def extract(image):
try:
binary = ocrolib.read_image_binary(image)
binary = 1-binary
scale = psegutils.estimate_scale(binary)
segmentation = compute_segmentation(binary,scale)
# ...lines = compute_lines(segmentation,scale)
# compute the reading order
lines = psegutils.compute_lines(segmentation,scale)
order = psegutils.reading_order([l.bounds for l in lines])
lsort = psegutils.topsort(order)
# renumber the labels so that they conform to the specs
nlabels = amax(compute_segmentation)+1
renumber = zeros(nlabels,'i')
for i,v in enumerate(lsort): renumber[lines[v].label] = 0x010000+(i+1)
segmentation = renumber[segmentation]
outputdir = "http://127.0.0.1:5000/uploads/"
lines = [lines[i] for i in lsort]
ocrolib.write_page_segmentation("%s.pseg.png"%outputdir,segmentation)
cleaned = ocrolib.remove_noise(binary,args.noise)
for i,l in enumerate(lines):
binline = psegutils.extract_masked(1-cleaned,l,pad=args.pad,expand=args.expand)
ocrolib.write_image_binary("%s/01%04x.bin.png"%(outputdir,i+1),binline)
#print "%6d"%i,fname,"%4.1f"%scale,len(lines)
except:
print ('error')
def caption_segment(binary):
'''
:param gray:待分析的析"标题栏"
:param bina:
:return:
'''
# 排除边界处干扰部分
bina = ocrolib.remove_noise(binary, 8)
scale = psegutils.estimate_scale(bina)
lines = morph.select_regions(bina, sl.dim1, min=2 * scale)
bina = bina - lines
bina = morph.select_regions(bina, sl.dim0, min=scale / 3)
#扩大文本区域,连接相邻文本
textlines = filters.maximum_filter(bina, (scale, scale / 2))
#计算候选文本区域起始位置
indexs_white = compute_index(textlines, th=scale / 2, n=1)
indexs_lists = []
if len(indexs_white) > 2:
index_fir = indexs_white[0]
#排除过小同时连接相邻的候选文本区域
for i, index in enumerate(indexs_white):
if index[1] - index[0] > scale / 2: #排除过小
if i != 0 and index[0] - index_fir[1] < scale / 3: #连接相近
index_acc = [index_fir[0], index[1]]
indexs_lists.remove(index_fir)
indexs_lists.append(index_acc)
index_fir = index_acc
else:
indexs_lists.append(index)
index_fir = index
return indexs_lists
def calc(self, objects, scale):
if self.binpage is None:
return
tt = time()
bottom, top, boxmap = compute_gradmaps(self.binpage, scale)
# DSHOW('hihi', [0.5*bottom+0.5*top,self.binpage, boxmap])
seeds0 = compute_line_seeds(self.binpage, bottom, top, scale)
seeds, _ = morph.label(seeds0)
llabels = morph.propagate_labels(boxmap, seeds, conflict=0)
spread = spread_labels(seeds, maxdist=scale)
llabels = where(llabels > 0, llabels, spread * self.binpage)
segmentation = llabels * self.binpage
self.binpage = ocrolib.remove_noise(self.binpage, args.noise)
lines = psegutils.compute_lines(segmentation, scale)
binpage_reversed = 1 - self.binpage
# print 'pre line ', time() - tt
tt = time()
self.lines = []
for i, l in enumerate(lines):
tt = time()
binline = psegutils.extract_masked(
binpage_reversed, l, pad=args.pad,
expand=args.expand) # black text
binline = (1 - binline)
le = lineest.CenterNormalizer(binline.shape[0]) # white text
binline = binline.astype(float)
le.measure(binline)
binline = le.normalize(binline)
binline = where(binline > 0.5, 0, 1) # black text
# print 'line time ', time()-tt
print '-----------------------'
pilimg = Image.fromarray((binline * 255).astype(uint8))
pred_legacy = pytesseract.image_to_string(pilimg,
lang='eng',
config='--oem 0 --psm 7')
print '00', pred_legacy
pred_lstm = pytesseract.image_to_string(pilimg,
lang='eng',
config='--oem 1 --psm 7')
print '11', pred_lstm
# ASHOW('line',binline, scale=2.0)
## pred_both = pytesseract.image_to_string(pilimg,lang='vie', config='--oem 2 --psm 7')
## print '22', pred_both
result = psegutils.record(bounds=l.bounds,
text1=pred_legacy,
text2=pred_lstm,
img=binline)
self.lines.append(result)
def printResult(self, outputfile):
# Some pre-process
# print 'text area before'
# cv2.imshow('patch', self.patch)
# cv2.waitKey(-1)
if self.name == 'CMND cu - 9 so':
k = 0.45
else:
k = 0.33
patch = sharpen(self.patch)
binary = sauvola(patch,
w=int(self.template.shape[1] / 24.5 * 2),
k=k,
scaledown=0.5,
reverse=True)
binary = cv2.bitwise_and(binary, binary, mask=self.patch_mask)
# print 'text area after'
# cv2.imshow('patch', binary*255)
# cv2.waitKey(-1)
dotremoved = binary
scale = self.scale
# Line extraction copied from Ocropus source code
bottom, top, boxmap = compute_gradmaps(dotremoved, scale)
