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Creates a semi random image, similar to a Jackson Pollock or Monet painting.

Python, 261 lines
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261``` ```# PREAMBLE # My original intention was to create some # disruptive visual camoflage, inspired by the pattern on # the bark of plane trees. The result was never quite what I intended # and several fixes had to be incorperated to aproximate what I wanted. # The end result is effective though and reminds me of a Jackson Pollock # or a Monet painting. # CODE from Tkinter import * from math import * from random import* # critical parameters, adjust to suit W = 900 # canvas dimensions H = 500 nLow = 25 # recursive limiter nLayers = 5 # number of repeated paint overs nCover = 0.8 # Adjusts probability of a particular area being painted over per sweep nMSpan = 5.0 # Same as above, These two parameters depend upon the number of recursions nCSpan = 2.0 # Same for colour range # scale factor per recursion. i.e. not scale invariant aScale = [0.1, .3, .5, 0.5,0.9,0.9,0.1,0.1,0.1,0,0,0,0,0,0,0,0,0,0,0,0] # colours are scale invariant aColour = [0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5] # The two functions AutumnPastels and DrawSplash # can be modified to suit def AutumnPastels(nCR, nCG, nCB): # colour scheme cR = ColStr(int((ZeroToOne(nCR, 8.0)) * 150) + 80) # red cG = ColStr(int((ZeroToOne(nCG, 8.0)) * 150) + 80) # green cB = ColStr(int((ZeroToOne(nCB, 8.0)) * 25) + 30) # blue return '#' + cR + cG + cB def DrawSplash(nX, nY, colour): # simulation of paint splatter or leaves nX2 = dist(nX, nLow / 2.0) nY2 = dist(nY, nLow / 2.0) for i in range(8): nX1 = dist(nX2, nLow / 3.0) nY1 = dist(nY2, nLow / 3.0) nL = dist(nLow / 6.0 + 2, nLow / 3.0) canvas.create_oval( nX1, nY1, nX1 + nL, nY1 + nL, fill = colour, width = 0) canvas.update() def ColStr( x): if x > 255: x = 0 if x < 0: x = 255 s = "%x" % x # converts x into a hexidecimal string if len( s) < 2: s = '0' + s return s # This is a clumbsy way of recording continuity across the plane # A dictionary would be more efficient, but it seems to work O.K. def Load(aGrid, nX, nY, n): # changes value if not set, or else it returns the value if aGrid[nX][nY] == -1: aGrid[nX][nY] = n return n return aGrid[nX][nY] def ZeroToOne(nM, nSpan): # maps the Real domain onto [0 , 1] return atan(nM * nSpan) / pi + 0.5 def mid(n1, n2): return int((n1 + n2) / 2) def dist(nP, nScale): return nP + (random() - 0.5) * nScale def FracDown(aGrid, nX1, nY1, nX2, nY2, nTL, nTR, nBL, nBR, nLim, nRecursive, nCR, nCG, nCB): # fractal lanscape grenerator dx = nX2 - nX1 dy = nY2 - nY1 nS = aScale[nRecursive] nT = dist((nTL + nTR) / 2, nS * nHorizFactor) nL = dist((nTL + nBL) / 2, nS) nR = dist((nTR + nBR) / 2, nS) nB = dist((nBL + nBR) / 2, nS * nHorizFactor) nM = dist((nTL + nTR + nBL + nBR) / 4, nS * nDiagFactor) nSC = aColour[nRecursive] nCR = dist(nCR, nSC) nCG = dist(nCG, nSC) nCB = dist(nCB, nSC) nXm = mid(nX1, nX2) nYm = mid(nY1, nY2) if dx <= nLow and dy <= nLow: if ZeroToOne(nM, nMSpan) > nLim: DrawSplash(nXm, nYm, AutumnPastels(nCR, nCG, nCB)) return nTL = Load(aGrid, nX1, nY2, nTL) nTR = Load(aGrid, nX2, nY2, nTR) nBL = Load(aGrid, nX1, nY1, nBL) nBR = Load(aGrid, nX1, nY1, nBR) t1 = (aGrid, nX1, nYm, nXm, nY2, nTL, nT, nL, nM, nLim, nRecursive + 1, nCR, nCG, nCB) t2 = (aGrid, nXm, nYm, nX2, nY2, nT, nTR, nM, nR, nLim, nRecursive + 1, nCR, nCG, nCB) t3 = (aGrid, nX1, nY1, nXm, nYm, nL, nM, nBL, nB, nLim, nRecursive + 1, nCR, nCG, nCB) t4 = (aGrid, nXm, nY1, nX2, nYm, nM, nR, nB, nBR, nLim, nRecursive + 1, nCR, nCG, nCB) aT = [t1,t2,t3,t4] shuffle(aT) for i in aT: apply(FracDown, i) def Frac(nLim): aGrid = [] for i in range(W): aGrid.append([-1] * H) FracDown(aGrid, 0, 0, W - 1, H - 1, 0.0, 0.0, 0.0, 0.0,nLim, 1, 0.0, 0.0, 0.0) seed() canvas = Canvas( width = W, height = H) canvas.pack(side = TOP) canvas.create_rectangle( 0, 0, W, H, fill = AutumnPastels(-0.8, -0.4, -3.0), width = 0, tag ='o') nHorizFactor = (W + 0.0) / (H + 0.0) nDiagFactor = sqrt(H**2 + W**2) / (H + 0.0) for i in range(nLayers): Frac(nCover) print 'done' ```

The program is for fun mainly. It is interesting to compare a computer generated artwork with those of abstract painters. Some research has been done into the fractal aspects of paintings.

The program may also be of use in the generation of disruptive camoflage.

 Created by James Coliins on Fri, 17 Feb 2012 (MIT)

### Required Modules

• (none specified)