Exemple #1
0
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)
Exemple #3
0
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