def viterbi_post_process(self, img_arr, results):
'''Go through all results and attempts to correct invalid syllables'''
final = [[] for i in range(len(results))]
for i, line in enumerate(results):
syllable = []
for j, char in enumerate(line):
if char[-1] in u'་། ' or not word_parts_set.intersection(char[-1]) or j == len(line)-1:
if syllable:
syl_str = ''.join(s[-1] for s in syllable)
if is_non_std(syl_str) and syl_str not in syllables:
print syl_str, 'HAS PROBLEMS. TRYING TO FIX'
bx = combine_many_boxes([ch[0:4] for ch in syllable])
bx = list(bx)
arr = img_arr[bx[1]:bx[1]+bx[3], bx[0]:bx[0]+bx[2]]
arr = fadd_padding(arr, 3)
try:
temp_dir = tempfile.mkdtemp()
tmpimg = os.path.join(temp_dir, 'tmp.tif')
Image.fromarray(arr*255).convert('L').save(tmpimg)
pgrec = PageRecognizer(tmpimg, Config(line_break_method='line_cut', page_type='book', postprocess=False, viterbi_postprocessing=True, clear_hr=False, detect_o=False))
prob, hmm_res = pgrec.recognize_page()
os.remove(tmpimg)
os.removedirs(temp_dir)
except TypeError:
print 'HMM run exited with an error.'
prob = 0
hmm_res = ''
logging.info(u'VPP Correction: %s\t%s' % (syl_str, hmm_res))
if prob == 0 and hmm_res == '':
print 'hit problem. using unmodified output'
for s in syllable:
final[i].append(s)
else:
bx.append(prob)
bx.append(hmm_res)
final[i].append(bx)
else:
for s in syllable:
final[i].append(s)
final[i].append(char)
syllable = []
else:
syllable.append(char)
if syllable:
for s in syllable:
final[i].append(s)
return final
def viterbi_post_process(img_arr, results):
'''Go through all results and attempts to correct invalid syllables'''
final = [[] for i in range(len(results))]
for i, line in enumerate(results):
syllable = []
for j, char in enumerate(line):
if char[-1] in u'་། ' or not word_parts.intersection(char[-1]) or j == len(line)-1:
if syllable:
syl_str = ''.join(s[-1] for s in syllable)
if is_non_std(syl_str) and syl_str not in syllables:
print syl_str, 'HAS PROBLEMS. TRYING TO FIX'
bx = combine_many_boxes([ch[0:4] for ch in syllable])
bx = list(bx)
arr = img_arr[bx[1]:bx[1]+bx[3], bx[0]:bx[0]+bx[2]]
arr = fadd_padding(arr, 3)
try:
prob, hmm_res = main(arr, Config(line_break_method='line_cut', page_type='book', postprocess=False, viterbi_postprocess=True, clear_hr=False), page_info={'flname':''})
except TypeError:
print 'HMM run exited with an error.'
