def clean_page(img, max_scale=defaults.CC_SCALE_MAX, min_scale=defaults.CC_SCALE_MIN):
#img = cv2.imread(sys.argv[1])
(h,w,d)=img.shape
gray = grayscale(img)
#create gaussian filtered and unfiltered binary images
sigma = arg.float_value('sigma',default_value=defaults.GAUSSIAN_FILTER_SIGMA)
if arg.boolean_value('verbose'):
print 'Binarizing image with sigma value of ' + str(sigma)
gaussian_filtered = scipy.ndimage.gaussian_filter(gray, sigma=sigma)
binary_threshold = arg.integer_value('binary_threshold',default_value=defaults.BINARY_THRESHOLD)
if arg.boolean_value('verbose'):
print 'Binarizing image with sigma value of ' + str(sigma)
gaussian_binary = binarize(gaussian_filtered, threshold=binary_threshold)
binary = binarize(gray, threshold=binary_threshold)
#Draw out statistics on average connected component size in the rescaled, binary image
average_size = cc.average_size(gaussian_binary)
#print 'Initial mask average size is ' + 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 = 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)
return (cv2.bitwise_not(binary), final_mask, cleaned)
def clean_page(img, max_scale=defaults.CC_SCALE_MAX, min_scale=defaults.CC_SCALE_MIN):
#img = cv2.imread(sys.argv[1])
(h,w,d)=img.shape
gray = grayscale(img)
#create gaussian filtered and unfiltered binary images
sigma = arg.float_value('sigma',default_value=defaults.GAUSSIAN_FILTER_SIGMA)
if arg.boolean_value('verbose'):
print 'Binarizing image with sigma value of ' + str(sigma)
gaussian_filtered = scipy.ndimage.gaussian_filter(gray, sigma=sigma)
binary_threshold = arg.integer_value('binary_threshold',default_value=defaults.BINARY_THRESHOLD)
if arg.boolean_value('verbose'):
print 'Binarizing image with sigma value of ' + str(sigma)
gaussian_binary = binarize(gaussian_filtered, threshold=binary_threshold)
binary = binarize(gray, threshold=binary_threshold)
#Draw out statistics on average connected component size in the rescaled, binary image
average_size = cc.average_size(gaussian_binary)
#print 'Initial mask average size is ' + 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 = 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)
return (cv2.bitwise_not(binary), final_mask, cleaned)
def filter_text_like_areas(img, segmentation, average_size):
#see if a given rectangular area (2d slice) is very text like
#First step is to estimate furigana like elements so they can be masked
furigana_areas = furigana.estimate_furigana(img, segmentation)
furigana_mask = np.array(furigana_areas == 0, 'B')
#binarize the image, clean it via the segmentation and remove furigana too
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)
segmentation_mask = np.array(segmentation != 0, 'B')
cleaned = binary * segmentation_mask * furigana_mask
inv_cleaned = cv2.bitwise_not(cleaned)
areas = cc.get_connected_components(segmentation)
text_like_areas = []
nontext_like_areas = []
for area in areas:
#if area_is_text_like(cleaned, area, average_size):
if text_like_histogram(cleaned, area, average_size):
text_like_areas.append(area)
else:
nontext_like_areas.append(area)
return (text_like_areas, nontext_like_areas)
def estimate_furigana(img, segmentation):
(w,h)=img.shape[:2]
if arg.boolean_value('verbose'):
print('Estimateding furigana in ' + str(h) + 'x' + str(w) + ' image.')
text_areas = segmentation
#form binary image from grayscale
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))
#apply mask and return images
text_mask = binary_mask(text_areas)
cleaned = cv2.bitwise_not(text_mask*binary)
cleaned_average_size = cc.average_size(cleaned)
if arg.boolean_value('verbose'):
print('average cc size for cleaned, binaryized grayscale image is ' + str(cleaned_average_size))
columns = scipy.ndimage.filters.gaussian_filter(cleaned,(defaults.FURIGANA_VERTICAL_SIGMA_MULTIPLIER*binary_average_size,defaults.FURIGANA_HORIZONTAL_SIGMA_MULTIPLIER*binary_average_size))
columns = clean.binarize(columns,threshold=defaults.FURIGANA_BINARY_THRESHOLD)
furigana = columns*text_mask
#go through the columns in each text area, and:
#1) Estimate the standard column width (it should be similar to the average connected component width)
#2) Separate out those columns which are significantly thinner (>75%) than the standard width
boxes = cc.get_connected_components(furigana)
furigana_lines = []
non_furigana_lines = []
lines_general = []
for box in boxes:
line_width = cc_width(box)
line_to_left = find_cc_to_left(box, boxes, max_dist=line_width*defaults.FURIGANA_DISTANCE_MULTIPLIER)
if line_to_left is None:
non_furigana_lines.append(box)
continue
left_line_width = cc_width(line_to_left)
if line_width < left_line_width * defaults.FURIGANA_WIDTH_THRESHOLD:
furigana_lines.append(box)
else:
non_furigana_lines.append(box)
furigana_mask = np.zeros(furigana.shape)
for f in furigana_lines:
furigana_mask[f[0].start:f[0].stop,f[1].start:f[1].stop]=255
#furigana_mask[f]=1
furigana = furigana_mask #furigana * furigana_mask
if arg.boolean_value('debug'):
furigana = 0.25*(columns*text_mask) + 0.25*img + 0.5*furigana
return furigana
def filter_text_like_areas(img, segmentation, average_size):
#see if a given rectangular area (2d slice) is very text like
#First step is to estimate furigana like elements so they can be masked
furigana_areas = furigana.estimate_furigana(img, segmentation)
furigana_mask = np.array(furigana_areas==0,'B')
#binarize the image, clean it via the segmentation and remove furigana too
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)
segmentation_mask = np.array(segmentation!=0,'B')
cleaned = binary * segmentation_mask * furigana_mask
inv_cleaned = cv2.bitwise_not(cleaned)
areas = cc.get_connected_components(segmentation)
text_like_areas = []
nontext_like_areas = []
for area in areas:
#if area_is_text_like(cleaned, area, average_size):
if text_like_histogram(cleaned, area, average_size):
text_like_areas.append(area)
else:
nontext_like_areas.append(area)
return (text_like_areas, nontext_like_areas)
def estimate_furigana(img, segmentation):
(w,h)=img.shape[:2]
if arg.boolean_value('verbose'):
print 'Estimateding furigana in ' + str(h) + 'x' + str(w) + ' image.'
