def assign_sizes_for_page(content,
stack_inx_pointer,
stacks_gmms,
stack_mean,
prev_size='',
smooth_method=''):
all_lines = []
if not content:
return None, all_lines
# First pass
for line in content:
# prev_size = 'b'
cur_line = []
for s in line:
if s[-1] in u'་། ()〈〉༽༼༔༑' or num_stacks(s[-1]) > 1:
# size = prev_size
if smooth_method == 'line_smooth':
cur_line.append(s[-1])
else:
all_lines.append(s[-1])
continue
# print s
try:
# size = big_or_small(stacks_gmms[s[-1]], s[2], stack_mean)
size = big_or_small(stacks_gmms[stack_inx_pointer[s[-1]]],
s[2], stack_mean)
except KeyError:
if smooth_method == 'line_smooth':
cur_line.append(s[-1])
else:
all_lines.append(s[-1])
# print 'KEY ERROR', s[-1]
continue
# prev_size = size
# assert size in 'bs', 'fatal error in first assignment pass'
if smooth_method == 'line_smooth':
cur_line.append(size)
else:
all_lines.append(size)
if smooth_method == 'line_smooth':
all_lines.append(cur_line)
cur_line = []
# all_lines.append(size)
# all_lines.append(cur_line)
# print '\n'
# second pass. smooth over abrupt size changes
if smooth_method == 'line_smooth':
second_pass = line_smooth(all_lines)
elif smooth_method == 'segment_smooth':
second_pass = segment_smooth(all_lines)
elif smooth_method == 'granular':
second_pass = granular_smooth(all_lines)
else:
second_pass = []
for i, s in enumerate(all_lines):
if s in u'་། ()〈〉༽༼༔༑':
s = prev_size
second_pass.append(prev_size)
else:
second_pass.append(s)
prev_size = s
if not second_pass:
mode = 's'
print 'WARNING: using default value for mode'
else:
mode = statsmode(second_pass)[0][0]
final_lines = []
prev_inx = 0
for line in content:
cur_inx = len(line) + prev_inx
final_lines.append(second_pass[prev_inx:cur_inx])
prev_inx = cur_inx
return mode, final_lines
def __init__(self, img_arr, fast_cls, small_coef=1, low_ink=False, \
page_type=None, flpath=None, detect_o=True,\
clear_hr = False): #lower coef means more filtering USE 3 for nying gyud
self.img_arr = img_arr
self.page_type = page_type
self.flpath = flpath
self.low_ink = low_ink
self.detect_o = detect_o
# self.clear_hr = clear_hr
# self.cached_features = {}
# self.cached_pred_prob = {}
self.cached_features = OrderedDict()
self.cached_pred_prob = OrderedDict()
# self.low_ink = True
# if page_type == 'pecha':
# self._contour_mode = cv.RETR_CCOMP
# else:
self._contour_mode = cv.RETR_TREE
### repeatedly called functions
ones = np.ones
uint8 = np.uint8
predict = fast_cls.predict
predict_proba = fast_cls.predict_proba
_, self.contours, self.hierarchy = self._contours()
self.boxes = []
self.indices = []
self.small_coef = small_coef
FILTERED_PUNC = (u'།', u'་', u']', u'[')
self._set_shape_measurements()
if page_type == 'pecha':
if clear_hr:
print 'Warning: clear_hr called on pecha format. For clearing text'
self.force_clear_hr()
self.set_pecha_layout()
if self.indices:
content_parent = int(
statsmode([self.hierarchy[0][i][3]
for i in self.indices])[0])
else:
print 'no content found'
else:
content_parent = int(
statsmode([hier[3] for hier in self.hierarchy[0]])[0])
self.indices = self.get_indices()
# if self.page_type != 'pecha':
### Find the parent with the most children. Call it 'content_parent'
# content_parent = int(statsmode([self.hierarchy[0][i][3] for i in self.indices])[0])
# width_measures = self.char_gaussians([b[2] for b in self.get_boxes() if (b[2] < .1*self.img_arr.shape[1]] and self.hierarchy[0][] ))
outer_contours = []
outer_widths = []
# pg = np.ones_like(img_arr)
## Iterate through all contours
for i in self.indices:
cbox = self.get_boxes()[i]
x, y, w, h = cbox
### THIS SECOND CONDITION IS CAUSING A LOT OF PROBLEMS. Recently
# added the len(indices) < 40 as a way to prevent exaggerated
# filtering of small lines where gaussian width measures
# are meaningless due to small sample size (too few contours)
# if self.hierarchy[0][i][3] == content_parent and (cbox[2] < .1*self.img_arr.shape[1] or len(self.indices) < 40 ):
if self.hierarchy[0][i][3] == content_parent and (
cbox[2] < .1 * self.img_arr.shape[1]
or len(self.indices) < 40):
# if self.hierarchy[0][i][3] == content_parent and cbox[2] < 3*self.char_mean: ### THIS SECOND CONDITION IS CAUSING A LOT OF PROBLEMS
# if self.hierarchy[0][i][3] == content_parent and cbox[2] < .075*self.img_arr.shape[1]: ### THIS SECOND CONDITION IS CAUSING A LOT OF PROBLEMS
outer_contours.append(i)
outer_widths.append(cbox[2])
# if cbox[2] > 50: print cbox[2],
# x,y,w,h = cbox
# cv.rectangle(self.img_arr, (x,y), (x+w, y+h), 0)
else:
# if cbox[2] > 100:
# print cbox
# raw_input('continue?')
if cbox[2] > .66 * self.img_arr.shape[1]:
print cbox[2] / float(self.img_arr.shape[1])
if clear_hr and .995*self.img_arr.shape[1] > cbox[2] > \
.66*self.img_arr.shape[1] and cbox[1] < .25*self.img_arr.shape[0]:
self.img_arr[0:cbox[1] + cbox[3], :] = 1
# print 'rejected box. too wide?', cbox[2] >= .1*self.img_arr.shape[1]
# print
# print max(outer_widths)
width_measures = self.char_gaussians(outer_widths)
# import Image
# Image.fromarray(self.img_arr*255).show()
# newarr = np.ones_like(img_arr)
# for o in self.indices:
# x,y,w,h = self.get_boxes()[o]
# cv.rectangle(newarr, (x,y), (x+w, y+h), 0)
# if self.hierarchy[0][o][3] == content_parent:
# self.draw_contour_and_children(o, newarr, (0,0))
#
# import Image
# Image.fromarray(newarr*255).show()
# import sys; sys.exit()
for i, j in zip(['char_mean', 'char_std', 'tsek_mean', 'tsek_std'],
width_measures):
setattr(self, i, j)
# print self.gmm.converged_
# print self.char_mean, self.char_std
# print self.tsek_mean, self.tsek_std
self.small_contour_indices = []
# self.contours = []
self.indices = [] # Need to reset!19
self.emph_symbols = []
self.naros = []
# print self.char_mean, self.char_std, self.tsek_mean
for i in outer_contours:
cbox = self.get_boxes()[i]
# if small and has no children, put in small list (this could backfire with false interiors e.g. from salt and pepper noise)
## NOTE: previously small was defined as less than tsek_mean + 3xtsek std
## however, this wasn't always working. changing to less than charmean
## minus 2xchar std however should watch to see if is ok for many different inputs...
x, y, w, h = cbox
tmparr = ones((h, w), dtype=uint8)
tmparr = self.draw_contour_and_children(i, tmparr, (-x, -y))
features = normalize_and_extract_features(tmparr)
self.cached_features[i] = features
prprob = predict_proba(features)
# all_feats = self.cached_features.values()
# all_probs = predict_proba(all_feats)
# all_probs = predict_proba(self.cached_features.values())
# for ix,i in enumerate(outer_contours):
# prprob = all_probs[ix]
# if recognizer == 'probout':
mxinx = prprob.argmax()
quick_prd = label_chars[mxinx]
self.cached_pred_prob[i] = (mxinx, prprob[0])
# self.cached_pred_prob[i] = (mxinx, prprob)
# else:
# quick_prd = label_chars[predict_proba(features).argmax()]
# quick_prd = label_chars[predict(features)[0]]
# is_emph_symbol = quick_prd in set([u'༷', u'༵', u'༼', u'༽', u'—'])
is_emph_symbol = quick_prd in set([u'༷', u'༵', u'༼', u'༽'])
# is_emph_symbol = quick_prd in set([u'༼', u'༽'])
# is_emph_symbol = quick_prd in set([u'༷', u'༵'])
# is_emph_symbol = quick_prd in set([u'༼', u'༽', u'—'])
# is_emph_symbol = quick_prd in set([u'༼', u'༽'])
# is_emph_symbol = quick_prd == '~~' # use this line if don't want this to actually get anything
# if is_emph_symbol: print 'found naro? ', is_emph_symbol
# import Image; Image.fromarray(tmparr*255).show()
if is_emph_symbol:
self.emph_symbols.append(i)
print 'EMPHSYMBOLFOUND', quick_prd
# cv.rectangle(self.img_arr, (x,y), (x+w, y+h), 0)
elif quick_prd == u'ོ' and self.detect_o:
self.naros.append(i)
elif cbox[2] < 7:
# elif cbox[2] < 9:
continue
# elif (cbox[2] <= self.char_mean - 2*self.char_std and
# elif (cbox[2] <= self.char_mean - 3*self.char_std and
# elif (cbox[2] <= self.tsek_mean*1.5 and
# elif (cbox[2] <= self.tsek_mean*.0 and
elif (cbox[2] <= self.tsek_mean * 3 and
# elif (cbox[2] <= self.char_mean - 4*self.char_std and
# self.hierarchy[0][i][2] < 0 and
quick_prd in FILTERED_PUNC
) and not self.low_ink: # default!!!