prob = 0
hmm_res = ''
# corrections[syl_str].append(hmm_res)
logging.info(u'VPP Correction: %s\t%s' % (syl_str, hmm_res))
if prob == 0 and hmm_res == '':
print 'hit problem. using unmodified output'
for s in syllable:
final[i].append(s)
else:
bx.append(prob)
bx.append(hmm_res)
final[i].append(bx)
else:
for s in syllable:
final[i].append(s)
final[i].append(char)
syllable = []
else:
syllable.append(char)
if syllable:
for s in syllable:
final[i].append(s)
return final
def __init__(self, shapes, k):
from sklearn.cluster import KMeans
self.shapes = shapes
self.k = k
self.page_array = shapes.img_arr
if shapes.conf['line_cluster_pos'] == 'top':
tops = array(shapes.get_tops(), dtype=float64)
elif shapes.conf['line_cluster_pos'] == 'center':
tops = array(
[t[1] + .5*shapes.char_mean for t in shapes.get_boxes() if t[3] > 2* shapes.tsek_mean],
dtype=float64
)
else:
raise ValueError, "The line_cluster_pos argument must be either 'top' or 'center'"
tops.shape = (len(tops), 1)
kmeans = KMeans(n_clusters=k)
# print tops
kmeans.fit(tops)
##################
######## mark cluster centroids on original image and show them
# img_arr = shapes.img_arr.copy()
# for centroid in kmeans.cluster_centers_:
## print centroid[0]
# img_arr[centroid[0],:] = 0
#
# import Image
# Image.fromarray(img_arr*255).show()
#######################3
lines = [[] for i in range(k)]
ind = shapes.get_indices()
### Assign char pointers (ind) to the appropriate line ###
# [lines[kmeans.labels_[i]].append(ind[i]) for i in range(len(ind))]
[lines[kmeans.predict(shapes.get_boxes()[ind[i]][1])[0]].append(ind[i]) for i in range(len(ind))]
lines = [l for l in lines if l]
self.k = len(lines)
boxes = shapes.get_boxes()
### Sort indices so they are in order from top to bottom using y from the first box in each line
sort_inx = list(argsort([boxes[line[0]][1] for line in lines]))
lines.sort(key=lambda line: boxes[line[0]][1])
### Get breaklines for splitting up lines
### Uses the topmost box in each line cluster to determine breakline
try:
topmosts = [min([boxes[i][1] for i in line]) for line in lines]
except ValueError:
print 'failed to get topmosts...'
raise
vsums = self.page_array.sum(axis=1)
breaklines = []
delta = 25
for c in topmosts:
if c - delta < 0:
lower = 0
else:
lower = c-delta
e = argmax(vsums[lower:c+delta])
c = c - delta + e
if c < 0:
c = 0
breaklines.append(c)
breaklines.append(self.page_array.shape[0])
self.baselines = []
for i, br in enumerate(breaklines[:-1]):
try:
baseline_area = vsums[br:breaklines[i+1]]
if baseline_area.any():
self.baselines.append(br + argmin(baseline_area))
else:
print i
print 'No baseline info'
except ValueError:
print 'ValueError. exiting...HERE'
import traceback;traceback.print_exc()
raise
final_ind = dict((i, []) for i in range(len(lines)))
self.new_contours = {}
for j, br in enumerate(breaklines[1:-1]):
topcount = 0
bottomcount = 0
for i in lines[j]:
# if char extends into next line, break it
# 253 is roughly global line height avg + 1 std
# The following lines says that a box/char must be extending over
# breakline by a non trivial amount eg. 30 px and must itself
# be a tall-ish box (roughly the height of average line) in order
# for it to be broken.
# if (bounding[i][1] + bounding[i][3]) - br >= 30 and bounding[i][3] > 205:
if (boxes[i][1] + boxes[i][3]) - br >= 30 and \
(boxes[i][1] + boxes[i][3]) - topmosts[j] > self.shapes.char_mean*2.85:
chars = ones((boxes[i][3]+2, boxes[i][2]+2), dtype=uint8)
contours = shapes.contours
cv.drawContours(chars, [contours[i]], -1,0, \
thickness = -1, offset=(-boxes[i][0]+1,-boxes[i][1]+1))
cv.dilate(chars, None, chars)
y_offset = boxes[i][1]
new_br = br - y_offset
prd_cut = []
