def load_norm_ivecs(self):
""" Load normalization i-vectors, scale and shift files and also pretrained model.
:returns: i-vectors
:rtype: numpy.array
"""
line = None
with open(self.norm_list, 'r') as f:
for line in f:
line = line.rstrip()
loginfo(
'[Diarization.load_norm_ivecs] Loading npy file {} ...'.
format(line))
try:
yield np.load('{}.npy'.format(
os.path.join(self.ivecs_dir, line))).flatten()
except IOError:
logwarning(
'[Diarization.load_norm_ivecs] No pickle file found for {}.'
.format(line))
self.scale = np.load(
os.path.join(self.ivecs_dir, os.path.dirname(line), 'scale.npy'))
self.shift = np.load(
os.path.join(self.ivecs_dir, os.path.dirname(line), 'shift.npy'))
try:
with open(
os.path.join(self.ivecs_dir, os.path.dirname(line),
'model.pkl')) as f:
self.model = pickle.load(f)
except IOError:
logwarning(
'[Diarization.load_norm_ivecs] No pretrained model found.')
def load_ivecs(self):
with open(self.input_list, 'r') as f:
for line in f:
loginfo('[Diarization.load_ivecs] Loading pickle file {} ...'.
format(line.rstrip().split()[0]))
line = line.rstrip()
try:
if len(line.split()) == 1:
with open(os.path.join(self.ivecs_dir,
line + '.pkl')) as i:
yield pickle.load(i)
elif len(line.split()) == 2:
file_name = line.split()[0]
num_spks = int(line.split()[1])
with open(
os.path.join(self.ivecs_dir,
file_name + '.pkl')) as i:
ivec_set = pickle.load(i)
ivec_set.num_speakers = num_spks
yield ivec_set
else:
raise DiarizationException(
'[Diarization.load_ivecs] Unexpected number of columns in input list {}.'
.format(self.input_list))
except IOError:
logwarning(
'[Diarization.load_ivecs] No pickle file found for {}.'
.format(line.rstrip().split()[0]))
def score(self):
scores_dict = {}
for ivecset in self.ivecs:
name = os.path.normpath(ivecset.name)
ivecs = ivecset.get_all()
loginfo('[Diarization.score] Scoring {} ...'.format(name))
size = ivecset.size()
if size > 0:
if ivecset.num_speakers is not None:
num_speakers = min(ivecset.num_speakers, size)
sklearnkmeans = sklearnKMeans(
n_clusters=num_speakers).fit(ivecs)
centroids = KMeans(sklearnkmeans.cluster_centers_,
num_speakers, self.plda).fit(ivecs)
else:
num_speakers, centroids = self.get_num_speakers(ivecs)
if self.norm_list is None:
scores_dict[name] = self.plda.score(
ivecs, centroids, self.scale, self.shift)
else:
scores_dict[name] = self.s_norm(ivecs, centroids)
else:
logwarning(
'[Diarization.score] No i-vectors to score in {}.'.format(
ivecset.name))
return scores_dict
def get_der(self, ref_file, scores):
""" Compute Diarization Error Rate from reference and scores.
:param ref_file: path to file with diarization reference
:type ref_file: str
:param scores: input scores from PLDA model
:type scores: numpy.array
"""
ref, hyp = self.init_annotations()
with open(ref_file, 'r') as f:
for line in f:
_, name, _, start, duration, _, _, speaker, _ = line.split()
ref[name][Segment(float(start),
float(start) + float(duration))] = speaker
for ivecset in self.ivecs:
if ivecset.size() > 0:
name, reg_name = ivecset.name, ivecset.name
# dirty trick, will be removed, watch out
if 'beamformed' in name:
reg_name = re.sub('beamformed/', '', name)
# # # # # # # # # # # # # # # # # # # # #
reg_name = re.sub('/.*', '', reg_name)
for i, ivec in enumerate(ivecset.ivecs):
start, end = ivec.window_start / 1000.0, ivec.window_end / 1000.0
hyp[reg_name][Segment(start,
end)] = np.argmax(scores[name].T[i])
else:
logwarning(
'[Diarization.get_der] No i-vectors to dump in {}.'.format(
ivecset.name))
der = DiarizationErrorRate()
der.collar = 0.25
names, values, summ = [], [], 0.0
for name in ref.keys():
names.append(name)
der_num = der(ref[name], hyp[name]) * 100
values.append(der_num)
summ += der_num
loginfo('[Diarization.get_der] {} DER = {}'.format(
name, '{0:.3f}'.format(der_num)))
loginfo('[Diarization.get_der] Average DER = {}'.format(
'{0:.3f}'.format(summ / float(len(ref.keys())))))
Diarization.plot_der(names, values)
def score(self):
""" Score i-vectors agains speaker clusters.