text_areas = segmentation
#form binary image from grayscale
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)
#apply mask and return images
text_mask = binary_mask(text_areas)
cleaned = cv2.bitwise_not(text_mask*binary)
cleaned_average_size = cc.average_size(cleaned)
if arg.boolean_value('verbose'):
print 'average cc size for cleaned, binaryized grayscale image is ' + str(cleaned_average_size)
columns = scipy.ndimage.filters.gaussian_filter(cleaned,(defaults.FURIGANA_VERTICAL_SIGMA_MULTIPLIER*binary_average_size,defaults.FURIGANA_HORIZONTAL_SIGMA_MULTIPLIER*binary_average_size))
columns = clean.binarize(columns,threshold=defaults.FURIGANA_BINARY_THRESHOLD)
furigana = columns*text_mask
#go through the columns in each text area, and:
#1) Estimate the standard column width (it should be similar to the average connected component width)
#2) Separate out those columns which are significantly thinner (>75%) than the standard width
boxes = cc.get_connected_components(furigana)
furigana_lines = []
non_furigana_lines = []
lines_general = []
for box in boxes:
line_width = cc_width(box)
line_to_left = find_cc_to_left(box, boxes, max_dist=line_width*defaults.FURIGANA_DISTANCE_MULTIPLIER)
if line_to_left is None:
non_furigana_lines.append(box)
continue
left_line_width = cc_width(line_to_left)
if line_width < left_line_width * defaults.FURIGANA_WIDTH_THRESHOLD:
furigana_lines.append(box)
else:
non_furigana_lines.append(box)
furigana_mask = np.zeros(furigana.shape)
for f in furigana_lines:
furigana_mask[f[0].start:f[0].stop,f[1].start:f[1].stop]=255
#furigana_mask[f]=1
furigana = furigana_mask #furigana * furigana_mask
if arg.boolean_value('debug'):
furigana = 0.25*(columns*text_mask) + 0.25*img + 0.5*furigana
return furigana
def main():
parser = arg.parser
parser = argparse.ArgumentParser(
description='Estimate areas of furigana in segmented raw manga scan.')
parser.add_argument('infile',
help='Input (color) raw Manga scan image to clean.')
parser.add_argument(
'segmentation_file',
help=
'Input 3 channel segmentation of input image, with text areas in R channel.'
)
parser.add_argument('-o',
'--output',
dest='outfile',
help='Output (color) cleaned raw manga scan image.')
#parser.add_argument('-m','--mask', dest='mask', default=None, help='Output (binary) mask for non-graphical regions.')
#parser.add_argument('-b','--binary', dest='binary', default=None, help='Binarized version of input file.')
parser.add_argument(
'-v',
'--verbose',
help='Verbose operation. Print status messages during processing',
action="store_true")
parser.add_argument(
'--display',
help='Display output using OPENCV api and block program exit.',
action="store_true")
parser.add_argument('-d',
'--debug',
help='Overlay input image into output.',
action="store_true")
#parser.add_argument('--sigma', help='Std Dev of gaussian preprocesing filter.',type=float,default=None)
#parser.add_argument('--segment_threshold', help='Threshold for nonzero pixels to separete vert/horiz text lines.',type=int,default=1)
arg.value = parser.parse_args()
infile = arg.string_value('infile')
segmentation_file = arg.string_value('segmentation_file')
outfile = arg.string_value('outfile',
default_value=infile + '.furigana.png')
if not os.path.isfile(infile) or not os.path.isfile(segmentation_file):
print(
'Please provide a regular existing input file. Use -h option for help.'
