Full Code of mekarpeles/captcha-decoder for AI

Repository: mekarpeles/captcha-decoder
Branch: master
Commit: 912dac971f83
Files: 14
Total size: 41.8 KB

Directory structure:
gitextract_0h0kn1hl/

├── .gitignore
├── .travis.yml
├── AUTHORS
├── CHANGES
├── LICENSE
├── MANIFEST.in
├── README.md
├── decaptcha/
│   ├── __init__.py
│   ├── cli.py
│   └── decoder.py
├── setup.cfg
├── setup.py
└── tests/
    ├── __init__.py
    └── test_captcha.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
*~
decaptcha/output/*

*.py[co]

# Packages
*.egg
*.egg-info
dist
build
eggs
parts
bin
var
sdist
develop-eggs
.installed.cfg

# Installer logs
pip-log.txt

# Unit test / coverage reports
.coverage
.tox

#Translations
*.mo

#Mr Developer
.mr.developer.cfg

# PyCharm
.idea/

================================================
FILE: .travis.yml
================================================
language: python
python:
  - "2.7"
  - "3.4"
matrix:
  allow_failures:
    - python: "2.7"
install:
  - pip install flake8 --use-mirrors
  - pip install pep8 --use-mirrors
  - pip install -q -e . --use-mirrors
script:
  - nosetests
  - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then flake8 decaptcha; fi
cache: apt
sudo: false

================================================
FILE: AUTHORS
================================================
Mek <michael.karpeles@gmail.com> @mekarpeles (maintainer)
Ben Boyter <bboyte01@gmail.com> @boyter (original author)
Nate Urwin <nateurwin@gmail.com> @nateurwin
Abel Molina




================================================
FILE: CHANGES
================================================
v0.0.1, Tue Jul 28 17:43:00 2015 -- Initial Release.


================================================
FILE: LICENSE
================================================
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================================================
FILE: MANIFEST.in
================================================
recursive-include decaptcha/iconset *
recursive-include examples *


================================================
FILE: README.md
================================================
captcha-decoder
===============

![Build Status](https://travis-ci.org/mekarpeles/captcha-decoder.png)

This module takes a captcha (image) as input, attempts to partition it into discrete segments, each (it hopes) containing a single symbol, and then runs basic vector space search to determine the similarity of each symbol against known characters (whose reference images are included). The objective of this project is to (a) make bboyte's code more accessible and (b) illustrate, in a readable way, the fundamentals of captcha cracking. It's primary goal is clarity and makes no claims or attempts at efficiency, accuracy, or practicality.

This work is a derivation of an original work by @boyter
<bboyte01@gmail.com>, http://www.boyter.org/decoding-captchas/ (see
origin tutorial at
https://web.archive.org/web/20121012023114/http://www.wausita.com/captcha/)

## Installation

On ubuntu, libjpeg-dev and libpng-dev may be system requirements for the Python Pillow (PIL) library 

    sudo apt-get install libjpeg-dev
    sudo apt-get install libpng-dev
    
Next, fetch and build the decaptcha library

    pip install git+https://github.com/mekarpeles/captcha-decoder.git

## Usage

The decaptcha library comes with a command line utility called `decaptca`. Running the command with `-h` will show a list of options. The <img> argument can be provided a filepath or a url:

    usage: decaptcha [-h] [-v] [-l LIMIT] [-c CHANNELS] [-t THRESHOLD] [--min MIN]
                     [--max MAX] [-o TOLERANCE]
                     [<img>]
    
    Python captcha cracking utility
    
    positional arguments:
      <img>                 Enter the filesystem path or url of a captcha image
    
    optional arguments:
      -h, --help            show this help message and exit
      -v                    Displays the decaptcha version
      -l LIMIT, --limit LIMIT
                            Package url
      -c CHANNELS, --channels CHANNELS
                            The number of prominant color channels to keep
      -t THRESHOLD, --threshold THRESHOLD
                            Accuracy threshold for matching decimal [0-1]
      --min MIN             Filter out colors darker than this [0-256]
      --max MAX             Filter out colors light than this [0-256]
      -o TOLERANCE, --tolerance TOLERANCE
                            Pixel tolerance for character segmentation. Higher is
                            more lenient/greedy, lower is strict.