:returns: PLDA scores
:rtype: numpy.array
"""
scores_dict = {}
for ivecset in self.ivecs:
name = os.path.normpath(ivecset.name)
ivecs = ivecset.get_all()
loginfo('[Diarization.score] Scoring {} ...'.format(name))
size = ivecset.size()
if size > 0:
if ivecset.num_speakers is not None:
num_speakers = ivecset.num_speakers
sklearnkmeans = sklearnKMeans(
n_clusters=num_speakers).fit(ivecs)
centroids = KMeans(sklearnkmeans.cluster_centers_,
num_speakers, self.plda).fit(ivecs)
else:
if self.norm_ivecs is not None:
num_speakers, centroids = self.get_num_speakers(ivecs)
else:
raise DiarizationException(
'[Diarization.score] Can not estimate number of speakers without training set.'
)
if self.norm_list is None:
scores_dict[name] = self.plda.score(
ivecs, centroids, self.scale, self.shift)
else:
scores_dict[name] = self.s_norm(ivecs, centroids)
else:
logwarning(
'[Diarization.score] No i-vectors to score in {}.'.format(
ivecset.name))
return scores_dict
def get_main_area(img, acta):
remove = cfg.remove_pixels if cfg.remove_pixels else [0, 0, 0, 0]
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
ret, thresh = cv.threshold(gray, 127, 255, cv.THRESH_BINARY_INV)
kernel = np.ones((7, 7), np.uint8)
gray = cv.dilate(thresh, kernel, iterations=1)
ctrs, hier = cv.findContours(gray.copy(), cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_SIMPLE)
areas = [(t[0], t[1], t[0] + t[2], t[1] + t[3])
for t in [cv.boundingRect(ctr) for ctr in ctrs]]
height, width, channels = img.shape
if not areas:
# No hay zonas de interes posiblemente imagen en blanco
areas = [(0, 0, width, height)]
lmin = list(map(min, zip(*areas)))
lmax = list(map(max, zip(*areas)))
add = 3
seguridad = (lmin[0] - add if lmin[0] - add > 0 else 0,
lmin[1] - add if lmin[1] - add > 0 else 0,
lmax[2] + add if lmax[2] + add < width else width,
lmax[3] + add if lmax[3] + add < height else height)
x1 = seguridad[0] if seguridad[0] < remove[0] else remove[0]
y1 = seguridad[1] if seguridad[1] < remove[1] else remove[1]
x2 = seguridad[2] if seguridad[2] > width - remove[2] else width - remove[2]
y2 = seguridad[
3] if seguridad[3] > height - remove[3] else height - remove[3]
crop = (x1, y1, x2, y2)
loginfo("Acta : {0}".format(acta))
loginfo("Recorte seguridad: {0}".format(str(seguridad)))
loginfo("Recorte final : {0}".format(str(crop)))
roi = img[crop[1]:crop[1] + crop[3], crop[0]:crop[0] + crop[2]]
return roi
def logos_from_pdf(cfg, pdf_file, quiet=False):
"""logos_from_pdf
Procesa un archivo PDF de boletines de Marcas para extraer logos y textos de cada acta
cfg: <Config> Objeto de congiguracón del proceso
pdf_file: <str> path al archivo PDF del boletín
"""
print("\nExtracción de logos y textos\n")
workpath = tempfile.mkdtemp()
filename, _ = os.path.splitext(os.path.basename(pdf_file))
outputpath_logos = os.path.join(cfg.outputdir, filename, "logos")
outputpath_txt = os.path.join(cfg.outputdir, filename, "txt")
os.makedirs(outputpath_txt, exist_ok=True)
os.makedirs(outputpath_logos, exist_ok=True)
dte = DataExtractor(workpath)
total_logos, total_textos = 0, 0
with open(pdf_file, 'rb') as fp:
rsrcmgr = PDFResourceManager()
laparams = LAParams()
device = PDFPageAggregator(rsrcmgr, laparams=laparams)
interpreter = PDFPageInterpreter(rsrcmgr, device)
pages = list(PDFPage.get_pages(fp))