seeds0 = compute_line_seeds(dotremoved, bottom, top, scale)
seeds, _ = morph.label(seeds0)
llabels = morph.propagate_labels(boxmap, seeds, conflict=0)
spread = spread_labels(seeds, maxdist=scale)
llabels = where(llabels > 0, llabels, spread * dotremoved)
segmentation = llabels * dotremoved
dotremoved = ocrolib.remove_noise(dotremoved, 8)
lines = psegutils.compute_lines(segmentation, scale / 2)
binpage_reversed = 1 - dotremoved
self.lines = []
readrs = dict.fromkeys(self.linepos1.keys(), u'')
lines = sorted(lines, key=lambda x: x.bounds[1].start)
for i, l in enumerate(lines):
# Line extraction copied from Ocropus source code
binline = psegutils.extract_masked(binpage_reversed,
l,
pad=int(scale / 2),
expand=0) # black text
binline = (1 - binline)
le = lineest.CenterNormalizer(binline.shape[0]) # white text
binline = binline.astype(float)
le.measure(binline)
binline = le.normalize(binline)
# print 'normalized'
# cv2.imshow('line', binline)
# cv2.waitKey(-1)
binline = cv2.resize(binline, None, fx=2.0, fy=2.0)
# print 'resized'
# cv2.imshow('line', binline)
# cv2.waitKey(-1)
binline = where(binline > 0.5, uint8(0), uint8(255)) # black text
# print 'black text'
# cv2.imshow('line', binline)
# cv2.waitKey(-1)
# pilimg = Image.fromarray(binline)
pos = l.bounds[0].stop
left = (l.bounds[1].start < self.template.shape[1] / 2)
# Prediction using Tesseract 4.0
if pos > self.linepos1['idNumber'][0] and pos < self.linepos1[
'idNumber'][1]: #ID, all numbers
pred = ocr(
binline,
config=
'--oem 0 --psm 7 -c tessedit_char_whitelist=0123456789')
readrs['idNumber'] += pred + u' '
elif pos > self.linepos1['dateOfBirth'][0] and pos < self.linepos1[
'dateOfBirth'][1]: # DOB, number, - , /
pred = ocr(
binline,
config=
'--oem 1 --psm 7 -c tessedit_char_whitelist=0123456789-/')
readrs['dateOfBirth'] += pred + u' '
elif left and pos > self.linepos1['Gender'][
0] and pos < self.linepos1['Gender'][1]:
pred = ocr(binline, config='--oem 1 --psm 7 -l vie')
readrs['Gender'] += pred + u' '
elif (not left) and pos > self.linepos1['Dantoc'][
0] and pos < self.linepos1['Dantoc'][1]:
pred = ocr(binline, config='--oem 1 --psm 7 -l vie')
readrs['Dantoc'] += pred + u' '
elif pos > self.linepos1['NguyenQuan'][0] and pos < self.linepos1[
'NguyenQuan'][1]:
pred = ocr(binline, config='--oem 1 --psm 7 -l vie')
readrs['NguyenQuan'] += pred + u' '
elif pos > self.linepos1['fullName'][0] and pos < self.linepos1[
'fullName'][1]:
pred = ocr(binline, config='--oem 1 --psm 7 -l vie')
readrs['fullName'] += pred + u' '
# else:
# pred = ocr(binline, config='--oem 1 --psm 7 -l vie')
# print 'unknown ', unicode2ascii(pred), 'y:', l.bounds[0], 'x:', l.bounds[1]
for k in readrs:
readrs[k] = (readrs[k].replace(u'²',
u'2').replace(u'º', u'o').replace(
u'»', u'-')).strip()
if len(readrs[k]) == 0:
readrs[k] = None
if self.name == 'CMND moi - 12 so':
readrs['type'] = 'CMND Mới - 12 Số'
elif self.name == 'Can Cuoc Cong Dan':
readrs['type'] = 'Căn Cước Công Dân'
elif self.name == 'CMND cu - 9 so':
readrs['type'] = 'CMND Cũ - 9 Số'
readrs['NgayHetHan'] = None
outputfile.write(json.dumps(readrs))
def _process_segment(self, page_image, page, textregion, region_xywh,
page_id, input_file, n):
LOG = getLogger('OcrdAnybaseocrTextline')
#check for existing text lines and whether to overwrite them
if textregion.get_TextLine():
if self.parameter['overwrite']:
LOG.info('removing existing TextLines in region "%s"', page_id)
textregion.set_TextLine([])
else:
LOG.warning('keeping existing TextLines in region "%s"',
page_id)
return
binary = ocrolib.pil2array(page_image)
if len(binary.shape) > 2:
binary = np.mean(binary, 2)
binary = np.array(1 - binary / np.amax(binary), 'B')
if self.parameter['scale'] == 0:
scale = psegutils.estimate_scale(binary)
else:
scale = self.parameter['scale']
if np.isnan(
scale) or scale > 1000.0 or scale < self.parameter['minscale']:
LOG.warning(str(scale) + ": bad scale; skipping!\n")
return
segmentation = self.compute_segmentation(binary, scale)
if np.amax(segmentation) > self.parameter['maxlines']:
LOG.warning("too many lines %i; skipping!\n",
(np.amax(segmentation)))
return
lines = psegutils.compute_lines(segmentation, scale)
