Improve FancyCaptcha resistance to OCR

Tesseract is a popular open source OCR package. Running it on
FancyCaptcha images, with no training or configuration, yielded a 56%
break rate. By restricting the character set, the OCR break rate was
improved to 66%.

So:
* Reduce k, increase wob scale, increase rr fuzz. The net effect of
  these three changes is to more reliably bend the baseline. In the old
  captcha, the baseline would often be bent by chance, but when it
  wasn't bent, it provided a very easy challenge for the OCR engine.
  This reduced the break rate from 66% to around 40%.
* Introduce additive noise, based on a bilinear upscale of a random
  greyscale image. This, combined with the above change, reduces the
  Tesseract break rate to 6%.

Change-Id: I05b5bb6475de9378cd89cce13b1b2f28b32cd405

captcha.py

@@ -39,6 +39,7 @@ try:
 	import ImageDraw
 	import ImageEnhance
 	import ImageOps
+	import ImageMath
 except:
 	sys.exit("This script requires the Python Imaging Library - http://www.pythonware.com/products/pil/")
 
@@ -49,7 +50,7 @@ def wobbly_copy(src, wob, col, scale, ang):
 	x, y = src.size
 	f = random.uniform(4*scale, 5*scale)
 	p = random.uniform(0, math.pi*2)
-	rr = ang+random.uniform(-30, 30) # vary, but not too much
+	rr = ang+random.uniform(-10, 10) # vary, but not too much
 	int_d = Image.new('RGB', src.size, 0) # a black rectangle
 	rot = src.rotate(rr, Image.BILINEAR)
 	# Do a cheap bounding-box op here to try to limit work below
@@ -86,8 +87,8 @@ def gen_captcha(text, fontname, fontsize, file_name):
 	x, y = im.size
 	# add the text to the image
 	d.text((x/2-dim[0]/2, y/2-dim[1]/2), text, font=font, fill=fgcolor)
-	k = 3
-	wob = 0.20*dim[1]/k
+	k = 2
+	wob = 0.09*dim[1]
 	rot = 45
 	# Apply lots of small stirring operations, rather than a few large ones
 	# in order to get some uniformity of treatment, whilst
@@ -102,6 +103,21 @@ def gen_captcha(text, fontname, fontsize, file_name):
 	bbox = im.getbbox()
 	bord = min(dim[0], dim[1])/4 # a bit of a border
 	im = im.crop((bbox[0]-bord, bbox[1]-bord, bbox[2]+bord, bbox[3]+bord))
+
+	# Create noise
+	nblock = 4
+	nsize = (im.size[0] / nblock, im.size[1] / nblock)
+	noise = Image.new('L', nsize, bgcolor)
+	data = noise.load()
+	for x in range(nsize[0]):
+		for y in range(nsize[1]):
+			r = random.randint(0, 100)
+			data[x, y] = r
+	# Turn speckles into blobs
+	noise = noise.resize(im.size, Image.BILINEAR)
+	# Add to the image
+	im = ImageMath.eval('convert(convert(a, "L") / 2 + b, "RGB")', a=im, b=noise)
+
 	# and turn into black on white
 	im = ImageOps.invert(im)