total_pages = len(pages)
loginfo("Total de páginas : {0}".format(total_pages))
if not cfg.debug_page:
firstp = cfg.from_page if cfg.from_page else (
cfg.ignore_first_pages + 1)
endp = cfg.to_page if cfg.to_page else (total_pages -
cfg.ignore_last_pages) + 1
if cfg.detect_export_pages and (not cfg.from_page
and not cfg.to_page):
firstp = 1
endp = total_pages
num_bars = (endp - firstp) + 1
else:
firstp = cfg.debug_page
endp = cfg.debug_page
num_bars = 1
if not quiet:
widgets = [
FormatLabel(''), ' ',
Percentage(), ' ',
Bar('#'), ' ',
ETA(), ' ',
RotatingMarker()
]
bar = ProgressBar(widgets=widgets, maxval=num_bars)
i = 1
for page in [
p for n, p in enumerate(pages, start=1)
if n >= firstp and n <= endp
]:
interpreter.process_page(page)
layout = device.get_result()
objetos = dte.get_data_from_layout(layout)
total_logos = total_logos + len(
[e for e in objetos if e[2] is not None])
total_textos = total_textos + len(objetos)
for acta, texto, filename in objetos:
if filename:
_, file_extension = os.path.splitext(filename)
shutil.copyfile(
filename,
os.path.join(outputpath_logos,
"{0}{1}".format(acta, file_extension)))
txt_file = os.path.join(outputpath_txt,
"{0}.{1}".format(acta, "txt"))
texto = texto.encode('latin-1',
'ignore').decode('utf-8', 'ignore')
with open(txt_file, 'w', errors="ignore") as f:
f.write(texto)
if not quiet:
widgets[0] = FormatLabel('[Página {0} de {1}]'.format(
i, total_pages))
bar.update(i)
i = i + 1
if not quiet:
bar.finish()
printmsg = True if not quiet else False
loginfo("", printmsg=printmsg)
loginfo("-- Estatus -------------------------------------------",
printmsg=printmsg)
loginfo("Carpeta temporal de trabajo : {0}".format(workpath),
printmsg=printmsg)
loginfo("Total de logos extraídos : {0}".format(total_logos),
printmsg=printmsg)
loginfo("Total de textos extraídos : {0}".format(total_textos),
printmsg=printmsg)
if not cfg.debug_page:
loginfo("Eliminamos carpeta de trabajo")
shutil.rmtree(workpath)
os.path.join(application_path, 'pboletin.ini'))
else:
configfile = args.configfile
if args.pdffile:
if args.logfile:
log_level = getattr(logging, args.loglevel.upper(), None)
logging.basicConfig(filename=args.logfile,
level=log_level,
format='%(asctime)s|%(levelname)s|%(message)s',
datefmt='%Y/%m/%d %I:%M:%S',
filemode='w')
try:
loginfo("Configuración : {0}".format(configfile))
cfg.set_file(configfile)
except IOError as msg:
cmdparser.error(str(msg))
sys.exit(-1)
if args.debug_page:
cfg.save_process_files = True
cfg.debug_page = args.debug_page
cfg.from_page = args.from_page
cfg.to_page = args.to_page
if args.inputpath:
cfg.inputdir = args.inputpath
args.pdffile = os.path.join(cfg.inputdir, args.pdffile)
def process_pdf(cfg, pdf_file, quiet=False):
lista_actas = []
total_actas = 0
total_regions = 0
total_pages = pdf_count_pages(pdf_file, cfg.pdfinfo_bin, cfg.rxcountpages)
print("\nRecorte de actas\n")
loginfo("{0} has {1} pages".format(pdf_file, total_pages))
if not cfg.debug_page:
firstp = cfg.from_page if cfg.from_page else (cfg.ignore_first_pages +
1)
endp = cfg.to_page if cfg.to_page else (total_pages -
cfg.ignore_last_pages) + 1
if cfg.detect_export_pages and (not cfg.from_page and not cfg.to_page):
firstp = 1
endp = total_pages
num_bars = (endp - firstp) + 1
else:
firstp = cfg.debug_page
endp = cfg.debug_page
num_bars = 1