order = psegutils.reading_order([l.bounds for l in lines])
lsort = psegutils.topsort(order)
# renumber the labels so that they conform to the specs
nlabels = np.amax(segmentation) + 1
renumber = np.zeros(nlabels, 'i')
for i, v in enumerate(lsort):
renumber[lines[v].label] = 0x010000 + (i + 1)
segmentation = renumber[segmentation]
lines = [lines[i] for i in lsort]
cleaned = ocrolib.remove_noise(binary, self.parameter['noise'])
for i, l in enumerate(lines):
#LOG.info('check this: ')
#LOG.info(type(l.bounds))
#LOG.info(l.bounds)
#line_points = np.where(l.mask==1)
#hull = MultiPoint([x for x in zip(line_points[0],line_points[1])]).convex_hull
#x,y = hull.exterior.coords.xy
#LOG.info('hull coords x: ',x)
#LOG.info('hull coords y: ',y)
min_x, max_x = (l.bounds[0].start, l.bounds[0].stop)
min_y, max_y = (l.bounds[1].start, l.bounds[1].stop)
line_polygon = [[min_x, min_y], [max_x, min_y], [max_x, max_y],
[min_x, max_y]]
#line_polygon = [x for x in zip(y, x)]
line_polygon = coordinates_for_segment(line_polygon, page_image,
region_xywh)
line_points = points_from_polygon(line_polygon)
img = cleaned[l.bounds[0], l.bounds[1]]
img = np.array(255 * (img > ocrolib.midrange(img)), 'B')
img = 255 - img
img = ocrolib.array2pil(img)
file_id = make_file_id(input_file, self.output_file_grp)
file_path = self.workspace.save_image_file(
img,
file_id + "_" + str(n) + "_" + str(i),
page_id=page_id,
file_grp=self.output_file_grp)
ai = AlternativeImageType(filename=file_path,
comments=region_xywh['features'])
line_id = '%s_line%04d' % (page_id, i)
line = TextLineType(custom='readingOrder {index:' + str(i) + ';}',
id=line_id,
Coords=CoordsType(line_points))
line.add_AlternativeImage(ai)
textregion.add_TextLine(line)
def analyze_page_layout(binary, gray, rgb=None):
hscale = 1.0 # Non-standard scaling of horizontal parameters.
vscale = 1.0 # Non-standard scaling of vertical parameters.
threshold = 0.2 # baseline threshold.
usegauss = True # Use gaussian instead of uniform.
maxseps = 0 # Maximum black column separators.
sepwiden = 10 # Widen black separators (to account for warping).
blackseps = True
maxcolseps = 3 # Maximum # whitespace column separators.
csminheight = 10 # Minimum column height (units=scale).
noise = 8 # Noise threshold for removing small components from lines.
gray_output = True # Output grayscale lines as well, which are extracted from the grayscale version of the pages.
pad = 3 # Padding for extracted lines.
expand = 3 # Expand mask for grayscale extraction.
if False:
bin_image_filepath = './ocropy_test.bin.png'
gray_image_filepath = './ocropy_test.nrm.png'
binary = ocrolib.read_image_binary(bin_image_filepath)
gray = ocrolib.read_image_gray(gray_image_filepath)
binary = 1 - binary # Invert.
scale = psegutils.estimate_scale(binary)
segmentation = compute_segmentation(binary,
scale,
blackseps,
maxseps,
maxcolseps,
csminheight,
sepwiden,
usegauss,
vscale,
hscale,
threshold,
quiet=True)
lines = psegutils.compute_lines(segmentation, scale)
order = psegutils.reading_order([l.bounds for l in lines])
lsort = psegutils.topsort(order)
# Renumber the labels so that they conform to the specs.
nlabels = np.amax(segmentation) + 1
renumber = np.zeros(nlabels, 'i')
for i, v in enumerate(lsort):
renumber[lines[v].label] = 0x010000 + (i + 1)
segmentation = renumber[segmentation] # Image.
lines = [lines[i] for i in lsort]
# Visualize bounding boxes.
if False:
if rgb is not None:
# REF [function] >> extract_masked() in ${OCROPY_HOME}/ocrolib/psegutils.py.
for l in lines:
y0, x0, y1, x1 = [
int(x) for x in [
l.bounds[0].start, l.bounds[1].start, l.bounds[0].stop,
l.bounds[1].stop
]
]
cv2.rectangle(rgb, (x0, y0), (x1, y1), (0, 0, 255), 1,
cv2.LINE_AA)
cv2.imshow('Image', rgb)
cv2.waitKey(0)
# Output everything.
if False:
if not os.path.exists(outputdir):
os.mkdir(outputdir)
ocrolib.write_page_segmentation("%s.pseg.png" % outputdir,
segmentation)
cleaned = ocrolib.remove_noise(binary, noise)
for i, l in enumerate(lines):
binline = psegutils.extract_masked(1 - cleaned,
l,
pad=pad,
expand=expand) # Image.
ocrolib.write_image_binary(
"%s/01%04x.bin.png" % (outputdir, i + 1), binline)
if gray_output:
grayline = psegutils.extract_masked(gray,
l,
pad=pad,
expand=expand) # Image.
