def GenerateTrainingData(cls,
captchas_dir,
training_data_dir,
max_size=500,
max_captcha_length=8):
image_shape = _GetShapeOfImagesUnderDir(captchas_dir)
training_data_shape = tuple(
[max_size] +
list(ImagePreprocessor.GetProcessedImageShape(image_shape)))
training_image_data = numpy.zeros(training_data_shape,
dtype=numpy.float32)
training_labels = numpy.zeros((max_size, max_captcha_length),
dtype=numpy.int32)
i = 0
for captcha_filepath in utils.GetFilePathsUnderDir(captchas_dir):
try:
image_data = ImagePreprocessor.GetImageData(captcha_filepath)
except Exception as e:
print e, captcha_filepath
continue
i += 1
index = i % max_size
training_image_data[index] = ImagePreprocessor.ProcessImage(
image_data)
captcha_ids = _GetCaptchaIdsFromImageFilename(captcha_filepath)
training_labels[index, :] = numpy.zeros(max_captcha_length,
dtype=numpy.int32)
training_labels[index, :captcha_ids.shape[0]] = captcha_ids
if i != 0 and (i % 1000) == 0:
print 'Generated {0} examples.'.format(i)
if i != 0 and i % max_size == 0:
print i
file_path = os.path.join(
training_data_dir,
"training_images_{0}.npy".format(i / max_size))
try:
cls.Save(file_path, training_image_data, training_labels)
except Exception as e:
print e
def _GetShapeOfImagesUnderDir(captchas_dir):
for captcha_filepath in utils.GetFilePathsUnderDir(captchas_dir):
image_data = ImagePreprocessor.GetImageData(captcha_filepath)
return image_data.shape
return None