if not quiet:
widgets = [
FormatLabel(''), ' ',
Percentage(), ' ',
Bar('#'), ' ',
ETA(), ' ',
RotatingMarker()
]
bar = ProgressBar(widgets=widgets, maxval=num_bars)
loginfo("Create temp dir")
workpath = tempfile.mkdtemp()
filename, _ = os.path.splitext(os.path.basename(pdf_file))
outputpath = os.path.join(cfg.outputdir, filename)
loginfo("Create outputp dir")
os.makedirs(outputpath, exist_ok=True)
loginfo("Extract PDF pages form {0}".format(pdf_file))
maxz = len(str(total_pages))
i = 1
############################################################################
# Creamos las subcarpetas para guardar las imagenes por extensión
############################################################################
for ext in cfg.imgext:
opath = os.path.join(outputpath, ext, "check")
os.makedirs(opath, exist_ok=True)
for p in range(firstp, endp + 1):
loginfo("Extract page {0} of {1}".format(i, num_bars))
if cfg.export_logos:
cmdline = '{0} -png -q -f {3} -l {4} {1} {2}/pagina-{5}'.format(
cfg.pdfimages_bin, pdf_file, workpath, p, p, p)
loginfo(cmdline)
with subprocess.Popen(cmdline, shell=True) as _:
pass
cmdline = '{0} -q -png -f {3} -l {4} -r {5} {1} {2}/pagina'.format(
cfg.pdftoppm_bin, pdf_file, workpath, p, p, cfg.resolution)
loginfo(cmdline)
with subprocess.Popen(cmdline, shell=True) as _:
pass
cmdline = '{0} -q -c -f {3} -l {4} {1} {2}/pagina'.format(
cfg.pdftohtml_bin, pdf_file, workpath, p, p)
loginfo(cmdline)
with subprocess.Popen(cmdline, shell=True) as _:
pass
with open(os.path.join(workpath, 'pagina-{0}.html'.format(str(p))),
'r',
encoding="Latin1") as f:
html = f.read()
img_file = "pagina-{0}.png".format(str(p).zfill(maxz))
img_file = os.path.join(workpath, img_file)
actas = get_metadata(cfg, html)
loginfo("Actas encontradas: {0}".format(str(actas)))
if not cfg.detect_export_pages or (cfg.detect_export_pages
and len(actas[2]) > 0):
last_acta = lista_actas[-1] if lista_actas else None
actas_pagina = [a[2] for a in actas[2]]
lista_actas.extend(actas_pagina)
total_actas = total_actas + (len(actas[2])
if actas is not None else 0)
try:
total_regions = total_regions + crop_regions(
img_file,
workpath,
outputpath,
last_acta=last_acta,
metadata=actas)
except Exception as msg:
logerror("Error:" + str(msg))
if not quiet:
widgets[0] = FormatLabel('[Página {0} de {1}]'.format(
i, num_bars))
if not quiet:
bar.update(i)
i = i + 1
loginfo("Remove temp dir")
if not quiet:
bar.finish()
printmsg = True if not quiet else False
loginfo("", printmsg=True)
loginfo("-- Estatus -------------------------------------------",
printmsg=printmsg)
loginfo("Carpeta temporal de trabajo : {0}".format(workpath),
printmsg=printmsg)
loginfo("Carpeta de salida : {0}".format(outputpath),
printmsg=printmsg)
if not cfg.debug_page:
loginfo("Eliminamos carpeta de trabajo")
shutil.rmtree(workpath)
actas_error = []
for a in lista_actas:
f = os.path.join(outputpath, cfg.imgext[0],
'{0}.{1}'.format(a, cfg.imgext[0]))
if not os.path.isfile(f):
actas_error.append(a)
loginfo("Total de actas : {0}".format(total_actas),
printmsg=True)
loginfo("Total de regiones recortadas : {0}".format(total_regions),
printmsg=True)
if actas_error:
loginfo("Actas no encontradas : {0}".format(
",".join(actas_error)),
printmsg=True)
if not cfg.debug_page:
# loginfo("Actas encontradas : {0}".format(",".join(set(lista_actas)-set(actas_error))), printmsg=True)
loginfo("-- Configuración -------------------------------------",
printmsg=True)
for linea in str(cfg).split("\n"):
loginfo(linea, printmsg=True)
loginfo("------------------------------------------------------",
printmsg=True)
loginfo("Finish process", printmsg=True)
if cfg.debug_page:
show_results(workpath, outputpath, lista_actas)
def save_crop(acta, crop, outputpath, boletin, index, last_acta):
loginfo("save_crop")
unique_colors = len(np.unique(crop.reshape(-1, crop.shape[2]), axis=0))
compression = [int(cv.IMWRITE_JPEG_QUALITY), cfg.jpg_compression]
fmerged = None
if unique_colors <= 1:
return 0
if not acta and last_acta:
loginfo("merging")
############################################################################################
# Es un Merged
############################################################################################
ext_compat = [e for e in cfg.imgext if e not in ['pcx']][0]
last_file = os.path.join(outputpath, ext_compat,
'{0}.{1}'.format(last_acta, ext_compat))
last_img = cv.imread(last_file)
max_width = 0 # find the max width of all the images
total_height = 0 # the total height of the images (vertical stacking)
images = [last_img, crop]
for img in images:
if img.shape[1] > max_width:
max_width = img.shape[1]
total_height += img.shape[0]
merged = np.zeros((total_height, max_width, 3), dtype=np.uint8)
merged.fill(255)
current_y = 0 # keep track of where your current image was last placed in the y coordinate
for image in images:
merged[current_y:image.shape[0] +
current_y, :image.shape[1], :] = image
current_y += image.shape[0]
unique_colors = len(
np.unique(merged.reshape(-1, merged.shape[2]), axis=0))
for ext in cfg.imgext:
# shutil.move(last_file, os.path.join(outputpath, ext, 'check'))
fmerged = os.path.join(outputpath, ext, 'check',
'{0}.merged.{1}'.format(last_acta, ext))
if ext.lower() == 'pcx':
# Mejorar esto por Dios
src = fmerged.replace(ext, cfg.imgext[0])
Image.open(src).save(fmerged)
else:
if unique_colors <= 256:
merged = cv.cvtColor(merged, cv.COLOR_BGR2GRAY)
loginfo("Saving : {0}".format(fmerged))
if ext.lower() == 'jpg':
cv.imwrite(fmerged, merged, compression)
add_resolution_to_jpg(fmerged, cfg.resolution)
else:
cv.imwrite(fmerged, merged, compression)
for ext in cfg.imgext:
opath = os.path.join(outputpath, ext)
if acta:
f = os.path.join(opath, '{0}.{1}'.format(acta, ext))
else:
f = os.path.join(opath, 'check',
'{0}_crop_{1}.{2}'.format(boletin, index, ext))
loginfo("Saving : {0}".format(f))
if ext.lower() == 'pcx':
# Mejorar esto por Dios
src = f.replace(ext, cfg.imgext[0])
Image.open(src).save(f)
else:
if unique_colors <= 256:
crop = cv.cvtColor(crop, cv.COLOR_BGR2GRAY)
if ext.lower() == 'jpg':
cv.imwrite(f, crop, compression)
add_resolution_to_jpg(f, cfg.resolution)
else:
cv.imwrite(f, crop, compression)
if fmerged:
for ext in cfg.imgext:
last_file = os.path.join(outputpath, ext,
'{0}.{1}'.format(last_acta, ext))
# Muevo el file anterior a check
outpath = os.path.join(outputpath, ext, 'check')
dst_filename = os.path.join(outpath, os.path.basename(last_file))
loginfo("Moving : {0} to {1}".format(
last_file, dst_filename))
shutil.move(last_file, outpath)
fmerged = os.path.join(outputpath, ext, 'check',
'{0}.merged.{1}'.format(last_acta, ext))
