def translate_page(img,
binary_threshold=defaults.BINARY_THRESHOLD,
boxes=False,
target_lang="en"):
gray = clean.grayscale(img)
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 = get_connected_components(segmented_image)
for component in components:
speech = img[component]
if speech.shape[0] <= 0 or speech.shape[1] <= 0:
continue
translation = helper.ocr(speech, target_lang)
if len(translation) > 0:
#print "added component", translation
white_out_text(img, component)
add_text(img, component, translation)
if boxes:
cc.draw_bounding_boxes(img, components, color=(255, 0, 0), line_size=2)
return img
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
'--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)
def estimate_furigana_from_files(filename, segmentation_filename): img = cv2.imread(filename) gray = clean.grayscale(img) seg = cv2.imread(segmentation_filename) segmentation = seg[:,:,2] return estimate_furigana(gray, segmentation)
def estimate_furigana_from_files(filename, segmentation_filename):
img = cv2.imread(filename)
gray = clean.grayscale(img)
seg = cv2.imread(segmentation_filename)
segmentation = seg[:, :, 2]
return estimate_furigana(gray, segmentation)
def segment_image_file(filename):
img = cv2.imread(filename)
gray = clean.grayscale(img)
return segment_image(gray)
#infile = arg.string_value('infile')
infile = r'img/same_text.png'
#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)
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)
def segment_image_file(filename): img = cv2.imread(filename) gray = clean.grayscale(img) return segment_image(gray)
