--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pycaptcha/Captcha/Visual/Distortions.py	Thu Feb 11 05:09:46 2010 -0500
@@ -0,0 +1,117 @@
+""" Captcha.Visual.Distortions
+
+Distortion layers for visual CAPTCHAs
+"""
+#
+# PyCAPTCHA Package
+# Copyright (C) 2004 Micah Dowty <micah@navi.cx>
+#
+
+from Captcha.Visual import Layer
+import ImageDraw, Image
+import random, math
+
+
+class WigglyBlocks(Layer):
+    """Randomly select and shift blocks of the image"""
+    def __init__(self, blockSize=3, sigma=0.01, iterations=300):
+        self.blockSize = blockSize
+        self.sigma = sigma
+        self.iterations = iterations
+        self.seed = random.random()
+
+    def render(self, image):
+        r = random.Random(self.seed)
+        for i in xrange(self.iterations):
+            # Select a block
+            bx = int(r.uniform(0, image.size[0]-self.blockSize))
+            by = int(r.uniform(0, image.size[1]-self.blockSize))
+            block = image.crop((bx, by, bx+self.blockSize-1, by+self.blockSize-1))
+
+            # Figure out how much to move it.
+            # The call to floor() is important so we always round toward
+            # 0 rather than to -inf. Just int() would bias the block motion.
+            mx = int(math.floor(r.normalvariate(0, self.sigma)))
+            my = int(math.floor(r.normalvariate(0, self.sigma)))
+
+            # Now actually move the block
+            image.paste(block, (bx+mx, by+my))
+
+
+class WarpBase(Layer):
+    """Abstract base class for image warping. Subclasses define a
+       function that maps points in the output image to points in the input image.
+       This warping engine runs a grid of points through this transform and uses
+       PIL's mesh transform to warp the image.
+       """
+    filtering = Image.BILINEAR
+    resolution = 10
+
+    def getTransform(self, image):
+        """Return a transformation function, subclasses should override this"""
+        return lambda x, y: (x, y)
+
+    def render(self, image):
+        r = self.resolution
+        xPoints = image.size[0] / r + 2
+        yPoints = image.size[1] / r + 2
+        f = self.getTransform(image)
+
+        # Create a list of arrays with transformed points
+        xRows = []
+        yRows = []
+        for j in xrange(yPoints):
+            xRow = []
+            yRow = []
+            for i in xrange(xPoints):
+                x, y = f(i*r, j*r)
+
+                # Clamp the edges so we don't get black undefined areas
+                x = max(0, min(image.size[0]-1, x))
+                y = max(0, min(image.size[1]-1, y))
+
+                xRow.append(x)
+                yRow.append(y)
+            xRows.append(xRow)
+            yRows.append(yRow)
+
+        # Create the mesh list, with a transformation for
+        # each square between points on the grid
+        mesh = []
+        for j in xrange(yPoints-1):
+            for i in xrange(xPoints-1):
+                mesh.append((
+                    # Destination rectangle
+                    (i*r, j*r,
+                     (i+1)*r, (j+1)*r),
+                    # Source quadrilateral
+                    (xRows[j  ][i  ], yRows[j  ][i  ],
+                     xRows[j+1][i  ], yRows[j+1][i  ],
+                     xRows[j+1][i+1], yRows[j+1][i+1],
+                     xRows[j  ][i+1], yRows[j  ][i+1]),
+                    ))
+
+        return image.transform(image.size, Image.MESH, mesh, self.filtering)
+
+
+class SineWarp(WarpBase):
+    """Warp the image using a random composition of sine waves"""
+
+    def __init__(self,
+                 amplitudeRange = (3, 6.5),
+                 periodRange    = (0.04, 0.1),
+                 ):
+        self.amplitude = random.uniform(*amplitudeRange)
+        self.period = random.uniform(*periodRange)
+        self.offset = (random.uniform(0, math.pi * 2 / self.period),
+                       random.uniform(0, math.pi * 2 / self.period))
+
+    def getTransform(self, image):
+        return (lambda x, y,
+                a = self.amplitude,
+                p = self.period,
+                o = self.offset:
+                (math.sin( (y+o[0])*p )*a + x,
+                 math.sin( (x+o[1])*p )*a + y))
+
+### The End ###