ocrolib.write_image_gray(
"%s/01%04x.nrm.png" % (outputdir, i + 1), grayline)
def processPngFile(outRoot, origFile, fileNum):
baseName = os.path.basename(origFile)
baseBase, _ = os.path.splitext(baseName)
outDir = os.path.join(outRoot, "%s.%03d" % (baseBase, fileNum))
inFile = os.path.join(outDir, baseName)
os.makedirs(outDir, exist_ok=True)
shutil.copy(origFile, inFile)
inBase, _ = ocrolib.allsplitext(inFile)
print("** inBase=%s" % inBase)
# print("** binBase=%s" % binBase)
fname = inFile
outputdir = inBase
binFile = inBase + ".bin.png"
outFile = inBase + ".out.png"
outRoot2, outDir2 = os.path.split(outRoot)
outFile2 = os.path.join(outRoot2, "%s.out" % outDir2, baseName)
print("outFile2=%s" % outFile2)
# assert False
grayFile = inBase + ".nrm.png"
psegFile = inBase + ".pseg.png"
print(" inFile=%s" % inFile)
print(" binFile=%s" % binFile)
print("grayFile=%s" % grayFile)
print(" outFile=%s" % outFile)
assert inFile and binFile
assert outFile != inFile
assert outFile != binFile
if not binarize(inFile, binFile, grayFile):
binExists = os.path.exists(binFile)
print("Couldn't binarize inFile=%s binFile=%s exists=%s" %
(inFile, binFile, binExists))
return False
binary = ocrolib.read_image_binary(binFile)
print("$$ %s=%s" % (binFile, desc(binary)))
height, width = binary.shape
checktype(binary, ABINARY2)
check = check_page(np.amax(binary) - binary)
if check is not None:
print("%s SKIPPED %s (use -n to disable this check)" % (inFile, check))
return False
# if args.gray:
# if os.path.exists(base+".nrm.png"):
# gray = ocrolib.read_image_gray(base+".nrm.png")
# checktype(gray, GRAYSCALE)
# else:
# print_error("Grayscale version %s.nrm.png not found. Use ocropus-nlbin for creating " +
# "normalized grayscale version of the pages as well." % base)
# return
binary = 1 - binary # invert
scale = psegutils.estimate_scale(binary)
print("scale %f" % scale)
if np.isnan(scale) or scale > 1000.0:
print("%s: bad scale (%g); skipping\n" % (fname, scale))
return False
# find columns and text lines
print("computing segmentation")
segmentation = compute_segmentation(binary, scale)
if np.amax(segmentation) > maxlines:
print("%s: too many lines %g" % (fname, np.amax(segmentation)))
return False
print("segmentation=%s" % desc(segmentation))
print("number of lines %g" % np.amax(segmentation))
# compute the reading order
print("finding reading order")
lines = psegutils.compute_lines(segmentation, scale)
order = psegutils.reading_order([l.bounds for l in lines])
lsort = psegutils.topsort(order)
print("$$ lsort = %d = %s...%s" % (len(lsort), lsort[:10], lsort[-10:]))
# renumber the labels so that they conform to the specs
nlabels = np.amax(segmentation) + 1
renumber = np.zeros(nlabels, 'i')
for i, v in enumerate(lsort):
renumber[lines[v].label] = 0x010000 + (i + 1)
segmentation = renumber[segmentation]
# finally, output everything
print("writing lines")
if not os.path.exists(outputdir):
os.mkdir(outputdir)
lines = [lines[i] for i in lsort]
ocrolib.write_page_segmentation("%s.pseg.png" % outputdir, segmentation)
cleaned = ocrolib.remove_noise(binary, noise)
for i, l in enumerate(lines):
binline = psegutils.extract_masked(1 - cleaned,
l,
pad=pad,
expand=expand)
ocrolib.write_image_binary("%s/01%04x.bin.png" % (outputdir, i + 1),
binline)
# if args.gray:
# grayline = psegutils.extract_masked(
# gray, l, pad=args.pad, expand=args.expand)
# ocrolib.write_image_gray("%s/01%04x.nrm.png" % (outputdir, i+1), grayline)
print("%6d %s %4.1f %d" % (i, fname, scale, len(lines)))
# to proceed, we need a pseg file and a subdirectory containing text lines
assert os.path.exists(psegFile), "%s: no such file" % psegFile
assert os.path.isdir(inBase), "%s: no such directory" % inBase
# iterate through the text lines in reading order, based on the page segmentation file
pseg = ocrolib.read_page_segmentation(psegFile)
print("$$ %s=%s" % (psegFile, desc(pseg)))
regions = ocrolib.RegionExtractor()
print("$$ regions=%s" % regions)
