwrite(outdir+'/error_%03d_prefilter.png'%(q+2),z*255)
z=np.sum(np.abs(filters.gaussian_filter(z,filtersize,mode='constant',cval=-shift/scale)*scale+shift)**2,2)
z=z/np.max(z)
cv2.imwrite(outdir+'/error_%03d_postfilter.png'%(q+2),z*255)
idx0=np.unravel_index(np.argmax(z),z.shape)
# z=img.astype(np.int)-orig.astype(np.int)
# zsq=z**2
# filt=mcircle(filtersize)
# zzsum=np.zeros_like(z)
# zzsumsq=np.zeros_like(z)
# for q in range(3):
# zzsum[:,:,q]=rank.sum(z[:,:,q],filt)
# zzsumsq[:,:,q]=rank.sum(zsq[:,:,q],filt)
# zzz=np.sum(zzsumsq-2./np.sum(filt)*zzsumsq*zzsum -zzsum,2).astype(np.float32)
# zzz=zzz-np.min(zzz)
# zzz=zzz/np.max(zzz)
# idx=np.unravel_index(np.argmax(zzz),zzz.shape)
# cv2.imwrite(outdir+'/error_%03d_postfilter.png'%(q+2),zzz*255)
#z=np.sum(np.abs(filters.gaussian_filter(z,filtersize,mode='constant',cval=-shift/scale)*scale+shift)**2,2)
#z=z/np.max(z)
#cv2.imwrite(outdir+'/error_%03d_postfilter.png'%(q+2),z*255)
#idx=np.unravel_index(np.argmax(z),z.shape)
# z=np.sum((img.astype(np.float32)-orig.astype(np.float32))**2,2)
# z=z/np.max(z)
# z=filters.gaussian_filter(z,filtersize,mode='constant')
# cv2.imwrite(outdir+'/error_%03d_oldfilter.png'%(q+2),z*255)
# idx1=np.unravel_index(np.argmax(z),z.shape)
# print idx0,idx1
#
params0=xopt[:]
# params1=xopt[:]
# bounds0=bounds[:]
# bounds1=bounds[:]
include=[False,]*len(params0)
params0.append(idx0[1])
bounds.append((idx0[1]-30,idx0[1]+30))
include.append(True)
params0.append(idx0[0])
bounds.append((idx0[0]-30,idx0[0]+30))
include.append(True)
params0.append(0)
bounds.append((-30,+30))
include.append(True)
params0.append(0)
bounds.append((-30,+30))
include.append(True)
c=orig[idx0[0],idx0[1],:]
params0.append(float(c[0]))
bounds.append((0,255))
include.append(True)
params0.append(float(c[1]))
bounds.append((0,255))
include.append(True)
params0.append(float(c[2]))
bounds.append((0,255))
include.append(True)
params0.append(11)
bounds.append((1,50))
include.append(True)
xopt0=localopt(optimizeme,params0,bounds,include,5)
# params1.append(idx1[1])
# bounds1.append((idx1[1]-30,idx1[1]+30))
# params1.append(idx1[0])
# bounds1.append((idx1[0]-30,idx1[0]+30))
# params1.append(0)
# bounds1.append((-30,+30))
# params1.append(0)
# bounds1.append((-30,+30))
# c=anArtist.toColorSubspace(orig[idx1[0],idx1[1],:])
# params1.append(c[0])
# bounds1.append((0,99))
# params1.append(c[1])
# bounds1.append((0,99))
# params1.append(11)
# bounds1.append((1,50))
# xopt1=localopt(optimizeme,params1,bounds1,include,5)
#
# img=anArtist.doit(xopt0)
# s0=score(img,orig)
# img=anArtist.doit(xopt1)
# s1=score(img,orig)
# if s0<s1:
# xopt=xopt0
# bounds=bounds0
# else:
# xopt=xopt1
# bounds=bounds1
xopt=localopt(optimizeme,xopt0,bounds,[True,]*len(bounds),1)
xtry=xopt[:-8]
img=anArtist.doit(xtry)
stry=score(img,orig)
img=anArtist.doit(xopt)
s=score(img,orig)
if stry > s:
break
else:
itercount+=1
print "Rejecting last circle as it is useless"
print "score with",s
print "score without",stry
print "Full params were",xopt
xopt=xtry
bounds=bounds[:-8]
filtersize=filtersize-1
if filtersize<1:
filtersize=10
print "Found",xopt
print "-------------------------------------"
#xopt=optimize.fmin_powell(optimizeme,params0,xtol=0.5)
#res=optimize.differential_evolution(optimizeme,bounds)
#xopt=res.x
prg=anArtist.makeProgram(xopt)
# print prg
fd=open(outdir+"/draw_%0d.py"%(q+1),'w')
print >>fd,prg,
fd.close()
cprg=compressProgram(prg)
fd=open(outdir+"/cdraw_%0d.py"%(q+1),'w')
print >>fd,cprg,
fd.close()
print "Program is",len(prg),len(cprg),cprg[10:14]
print "At",time.asctime()
print "%d circles score: %f"%(q+1,s)
print >>scorefile,q+1,s,len(prg),len(cprg),cprg[10:14]
cv2.imwrite(outdir+'/test_%03d.png'%(q+2),img)
if s>=lastscore:
print "Score has not improved, stopping"
break
return xopt,bounds
orig=cv2.imread('ORIGINAL.png')
mval=np.mean(orig,(0,1))
params0=[orig.shape[0]//2,int(mval[0]),int(mval[1]),int(mval[2]),int(mval[0]),int(mval[1]),int(mval[2]), 5, 94, 267, 0, -10, 43, 43, 38, 30]
bounds=[(1,orig.shape[0]),(0,255),(0,255),(0,255),(0,255),(0,255),(0,255),(1,10), (94-20,94+20),(267-20,267+20),(-30,30),(-30,30),(0,255),(0,255),(0,255),(3,50)]
x,b=run("outwithblurbrushv6",orig,params0,bounds,120)
params0=[orig.shape[0]//2,int(mval[0]),int(mval[1]),int(mval[2]),int(mval[0]),int(mval[1]),int(mval[2]), 94, 267, 0, -10, 43, 43, 38, 30]
bounds=[(1,orig.shape[0]),(0,255),(0,255),(0,255),(0,255),(0,255),(0,255), (94-20,94+20),(267-20,267+20),(-30,30),(-30,30),(0,255),(0,255),(0,255),(3,50)]
run("outnoblurbrushv6",orig,params0,bounds,120)
run("outremoveblurbrushv6",orig,x[:7]+x[8:],b[:7]+b[8:],2)
#plt.imshow(img[:,:,(2,1,0)])
#plt.show()