)
sys.exit(-1)
if arg.boolean_value('verbose'):
print('\tProcessing file ' + infile)
print('\tWith segmentation file ' + segmentation_file)
print('\tAnd generating output ' + outfile)
furigana = estimate_furigana_from_files(infile, segmentation_file)
imsave(outfile, furigana)
if arg.boolean_value('display'):
cv2.imshow('Furigana', furigana)
if cv2.waitKey(0) == 27:
cv2.destroyAllWindows()
cv2.destroyAllWindows()
def cleaned2segmented(cleaned, average_size):
vertical_smoothing_threshold = defaults.VERTICAL_SMOOTHING_MULTIPLIER * average_size
horizontal_smoothing_threshold = defaults.HORIZONTAL_SMOOTHING_MULTIPLIER * average_size
(h, w) = cleaned.shape[:2]
if arg.boolean_value('verbose'):
print 'Applying run length smoothing with vertical threshold ' + str(vertical_smoothing_threshold) \
+' and horizontal threshold ' + str(horizontal_smoothing_threshold)
run_length_smoothed = rls.RLSO(cv2.bitwise_not(cleaned),
vertical_smoothing_threshold,
horizontal_smoothing_threshold)
components = cc.get_connected_components(run_length_smoothed)
text = np.zeros((h, w), np.uint8)
#text_columns = np.zeros((h,w),np.uint8)
#text_rows = np.zeros((h,w),np.uint8)
for component in components:
seg_thresh = arg.integer_value('segment_threshold', default_value=1)
(aspect, v_lines,
h_lines) = ocr.segment_into_lines(cv2.bitwise_not(cleaned),
component,
min_segment_threshold=seg_thresh)
if len(v_lines) < 2 and len(h_lines) < 2: continue
ocr.draw_2d_slices(text, [component], color=255, line_size=-1)
#ocr.draw_2d_slices(text_columns,v_lines,color=255,line_size=-1)
#ocr.draw_2d_slices(text_rows,h_lines,color=255,line_size=-1)
return text
def cleaned2segmented(cleaned, average_size):
"cleaned是已经把图像中的字细化了"
vertical_smoothing_threshold = defaults.VERTICAL_SMOOTHING_MULTIPLIER*average_size
horizontal_smoothing_threshold = defaults.HORIZONTAL_SMOOTHING_MULTIPLIER*average_size
(h,w) = cleaned.shape[:2]
if arg.boolean_value('verbose'):
print 'Applying run length smoothing with vertical threshold ' + str(vertical_smoothing_threshold) \
+' and horizontal threshold ' + str(horizontal_smoothing_threshold)
run_length_smoothed = rls.RLSO( cv2.bitwise_not(cleaned), vertical_smoothing_threshold, horizontal_smoothing_threshold)
components = cc.get_connected_components(run_length_smoothed)
text = np.zeros((h,w),np.uint8)
#text_columns = np.zeros((h,w),np.uint8)
#text_rows = np.zeros((h,w),np.uint8)
for component in components:
seg_thresh = arg.integer_value('segment_threshold',default_value=1)
(aspect, v_lines, h_lines) = ocr.segment_into_lines(cv2.bitwise_not(cleaned), component,min_segment_threshold=seg_thresh)
if len(v_lines)<2 and len(h_lines)<2:continue
ocr.draw_2d_slices(text,[component],color=255,line_size=-1)
#ocr.draw_2d_slices(text_columns,v_lines,color=255,line_size=-1)
#ocr.draw_2d_slices(text_rows,h_lines,color=255,line_size=-1)
return text
def main():
parser = arg.parser
parser = argparse.ArgumentParser(description='Segment raw Manga scan image.')
parser.add_argument('infile', help='Input (color) raw Manga scan image to clean.')
parser.add_argument('-o','--output', dest='outfile', help='Output (color) cleaned raw manga scan image.')
#parser.add_argument('-m','--mask', dest='mask', default=None, help='Output (binary) mask for non-graphical regions.')
#parser.add_argument('-b','--binary', dest='binary', default=None, help='Binarized version of input file.')
parser.add_argument('-v','--verbose', help='Verbose operation. Print status messages during processing', action="store_true")
parser.add_argument('--display', help='Display output using OPENCV api and block program exit.', action="store_true")
parser.add_argument('-d','--debug', help='Overlay input image into output.', action="store_true")
parser.add_argument('--sigma', help='Std Dev of gaussian preprocesing filter.',type=float,default=None)
parser.add_argument('--binary_threshold', help='Binarization threshold value from 0 to 255.',type=float,default=defaults.BINARY_THRESHOLD)
parser.add_argument('--furigana', help='Attempt to suppress furigana characters to improve OCR.', action="store_true")
parser.add_argument('--segment_threshold', help='Threshold for nonzero pixels to separete vert/horiz text lines.',type=int,default=defaults.SEGMENTATION_THRESHOLD)
parser.add_argument('--additional_filtering', help='Attempt to filter false text positives by histogram processing.', action="store_true")
arg.value = parser.parse_args()
infile = arg.string_value('infile')
outfile = arg.string_value('outfile', default_value=infile + '.segmented.png')
binary_outfile = infile + '.binary.png'
if not os.path.isfile(infile):
print 'Please provide a regular existing input file. Use -h option for help.'