regions.setPageLines(pseg)
im = Image.open(inFile)
print("~~%s %s" % (inFile, im.size))
print("$$ regions=%s=%s" % (regions, sorted(regions.__dict__)))
print("$$ regions.length=%s" % regions.length())
n = regions.length()
for i in range(1, n):
id = regions.id(i)
y0, x0, y1, x1 = regions.bbox(i)
# print("%5d: 0x%05X %s %d x %d" %
# (i, id, [y0, x0, y1, x1], y1 - y0, x1 - x0))
draw = ImageDraw.Draw(im)
draw.rectangle((x0, y0, x1, y1), outline=(255, 0, 0), width=3)
draw.rectangle((x0, y0, x1, y1), outline=(0, 0, 255), width=0)
# draw.rectangle((x0, y0, x1, y1), outline=255, width=5)
# draw.rectangle((x0, y0, x1, y1), outline=10, width=1)
del draw
# write output files
print("outFile=%s" % outFile)
im.save(outFile, "PNG")
print("outFile2=%s" % outFile2)
outDir2 = os.path.dirname(outFile2)
os.makedirs(outDir2, exist_ok=True)
im.save(outFile2, "PNG")
assert os.path.exists(outFile2)
# outFile3, _ = os.path.splitext(outFile)
# outFile3 = "%s.jpg" % outFile3
# print("outFile3=%s" % outFile3)
# im.save(outFile3, "JPEG")
# assert os.path.exists(outFile3)
return True
def Segment(fname, save_path):
# pdb.set_trace()
# 清理上次执行的缓存结果
if os.path.exists(save_path):
shutil.rmtree(save_path)
os.makedirs(save_path)
#读取图像数据
raw = read_image_gray(fname)
#二值化,抗旋转,抗明暗度变化
gray_o, bina_o = Binarization(raw)
#出现类型错误，返回分割失败标识0
if gray_o is None and bina_o is None:
new_fname = os.path.basename(fname)
cv2.imwrite(os.path.join(save_path, new_fname))
return 0
#估计文本宽度
bina_o = ocrolib.remove_noise(bina_o, 8)
scale = psegutils.estimate_scale(bina_o)
#页面分块
block_grays, block_binas = split_columns_vertical(gray_o, bina_o, scale)
if len(block_grays) > 2: # 图片格式出现特殊情况,即指标栏之间均以垂直黑线分隔开,直接进行行分割
mb_dics = {} # 存储删除空白行记录后的所有文件记录
mb_block = [] # 记录每块中的行列数
for i, gray_i in enumerate(block_grays):
mb_dic = {}
bina_i = block_binas[i]
#分离属性栏区域和核心指标栏
cp_gray, cp_bina, mb_gray, mb_bina = get_caption_mainbody(
gray_i, bina_i, scale)
#行分割，并将结果分割结果及存储名称以字典形式存放
mb_dic, max_row = mainbody_textline_segment(
mb_gray, mb_bina, scale, i, 0, mb_dic)
max_col = 0
max_row += 1
#表单结构化初步调整：调整空白栏
mb_dics = modify_fname_dictionary(mb_dic, mb_dics)
mb_block.append([i, max_col, max_row])
#表单结构化后处理：合并多块
res_mb_dics = modify_mainbody_display(mb_dics, mb_block)
#去除分块标识
res_mb_dics = add_flag(res_mb_dics)
else: # 正常的格式,即单块或者两块
mb_dics = {} # 存储删除空白行记录后的所有文件记录
mb_block = [] # 记录每块中的行列数
for i, gray_i in enumerate(block_grays):
bina_i = block_binas[i]
#分离属性栏区域和核心指标栏区域
cp_gray, cp_bina, mb_gray, mb_bina = get_caption_mainbody(
gray_i, bina_i, scale)
#属性栏列方向分割,获得各属性分割位置,以列表形式存放
cp_index_list = []
if cp_bina is not None:
cp_index_list = caption_segment(cp_bina)
#核心指标栏区域列方向分割,获得各属性列分隔位置,并截取图像数据,以列表形式存放
mb_grays, mb_binas = mainbody_segment(mb_gray, mb_bina, scale,
cp_index_list)
######----------"核心指标栏":文本行分割----------########
max_col = 0 # 第一块中列数
max_row = 0 # 第一块中最大行数
mb_dic = {} # 存储每块中的文件记录
for j, bina_j in enumerate(mb_binas):
if j > max_col:
max_col = j
gray_j = mb_grays[j]
#文本行分割,获得文本行分割结果，以字典形式存储
mb_dic, row = mainbody_textline_segment(
gray_j, bina_j, scale, i, j, mb_dic)
if row > max_row:
max_row = row
max_col += 1
max_row += 1
if mb_dic is not {}:
# 表单结构化初步调整：调整空白栏
mb_dics = modify_fname_dictionary(mb_dic, mb_dics)
mb_block.append([i, max_col, max_row])
# 表单结构化后处理：合并多块
res_mb_dics = modify_mainbody_display(mb_dics,
mb_block) # 存储经过显示调整的所有文件记录
# 去除块标识
res_mb_dics = add_flag(res_mb_dics)
#根据字典数据保存分割结果，并返回分割成功标识１
save_img_from_dic(save_path, res_mb_dics)
return 1
def mainbody_textline_segment(gray, bina, scale, black_id, col_id, dictionary):
'''
:param gray: "核心指标栏"中某属性列灰度图
:param bina: "核心指标栏"中某属性列二值图
:param black_id: "核心指标栏"中某属性列所属块id
:param col_id: "核心指标栏"中某属性列所属列id
:param dictionary: 文件存储记录
:return: 文件存储记录和此属性列所含行数
'''
#排除多种干扰
bina = 1 * (gray < 0.5)
bina = ocrolib.remove_noise(bina, 5) #希望排除一定的噪声干扰
scale = psegutils.estimate_scale(bina)
height, width = gray.shape
lines = morph.select_regions(bina, sl.dim0,
min=2 * scale) #希望排除水平方向边缘处的亮斑干扰
