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1 """ Captcha.Visual.Distortions
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2
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3 Distortion layers for visual CAPTCHAs
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4 """
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5 #
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6 # PyCAPTCHA Package
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7 # Copyright (C) 2004 Micah Dowty <micah@navi.cx>
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8 #
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9
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10 from Captcha.Visual import Layer
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11 import ImageDraw, Image
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12 import random, math
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13
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14
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15 class WigglyBlocks(Layer):
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16 """Randomly select and shift blocks of the image"""
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17 def __init__(self, blockSize=3, sigma=0.01, iterations=300):
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18 self.blockSize = blockSize
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19 self.sigma = sigma
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20 self.iterations = iterations
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21 self.seed = random.random()
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22
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23 def render(self, image):
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24 r = random.Random(self.seed)
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25 for i in xrange(self.iterations):
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26 # Select a block
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27 bx = int(r.uniform(0, image.size[0]-self.blockSize))
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28 by = int(r.uniform(0, image.size[1]-self.blockSize))
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29 block = image.crop((bx, by, bx+self.blockSize-1, by+self.blockSize-1))
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30
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31 # Figure out how much to move it.
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32 # The call to floor() is important so we always round toward
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33 # 0 rather than to -inf. Just int() would bias the block motion.
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34 mx = int(math.floor(r.normalvariate(0, self.sigma)))
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35 my = int(math.floor(r.normalvariate(0, self.sigma)))
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36
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37 # Now actually move the block
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38 image.paste(block, (bx+mx, by+my))
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39
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40
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41 class WarpBase(Layer):
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42 """Abstract base class for image warping. Subclasses define a
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43 function that maps points in the output image to points in the input image.
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44 This warping engine runs a grid of points through this transform and uses
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45 PIL's mesh transform to warp the image.
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46 """
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47 filtering = Image.BILINEAR
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48 resolution = 10
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49
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50 def getTransform(self, image):
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51 """Return a transformation function, subclasses should override this"""
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52 return lambda x, y: (x, y)
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53
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54 def render(self, image):
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55 r = self.resolution
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56 xPoints = image.size[0] / r + 2
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57 yPoints = image.size[1] / r + 2
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58 f = self.getTransform(image)
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59
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60 # Create a list of arrays with transformed points
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61 xRows = []
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62 yRows = []
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63 for j in xrange(yPoints):
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64 xRow = []
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65 yRow = []
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66 for i in xrange(xPoints):
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67 x, y = f(i*r, j*r)
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68
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69 # Clamp the edges so we don't get black undefined areas
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70 x = max(0, min(image.size[0]-1, x))
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71 y = max(0, min(image.size[1]-1, y))
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72
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73 xRow.append(x)
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74 yRow.append(y)
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75 xRows.append(xRow)
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76 yRows.append(yRow)
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77
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78 # Create the mesh list, with a transformation for
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79 # each square between points on the grid
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80 mesh = []
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81 for j in xrange(yPoints-1):
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82 for i in xrange(xPoints-1):
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83 mesh.append((
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84 # Destination rectangle
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85 (i*r, j*r,
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86 (i+1)*r, (j+1)*r),
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87 # Source quadrilateral
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88 (xRows[j ][i ], yRows[j ][i ],
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89 xRows[j+1][i ], yRows[j+1][i ],
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90 xRows[j+1][i+1], yRows[j+1][i+1],
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91 xRows[j ][i+1], yRows[j ][i+1]),
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92 ))
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93
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94 return image.transform(image.size, Image.MESH, mesh, self.filtering)
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95
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96
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97 class SineWarp(WarpBase):
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98 """Warp the image using a random composition of sine waves"""
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99
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100 def __init__(self,
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101 amplitudeRange = (3, 6.5),
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102 periodRange = (0.04, 0.1),
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103 ):
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104 self.amplitude = random.uniform(*amplitudeRange)
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105 self.period = random.uniform(*periodRange)
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106 self.offset = (random.uniform(0, math.pi * 2 / self.period),
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107 random.uniform(0, math.pi * 2 / self.period))
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108
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109 def getTransform(self, image):
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110 return (lambda x, y,
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111 a = self.amplitude,
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112 p = self.period,
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113 o = self.offset:
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114 (math.sin( (y+o[0])*p )*a + x,
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115 math.sin( (x+o[1])*p )*a + y))
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116
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117 ### The End ###
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