sys.exit(-1)
if arg.boolean_value('verbose'):
print '\tProcessing file ' + infile
print '\tGenerating output ' + outfile
segmented = segment_image_file(infile)
imsave(outfile,segmented)
#cv2.imwrite(outfile,segmented)
#if binary is not None:
# cv2.imwrite(binary_outfile, binary)
if arg.boolean_value('display'):
cv2.imshow('Segmented', segmented)
#cv2.imshow('Cleaned',cleaned)
#if args.mask is not None:
# cv2.imshow('Mask',mask)
if cv2.waitKey(0) == 27:
cv2.destroyAllWindows()
cv2.destroyAllWindows()
def main():
parser = arg.parser
parser = argparse.ArgumentParser(description='Segment raw Manga scan image.')
parser.add_argument('infile', help='Input (color) raw Manga scan image to clean.')
parser.add_argument('-o','--output', dest='outfile', help='Output (color) cleaned raw manga scan image.')
#parser.add_argument('-m','--mask', dest='mask', default=None, help='Output (binary) mask for non-graphical regions.')
#parser.add_argument('-b','--binary', dest='binary', default=None, help='Binarized version of input file.')
parser.add_argument('-v','--verbose', help='Verbose operation. Print status messages during processing', action="store_true")
parser.add_argument('--display', help='Display output using OPENCV api and block program exit.', action="store_true")
parser.add_argument('-d','--debug', help='Overlay input image into output.', action="store_true")
parser.add_argument('--sigma', help='Std Dev of gaussian preprocesing filter.',type=float,default=None)
parser.add_argument('--binary_threshold', help='Binarization threshold value from 0 to 255.',type=float,default=defaults.BINARY_THRESHOLD)
parser.add_argument('--furigana', help='Attempt to suppress furigana characters to improve OCR.', action="store_true")
parser.add_argument('--segment_threshold', help='Threshold for nonzero pixels to separete vert/horiz text lines.',type=int,default=defaults.SEGMENTATION_THRESHOLD)
parser.add_argument('--additional_filtering', help='Attempt to filter false text positives by histogram processing.', action="store_true")
arg.value = parser.parse_args()
infile = arg.string_value('infile')
outfile = arg.string_value('outfile', default_value=infile + '.segmented.png')
binary_outfile = infile + '.binary.png'
if not os.path.isfile(infile):
print('Please provide a regular existing input file. Use -h option for help.')
sys.exit(-1)
if arg.boolean_value('verbose'):
print('\tProcessing file ' + infile)
print('\tGenerating output ' + outfile)
segmented = segment_image_file(infile)
imsave(outfile,segmented)
#cv2.imwrite(outfile,segmented)
#if binary is not None:
# cv2.imwrite(binary_outfile, binary)
if arg.boolean_value('display'):
cv2.imshow('Segmented', segmented)
#cv2.imshow('Cleaned',cleaned)
#if args.mask is not None:
# cv2.imshow('Mask',mask)
if cv2.waitKey(0) == 27:
cv2.destroyAllWindows()
cv2.destroyAllWindows()
def main():
parser = arg.parser
parser = argparse.ArgumentParser(description='Estimate areas of furigana in segmented raw manga scan.')
parser.add_argument('infile', help='Input (color) raw Manga scan image to clean.')
parser.add_argument('segmentation_file', help='Input 3 channel segmentation of input image, with text areas in R channel.')
parser.add_argument('-o','--output', dest='outfile', help='Output (color) cleaned raw manga scan image.')
#parser.add_argument('-m','--mask', dest='mask', default=None, help='Output (binary) mask for non-graphical regions.')
#parser.add_argument('-b','--binary', dest='binary', default=None, help='Binarized version of input file.')
parser.add_argument('-v','--verbose', help='Verbose operation. Print status messages during processing', action="store_true")
parser.add_argument('--display', help='Display output using OPENCV api and block program exit.', action="store_true")
parser.add_argument('-d','--debug', help='Overlay input image into output.', action="store_true")
#parser.add_argument('--sigma', help='Std Dev of gaussian preprocesing filter.',type=float,default=None)
#parser.add_argument('--segment_threshold', help='Threshold for nonzero pixels to separete vert/horiz text lines.',type=int,default=1)
arg.value = parser.parse_args()
infile = arg.string_value('infile')
segmentation_file = arg.string_value('segmentation_file')
outfile = arg.string_value('outfile',default_value=infile + '.furigana.png')
if not os.path.isfile(infile) or not os.path.isfile(segmentation_file):
print 'Please provide a regular existing input file. Use -h option for help.'
sys.exit(-1)
if arg.boolean_value('verbose'):
print '\tProcessing file ' + infile
print '\tWith segmentation file ' + segmentation_file
print '\tAnd generating output ' + outfile
furigana = estimate_furigana_from_files(infile, segmentation_file)
imsave(outfile,furigana)
if arg.boolean_value('display'):
cv2.imshow('Furigana', furigana)
if cv2.waitKey(0) == 27:
cv2.destroyAllWindows()
cv2.destroyAllWindows()
def main():
parser = arg.parser
parser = argparse.ArgumentParser(description='Basic OCR on raw manga scan.')