# Example

    $ decaptcha http://www.mondor.org/img/capex.jpg  --min 0 --max 20 --limit 5 --channels 5 --tolerance 7
    
    Character 0 of 7:
            t (confidence of 0.839150063096)
            e (confidence of 0.827405543276)
    Character 1 of 7:
            0 (confidence of 0.834057656228)
            l (confidence of 0.771064160322)
    Character 2 of 7:
            t (confidence of 0.309437274354)
            e (confidence of 0.303227199152)
    Character 3 of 7:
    Character 4 of 7:
            t (confidence of 0.267644230239)
            7 (confidence of 0.266067912114)
    Character 5 of 7:
            0 (confidence of 0.834057656228)
            l (confidence of 0.789422830806)
    Character 6 of 7:
            t (confidence of 0.835510535512)
            e (confidence of 0.835221298415)


## Further Reading

The following implementations and techniques are recommended as more practical and accurate alternatives for this project:

1. http://www.codeproject.com/Articles/106583/Handwriting-Recognition-Revisited-Kernel-Support-V


================================================
FILE: decaptcha/__init__.py
================================================
# -*- coding: utf-8 -*-

"""
   decaptcha
   ~~~~~~~~~

   Basic Captcha Cracker
"""

__version__ = '0.0.1'
__author__ = [
    'Mek <michael.karpeles@gmail.com>'
]
__license__ = 'see LICENSE (creative commons)'
__contributors__ = 'see AUTHORS'
__title__ = 'Python captcha cracking utility'

import sys
from .decoder import Captcha  # NOQA
from .decoder import trim, channel, monochrome, regions, decode  # NOQA
from .cli import main

if __name__ == '__main__':
    sys.exit(main())


================================================
FILE: decaptcha/cli.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-

import argparse
from . import Captcha, __title__, __version__


def threshold(x):
    x = float(x)
    if 1 > x < 0:
        raise argparse.ArgumentTypeError(
            "Threshold must be a value between 0 and 1.")
    return x


def argparser():
    """Creates a command line ArgumentParser for decaptcha."""
    parser = argparse.ArgumentParser(description=__title__)
    parser.add_argument('-v', help='Displays the decaptcha version',
                        action='version', version='%s v%s'
                        % (__title__, __version__))
    parser.add_argument('captcha', nargs='?', metavar='<img>',
                        help='Enter the filesystem path or url '
                        'of a captcha image')
    parser.add_argument('-l', '--limit', dest='limit', help='Package url',
                        type=int, default=3)
    parser.add_argument('-c', '--channels', dest='channels',
                        help='The number of prominant color channels to keep',
                        type=int, default=3)
    parser.add_argument('-t', '--threshold', dest='threshold',
                        help='Accuracy threshold for matching decimal [0-1]',
                        type=threshold, default=0)
    parser.add_argument('--min', dest='min', type=int, default=0,
                        help='Filter out colors darker than this [0-256]')
    parser.add_argument('--max', dest='max', type=int, default=230,
                        help='Filter out colors light than this [0-256]')
    parser.add_argument('-o', '--tolerance', dest='tolerance', type=int,
                        default=3, help='Pixel tolerance for character '
                        'segmentation. Higher is more lenient/greedy, '
                        'lower is strict.')
    return parser


def prettyprint(guesses):
    regions = len(guesses)
    for i, guess in enumerate(guesses):
        print('Character %s of %s:' % (i + 1, regions))
        for result in guess:
            confidence, symbol = result
            print('\t%s (%s confidence)' % (symbol, confidence))


def main():
    parser = argparser()
    args = parser.parse_args()

    if not args.captcha:
        raise ValueError('No captcha input image provided')

    prettyprint(Captcha(args.captcha).decode(channels=args.channels,
                                             limit=args.limit,
                                             threshold=args.threshold,
                                             tolerance=args.tolerance,
                                             _min=args.min,
                                             _max=args.max))


if __name__ == "__main__":
    main()


================================================
FILE: decaptcha/decoder.py
================================================
# -*- coding: utf-8 -*-

"""
    crack.py
    ~~~~~~~~

    This module takes captcha images as input and partitions them into
    n new images, 1 image per character found within the captcha.