### Iterate through potential cut-points and
### and cut where top half has the highest probability
### that is not a tsek
# print 'bottom bound cut point', int(.75*shapes.tsek_mean)
for delta in range(-3, int(.75*shapes.tsek_mean), 1):
# for delta in range(-3, 100, 1):
cut_point = new_br + delta
# chars[cut_point, :] = 0
# import Image
# Image.fromarray(chars*255).show()
tchr = chars[:cut_point,:]
tchr = ftrim(tchr)
if not tchr.any():
continue
tchr = normalize_and_extract_features(tchr)
probs = cls.predict_proba(tchr)
max_prob_ind = argmax(probs)
chr = label_chars[max_prob_ind]
prd_cut.append((probs[0,max_prob_ind], chr, cut_point))
prd_cut = [q for q in prd_cut if q[1] != u'་']
try:
cut_point = max(prd_cut)[-1]
except:
print 'No max prob for vertical char break, using default breakline. Usually this means the top half of the attempted segmentation looks like a tsek blob'
cut_point = br-boxes[i][1]
#######FOLLWNG NOT WORKING ATTEMPTS TO GET A BETTER BREAK LINE
# br2 = br-bounding[i][1]
#
# csum = chars.sum(axis=1)
# bzone = csum[br2-25:br2+40]
# if bzone.any():
# br2 = np.argmax(bzone) + (br-25)
## print br, 'br'
# chars = chars*255
# nbr = br
# cv.line(chars, (0, br2), (chars.shape[1], br2), 0)
# Image.fromarray(chars).save('/tmp/outt.tiff')
# sys.exit()
#############
tarr = chars[:cut_point,:]
tarr, top_offset = ftrim(tarr, new_offset=True)
tarr = fadd_padding(tarr, 3)
barr = chars[cut_point:,:]
barr = ftrim(barr, sides='brt')
barr = fadd_padding(barr, 3)
c1, h = cv.findContours(image=tarr, mode=cv.RETR_LIST, method=cv.CHAIN_APPROX_SIMPLE, offset=(boxes[i][0]+top_offset['left'],boxes[i][1]))
c1 = c1[argmax([len(t) for t in c1])] # use the most complex contour
bnc1 = cv.boundingRect(c1)
c2, h = cv.findContours(barr, mode=cv.RETR_LIST,
method=cv.CHAIN_APPROX_SIMPLE,
offset=(boxes[i][0]-3,boxes[i][1]+cut_point-3))
c2 = c2[argmax([len(t) for t in c2])]
bnc2 = cv.boundingRect(c2)
topbox_name = 't%d_%d' % (j, topcount)
final_ind[j].append(topbox_name)
self.new_contours[topbox_name] = (bnc1, c1)
topcount += 1
if bnc2[-1] > 8: #only add bottom contour if not trivially small
bottombox_name = 'b%d_%d' % (j, bottomcount)
final_ind[j+1].append(bottombox_name)
self.new_contours[bottombox_name] = (bnc2, c2)
bottomcount += 1
else:
final_ind[j].append(i)
# Don't forget to include the last line
map(final_ind[len(lines)-1].append, lines[len(lines)-1])
self.lines_chars = final_ind
cctops = [self.shapes.get_boxes()[i][1] for i in self.shapes.small_contour_indices]
char_tops = zip(cctops, self.shapes.small_contour_indices)
char_tops.sort(key=lambda x: x[0])
sorted_indices = [i[1] for i in char_tops]
_line_insert_indxs = []
_line_insert_indxs.extend([bisect_right(char_tops, (i - 1,))
for i in breaklines])
self.small_cc_lines_chars = []
if not _line_insert_indxs: sys.exit()
for i, l in enumerate(_line_insert_indxs[:-1]):
self.small_cc_lines_chars.append(sorted_indices[l:_line_insert_indxs[i+1]])
self.small_cc_lines_chars.append(sorted_indices[_line_insert_indxs[-1]:])
self.small_cc_lines_chars = [self.small_cc_lines_chars[i] for i in range(len(self.lines_chars)) if self.lines_chars[i]]
cctops = [self.shapes.get_boxes()[i][1] for i in self.shapes.emph_symbols]
char_tops = zip(cctops, self.shapes.emph_symbols)
char_tops.sort(key=lambda x: x[0])
empred = [kmeans.predict(shapes.get_boxes()[i][1])[0] for i in self.shapes.emph_symbols]
self.emph_lines = [[] for i in range(k)]
for nn, e in enumerate(empred):
self.emph_lines[sort_inx.index(e)].append(self.shapes.emph_symbols[nn])
if self.shapes.detect_o:
cctops = [self.shapes.get_boxes()[i][1] for i in self.shapes.naros]
char_tops = zip(cctops, self.shapes.naros)
char_tops.sort(key=lambda x: x[0])
sorted_indices = [i[1] for i in char_tops]
_line_insert_indxs = []
_line_insert_indxs.extend([bisect_right(char_tops, (i - 1,))
for i in breaklines])
if not _line_insert_indxs: sys.exit()