parser.add_argument('infile', help='Input (color) raw Manga scan image to clean.')
parser.add_argument('-o','--output', dest='outfile', help='Output (color) cleaned raw manga scan image.')
parser.add_argument('-v','--verbose', help='Verbose operation. Print status messages during processing', action="store_true")
#parser.add_argument('-d','--debug', help='Overlay input image into output.', action="store_true")
parser.add_argument('--sigma', help='Std Dev of gaussian preprocesing filter.',type=float,default=None)
parser.add_argument('--binary_threshold', help='Binarization threshold value from 0 to 255.',type=int,default=defaults.BINARY_THRESHOLD)
parser.add_argument('--furigana', help='Attempt to suppress furigana characters to improve OCR.', action="store_true")
parser.add_argument('--segment_threshold', help='Threshold for nonzero pixels to separete vert/horiz text lines.',type=int,default=defaults.SEGMENTATION_THRESHOLD)
arg.value = parser.parse_args()
infile = arg.string_value('infile')
outfile = arg.string_value('outfile', default_value=infile + '.html')
if not os.path.isfile(infile):
print 'Please provide a regular existing input file. Use -h option for help.'
sys.exit(-1)
if arg.boolean_value('verbose'):
print '\tProcessing file ' + infile
print '\tGenerating output ' + outfile
img = cv2.imread(infile)
gray = clean.grayscale(img)
binary = clean.binarize(gray)
segmented = segmentation.segment_image_file(infile)
components = cc.get_connected_components(segmented)
#perhaps do more strict filtering of connected components because sections of characters
#will not be dripped from run length smoothed areas? Yes. Results quite good.
#filtered = cc.filter_by_size(img,components,average_size*100,average_size*1)
blurbs = ocr_on_bounding_boxes(binary, components)
for blurb in blurbs:
print str(blurb.x)+','+str(blurb.y)+' '+str(blurb.w)+'x'+str(blurb.h)+' '+ str(blurb.confidence)+'% :'+ blurb.text
def main():
parser = arg.parser
parser = argparse.ArgumentParser(description='Basic OCR on raw manga scan.')
parser.add_argument('infile', help='Input (color) raw Manga scan image to clean.')
parser.add_argument('-o','--output', dest='outfile', help='Output (color) cleaned raw manga scan image.')
parser.add_argument('-v','--verbose', help='Verbose operation. Print status messages during processing', action="store_true")
#parser.add_argument('-d','--debug', help='Overlay input image into output.', action="store_true")
parser.add_argument('--sigma', help='Std Dev of gaussian preprocesing filter.',type=float,default=None)
parser.add_argument('--binary_threshold', help='Binarization threshold value from 0 to 255.',type=int,default=defaults.BINARY_THRESHOLD)
parser.add_argument('--furigana', help='Attempt to suppress furigana characters to improve OCR.', action="store_true")
parser.add_argument('--segment_threshold', help='Threshold for nonzero pixels to separete vert/horiz text lines.',type=int,default=defaults.SEGMENTATION_THRESHOLD)
arg.value = parser.parse_args()
infile = arg.string_value('infile')
outfile = arg.string_value('outfile', default_value=infile + '.html')
if not os.path.isfile(infile):
print ('Please provide a regular existing input file. Use -h option for help.')
sys.exit(-1)
if arg.boolean_value('verbose'):
print ('\tProcessing file ' + infile)
print ('\tGenerating output ' + outfile)
img = cv2.imread(infile)
gray = clean.grayscale(img)
binary = clean.binarize(gray)
segmented = segmentation.segment_image_file(infile)
components = cc.get_connected_components(segmented)
#perhaps do more strict filtering of connected components because sections of characters
#will not be dripped from run length smoothed areas? Yes. Results quite good.
#filtered = cc.filter_by_size(img,components,average_size*100,average_size*1)
blurbs = ocr_on_bounding_boxes(binary, components)
for blurb in blurbs:
print (str(blurb.x)+','+str(blurb.y)+' '+str(blurb.w)+'x'+str(blurb.h)+' '+ str(blurb.confidence)+'% :'+ blurb.text)
def text_like_histogram(img, area, average_size):
if not arg.boolean_value('additional_filtering'):
return True
(x, y, w, h) = dimensions_2d_slice(area)
x_subimage = np.copy(img)
x_histogram = np.zeros(w,int)
y_subimage = np.copy(img)
y_histogram = np.zeros(h,int)
aoi = img[area]
ccs = cc.get_connected_components(aoi)
if( len(ccs) < 2):
return False
#avg = average_size
avg = cc.average_size(aoi)
mean_width = cc.mean_width(aoi)
mean_height = cc.mean_height(aoi)
if arg.boolean_value('verbose'):
print 'average size = ' + str(avg) + ' mean width = ' + str(mean_width) + ' mean height = ' + str(mean_height)
if math.isnan(avg) or avg==0:
if arg.boolean_value('verbose'):
print 'Rejecting area since average size is NaN'
#return False
#in a text area, the average size of a blob (cc) will reflect
#that of the used characters/typeface. Thus if there simply aren't
#enough pixels per character, we can drop this as a text candidate
#note the following is failing in odd situations, probably due to incorrect
#calculation of 'avg size'
#TODO: replace testing against "average size" with testing against
#hard thresholds for connected component width and height. i.e.