    Original Code (http://tinyurl.com/puq6alb) by bboyte01@gmail.com
    https://web.archive.org/web/20121012023114/http://www.wausita.com/captcha/
    http://www.wausita.com/captcha/

    :copyright: (c) 2012 by Mek
    :license: see LICENSE for more details.
"""

import os
import string
from operator import itemgetter
from math import sqrt
from PIL import Image, ImageChops
from io import BytesIO

try:
    from urllib.request import urlopen
except ImportError:
    from urllib import urlopen

SYMBOLS = list(string.ascii_lowercase + string.digits)
ICONS_PATH = os.path.abspath(
    os.path.join(os.path.dirname(__file__), 'iconset'))
IMAGESET = []
WHITE = 255


def imageset():
    """Loads icons of various characters"""
    imageset = []
    for symbol in SYMBOLS:
        for imfile in os.listdir(os.path.join(ICONS_PATH, symbol)):
            path = os.path.join(ICONS_PATH, symbol, imfile)
            imageset.append({symbol: Image.open(path)})
    return imageset


def trim(im, color=WHITE):
    """Tims image to remove excess color (default: whitespace)"""
    bg = Image.new(im.mode, im.size, WHITE)
    diff = ImageChops.difference(im, bg)
    diff = ImageChops.add(diff, diff, 2.0, -100)
    return im.crop(diff.getbbox())


def channel(im, *colors, **kwargs):
    """Composes an new image with the same dimensions as `im` but
    draws only pixels of the specified color channels on a `bg`
    colored background.
    """
    bg = kwargs.get('bg', WHITE)
    sample = Image.new('P', im.size, bg)
    width, height = im.size
    for col in range(width):
        for row in range(height):
            pixel = im.getpixel((col, row))
            if pixel in colors:
                sample.putpixel((col, row), pixel)
    return sample


def monochrome(im, threshold=255):
    """Converts all colors in gif image which are less than threshold
    to black"""
    return im.point(lambda x: 0 if x < 255 else 255, '1')


def regions(im, threshold=1):
    """Iterates over the columns of an image from left-to-right and
    composes an ordered list of (start, end) column ranges referring
    to discrete, contiguous columns which contain at least `threshold`
    non-white pixel.
    """
    regions = []
    start = None
    width, height = im.size
    for col in range(width):
        # if column contains at least one pixel
        if sum([im.getpixel((col, row)) is not WHITE
                for row in range(height)]) >= threshold:
            start = start if start else col
        elif start:
            regions.append((start, col))
            start = None  # reset start
    return regions


def similarity(im1, im2, equalize=False):
    """Takes in two images, vectorizes them into concordance
    dictionaries and spits out a number from 0 to 1 indicating how
    related they are. 0 means no relation and 1 indicates they are the
    same.

    params:
        stretch - stretch im2 to be the same dimensions as 1
    """
    def scale(im1, im2):
        """Scales the image with the greater height to match the one
        with the smaller height
        """
        if im1.size[1] > im2.size[1]:
            return im1.resize(im2.size, Image.ANTIALIAS), im2
        elif im1.size[1] < im2.size[1]:
            return im1, im2.resize(im1.size, Image.ANTIALIAS)
        return im1, im2

    def vectorize(im):
        """im.getdata returns the contents of an image as a flattened
        sequence object containing pixel values.
        """
        d1 = {}
        for count, i in enumerate(im.getdata()):
            d1[count] = i
        return d1

    def magnitude(concordance):
        return sqrt(sum(count ** 2 for word, count in concordance.items()))

    c1, c2 = [vectorize(im) for im in
              (scale(im1, im2) if equalize else (im1, im2))]
    topvalue = 0
    for word, count in c1.items():
        if word in c2:
            topvalue += count * c2[word]
    return topvalue / (magnitude(c1) * magnitude(c2))


class Captcha(object):

    def __init__(self, imgpath):
        self.imgpath = imgpath

    @property
    def im(self):
        """Fetches captcha's image from disk or url"""
        try:
            im = Image.open(self.imgpath)
        except:
            im = Image.open(BytesIO(urlopen(self.imgpath).read()))
        return self.gif(im)

    @property
    def histogram(self):
        with self.im as im:
            return im.histogram()

    def decode(self, channels=3, limit=3, threshold=0, tolerance=3,
               _min=0, _max=245):
        """Attempts to decode a captcha by:

        - Finding the `n` most prominant colors in the image
        - Sampling the captcha into `n` images, each discretely composed
          of a differnet prominant colors.
        - Segmenting each sample into regions of contiguous columns