dst_filename = os.path.join(outpath, os.path.basename(fmerged))
loginfo("Moving : {0} to {1}".format(
fmerged, dst_filename))
shutil.move(fmerged, last_file)
return 1
def crop_regions(filepath, workpath, outputpath, last_acta, metadata=None):
filename, _ = os.path.splitext(os.path.basename(filepath))
############################################################################
# El calculo de todo esta hecho sobre una base de 300 dpi
# Hay que compensar si la resolucion es distinta
############################################################################
cfg.compensation = (cfg.resolution / 300)
############################################################################
# Lectura inicial de la imagen
############################################################################
loginfo("Abriendo archivo: {0}".format(filepath))
src = cv.imread(filepath)
if src is None:
logerror('opening {0}!'.format(filepath))
return -1
height, width, channels = src.shape
############################################################################
# Me quedo solo con el color de las lineas rectas y el texto b y n (negativo)
############################################################################
loginfo("Mask image (from {0} to {1})".format(cfg.linecolor_from,
cfg.linecolor_to))
mask_bw_negative = cv.inRange(src, cfg.linecolor_from, cfg.linecolor_to)
############################################################################
# Quito artefactos de hasta una cierta superficie
############################################################################
loginfo("Remove artifacts")
nb_components, output, stats, centroids = cv.connectedComponentsWithStats(
mask_bw_negative, connectivity=8)
sizes = stats[1:, -1]
nb_components = nb_components - 1
clean_mask = np.zeros((output.shape[0], output.shape[1], 3), dtype="uint8")
for i in range(0, nb_components):
if sizes[i] >= cfg.artifact_min_size * cfg.compensation:
clean_mask[output == i + 1] = 255
############################################################################
loginfo("Copy original")
original_con_lineas = np.copy(src)
original_original_con_lineas = np.copy(src)
final = src
############################################################################
# Engroso la máscara para no perder lineas rectas
############################################################################
loginfo("Dilate")
clean_mask_gray = cv.Canny(clean_mask, 50, 150, apertureSize=3)
kernel = cv.getStructuringElement(cv.MORPH_CROSS, (2, 2))
clean_mask_gray = cv.dilate(clean_mask_gray, kernel, iterations=1)
############################################################################
# Detección de líneas rectas y generación de máscara de recorte
############################################################################
height, width, channels = final.shape
crop_mask = np.zeros((height, width, 3), np.uint8)
minLineLength = int(cfg.line_min_length * cfg.compensation)
maxLineGap = int(cfg.line_max_gap * cfg.compensation)
theta = int(cfg.theta)
thres = int(cfg.line_thres * cfg.compensation)
rho = cfg.line_rho
loginfo("Lines detection")
linesP = None
linesP = cv.HoughLinesP(clean_mask_gray,
rho,
np.pi / theta,
thres,
minLineLength=minLineLength,
maxLineGap=maxLineGap)
cv.imwrite(os.path.join(workpath, '01.original.png'), src)