#if they're all thin small ccs, we can drop this area
#if avg < defaults.MINIMUM_TEXT_SIZE_THRESHOLD:
if mean_width < defaults.MINIMUM_TEXT_SIZE_THRESHOLD or \
mean_height < defaults.MINIMUM_TEXT_SIZE_THRESHOLD:
if arg.boolean_value('verbose'):
print 'Rejecting area since average width or height is less than threshold.'
return False
#check the basic aspect ratio of the ccs
if mean_width/mean_height < 0.5 or mean_width/mean_height > 2:
if arg.boolean_value('verbose'):
print 'Rejecting area since mean cc aspect ratio not textlike.'
return False
width_multiplier = float(avg)
height_multiplier = float(avg)
#gaussian filter the subimages in x,y directions to emphasise peaks and troughs
x_subimage = scipy.ndimage.filters.gaussian_filter(x_subimage,(0.01*width_multiplier,0))
y_subimage = scipy.ndimage.filters.gaussian_filter(y_subimage,(0,0.01*height_multiplier))
#put together the histogram for black pixels over the x directon (along columns) of the component
for i,col in enumerate(range(x,x+w)):
black_pixel_count = np.count_nonzero(y_subimage[y:y+h,col])
x_histogram[i] = black_pixel_count
#and along the y direction (along rows)
for i,row in enumerate(range(y,y+h)):
black_pixel_count = np.count_nonzero(x_subimage[row,x:x+w])
y_histogram[i] = black_pixel_count
h_white_runs = get_white_runs(x_histogram)
num_h_white_runs = len(h_white_runs)
h_black_runs = get_black_runs(x_histogram)
num_h_black_runs = len(h_black_runs)
(h_spacing_mean, h_spacing_variance) = slicing_list_stats(h_white_runs)
(h_character_mean, h_character_variance) = slicing_list_stats(h_black_runs)
v_white_runs = get_white_runs(y_histogram)
num_v_white_runs = len(v_white_runs)
v_black_runs = get_black_runs(y_histogram)
num_v_black_runs = len(v_black_runs)
(v_spacing_mean, v_spacing_variance) = slicing_list_stats(v_white_runs)
(v_character_mean, v_character_variance) = slicing_list_stats(v_black_runs)
if arg.boolean_value('verbose'):
print 'x ' + str(x) + ' y ' +str(y) + ' w ' + str(w) + ' h ' + str(h)
print 'white runs ' + str(len(h_white_runs)) + ' ' + str(len(v_white_runs))
print 'white runs mean ' + str(h_spacing_mean) + ' ' + str(v_spacing_mean)
print 'white runs std ' + str(h_spacing_variance) + ' ' + str(v_spacing_variance)
print 'black runs ' + str(len(h_black_runs)) + ' ' + str(len(v_black_runs))
print 'black runs mean ' + str(h_character_mean) + ' ' + str(v_character_mean)
print 'black runs std ' + str(h_character_variance) + ' ' + str(v_character_variance)
if num_h_white_runs < 2 and num_v_white_runs < 2:
if arg.boolean_value('verbose'):
print 'Rejecting area since not sufficient amount post filtering whitespace.'
return False
if v_spacing_variance > defaults.MAXIMUM_VERTICAL_SPACE_VARIANCE:
if arg.boolean_value('verbose'):
print 'Rejecting area since vertical inter-character space variance too high.'
return False
if v_character_mean < avg*0.5 or v_character_mean > avg*2.0:
pass
#return False
if h_character_mean < avg*0.5 or h_character_mean > avg*2.0:
pass
#return False
return True
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 text_like_histogram(img, area, average_size):
if not arg.boolean_value('additional_filtering'):
return True
(x, y, w, h) = dimensions_2d_slice(area)
x_subimage = np.copy(img)
x_histogram = np.zeros(w, int)
y_subimage = np.copy(img)
y_histogram = np.zeros(h, int)
aoi = img[area]
ccs = cc.get_connected_components(aoi)
if (len(ccs) < 2):
return False
#avg = average_size
avg = cc.average_size(aoi)
mean_width = cc.mean_width(aoi)
mean_height = cc.mean_height(aoi)
if arg.boolean_value('verbose'):
print 'average size = ' + str(avg) + ' mean width = ' + str(
mean_width) + ' mean height = ' + str(mean_height)
if math.isnan(avg) or avg == 0:
if arg.boolean_value('verbose'):
print 'Rejecting area since average size is NaN'
#return False
#in a text area, the average size of a blob (cc) will reflect
#that of the used characters/typeface. Thus if there simply aren't
#enough pixels per character, we can drop this as a text candidate
#note the following is failing in odd situations, probably due to incorrect
#calculation of 'avg size'
#TODO: replace testing against "average size" with testing against
#hard thresholds for connected component width and height. i.e.
