def saveCharacters(self, characters):
characters_hashes = []
# Get characters' hashes
for character in characters:
m = hashlib.md5()
# Get image as a string of 0 and 1 (for 255) and n (for new line)
ones_array = map(lambda lst: map(lambda x: "0" if x == 0 else "1", lst), numpy.array(character))
ones_string = "n".join(map(lambda lst: "".join(lst), ones_array))
m.update(ones_string)
characters_hashes.append(m.hexdigest())
saveAsCategorized = False
if self.options.auto_train:
# Parse file name and find code
basename = os.path.basename(self._captcha)
code = self.getCodeFromString(basename)
if len(code) != len(characters):
saveAsCategorized = False
Logger.error(
"Error! Training found {0} chars while in file name are specified {1} chars. Real code is: {2}".format(
len(characters), len(code), basename
),
1,
)
else:
saveAsCategorized = True
if self.options.verbose:
if saveAsCategorized:
Logger.subheader("Saving characters into categorized folders")
else:
Logger.log("Saving characters into output folder")
i = 0
for character in characters:
character_hash = characters_hashes[i]
if saveAsCategorized:
character_symbol = code[i]
dst = os.path.join(self.options.mod_dir, "char", character_symbol.lower(), character_hash + ".gif")
else:
dst = os.path.join(self.options.output_char_dir, character_hash + ".gif")
character.save(dst)
if saveAsCategorized:
Logger.log("Saving {0} into mod folder".format(character_symbol))
i += 1
def crack(self):
Logger.info("\nTrain on " + self._captcha)
options = self.options
if not options.mod:
Logger.error("Can't crack without a mod")
return None
supposed_words = []
processed = self.getImage()
processed = self.cleanImage(processed)
# Ensure that image is in black and white
processed = self.blackAndWhite(processed)
# List of images
characters = self.getCharacters(processed)
trained_chars = self.loadTrainedChars()
Logger.subheader("Detected characters")
# Go through each character
for character in characters:
# Transform image to list of lists
a_char = self.imageTo2DBinaryList(character)
similarity_best = 0.0
similarity_letter = None
for char, chars_list in trained_chars.iteritems():
for a_trained_char in chars_list:
similarity = self.computeSimilarity(a_char, a_trained_char)
if similarity > similarity_best:
similarity_best = similarity
similarity_letter = char
supposed_words.append((similarity_best, similarity_letter))
Logger.log("{} {}%".format(similarity_letter, round(similarity_best, 2)))
guess = ""
multiple_probability = 1.0
average_probability = 0.0
all_guesed = True
for probability, letter in supposed_words:
if letter:
guess += str(letter)
else:
guess += "_"
all_guesed = False
multiple_probability *= probability / 100
average_probability += probability
if len(guess):
average_probability /= len(guess)
Logger.subheader("Results")
if all_guesed:
Logger.success(guess)
else:
Logger.info("characters marked with _ are the ones that are not guessed:")
Logger.error(guess)
Logger.info("{}% Overall probability".format(round(multiple_probability * 100, 2)))
Logger.info("{}% Average probability".format(round(average_probability, 2)))
if not options.verbose:
Logger.log(guess, True)
return guess