# quick_prd in (u'་')) and not self.low_ink:
# quick_prd not in word_parts_set) and not self.low_ink :
self.small_contour_indices.append(i)
# self.indices.append(i) #DEFAULT
# elif (cbox[2] <= self.tsek_mean*.8 and
# elif (cbox[2] <= self.tsek_mean*.3 and
# elif (cbox[2] <= self.char_mean - 4*self.char_std and
# self.hierarchy[0][i][2] < 0 and not self.low_ink):
# cv.rectangle(self.img_arr, (x,y), (x+w, y+h), 0)
# continue
else:
# cv.rectangle(self.img_arr, (x,y), (x+w, y+h), 0)
self.indices.append(i)
# if (cbox[2] <= self.tsek_mean*1.5 and
## elif (cbox[2] <= self.char_mean - 4*self.char_std and
# self.hierarchy[0][i][2] < 0 and
# quick_prd in (u'།', u'་')):
# self.small_contour_indices.append(i)
# import Image
# Image.fromarray(tmparr*255).convert('L').save('/tmp/examples/%04d.tif' % i)
# print len(self.small_contour_indices), 'len small contour ind'
# import Image
# Image.fromarray(self.img_arr*255).show()
# print scount
# raw_input()
if self.detect_o:
print 'pre-filtered na-ro vowel', len(self.naros), 'found'
# for i in self.indices:
# if cbox[2] > 50: print cbox[2],
# bx = self.boxes[i]
# x,y,w,h = bx
# cv.rectangle(img_arr, (x,y), (x+w, y+h), 0)
# import Image
# Image.fromarray(img_arr*255).show()
# raw_input()
# for i in self.indices:
# if self.hierarchy[0][i][2] >= 0:
# char = self.draw_contour_and_children(i)
#
# Image.fromarray(char*255).show()
# raw_input()
# from matplotlib import pyplot as plt
# from matplotlib.mlab import normpdf
# plt.subplot(111)
# plt.title('tsek-char distributions, pre-segmentation')
#
## widths = [self.boxes[i][2] for i in self.get_indices()]
# n,bins,p = plt.hist(outer_widths, 200, range=(0,75), normed=True, color='#3B60FA')
# plt.vlines([self.char_mean, self.tsek_mean], 0, np.array([max(n), max(n)]), linestyles='--')
# plt.plot(bins, normpdf(bins, self.tsek_mean, self.tsek_std), label='fit', linewidth=1)
# plt.fill_between(bins, normpdf(bins, self.tsek_mean, self.tsek_std), color=(.58,.63,.8), alpha=0.09)
# plt.plot(bins, normpdf(bins, self.char_mean, self.char_std), label='fit', linewidth=1)
# plt.fill_between(bins, normpdf(bins, self.char_mean, self.char_std), color=(.58,.63,.8), alpha=0.01)
# plt.show()
# print self.tsek_mean, self.tsek_std
# print len(self.boxes)
# font_detector.save_info(self.char_mean, self.char_std, self.tsek_mean, self.tsek_std)
# self.low_ink = False
if self.low_ink:
self._low_ink_setting()
def __init__(self, img_arr, fast_cls, small_coef=1, low_ink=False, \
page_type=None, flpath=None, detect_o=True,\
clear_hr = False): #lower coef means more filtering USE 3 for nying gyud
self.img_arr = img_arr
self.page_type = page_type
self.flpath = flpath
self.low_ink = low_ink
self.detect_o = detect_o
self.cached_features = OrderedDict()
self.cached_pred_prob = OrderedDict()
self._contour_mode = cv.RETR_TREE
### repeatedly called functions
ones = np.ones
uint8 = np.uint8
predict = fast_cls.predict
predict_proba = fast_cls.predict_proba
self.contours, self.hierarchy = self._contours()
self.boxes = []
self.indices = []