#if they're all thin small ccs, we can drop this area
#if avg < defaults.MINIMUM_TEXT_SIZE_THRESHOLD:
if mean_width < defaults.MINIMUM_TEXT_SIZE_THRESHOLD or \
mean_height < defaults.MINIMUM_TEXT_SIZE_THRESHOLD:
if arg.boolean_value('verbose'):
print 'Rejecting area since average width or height is less than threshold.'
return False
#check the basic aspect ratio of the ccs
if mean_width / mean_height < 0.5 or mean_width / mean_height > 2:
if arg.boolean_value('verbose'):
print 'Rejecting area since mean cc aspect ratio not textlike.'
return False
width_multiplier = float(avg)
height_multiplier = float(avg)
#gaussian filter the subimages in x,y directions to emphasise peaks and troughs
x_subimage = scipy.ndimage.filters.gaussian_filter(
x_subimage, (0.01 * width_multiplier, 0))
y_subimage = scipy.ndimage.filters.gaussian_filter(
y_subimage, (0, 0.01 * height_multiplier))
#put together the histogram for black pixels over the x directon (along columns) of the component
for i, col in enumerate(range(x, x + w)):
black_pixel_count = np.count_nonzero(y_subimage[y:y + h, col])
x_histogram[i] = black_pixel_count
#and along the y direction (along rows)
for i, row in enumerate(range(y, y + h)):
black_pixel_count = np.count_nonzero(x_subimage[row, x:x + w])
y_histogram[i] = black_pixel_count
h_white_runs = get_white_runs(x_histogram)
num_h_white_runs = len(h_white_runs)
h_black_runs = get_black_runs(x_histogram)
num_h_black_runs = len(h_black_runs)
(h_spacing_mean, h_spacing_variance) = slicing_list_stats(h_white_runs)
(h_character_mean, h_character_variance) = slicing_list_stats(h_black_runs)
v_white_runs = get_white_runs(y_histogram)
num_v_white_runs = len(v_white_runs)
v_black_runs = get_black_runs(y_histogram)
num_v_black_runs = len(v_black_runs)
(v_spacing_mean, v_spacing_variance) = slicing_list_stats(v_white_runs)
(v_character_mean, v_character_variance) = slicing_list_stats(v_black_runs)
if arg.boolean_value('verbose'):
print 'x ' + str(x) + ' y ' + str(y) + ' w ' + str(w) + ' h ' + str(h)
print 'white runs ' + str(len(h_white_runs)) + ' ' + str(
len(v_white_runs))
print 'white runs mean ' + str(h_spacing_mean) + ' ' + str(
v_spacing_mean)
print 'white runs std ' + str(h_spacing_variance) + ' ' + str(
v_spacing_variance)
print 'black runs ' + str(len(h_black_runs)) + ' ' + str(
len(v_black_runs))
print 'black runs mean ' + str(h_character_mean) + ' ' + str(
v_character_mean)
print 'black runs std ' + str(h_character_variance) + ' ' + str(
v_character_variance)
if num_h_white_runs < 2 and num_v_white_runs < 2:
if arg.boolean_value('verbose'):
print 'Rejecting area since not sufficient amount post filtering whitespace.'
return False
if v_spacing_variance > defaults.MAXIMUM_VERTICAL_SPACE_VARIANCE:
if arg.boolean_value('verbose'):
print 'Rejecting area since vertical inter-character space variance too high.'
return False
if v_character_mean < avg * 0.5 or v_character_mean > avg * 2.0:
pass
#return False
if h_character_mean < avg * 0.5 or h_character_mean > avg * 2.0:
pass
#return False
return True
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
import scipy.ndimage
import glob
if __name__ == '__main__':
AbsPath = 'C:/Users/blue.i/Desktop/New folder/MangaTextDetection-master/'
for filename in glob.glob(os.path.join(AbsPath, '*.jpg')):
infile = "" + filename
print(infile)
outfile = infile + '.text_areas.jpg'
img = cv2.imread(infile)
gray = clean.grayscale(img)
binary_threshold = arg.integer_value(
'binary_threshold', default_value=defaults.BINARY_THRESHOLD)
if arg.boolean_value('verbose'):
print('Binarizing with threshold value of ' +
str(binary_threshold))
inv_binary = cv2.bitwise_not(
clean.binarize(gray, threshold=binary_threshold))
binary = clean.binarize(gray, threshold=binary_threshold)
segmented_image = seg.segment_image(gray)
segmented_image = segmented_image[:, :, 2]
myImage = np.copy(segmented_image)
image, contours, hierarchy = cv2.findContours(myImage, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
if cv2.contourArea(cnt) > 4000:
cv2.drawContours(img, [cnt], 0, (255, 255, 255), cv2.FILLED)
imsave(outfile, img)
'-v',
'--verbose',
help='Verbose operation. Print status messages during processing',
action="store_true")
parser.add_argument(
'--display',
help='Display output using OPENCV api and block program exit.',
action="store_true")
arg.value = parser.parse_args()
infile = arg.string_value('infile')
outfile = arg.string_value('outfile',
default_value=infile + '.cleaned.png')
binary_outfile = arg.string_value('binary',
default_value=infile + '.binary.png')
mask = arg.boolean_value('mask')
if not os.path.isfile(infile):
print(
'Please provide a regular existing input file. Use -h option for help.'