bina = bina - lines
lines = morph.select_regions(bina, sl.dim1,
min=2 * scale) #希望排除垂直方向边缘处的亮斑干扰
bina = bina - lines
#字符合并
textlines = filters.maximum_filter(bina, (0, scale))
textlines = morph.rb_erosion(textlines, (3, 0))
textlines = morph.rb_dilation(textlines, (0, scale))
#统计文本行位置
textpixe_num = np.sum(textlines, axis=1)
textpixe_num = 1 * ((1.0 * textpixe_num / scale) > 1)
textpixe_num = list(textpixe_num)
text_index = [i for i, a in enumerate(textpixe_num) if a == 1]
indexs = []
max_row = 0
if len(text_index) > 0:
beg_index = text_index[0]
end_index = text_index[0]
for i in range(1, len(text_index) - 1):
if text_index[i] - text_index[i - 1] != 1:
end_index = text_index[i - 1]
indexs.append([beg_index, end_index])
beg_index = text_index[i]
end_index = text_index[i]
indexs.append([beg_index, end_index])
#选取有效的文本行
results_indexs = []
if len(indexs) > 0:
for index in indexs:
if index[1] - index[0] >= scale / 4:
results_indexs.append(index)
# res_index = []
# if len(results_indexs)>0:
# i=0
# beg_index=results_indexs[i][0]/2
# for i in range(len(results_indexs)-1):
# end_index=(results_indexs[i][1]+results_indexs[i+1][0])/2
# res_index.append([beg_index, end_index])
# beg_index = end_index
# if i==0:
# end_index = (results_indexs[i][1] + height) / 2
# else:
# end_index = (results_indexs[i+1][1] + height) / 2
#
# res_index.append([beg_index,end_index])
for row_id, index in enumerate(results_indexs):
key = '%d.%d.%d.png' % (black_id, col_id, row_id)
data = 255 * gray[max(0, index[0] - 5):min(height, index[1] +
5), :]
value = name_dic(index, data)
dictionary[key] = value
max_row = row_id
return dictionary, max_row
def mainbody_segment(gray, binary, scale, index_list):
'''
:param gray:
:param binary: 待测的"核心指标栏"
:param scale: 字符宽度, float类型
:param index_list: "标题栏"中文本沿着水平方向所在的位置列表, list类型
:return: 沿着空白区域分割的分割图, array类型
'''
# 当存在"标题栏"情况下,计算垂直空白分割位置
def search_sep_index1(bina, th, n=2):
'''
:param bina:待检测图像
:param n: 匹配的模板列数
:return: 返回待测图中连续n列白点最少的位置,如果存在多个最少,则取两个最少中间的位置
'''
height, width = bina.shape
beg_index = []
end_index = []
min_sum = n * height
all_sum = np.sum(bina, axis=0)
for i in range(0, width - 1): #以非重复方式递进
num_sum = sum(all_sum[i:i + n])
if num_sum < min_sum:
min_sum = num_sum
beg_index = [i, i + 1]
end_index = [i, i + 1]
elif num_sum == min_sum:
end_index = [i, i + 1]
if len(beg_index) > 0 and len(end_index) > 0:
res_index = (beg_index[1] + end_index[0]) / 2
if np.sum(bina[:, res_index]) < th: #白像素个数小于一定数目,才认为是真的分割位置
return res_index
else:
return None
# 当不存在"标题栏"情况下,计算垂直白色空白位置
def search_sep_index2(bina, scale):
'''
:param binary:待检测的"核心指标"图,array类型
:param scale: 字体宽度, float类型
:return: 返回待测图中各空白区域处的中间位置, int类型
'''
indexs = np.sum(bina, axis=0)
indexs = list(1.0 * indexs / scale) # 排除噪声干扰:当某列中像素点数小于一定量时候,排除干扰
text_index_temp = [i for i, index in enumerate(indexs)
if index > 1] # 候选文本位置列表
text_index_acct = [] # 真正的文本位置列表
if len(text_index_temp) > 0:
beg_index = text_index_temp[0]
end_index = text_index_temp[0]
for i in range(1, len(text_index_temp)):
end_index = text_index_temp[i]
if text_index_temp[i] - text_index_temp[
i - 1] > 4: # 当文本间隔超过一定阈值时候,才认为文本从新开始
end_index = text_index_temp[i - 1]
if end_index - beg_index > scale: # 当文本宽度大于一个字符跨度时候,才认为是真正的文本
text_index_acct.append([beg_index, end_index])
beg_index = text_index_temp[i]
text_index_acct.append([beg_index, end_index])
text_index_acct.sort(key=lambda x: x[0])
res_index = []
for i in range(len(text_index_acct) - 1):
index = (text_index_acct[i + 1][0] + text_index_acct[i][1]) / 2
res_index.append(index)
return res_index
# import pdb
# pdb.set_trace()
# 排除边界处干扰部分
bina = ocrolib.remove_noise(binary, 8)
lines = morph.select_regions(bina, sl.dim1, min=2 * scale)
bina = bina - lines
lines = morph.select_regions(bina, sl.dim0, min=2 * scale)
bina = bina - lines
# 存在"标题栏"
if 6 > len(index_list) > 3:
colsep_index = []
# 线性扩张:白色区域变大
bina_d = filters.maximum_filter(bina, (scale, scale))
i = 0
while len(index_list):
