def segment_image(img,
max_scale=defaults.CC_SCALE_MAX,
min_scale=defaults.CC_SCALE_MIN):
(h, w) = img.shape[:2]
if arg.boolean_value('verbose'):
print 'Segmenting ' + str(h) + 'x' + str(w) + ' image.'
#create gaussian filtered and unfiltered binary images
binary_threshold = arg.integer_value(
'binary_threshold', default_value=defaults.BINARY_THRESHOLD)
if arg.boolean_value('verbose'):
print 'binarizing images with threshold value of ' + str(
binary_threshold)
binary = clean.binarize(img, threshold=binary_threshold)
binary_average_size = cc.average_size(binary)
if arg.boolean_value('verbose'):
print 'average cc size for binaryized grayscale image is ' + str(
binary_average_size)
'''
The necessary sigma needed for Gaussian filtering (to remove screentones and other noise) seems
to be a function of the resolution the manga was scanned at (or original page size, I'm not sure).
Assuming 'normal' page size for a phonebook style Manga is 17.5cmx11.5cm (6.8x4.5in).
A scan of 300dpi will result in an image about 1900x1350, which requires a sigma of 1.5 to 1.8.
I'm encountering many smaller images that may be nonstandard scanning dpi values or just smaller
magazines. Haven't found hard info on this yet. They require sigma values of about 0.5 to 0.7.
I'll therefore (for now) just calculate required (nonspecified) sigma as a linear function of vertical
image resolution.
'''
sigma = (0.8 / 676.0) * float(h) - 0.9
sigma = arg.float_value('sigma', default_value=sigma)
if arg.boolean_value('verbose'):
print 'Applying Gaussian filter with sigma (std dev) of ' + str(sigma)
gaussian_filtered = scipy.ndimage.gaussian_filter(img, sigma=sigma)
gaussian_binary = clean.binarize(gaussian_filtered,
threshold=binary_threshold)
#Draw out statistics on average connected component size in the rescaled, binary image
average_size = cc.average_size(gaussian_binary)
if arg.boolean_value('verbose'):
print 'Binarized Gaussian filtered image average cc size: ' + str(
average_size)
max_size = average_size * max_scale
min_size = average_size * min_scale
#primary mask is connected components filtered by size
mask = cc.form_mask(gaussian_binary, max_size, min_size)
#secondary mask is formed from canny edges
canny_mask = clean.form_canny_mask(gaussian_filtered, mask=mask)
#final mask is size filtered connected components on canny mask
final_mask = cc.form_mask(canny_mask, max_size, min_size)
#apply mask and return images
cleaned = cv2.bitwise_not(final_mask * binary)
text_only = cleaned2segmented(cleaned, average_size)
#if desired, suppress furigana characters (which interfere with OCR)
suppress_furigana = arg.boolean_value('furigana')
if suppress_furigana:
if arg.boolean_value('verbose'):
print 'Attempting to suppress furigana characters which interfere with OCR.'
furigana_mask = furigana.estimate_furigana(cleaned, text_only)
furigana_mask = np.array(furigana_mask == 0, 'B')
cleaned = cv2.bitwise_not(cleaned) * furigana_mask
cleaned = cv2.bitwise_not(cleaned)
text_only = cleaned2segmented(cleaned, average_size)
(text_like_areas,
nontext_like_areas) = filter_text_like_areas(img,
segmentation=text_only,
average_size=average_size)
if arg.boolean_value('verbose'):
print '**********there are ' + str(
len(text_like_areas)) + ' text like areas total.'
text_only = np.zeros(img.shape)
cc.draw_bounding_boxes(text_only,
text_like_areas,
color=(255),
line_size=-1)
if arg.boolean_value('debug'):
text_only = 0.5 * text_only + 0.5 * img
#text_rows = 0.5*text_rows+0.5*gray
#text_colums = 0.5*text_columns+0.5*gray
#text_only = filter_text_like_areas(img, segmentation=text_only, average_size=average_size)
segmented_image = np.zeros((h, w, 3), np.uint8)
segmented_image[:, :, 0] = img
segmented_image[:, :, 1] = text_only
segmented_image[:, :, 2] = text_only
return segmented_image
def segment_image(img, max_scale=defaults.CC_SCALE_MAX, min_scale=defaults.CC_SCALE_MIN):
(h,w)=img.shape[:2]
if arg.boolean_value('verbose'):
print 'Segmenting ' + str(h) + 'x' + str(w) + ' image.'