self.line_naros = []
for i, l in enumerate(_line_insert_indxs[:-1]):
#
self.line_naros.append(sorted_indices[l:_line_insert_indxs[i+1]])
self.line_naros.append(sorted_indices[_line_insert_indxs[-1]:])
self.line_naros = [self.line_naros[i] for i in range(len(self.lines_chars)) if self.lines_chars[i]]
self.line_naro_spans = []
for ll, mm in enumerate(self.line_naros):
thisline = []
for nn, naro in enumerate(mm):
box = self.get_box(naro)
thisline.append(box)
thisline.sort(key=lambda x: x[0])
self.line_naros[ll].sort(key=lambda x: self.get_box(x)[0])
self.line_naro_spans.append(thisline)
if self.shapes.low_ink:
cctops = [lib[1] for lib in self.shapes.low_ink_boxes]
char_tops = zip(cctops, self.shapes.low_ink_boxes)
char_tops.sort(key=lambda x: x[0])
sorted_indices = [i[1] for i in char_tops]
_line_insert_indxs = []
_line_insert_indxs.extend([bisect_right(char_tops, (i - 1,))
for i in breaklines])
self.low_ink_boxes = []
if not _line_insert_indxs: sys.exit()
for i, l in enumerate(_line_insert_indxs[:-1]):
self.low_ink_boxes.append(sorted_indices[l:_line_insert_indxs[i+1]])
self.low_ink_boxes.append(sorted_indices[_line_insert_indxs[-1]:])
self.low_ink_boxes = [self.low_ink_boxes[i] for i in range(len(self.lines_chars)) if self.lines_chars[i]]
def construct_vector_set_experimental(self):
NINF = -np.inf
final_box_info = CombineBoxesForPage(self.line_info)
self.final_box_info = final_box_info
final_boxes = final_box_info.final_boxes
final_indices = final_box_info.final_indices
scales = final_box_info.transitions
self.vectors = [[] for i in range(self.line_info.k)]
self.new_boxes = [[] for i in range(self.line_info.k)] #
cur_mean = self.final_box_info.char_mean
cur_std = self.final_box_info.char_std
BREAKWIDTH = self.breakwidth
rbfcls = self.line_info.rbfcls
for l in range(len(final_indices)): # for each line
try:
scale_l = scales[l]
except:
print 'ERROR AT ', l, len(scales)
raise
char_mean_int = floor(final_box_info.char_mean)
char_std_int = ceil(final_box_info.char_std)
try:
lb = range(len(final_indices[l]))
except IndexError:
print 'index error'
continue
segmented = 0
for i in lb: # for each line box
## New draw, takes into account tree hierarchy of contours
x, y, w, h = final_boxes[l][i]
letter = ones((h, w), dtype=uint8)
for k in final_indices[l][i]:
if not isinstance(k, str):
letter = self.line_info.shapes.draw_contour_and_children(
k, char_arr=letter, offset=(-x, -y))
else:
cv.drawContours(letter,
[self.line_info.get_contour(k)],
-1,
0,
thickness=-1,
offset=(-x, -y))
letter = cv.resize(letter,
dsize=(0, 0),
fx=scale_l,
fy=scale_l)
if letter.shape[1] >= (final_box_info.char_mean +
BREAKWIDTH * final_box_info.char_std
): # if a box is too large, break it
#
segmented += 1
sw = w * scale_l
sh = h * scale_l
vsum = letter.sum(axis=0)
chars = sw // (final_box_info.char_mean -
1.5 * final_box_info.char_std
) # important, floor division
if 10.0 > chars > 1.0: # Assume chars-to-be-broken don't span > 10
# if chars:
w = sw
h = sh
best_box_dim = []
best_prob = 0.0
best_seq = None
## Iterate through a range of variable chars if
## chars is greater than 2. This allows potential
## breaks for chars-1, chars-2
# all_choices = []
for chars in range(int(chars), 1, -1):
for z in range(0, 21, 2):
segs = []
prev_breakline = 0
for pos in range(int(chars - 1)):
if char_mean_int - z >= 0:
upper_range = [
int(
np.round((pos + 1) *
(char_mean_int - z))),
int(
np.round((pos + 1) *
(char_mean_int + z)))
]
vsum_range = vsum[
upper_range[0]:upper_range[1]]
if vsum_range.any():
breakline = int(
np.round((pos + 1) *
(char_mean_int - z) +
argmax(vsum_range)))
else:
breakline = None
if breakline:
sg = letter[:, prev_breakline:
breakline]
prev_breakline = breakline
else:
sg = letter[:,
int(
np.round(pos * (
char_mean_int -
z))
):int(
np.