)
sys.exit(-1)
if arg.boolean_value('verbose'):
print('\tProcessing file ' + infile)
print('\tGenerating output ' + outfile)
(binary, mask, cleaned) = clean_image_file(infile)
cv2.imwrite(outfile, cleaned)
if binary is not None:
if __name__ == '__main__':
parser = arg.parser
parser = argparse.ArgumentParser(description='Clean raw Manga scan image.')
parser.add_argument('infile', help='Input (color) raw Manga scan image to clean.')
parser.add_argument('-o','--output', dest='outfile', help='Output (color) cleaned raw manga scan image.')
parser.add_argument('-m','--mask', dest='mask', default=None, help='Output (binary) mask for non-graphical regions.')
parser.add_argument('-b','--binary', dest='binary', default=None, help='Binarized version of input file.')
parser.add_argument('-v','--verbose', help='Verbose operation. Print status messages during processing', action="store_true")
parser.add_argument('--display', help='Display output using OPENCV api and block program exit.', action="store_true")
arg.value = parser.parse_args()
infile = arg.string_value('infile')
outfile = arg.string_value('outfile',default_value=infile + '.cleaned.png')
binary_outfile = arg.string_value('binary',default_value=infile + '.binary.png')
mask = arg.boolean_value('mask')
if not os.path.isfile(infile):
print 'Please provide a regular existing input file. Use -h option for help.'
sys.exit(-1)
if arg.boolean_value('verbose'):
print '\tProcessing file ' + infile
print '\tGenerating output ' + outfile
(binary,mask,cleaned) = clean_image_file(infile)
cv2.imwrite(outfile,cleaned)
if binary is not None:
cv2.imwrite(binary_outfile, binary)
parser.add_argument('--binary_threshold', help='Binarization threshold value from 0 to 255.',type=int,default=defaults.BINARY_THRESHOLD)
#parser.add_argument('--segment_threshold', help='Threshold for nonzero pixels to separete vert/horiz text lines.',type=int,default=1)
parser.add_argument('--additional_filtering', help='Attempt to filter false text positives by histogram processing.', action="store_true")
arg.value = parser.parse_args()
infile = arg.string_value('infile')
outfile = arg.string_value('outfile',default_value=infile + '.text_areas.png')
if not os.path.isfile(infile):
print 'Please provide a regular existing input file. Use -h option for help.'
sys.exit(-1)
img = cv2.imread(infile)
gray = clean.grayscale(img)
binary_threshold=arg.integer_value('binary_threshold',default_value=defaults.BINARY_THRESHOLD)
if arg.boolean_value('verbose'):
print 'Binarizing with threshold value of ' + str(binary_threshold)
inv_binary = cv2.bitwise_not(clean.binarize(gray, threshold=binary_threshold))
binary = clean.binarize(gray, threshold=binary_threshold)
segmented_image = seg.segment_image(gray)
segmented_image = segmented_image[:,:,2]
components = cc.get_connected_components(segmented_image)
cc.draw_bounding_boxes(img,components,color=(255,0,0),line_size=2)
imsave(outfile, img)
if arg.boolean_value('display'):
cv2.imshow('segmented_image',segmented_image)
if cv2.waitKey(0) == 27:
infile = arg.string_value('infile')
outfile = arg.string_value('outfile',
default_value=infile + '.text_areas.png')
if not os.path.isfile(infile):
print(
'Please provide a regular existing input file. Use -h option for help.'
)
sys.exit(-1)
img = cv2.imread(infile)
gray = clean.grayscale(img)
binary_threshold = arg.integer_value(
'binary_threshold', default_value=defaults.BINARY_THRESHOLD)
if arg.boolean_value('verbose'):
print('Binarizing with threshold value of ' + str(binary_threshold))
inv_binary = cv2.bitwise_not(
clean.binarize(gray, threshold=binary_threshold))
binary = clean.binarize(gray, threshold=binary_threshold)
segmented_image = seg.segment_image(gray)
segmented_image = segmented_image[:, :, 2]
components = cc.get_connected_components(segmented_image)
cc.draw_bounding_boxes(img, components, color=(255, 0, 0), line_size=2)
imsave(outfile, img)
if arg.boolean_value('display'):
cv2.imshow('segmented_image', segmented_image)
if not os.path.isfile(infile):
print 'Please provide a regular existing input file. Use -h option for help.'
sys.exit(-1)
img = cv2.imread(infile)
cv2.imshow('srcimg', img)
gray = clean.grayscale(img)
binary_threshold = arg.integer_value('binary_threshold', default_value=defaults.BINARY_THRESHOLD)
if arg.boolean_value('verbose'):
print 'Binarizing with threshold value of ' + str(binary_threshold)
inv_binary = cv2.bitwise_not(clean.binarize(gray, threshold=binary_threshold))
#cv2.imshow('inv_binary', inv_binary)
binary = clean.binarize(gray, threshold=binary_threshold)
#cv2.imshow('binary', binary)
segmented_image = seg.segment_image(gray)
cv2.imshow('segmented_image', segmented_image)