          containing any pixels pixelation (which are hopefully
          equates to individual alphanumeric characters), and finally
        - Guessing which character appears in each segment

        XXX Prettier output and organizing of results required
        """
        colors = [color for color, _ in self
                  .prominant_colors(n=channels, _min=_min, _max=_max)]
        sample = monochrome(self.channel(*colors))
        return [self.guess_character(segment, limit=limit, threshold=threshold)
                for segment in self.segments(sample, tolerance=tolerance)]

    def prominant_colors(self, n=5, _min=0, _max=256):
        """Calculates the n most prominant colors of an image as an
        ordered list of (color, frequency) tuples.

        params:
            n - limit the number of colors to `n`
            _min - exclude any colors below this number (filter
                   out dark colors, like black/0)
            _max - exclude any colors above this number (filter out
                   light colors, like white/256)

        XXX consider sorted(im.getcolors(n), reverse=True)
        """
        hist = self.histogram[_min:_max]
        return sorted([(c, f) for c, f in enumerate(hist)],
                      key=itemgetter(1), reverse=True)[:n]

    def channel(self, *colors, **kwargs):
        """Composes an image with the same dimensions as `im` but
        draws only pixels of the specified colors on a `bg` colored
        background.
        """
        with self.im as im:
            return channel(im, *colors, **kwargs)

    @staticmethod
    def gif(im):
        """Converts captcha to a GIF (makes things easier since it has
        255 colors) and finds the most prominent colors in the image
        """
        return im if im.mode is 'P' else im.convert('P')

    @classmethod
    def segments(cls, im, tolerance=3, crop=True):
        """Discover """
        return [cls.segment(im, region, crop=crop) for
                region in regions(im, threshold=tolerance)]

    @classmethod
    def segment(cls, im, region, crop=True):
        """Returns a cropped image segment (hopefully of an
        alphanumeric character) within the range of the region
        """
        start, end = 0, 1
        segment = im.crop((region[start], 0, region[end], im.size[1]))
        return trim(segment) if crop else segment

    @staticmethod
    def guess_character(im, threshold=0, limit=None):
        """Guess alphanumeric character in image using Basic Vector
        Space Search algorithm.

        http://la2600.org/talks/files/20040102/Vector_Space_Search_Engine_Theory.pdf
        """
        global IMAGESET  # lazy-ish style iconset loading
        if not IMAGESET:
            IMAGESET = imageset()

        guesses = []
        for icon in IMAGESET:
            for symbol, im2 in icon.items():
                guess = similarity(im, im2, equalize=True)
                if guess >= threshold:
                    guesses.append((guess, symbol))
        return sorted(guesses, reverse=True)[:limit]


def decode(captcha, channels=1, limit=3, threshold=0, tolerance=3,
           _min=0, _max=256):
    """Backward compatible method for decoding a captcha"""
    return Captcha(captcha).decode(
        channels=channels,
        limit=limit,
        threshold=threshold,
        tolerance=tolerance,
        _min=_min, _max=_max)


================================================
FILE: setup.cfg
================================================
[bdist_wheel]
universal=1


================================================
FILE: setup.py
================================================
# -*- coding: utf-8 -*-

"""
    decaptcha
    ~~~~~~~~~

"""

import codecs
import os
import re
from setuptools import setup

here = os.path.abspath(os.path.dirname(__file__))


def read(*parts):
    """Taken from pypa pip setup.py:
    intentionally *not* adding an encoding option to open, See:
    https://github.com/pypa/virtualenv/issues/201#issuecomment-3145690
    """
    return codecs.open(os.path.join(here, *parts), 'r').read()


def find_version(*file_paths):
    version_file = read(*file_paths)
    version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]",
                              version_file, re.M)
    if version_match:
        return version_match.group(1)
    raise RuntimeError("Unable to find version string.")


setup(
    name='decaptcha',
    version=find_version("decaptcha", "__init__.py"),
    description='Python captcha cracking utility',
    long_description=read('README.md'),
    url='http://github.com/mekarpeles/captcha-decoder',
    author='mek',
    author_email='michael.karpeles@gmail.com',
    packages=[
        'decaptcha',
        ],
    platforms='any',
    license='LICENSE',
    classifiers=[