# 取标题栏中对应的连续两个位置的中间值,组成新的位置,作为待测位置
# eg:假设"标题栏"中两个相邻的文本区域,在对水平方向应的位置分别是是[x00,x01],[x10,x11],
# 则:分割线应该出现在"核心指标栏"中水平方向[(x00+x01)/2, (x10+x11)/2]范围内.
# TODO:此处有个bug,即当标题栏只有一个属性的情况下.该如何分割指标区域
sep_index = None
while sep_index is None and i < len(index_list) - 1:
index = [(index_list[i][0] + index_list[i][1]) / 2,
(index_list[i + 1][0] + index_list[i + 1][1]) / 2]
bina_i = bina_d[:, index[0]:index[1]]
sep_index = search_sep_index1(bina_i, 10 * scale) # 返回计算得到分割位置
if sep_index is None: # 意味着标题栏初始分割失败
index_re1 = index_list[i]
index_re2 = index_list[i + 1]
index_new = [index_re1[0], index_re2[1]]
index_list.remove(index_re1)
index_list.remove(index_re2)
index_list.insert(i, index_new)
if len(colsep_index) > 0:
b = colsep_index.pop()
if i > 0:
i = i - 1
if sep_index is not None:
sep_index = sep_index + index[0]
colsep_index.append(sep_index)
if i > 0:
i = i - 1
index_list.remove(index_list[i])
else:
index_list.remove(index_list[0])
i += 1
# 不存在"标题栏"
else:
bina_d = filters.maximum_filter(bina, (scale, scale / 2)) #改为２＊scale？
colsep_index = search_sep_index2(bina_d, scale)
colsep_index.append(0)
colsep_index.append(bina.shape[1])
colsep_index.sort(key=lambda x: x)
# 返回最终的文本位置列表
res_index = []
for i in range(len(colsep_index) - 1):
beg_index = colsep_index[i]
end_index = colsep_index[i + 1]
res_index.append([beg_index, end_index])
bina_lists = []
gray_lists = []
for index in res_index:
gray_i = gray[:, index[0]:index[1]]
bina_i = binary[:, index[0]:index[1]]
gray_lists.append(gray_i)
bina_lists.append(bina_i)
# plt.imshow(bina_i, 'gray'), plt.show()
return gray_lists, bina_lists
def process(job):
imagepath, i = job
global base
base, _ = ocrolib.allsplitext(imagepath)
outputdir = base
imagename_base = os.path.basename(os.path.normpath(base))
try:
binary = ocrolib.read_image_binary(imagepath)
except IOError:
if ocrolib.trace: traceback.print_exc()
print_error("cannot open either %s.bin.png or %s" % (base, imagepath))
return
checktype(binary, ABINARY2)
if not args['nocheck']:
check = check_page(amax(binary) - binary)
if check is not None:
print_error("%s SKIPPED %s (use -n to disable this check)" %
(imagepath, check))
return
binary = 1 - binary # invert
if args['scale'] == 0:
scale = psegutils.estimate_scale(binary)
else:
scale = args['scale']
print_info("scale %f" % (scale))
if isnan(scale) or scale > 1000.0:
print_error("%s: bad scale (%g); skipping\n" % (imagepath, scale))
return
if scale < args['minscale']:
print_error("%s: scale (%g) less than --minscale; skipping\n" %
(imagepath, scale))
return
# find columns and text lines
if not args['quiet']: print_info("computing segmentation")
segmentation = compute_segmentation(binary, scale)
if amax(segmentation) > args['maxlines']:
print_error("%s: too many lines %g" % (imagepath, amax(segmentation)))
return
if not args['quiet']: print_info("number of lines %g" % amax(segmentation))
# compute the reading order
if not args['quiet']: print_info("finding reading order")
lines = psegutils.compute_lines(segmentation, scale)
order = psegutils.reading_order([l.bounds for l in lines])
lsort = psegutils.topsort(order)
# renumber the labels so that they conform to the specs
nlabels = amax(segmentation) + 1
renumber = zeros(nlabels, 'i')
for i, v in enumerate(lsort):
renumber[lines[v].label] = 0x010000 + (i + 1)
segmentation = renumber[segmentation]
# finally, output everything
if not args['quiet']: print_info("writing lines")
if not os.path.exists(outputdir):
os.mkdir(outputdir)
lines = [lines[i] for i in lsort]
ocrolib.write_page_segmentation("%s.pseg.png" % outputdir, segmentation)
cleaned = ocrolib.remove_noise(binary, args['noise'])
for i, l in enumerate(lines):
binline = psegutils.extract_masked(1 - cleaned,
l,
pad=args['pad'],
expand=args['expand'])
ocrolib.write_image_binary(
"%s/%s_01%04x.bin.png" % (outputdir, imagename_base, i + 1),
binline)
print_info("%6d %s %4.1f %d" % (i, imagepath, scale, len(lines)))
return outputdir
def process1(job):
fname, i = job
global base
base, _ = ocrolib.allsplitext(fname)
outputdir = base
try:
binary = ocrolib.read_image_binary(base + ".bin.png")
except IOError:
try:
binary = ocrolib.read_image_binary(fname)
except IOError:
if ocrolib.trace:
traceback.print_exc()
print("cannot open either", base + ".bin.png", "or", fname)
return
checktype(binary, ABINARY2)
if not args.nocheck:
check = check_page(amax(binary) - binary)
if check is not None:
print(fname, "SKIPPED", check, "(use -n to disable this check)")
return
if args.gray:
if os.path.exists(base + ".nrm.png"):
gray = ocrolib.read_image_gray(base + ".nrm.png")
checktype(gray, GRAYSCALE)
binary = 1 - binary # invert
if args.scale == 0:
scale = psegutils.estimate_scale(binary)
else:
scale = args.scale
print("scale", scale)
if isnan(scale) or scale > 1000.0:
sys.stderr.write("%s: bad scale (%g); skipping\n" % (fname, scale))
return
if scale < args.minscale:
sys.stderr.write("%s: scale (%g) less than --minscale; skipping\n" %
(fname, scale))
return
# find columns and text lines
if not args.quiet:
print("computing segmentation")
segmentation = compute_segmentation(binary, scale)
if amax(segmentation) > args.maxlines:
print(fname, ": too many lines", amax(segmentation))
return
if not args.quiet:
print("number of lines", amax(segmentation))
# compute the reading order
if not args.quiet:
print("finding reading order")
lines = psegutils.compute_lines(segmentation, scale)
order = psegutils.reading_order([l.bounds for l in lines])
lsort = psegutils.topsort(order)
# renumber the labels so that they conform to the specs
nlabels = amax(segmentation) + 1
renumber = zeros(nlabels, 'i')
for i, v in enumerate(lsort):
renumber[lines[v].label] = 0x010000 + (i + 1)
segmentation = renumber[segmentation]
# finally, output everything
if not args.quiet:
print("writing lines")
if not os.path.exists(outputdir):
os.mkdir(outputdir)
lines = [lines[i] for i in lsort]
ocrolib.write_page_segmentation("%s.pseg.png" % outputdir, segmentation)
cleaned = ocrolib.remove_noise(binary, args.noise)
for i, l in enumerate(lines):
binline = psegutils.extract_masked(1 - cleaned,
l,
pad=args.pad,
expand=args.expand)
ocrolib.write_image_binary("%s/01%04x.bin.png" % (outputdir, i + 1),
binline)
if args.gray:
grayline = psegutils.extract_masked(gray,
l,
pad=args.pad,
expand=args.expand)
ocrolib.write_image_gray("%s/01%04x.nrm.png" % (outputdir, i + 1),
grayline)
print("%6d" % i, fname, "%4.1f" % scale, len(lines))
def _process_segment(self, page_image, page, region_xywh, page_id,
input_file, n):
binary = ocrolib.pil2array(page_image)
binary = np.array(1 - binary / np.amax(binary), 'B')
if page.get_TextRegion() is None or len(page.get_TextRegion()) < 1:
min_x, max_x = (0, binary.shape[0])
min_y, max_y = (0, binary.shape[1])
textregion = TextRegionType(
Coords=CoordsType("%i,%i %i,%i %i,%i %i,%i" %
(min_x, min_y, max_x, min_y, max_x, max_y,
min_x, max_y)))
page.add_TextRegion(textregion)
else:
textregion = page.get_TextRegion()[-1]
ocrolib.write_image_binary("test.bin.png", binary)
if self.parameter['scale'] == 0:
scale = psegutils.estimate_scale(binary)
else:
scale = self.parameter['scale']
if np.isnan(
scale) or scale > 1000.0 or scale < self.parameter['minscale']:
LOG.warning("%s: bad scale (%g); skipping\n" % (fname, scale))
return
segmentation = self.compute_segmentation(binary, scale)
if np.amax(segmentation) > self.parameter['maxlines']:
LOG.warning("%s: too many lines %i",
(fname, np.amax(segmentation)))
return
lines = psegutils.compute_lines(segmentation, scale)
order = psegutils.reading_order([l.bounds for l in lines])
lsort = psegutils.topsort(order)
# renumber the labels so that they conform to the specs
nlabels = np.amax(segmentation) + 1
renumber = np.zeros(nlabels, 'i')
for i, v in enumerate(lsort):
renumber[lines[v].label] = 0x010000 + (i + 1)
segmentation = renumber[segmentation]
lines = [lines[i] for i in lsort]
cleaned = ocrolib.remove_noise(binary, self.parameter['noise'])
region_xywh['features'] += ",textline"
for i, l in enumerate(lines):
ocrolib.write_image_binary("test.bin.png", binary[l.bounds[0],
l.bounds[1]])
min_x, max_x = (l.bounds[0].start, l.bounds[0].stop)
min_y, max_y = (l.bounds[1].start, l.bounds[1].stop)
img = binary[l.bounds[0], l.bounds[1]]
img = np.array(255 * (img > ocrolib.midrange(img)), 'B')
img = ocrolib.array2pil(img)
file_id = input_file.ID.replace(self.input_file_grp,
self.image_grp)
if file_id == input_file.ID:
file_id = concat_padded(self.image_grp, n)
file_path = self.workspace.save_image_file(img,
file_id + "_" + str(i),
page_id=page_id,
file_grp=self.image_grp)
ai = AlternativeImageType(filename=file_path,
comments=region_xywh['features'])
line = TextLineType(
Coords=CoordsType("%i,%i %i,%i %i,%i %i,%i" %
(min_x, min_y, max_x, min_y, max_x, max_y,
min_x, max_y)))
line.add_AlternativeImage(ai)
textregion.add_TextLine(line)