#create gaussian filtered and unfiltered binary images
binary_threshold = arg.integer_value('binary_threshold',default_value=defaults.BINARY_THRESHOLD)
if arg.boolean_value('verbose'):
print 'binarizing images with threshold value of ' + str(binary_threshold)
binary = clean.binarize(img,threshold=binary_threshold)
binary_average_size = cc.average_size(binary)
if arg.boolean_value('verbose'):
print 'average cc size for binaryized grayscale image is ' + str(binary_average_size)
'''
The necessary sigma needed for Gaussian filtering (to remove screentones and other noise) seems
to be a function of the resolution the manga was scanned at (or original page size, I'm not sure).
Assuming 'normal' page size for a phonebook style Manga is 17.5cmx11.5cm (6.8x4.5in).
A scan of 300dpi will result in an image about 1900x1350, which requires a sigma of 1.5 to 1.8.
I'm encountering many smaller images that may be nonstandard scanning dpi values or just smaller
magazines. Haven't found hard info on this yet. They require sigma values of about 0.5 to 0.7.
I'll therefore (for now) just calculate required (nonspecified) sigma as a linear function of vertical
image resolution.
'''
sigma = (0.8/676.0)*float(h)-0.9
sigma = arg.float_value('sigma',default_value=sigma)
if arg.boolean_value('verbose'):
print 'Applying Gaussian filter with sigma (std dev) of ' + str(sigma)
gaussian_filtered = scipy.ndimage.gaussian_filter(img, sigma=sigma)
gaussian_binary = clean.binarize(gaussian_filtered,threshold=binary_threshold)
#Draw out statistics on average connected component size in the rescaled, binary image
average_size = cc.average_size(gaussian_binary)
if arg.boolean_value('verbose'):
print 'Binarized Gaussian filtered image average cc size: ' + str(average_size)
max_size = average_size*max_scale
min_size = average_size*min_scale
#primary mask is connected components filtered by size
mask = cc.form_mask(gaussian_binary, max_size, min_size)
#secondary mask is formed from canny edges
canny_mask = clean.form_canny_mask(gaussian_filtered, mask=mask)
#final mask is size filtered connected components on canny mask
final_mask = cc.form_mask(canny_mask, max_size, min_size)
#apply mask and return images
cleaned = cv2.bitwise_not(final_mask * binary)
text_only = cleaned2segmented(cleaned, average_size)
#if desired, suppress furigana characters (which interfere with OCR)
suppress_furigana = arg.boolean_value('furigana')
if suppress_furigana:
if arg.boolean_value('verbose'):
print 'Attempting to suppress furigana characters which interfere with OCR.'
furigana_mask = furigana.estimate_furigana(cleaned, text_only)
furigana_mask = np.array(furigana_mask==0,'B')
cleaned = cv2.bitwise_not(cleaned)*furigana_mask
cleaned = cv2.bitwise_not(cleaned)
text_only = cleaned2segmented(cleaned, average_size)
(text_like_areas, nontext_like_areas) = filter_text_like_areas(img, segmentation=text_only, average_size=average_size)
if arg.boolean_value('verbose'):
print '**********there are ' + str(len(text_like_areas)) + ' text like areas total.'
text_only = np.zeros(img.shape)
cc.draw_bounding_boxes(text_only, text_like_areas,color=(255),line_size=-1)
if arg.boolean_value('debug'):
text_only = 0.5*text_only + 0.5*img
#text_rows = 0.5*text_rows+0.5*gray
#text_colums = 0.5*text_columns+0.5*gray
#text_only = filter_text_like_areas(img, segmentation=text_only, average_size=average_size)
segmented_image = np.zeros((h,w,3), np.uint8)
segmented_image[:,:,0] = img
segmented_image[:,:,1] = text_only
segmented_image[:,:,2] = text_only
return segmented_image