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 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