self.small_coef = small_coef
FILTERED_PUNC = (u'།', u'་', u']', u'[')
self._set_shape_measurements()
if page_type == 'pecha':
if clear_hr:
print 'Warning: clear_hr called on pecha format. For clearing text'
self.force_clear_hr()
self.set_pecha_layout()
if self.indices:
content_parent = int(
statsmode([self.hierarchy[0][i][3]
for i in self.indices])[0])
else:
print 'no content found'
else:
content_parent = int(
statsmode([hier[3] for hier in self.hierarchy[0]])[0])
self.indices = self.get_indices()
outer_contours = []
outer_widths = []
## Iterate through all contours
for i in self.indices:
cbox = self.get_boxes()[i]
x, y, w, h = cbox
### THIS SECOND CONDITION IS CAUSING A LOT OF PROBLEMS. Recently
# added the len(indices) < 40 as a way to prevent exaggerated
# filtering of small lines where gaussian width measures
# are meaningless due to small sample size (too few contours)
if self.hierarchy[0][i][3] == content_parent and (
cbox[2] < .1 * self.img_arr.shape[1]
or len(self.indices) < 40):
# if self.hierarchy[0][i][3] == content_parent and cbox[2] < 3*self.char_mean: ### THIS SECOND CONDITION IS CAUSING A LOT OF PROBLEMS
outer_contours.append(i)
outer_widths.append(cbox[2])
else:
if cbox[2] > .66 * self.img_arr.shape[1]:
print cbox[2] / float(self.img_arr.shape[1])
if clear_hr and .995*self.img_arr.shape[1] > cbox[2] > \
.66*self.img_arr.shape[1] and cbox[1] < .25*self.img_arr.shape[0]:
self.img_arr[0:cbox[1] + cbox[3], :] = 1
# print 'rejected box. too wide?', cbox[2] >= .1*self.img_arr.shape[1]
width_measures = self.char_gaussians(outer_widths)
for i, j in zip(['char_mean', 'char_std', 'tsek_mean', 'tsek_std'],
width_measures):
setattr(self, i, j)
self.small_contour_indices = []
self.indices = [] # Need to reset!19
self.emph_symbols = []
self.naros = []
for i in outer_contours:
cbox = self.get_boxes()[i]
# if small and has no children, put in small list (this could backfire with false interiors e.g. from salt and pepper noise)
## NOTE: previously small was defined as less than tsek_mean + 3xtsek std
## however, this wasn't always working. changing to less than charmean
## minus 2xchar std however should watch to see if is ok for many different inputs...
x, y, w, h = cbox
tmparr = ones((h, w), dtype=uint8)
tmparr = self.draw_contour_and_children(i, tmparr, (-x, -y))
features = normalize_and_extract_features(tmparr)
self.cached_features[i] = features
prprob = predict_proba(features)
mxinx = prprob.argmax()
quick_prd = label_chars[mxinx]
self.cached_pred_prob[i] = (mxinx, prprob[0])
is_emph_symbol = quick_prd in set([u'༷', u'༵', u'༼', u'༽'])
if is_emph_symbol:
self.emph_symbols.append(i)
print 'EMPHSYMBOLFOUND', quick_prd
elif quick_prd == u'ོ' and self.detect_o:
self.naros.append(i)
elif cbox[2] < 7:
continue
elif (cbox[2] <= self.tsek_mean * 3 and quick_prd
in FILTERED_PUNC) and not self.low_ink: # default!!!
self.small_contour_indices.append(i)
else:
self.indices.append(i)
if self.detect_o:
print 'pre-filtered na-ro vowel', len(self.naros), 'found'
if self.low_ink:
self._low_ink_setting()