        'Development Status :: 2 - Pre-Alpha',
        "Intended Audience :: Developers",
        "Programming Language :: Python :: 2.7",
        "Programming Language :: Python :: 3"
        ],
    install_requires=[
        'Pillow >= 2.9.0'
        ],
    entry_points={
        'console_scripts': ['decaptcha=decaptcha.cli:main'],
        },
    extras_require={
        ':python_version=="2.7"': ['argparse']
        },
    include_package_data=True,
    package_data={'': ['iconset/**/*.gif']},
)


================================================
FILE: tests/__init__.py
================================================


================================================
FILE: tests/test_captcha.py
================================================
# -*- coding: utf-8 -*-

"""
    tests
    ~~~~~
    Test cases for the decaptcha package

    :copyright: (c) 2012 by Mek
    :license: see LICENSE for more details.
"""

import os.path
import unittest
from PIL import Image
import decaptcha

TEST_IMG_DIR = os.path.abspath(
    os.path.join(
        os.path.dirname(os.path.realpath(__file__)),
        os.pardir, 'tests', 'images'))
TEST_CAPTCHA_IMG = os.path.join(TEST_IMG_DIR, 'captcha.gif')
TEST_CHANNEL_IMG = os.path.join(TEST_IMG_DIR, 'channel.gif')
TEST_SEGMENT_IMG = lambda i: os.path.join(TEST_IMG_DIR, 'segment%s.gif' % i)
EXPECTED_HISTOGRAM = [
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
    0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0,
    2, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 1, 2, 0, 0, 0, 1, 2, 0, 1, 0, 0, 1, 0, 2, 0, 0, 1, 0, 0, 2, 0, 0, 0,
    0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 3, 1, 3, 3, 0, 0, 0, 0, 0, 0, 1, 0, 3,
    2, 132, 1, 1, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 15, 0, 1, 0, 1, 0, 0,
    8, 1, 0, 0, 0, 0, 1, 6, 0, 2, 0, 0, 0, 0, 18, 1, 1, 1, 1, 1, 2, 365,
    115, 0, 1, 0, 0, 0, 135, 186, 0, 0, 1, 0, 0, 0, 116, 3, 0, 0, 0, 0, 0,
    21, 1, 1, 0, 0, 0, 2, 10, 2, 0, 0, 0, 0, 2, 10, 0, 0, 0, 0, 1, 0, 625
    ]
EXPECTED_DOMINANT_COLORS = [
    (255, 625), (212, 365), (220, 186), (219, 135), (169, 132), (227, 116),
    (213, 115), (234, 21), (205, 18), (184, 15)
    ]
EXPECTED_REGIONS = [
    (6, 14), (15, 25), (27, 35), (37, 46), (48, 56), (57, 67)
    ]
EXPECTED_OUTPUT = (1.0, '7')


class CaptchaDecoderTest(unittest.TestCase):

    def setUp(self):
        self.captcha = decaptcha.Captcha(TEST_CAPTCHA_IMG)

    def test_histogram(self):
        self.assertTrue(self.captcha.histogram == EXPECTED_HISTOGRAM,
                        "Captcha histogram different from expected")

    def test_prominant_colors(self):
        self.assertTrue(self.captcha.prominant_colors(n=10) ==
                        EXPECTED_DOMINANT_COLORS,
                        "Captcha's dominant colors differ from expected")

    def test_channels(self):
        channel = decaptcha.monochrome(self.captcha.channel(220, 227))
        print(channel.histogram())
        self.assertTrue(channel.histogram() ==
                        Image.open(TEST_CHANNEL_IMG).histogram(),
                        "Channel sample did not match expected image")

    def test_regions(self):
        sample = decaptcha.monochrome(self.captcha.channel(220, 227))
        regions = decaptcha.regions(sample, threshold=1)
        self.assertTrue(regions == EXPECTED_REGIONS,
                        "Expected regions %s, instead got %s"
                        % (EXPECTED_REGIONS, regions))

    def test_segmentation(self):
        """Note, in the test cases of the original publication
        (http://tinyurl.com/phvggox), output segments #3 and #5 (which
        are both the number 9) are mistakenly swapped.
        """
        sample = decaptcha.monochrome(self.captcha.channel(220, 227))
        segments = self.captcha.segments(sample, crop=False, tolerance=1)
        for i, segment in enumerate(segments):
            EXPECTED_SEGMENT = Image.open(TEST_SEGMENT_IMG(i+1))
            self.assertTrue(segment.histogram() ==
                            EXPECTED_SEGMENT.histogram(),
                            "Segment #%s is wrong." % (i+1))

    def test_guess_character(self):
        sample = decaptcha.monochrome(self.captcha.channel(220, 227))
        regions = decaptcha.regions(sample)
        segment = self.captcha.segment(sample, regions[0], crop=False)
        exp_seg = Image.open(TEST_SEGMENT_IMG(1))
        self.assertTrue(segment.histogram() == exp_seg.histogram(),
                        "Segment is wrong")
        predictions = self.captcha.guess_character(segment)
        self.assertTrue(predictions[0] == EXPECTED_OUTPUT,
                        "Expected %s, got %s" %
                        (EXPECTED_OUTPUT, predictions[0]))

    def test_decoder(self):
        self.captcha.decode()