def main():
c.clear_temp()
img = dataset_manager.get_training_image()
recognizer = captcha_recognizer.CaptchaRecognizer()
mpimg.imsave(c.temp_path('00.origin.png'), img)
# 1
img_01 = time_func(
'remove_noise_with_hsv',
lambda: recognizer.remove_noise_with_hsv(img)
)
mpimg.imsave(c.temp_path('01.hsv.png'), img_01, cmap=cm_greys)
# 2
img_02 = time_func(
'remove_noise_with_neighbors',
lambda: repeat(recognizer.remove_noise_with_neighbors, 2)(img_01)
)
mpimg.imsave(c.temp_path('02.neighbor.png'), img_02, cmap=cm_greys)
img_03a = time_func(
'skeletonize',
lambda: morph.skeletonize(img_02)
)
mpimg.imsave(c.temp_path('03a.skeleton.png'), img_03a, cmap=cm_greys)
def partition(self, img, save_intermediate=False, verbose=False, force_partition=True):
weak_confidence = 0
if save_intermediate:
mpimg.imsave(c.temp_path("00.origin.png"), img)
# step 1
# remove noise with hsv
img_01 = time_func("remove_noise_with_hsv" if verbose else None, lambda: self.remove_noise_with_hsv(img))
if save_intermediate:
mpimg.imsave(c.temp_path("01.hsv.png"), img_01, cmap=cm_greys)
# step 2
# remove noise with neighbors
img_02 = time_func(
"remove_noise_with_neighbors" if verbose else None,
lambda: repeat(self.remove_noise_with_neighbors, 2)(img_01),
)
if save_intermediate:
mpimg.imsave(c.temp_path("02.neighbor.png"), img_02, cmap=cm_greys)
# step 3
# partition stage 1
labels, object_slices = time_func(
"segment_with_label" if verbose else None, lambda: self.segment_with_label(img_02)
)
if verbose:
print("{} connected components found".format(len(object_slices)))
if save_intermediate:
mpimg.imsave(c.temp_path("03.00000.png"), labels)
# step 4
# Arrange the segments from left to right and probably partition stage 2
xmin_arr = np.array([s[1].start for s in object_slices])
sort_index = xmin_arr.argsort()
char_images = []
for i in list(sort_index):
char_image = img_02.copy()
char_image[labels != i + 1] = 0
char_image = char_image[object_slices[i]]
char_images.append(char_image)
if force_partition and len(char_images) == self.character_num - 1:
weak_confidence = 1
char_images = self.force_partition(char_images)
# step 5
# Check if segmentation was successful and get characters
if len(char_images) == self.character_num:
shapes = np.array(list(map(np.shape, char_images)))
heights, widths = shapes[:, 0], shapes[:, 1]
if verbose:
print("Heights {}".format(heights))
print("Widths {}".format(widths))
# noinspection PyTypeChecker
if (
np.all(heights >= self.char_height_min)
and np.all(heights <= self.char_height_max)
and np.all(widths >= self.char_width_min)
and np.all(widths <= self.char_width_max)
):
if save_intermediate:
for i in range(len(char_images)):
mpimg.imsave(c.temp_path("03.char.{}.png".format(i + 1)), char_images[i], cmap=cm_greys)
return char_images, weak_confidence
if verbose:
print("Warning: partition failed!")
