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 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 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 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 area_is_text_like(img, area, average_size):
#use basic 'ladder' building technique to see if area features somewhat
#regularly spaced vertical japanese characters
#this is done by attempting to draw a regular grid in the area, and converging on
#the grid dimension (cells are square) that minimizes the number of back pixels at the boundaries
#We should also count X black pixels at multiple boundaries as worse than X black pixels at one
(image_h, image_w)=img.shape[:2]
(x, y, w, h) = dimensions_2d_slice(area)
#get the average size (width, height?) of connected components in the area of interest
aoi = img[area]
#ccs = cc.get_connected_components(aoi)
aoi_average_size = cc.average_size(aoi)
if math.isnan(aoi_average_size):
return False
#if possible,expand the area by one pixel in all directions, which should be white pixels
if(x>0 and x+w<image_w-1):
x=x-1
w=w+2
if(y>0 and y+h<image_h-1):
y=y-1
h=h+2
#TODO: get the average connected component WIDTH and HEIGHT (not just size) of just
#elements within this area. Use THAT to form ranges over which we'll try to create ladders
#if w<average_size and h<average_size:
# return False
initial_height = int(aoi_average_size/2)
final_height = int(aoi_average_size * 1.5)
initial_width = int(aoi_average_size/2)
final_width = int(aoi_average_size * 2.0)
#if w<h:final_height=w
minimum_black_pixel_count_height = w*h
minimum_height = initial_height
minimum_black_pixel_count_width = w*h
minimum_width = initial_width
#TODO: vary width and height independently. Only way we'll find decent correct segmentation
#TODO: start the ladder (grid) outside the area so it wont' always 'hit' black pixels for the
#zeroeth row and column
for s in range(initial_height,final_height):
black_pixel_count = 0
#horizontal_steps = int(w/s)+1
#vertical_steps = int(h/s)+1
num_steps = int(h/s)+1
for vertical_step in range(0, num_steps):
#cound black pixels along horizontal, vertically stepped lines
#we can count black pixels with "nonzero" because we're using an inverse image
black_pixel_count = black_pixel_count + np.count_nonzero(img[y+vertical_step*s,x:x+w])
if black_pixel_count < minimum_black_pixel_count_height:
minimum_black_pixel_count_height = black_pixel_count
minimum_height = s
for s in range(initial_width,final_width):
black_pixel_count = 0
#horizontal_steps = w/s+1
#vertical_steps = h/s+1
num_steps = int(w/s)+1
for horizontal_step in range(0, num_steps):
#count black pixels along vertical, horizontally stepped lines
#height = vertical_steps*s
black_pixel_count = black_pixel_count + np.count_nonzero(img[y:y+h,x+horizontal_step*s])
if black_pixel_count < minimum_black_pixel_count_width:
minimum_black_pixel_count_width = black_pixel_count
minimum_width = s
#print 'at location ' + str(x) + ' ' + str(y) + ' ' + str(w) + ' ' + str(h)
#print 'found minimum cell height ' + str(minimum_height) + ' with black pixel count ' + str(minimum_black_pixel_count)
#draw the finalized grid on our img
num_horizontal_steps = int(w/minimum_width)+1
if (num_horizontal_steps-1) * minimum_width < (w-minimum_width/4):
#print 'increading width steps count by one'
num_horizontal_steps = num_horizontal_steps + 1
total_width = (num_horizontal_steps-1) * minimum_width
num_vertical_steps = int(h/minimum_height)+1
if (num_vertical_steps-1) * minimum_height < (h-minimum_height/4):
#print 'increasing height steps count by one'
num_vertical_steps = num_vertical_steps + 1
total_height = (num_vertical_steps-1) * minimum_height
#print 'height is ' + str(h) + ' and total line height is ' + str(total_height)
#print 'number of steps is ' + str(num_vertical_steps) + ' and num_cells*min height ' + str(num_vertical_steps*minimum_height)
for vertical_step in range(0, num_vertical_steps):
#pass
img[y+vertical_step*minimum_height,x:x+total_width]=255
for horizontal_step in range(0, num_horizontal_steps):
#pass
img[y:y+total_height,x+horizontal_step*minimum_width]=255
'''
img[y,x:x+w]=255
img[y+h,x:x+w]=255
img[y:y+h,x]=255
img[y:y+h,x+w]=255
img[y,x:x+total_width]=255
img[y+total_height,x:x+total_width]=255
img[y:y+total_height,x]=255