round((pos + 1) * (
char_mean_int -
z)))]
prev_breakline = int(
np.round((pos + 1) *
(char_mean_int - z)))
segs.append(sg)
segs.append(
letter[:,
int(
np.round((chars - 1) *
(char_mean_int -
z))):])
segs = [fadd_padding(sg, 2) for sg in segs]
seg_ctrs = [
cv.findContours(
sg.copy(),
mode=cv.RETR_CCOMP,
method=cv.CHAIN_APPROX_SIMPLE)
for sg in segs
]
try:
seg_bxs = [[
cv.boundingRect(k) for k in sgc[0]
] for sgc in seg_ctrs]
except:
print sgc
raise
bxs = []
nsegs = []
prev_w = 0
for zi, ltb in enumerate(seg_bxs):
seg = segs[zi]
for b in ltb:
if b[2] < (
final_box_info.tsek_mean +
4 * final_box_info.tsek_std
) or b[3] < final_box_info.tsek_mean + 4 * final_box_info.tsek_std:
seg[b[1] - 1:b[1] + b[3] + 1,
b[0] - 1:b[0] + b[2] +
1] = True
seg, ofst = ftrim(seg, new_offset=True)
bx = [
x + prev_w + (ofst['left'] / scale_l),
y + (ofst['top'] / scale_l),
seg.shape[1] / scale_l,
seg.shape[0] / scale_l
]
prev_w += seg.shape[1] / scale_l
bxs.append(bx)
nsegs.append(seg)
xt = [
normalize_and_extract_features(sg)
for sg in nsegs if 0 not in sg.shape
]
prd_probs = cls.predict_log_proba(xt)
prd_probs = prd_probs.astype(np.float32)
prob, prds = viterbi_cython(
prd_probs.shape[0], n_states, start_p,
trans_p, prd_probs)
prob = np.exp(prob)
if prob > best_prob:
best_prob = prob
best_seq = prds
best_box_dim = bxs
best_xt = xt
if not best_box_dim:
best_prob = prob
best_seq = prds
best_box_dim = bxs
best_xt = xt
for u in range(len(best_seq)):
self.vectors[l].append(label_chars[best_seq[u]])
best_box = best_box_dim[u]
best_box = [int(np.round(ii)) for ii in best_box]
best_box.append(best_prob)
best_box.append(label_chars[best_seq[u]])
self.new_boxes[l].append(best_box)
try:
self.line_info.shapes.img_arr[
best_box[1]:best_box[1] + best_box[3],
best_box[0] + best_box[2]] = 1
except:
pass
else:
self.new_boxes[l].append([x, y, w, h])
vect = normalize_and_extract_features(letter)
self.vectors[l].append(vect)
else:
self.new_boxes[l].append([x, y, w, h])
vect = normalize_and_extract_features(letter)
self.vectors[l].append(vect)
if not any(self.vectors):
print 'no vectors'
return
else:
if self.line_info.shapes.detect_o:
for i, l in enumerate(self.new_boxes):
for n in self.line_info.line_naros[i]:
box = self.line_info.get_box(n)
x, y, w, h = box
r0 = x + w
for k, b in enumerate(l):
if ((b[2] + w) - abs(b[0] - x) - abs(
(b[0] + b[2]) - r0)) / (
2 * float(min(w, b[2]))) > .8:
try:
nbox = list(combine_many_boxes([box, b]))
except:
print nbox[3]
raise
if isinstance(self.vectors[i][k], unicode):
self.vectors[i][k] += u'ོ'
nbox = b
nbox[-1] = self.vectors[i][k]
else:
probs = cls.predict_log_proba(
self.vectors[i][k])
mx = np.argmax(probs)
prob = probs[0][mx]
mx = rbfcls.predict(self.vectors[i][k])[0]
ch = label_chars[mx] + u'ོ'
self.vectors[i][k] = ch
nbox.append(prob)
nbox.append(ch)
self.new_boxes[i][k] = nbox