return None, weak_confidence
def partition(self, img, save_intermediate=False, verbose=False,
force_partition=True):
weak_confidence = 0
if save_intermediate:
mpimg.imsave(c.temp_path('00.origin.png'), img)
# step 1
# remove noise with hsv
img_01 = time_func(
'remove_noise_with_hsv' if verbose else None,
lambda: self.remove_noise_with_hsv(img)
)
if save_intermediate:
mpimg.imsave(c.temp_path('01.hsv.png'), img_01, cmap=cm_greys)
# step 2
# remove noise with neighbors
img_02 = time_func(
'remove_noise_with_neighbors' if verbose else None,
lambda: repeat(self.remove_noise_with_neighbors, 2)(img_01)
)
if save_intermediate:
mpimg.imsave(c.temp_path('02.neighbor.png'), img_02, cmap=cm_greys)
# step 3
# partition stage 1
labels, object_slices = time_func(
'segment_with_label' if verbose else None,
lambda: self.segment_with_label(img_02)
)
if verbose:
print('{} connected components found'.format(len(object_slices)))
if save_intermediate:
mpimg.imsave(c.temp_path('03.00000.png'), labels)
# step 4
# Arrange the segments from left to right and probably partition stage 2
xmin_arr = np.array([s[1].start for s in object_slices])
sort_index = xmin_arr.argsort()
char_images = []
for i in list(sort_index):
char_image = img_02.copy()
char_image[labels != i + 1] = 0
char_image = char_image[object_slices[i]]
char_images.append(char_image)
if force_partition and len(char_images) == self.character_num - 1:
weak_confidence = 1
char_images = self.force_partition(char_images)
# step 5
# Check if segmentation was successful and get characters
if len(char_images) == self.character_num:
shapes = np.array(list(map(np.shape, char_images)))
heights, widths = shapes[:, 0], shapes[:, 1]
if verbose:
print('Heights {}'.format(heights))
print('Widths {}'.format(widths))
# noinspection PyTypeChecker
if (np.all(heights >= self.char_height_min) and
np.all(heights <= self.char_height_max) and
np.all(widths >= self.char_width_min) and
np.all(widths <= self.char_width_max)):
if save_intermediate:
for i in range(len(char_images)):
mpimg.imsave(
c.temp_path('03.char.{}.png'.format(i + 1)),
char_images[i], cmap=cm_greys)
return char_images, weak_confidence
if verbose:
print('Warning: partition failed!')
return None, weak_confidence
def partition(self, img, save_intermediate=False, verbose=False):
if save_intermediate:
mpimg.imsave(c.temp_path('00.origin.png'), img)
# 1
img_01 = time_func(
'remove_noise_with_hsv' if verbose else None,
lambda: self.remove_noise_with_hsv(img)
)
if save_intermediate:
mpimg.imsave(c.temp_path('01.hsv.png'), img_01, cmap=cm_greys)
# 2
img_02 = time_func(
'remove_noise_with_neighbors' if verbose else None,
lambda: repeat(self.remove_noise_with_neighbors, 2)(img_01)
)
if save_intermediate:
mpimg.imsave(c.temp_path('02.neighbor.png'), img_02, cmap=cm_greys)
# 3
labels, object_slices = time_func(
'segment_with_label' if verbose else None,
lambda: self.segment_with_label(img_02)
)
if verbose:
print('{} connected components found'.format(len(object_slices)))
if save_intermediate:
mpimg.imsave(c.temp_path('03.00000.png'), labels)
# Arrange the segments from left to right
xmin_arr = np.array([s[1].start for s in object_slices])
sort_index = xmin_arr.argsort()
char_images = []
# noinspection PyTypeChecker
for i in sort_index:
char_image = img_02.copy()
char_image[labels != i + 1] = 0
char_image = char_image[object_slices[i]]
char_images.append(char_image)
# Check if segmentation was successful
if len(char_images) == self.character_num:
shapes = np.array(list(map(np.shape, char_images)))
heights, widths = shapes[:, 0], shapes[:, 1]
if verbose:
print('Heights {}'.format(heights))
print('Widths {}'.format(widths))
# noinspection PyTypeChecker
if (np.all(heights >= self.char_height_min) and
np.all(heights <= self.char_height_max) and
np.all(widths >= self.char_width_min) and
np.all(widths <= self.char_width_max)):
if save_intermediate:
for i in range(len(char_images)):
mpimg.imsave(
c.temp_path('03.char.{}.png'.format(i + 1)),
char_images[i], cmap=cm_greys)
return char_images
if verbose:
print('Warning: partition failed!')
return None