img[y:y+total_height,x+total_width]=255
'''
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 area_is_text_like(img, area, average_size):
#use basic 'ladder' building technique to see if area features somewhat
#regularly spaced vertical japanese characters
#this is done by attempting to draw a regular grid in the area, and converging on
#the grid dimension (cells are square) that minimizes the number of back pixels at the boundaries
#We should also count X black pixels at multiple boundaries as worse than X black pixels at one
(image_h, image_w)=img.shape[:2]
(x, y, w, h) = dimensions_2d_slice(area)
#get the average size (width, height?) of connected components in the area of interest
aoi = img[area]
#ccs = cc.get_connected_components(aoi)
aoi_average_size = cc.average_size(aoi)
if math.isnan(aoi_average_size):
return False
#if possible,expand the area by one pixel in all directions, which should be white pixels
if(x>0 and x+w<image_w-1):
x=x-1
w=w+2
if(y>0 and y+h<image_h-1):
y=y-1
h=h+2
#TODO: get the average connected component WIDTH and HEIGHT (not just size) of just
#elements within this area. Use THAT to form ranges over which we'll try to create ladders
#if w<average_size and h<average_size:
# return False
initial_height = int(aoi_average_size/2)
final_height = int(aoi_average_size * 1.5)
initial_width = int(aoi_average_size/2)
final_width = int(aoi_average_size * 2.0)
#if w<h:final_height=w
minimum_black_pixel_count_height = w*h
minimum_height = initial_height
minimum_black_pixel_count_width = w*h
minimum_width = initial_width
#TODO: vary width and height independently. Only way we'll find decent correct segmentation
#TODO: start the ladder (grid) outside the area so it wont' always 'hit' black pixels for the
#zeroeth row and column
for s in range(initial_height,final_height):
black_pixel_count = 0
#horizontal_steps = int(w/s)+1
#vertical_steps = int(h/s)+1
num_steps = int(h/s)+1
for vertical_step in range(0, num_steps):
#cound black pixels along horizontal, vertically stepped lines
#we can count black pixels with "nonzero" because we're using an inverse image
black_pixel_count = black_pixel_count + np.count_nonzero(img[y+vertical_step*s,x:x+w])
if black_pixel_count < minimum_black_pixel_count_height:
minimum_black_pixel_count_height = black_pixel_count
minimum_height = s
for s in range(initial_width,final_width):
black_pixel_count = 0
#horizontal_steps = w/s+1
#vertical_steps = h/s+1
num_steps = int(w/s)+1
for horizontal_step in range(0, num_steps):
#count black pixels along vertical, horizontally stepped lines
#height = vertical_steps*s
black_pixel_count = black_pixel_count + np.count_nonzero(img[y:y+h,x+horizontal_step*s])
if black_pixel_count < minimum_black_pixel_count_width:
minimum_black_pixel_count_width = black_pixel_count
minimum_width = s
#print 'at location ' + str(x) + ' ' + str(y) + ' ' + str(w) + ' ' + str(h)
#print 'found minimum cell height ' + str(minimum_height) + ' with black pixel count ' + str(minimum_black_pixel_count)
#draw the finalized grid on our img
num_horizontal_steps = int(w/minimum_width)+1
if (num_horizontal_steps-1) * minimum_width < (w-minimum_width/4):
#print 'increading width steps count by one'
num_horizontal_steps = num_horizontal_steps + 1
total_width = (num_horizontal_steps-1) * minimum_width
num_vertical_steps = int(h/minimum_height)+1
if (num_vertical_steps-1) * minimum_height < (h-minimum_height/4):
#print 'increasing height steps count by one'
num_vertical_steps = num_vertical_steps + 1
total_height = (num_vertical_steps-1) * minimum_height
#print 'height is ' + str(h) + ' and total line height is ' + str(total_height)
#print 'number of steps is ' + str(num_vertical_steps) + ' and num_cells*min height ' + str(num_vertical_steps*minimum_height)
for vertical_step in range(0, num_vertical_steps):
#pass
img[y+vertical_step*minimum_height,x:x+total_width]=255
for horizontal_step in range(0, num_horizontal_steps):
#pass
img[y:y+total_height,x+horizontal_step*minimum_width]=255
'''
img[y,x:x+w]=255
img[y+h,x:x+w]=255
img[y:y+h,x]=255
img[y:y+h,x+w]=255
img[y,x:x+total_width]=255
img[y+total_height,x:x+total_width]=255
img[y:y+total_height,x]=255
img[y:y+total_height,x+total_width]=255
'''
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