def _sample_widths_method(self,
chars,
letter,
letter_box,
oo_scale_l,
line_num=None):
x, y, w, h = letter_box
################default
cur_mean = self.final_box_info.char_mean * .97
cur_std = .295 * self.final_box_info.char_std
#################
best_prob = -np.inf
if chars > 1:
letter = cv.dilate(letter.copy(), None, iterations=1)
padding_amount = 3
for n in range(15):
widths = [gauss(cur_mean, cur_std) for i in range(chars)]
prev = 0
vecs = []
wdthprobs = 0
boxes = []
for i, val in enumerate(widths):
if i == chars - 1:
end = letter.shape[1]
else:
end = prev + val
wdthprobs += gausslogprob(cur_mean, cur_std, end - prev)
s = fadd_padding(letter[:, int(prev):int(end)],
padding_amount)
_, ctrs, hier = cv.findContours(
s.copy(),
mode=cv.RETR_TREE,
method=cv.CHAIN_APPROX_NONE)
bounding = map(boundingRect, ctrs)
for k, b in enumerate(bounding):
if (b[2] < 23 or b[3] < 23) and hier[0][k][3] == 0:
s[b[1] - 1:b[1] + b[3] + 1,
b[0] - 1:b[0] + b[2] + 1] = 1
s = s[padding_amount:-padding_amount,
padding_amount:-padding_amount]
s, ofst = ftrim(s, new_offset=True)
if 0 not in s.shape:
nnbox = [
x + (prev + ofst['left']) * oo_scale_l,
y + (ofst['top'] * oo_scale_l),
s.shape[1] * oo_scale_l, s.shape[0] * oo_scale_l
]
if line_num is not None:
naro = self.line_info.check_naro_overlap(
line_num, nnbox)
if naro != False:
naro_box = self.line_info.get_box(naro)
nnbox = combine_many_boxes([nnbox, naro_box])
ss = cv.resize(s,
dsize=(0, 0),
fx=oo_scale_l,
fy=oo_scale_l)
ss = np.vstack((ones(
(nnbox[3] - ss.shape[0], ss.shape[1]),
dtype=ss.dtype), ss))
ss = hstack(
(ss,
ones(
(ss.shape[0], nnbox[2] - ss.shape[1]),
dtype=ss.dtype)))
cv.drawContours(
ss, [self.line_info.get_contour(naro)],
-1,
0,
thickness=-1,
offset=(-naro_box[0], -naro_box[1]))
s = ss
vecs.append(normalize_and_extract_features(s))
boxes.append(nnbox)
else:
break
prev += val
if not vecs: continue
xn = len(vecs)
vecs = np.array(vecs).reshape(xn, 346) # 346 is len(vecs[0])
probs = predict_log_proba(vecs)
probs = probs.astype(np.float32)
if n % 10 == 0 and n != 0:
cur_mean = self.final_box_info.char_mean * (
.97 - (3 * n / 1000.0))
prob, prds = viterbi_cython(xn, n_states, start_p, trans_p,
probs)
prob = prob + wdthprobs
if prob > best_prob:
best_prob = prob
best_prd = prds
best_boxes = boxes
else:
best_boxes = [letter_box]
probs = predict_log_proba(normalize_and_extract_features(letter))
amx = probs[0].argmax()
try:
startprob = start_p[amx]
except IndexError:
startprob = 1e-10
best_prob = probs[0][amx] + gausslogprob(
cur_mean, cur_std, letter_box[2] / oo_scale_l) + startprob
best_prd = [amx]
final_prob = best_prob
res = []
for i, val in enumerate(best_prd):
best_boxes[i] = [int(np.round(k)) for k in best_boxes[i]]
best_boxes[i].extend([float(np.exp(final_prob)), label_chars[val]])
res.append(best_boxes[i])
return (final_prob, res)