cv.imwrite(os.path.join(workpath, '02.mask_bw_negative.png'),
mask_bw_negative)
cv.imwrite(os.path.join(workpath, '03.clean_mask.png'), clean_mask)
cv.imwrite(os.path.join(workpath, '04.clean_mask_gray.png'),
clean_mask_gray)
if linesP is not None:
llorig = [e[0] for e in np.array(linesP).tolist()]
for linea in [e[1] for e in enumerate(llorig)]:
cv.line(original_original_con_lineas, (linea[0], linea[1]),
(linea[2], linea[3]), (0, 0, 255), 3, cv.LINE_AA)
ll = process_lines(src, llorig, cfg.resolution, cfg.only_horizontal,
cfg.h_line_gap, cfg.v_line_gap, cfg.compensation)
for linea in [e[1] for e in enumerate(ll)]:
cv.line(original_con_lineas, (linea[0], linea[1]),
(linea[2], linea[3]), (0, 0, 255), 3, cv.LINE_AA)
cv.line(crop_mask, (linea[0], linea[1]), (linea[2], linea[3]),
(0, 0, 255), 3, cv.LINE_AA)
if cfg.save_process_files:
cv.imwrite(os.path.join(workpath, '05.crop_mask.png'), crop_mask)
cv.imwrite(os.path.join(workpath, '06.original_con_lineas.png'),
original_con_lineas)
############################################################################
# En base a la mascara obtengo los rectangulos de interes
############################################################################
loginfo("Contours")
gray = cv.cvtColor(crop_mask,
cv.COLOR_BGR2GRAY) # convert to grayscale
retval, thresh_gray = cv.threshold(gray,
thresh=1,
maxval=255,
type=cv.THRESH_BINARY_INV)
contours, hierarchy = cv.findContours(thresh_gray, cv.RETR_CCOMP,
cv.CHAIN_APPROX_SIMPLE)
############################################################################
# Recorto los rectangulos
# Si las coordenadas de algun acta entran dentro de la zona de recorte
# Bien! podemos asociar la zona con el número de acta
############################################################################
max_area = cfg.max_area * cfg.compensation
min_area = cfg.min_area * cfg.compensation
if metadata:
(x, y, actas) = metadata
relation = sum([height / y, width / x]) / 2
contornos = []
for cont in contours:
x, y, w, h = cv.boundingRect(cont)
area = w * h
contornos.append((x, y, w, h, area))
final = final.astype(np.uint8)
contornos.sort(key=lambda x: x[4])
i = 1
adj = 3 # Para que no entren las líneas rectas
for recorte in contornos[:-2]:
x, y, w, h, area = recorte
x = x + adj
y = y + adj
w = w - (adj * 2)
h = h - (adj * 2)
if area < max_area and area > min_area:
acta = get_acta(actas, (x, y, x + w, y + h), relation)
roi = final[y:y + h, x:x + w]
roi = get_main_area(roi, acta)
save_crop(acta, roi, outputpath, filename, i, last_acta)
i = i + 1
loginfo("End crop_regions")
return i - 1
return 0
os.path.join(application_path, 'pboletin.ini'))
else:
configfile = args.configfile
if args.pdffile:
if args.logfile:
log_level = getattr(logging, args.loglevel.upper(), None)
logging.basicConfig(filename=args.logfile,
level=log_level,
format='%(asctime)s|%(levelname)s|%(message)s',
datefmt='%Y/%m/%d %I:%M:%S',
filemode='w')
try:
loginfo("Config file: {0}".format(configfile))
cfg.set_file(configfile)
except IOError as msg:
cmdparser.error(str(msg))
sys.exit(-1)
if args.debug_page:
cfg.save_process_files = True
cfg.debug_page = args.debug_page
cfg.from_page = args.from_page
cfg.to_page = args.to_page
if args.inputpath:
cfg.inputdir = args.inputpath
args.pdffile = os.path.join(cfg.inputdir, args.pdffile)
