def run(panels):
logger = commands.get_logger(__name__, threading.get_ident())
try:
logger.info('Text detection')
paths = map(lambda panel: panel['path'].as_posix(), panels)
for (path, texts) in images.text.detect(paths):
text = ''.join(map(lambda x: x['description'], filter(lambda x: 'locale' in x,texts)))
logger.info('-> {0}'.format(path))
logger.info(text)
db.metadata.update(path, auto_script=text)
logger.info('Done')
except Exception as e:
logger.exception(e)
def run(panels):
logger = commands.get_logger(__name__, threading.get_ident())
try:
logger.info('Face detection')
for panel in panels:
path = panel['path'].as_posix()
logger.info(path)
crops = list(images.faces.detect(path))
for crop in crops:
logger.info(crop)
db.faces.update(panel, crops)
logger.info('Done')
except Exception as e:
logger.exception(e)
def run(id, panels, tags):
logger = commands.get_logger(__name__, threading.get_ident())
try:
logger.info('Learn characters')
data = []
for panel in panels:
for (face, character) in zip(panel['faces'], panel['metadata']['characters']):
logger.info('%s %s' % (face, character))
if character in tags:
data.append((face.as_posix(), tags.index(character)))
images.learn.train(logger, db.models.path_for(id).as_posix(), len(tags), data)
db.models.update(id, tags)
logger.info('Done')
except Exception as e:
logger.exception(e)
def run(id, panels):
logger = commands.get_logger(__name__, threading.get_ident())
try:
logger.info('Character infer detection')
tags = db.models.tags(id)
for panel in panels:
logger.info(panel['path'])
auto_characters = []
for face in panel['faces']:
logger.info(face)
prediction = images.learn.infer(db.models.path_for(id).as_posix(), len(tags), face.as_posix())
tag = tags[prediction]
logger.info('{0} => {1}'.format(prediction, tag))
auto_characters.append(tag)
db.metadata.update(panel['path'].as_posix(), auto_characters=auto_characters)
logger.info('Done')
except Exception as e:
logger.exception(e)
def run(id, volume, images):
logger = commands.get_logger(__name__, threading.get_ident())
try:
logger.info('Cut {0} {1}'.format(id, volume))
cut = Cut()
for (filename, path) in images:
logger.info('statistics: {0}'.format(filename))
cut.statistics(path)
for (filename, path) in images:
logger.info('crop: {0}'.format(filename))
for i, image in enumerate(cut.crop(path)):
logger.info('found: {0} -> {1}'.format(filename, i))
db.panels.update(id, volume, filename, i, image)
except Exception as e:
logger.exception(e)
def run(id, volume, images):
logger = commands.get_logger(__name__, threading.get_ident())
try:
logger.info('Cut {0} {1}'.format(id, volume))
cut = Cut()
for (filename, path) in images:
logger.info('statistics: {0}'.format(filename))
cut.statistics(path)
for (filename, path) in images:
logger.info('crop: {0}'.format(filename))
for i, image in enumerate(cut.crop(path)):
logger.info('found: {0} -> {1}'.format(filename, i))
db.panels.update(id, volume, filename, i, image)
except Exception as e:
logger.exception(e)
def run(id, panels):
logger = commands.get_logger(__name__, threading.get_ident())
try:
logger.info('Character infer detection')
tags = db.models.tags(id)
for panel in panels:
logger.info(panel['path'])
auto_characters = []
for face in panel['faces']:
logger.info(face)
prediction = images.learn.infer(
db.models.path_for(id).as_posix(), len(tags),
face.as_posix())
tag = tags[prediction]
logger.info('{0} => {1}'.format(prediction, tag))
auto_characters.append(tag)
db.metadata.update(panel['path'].as_posix(),
auto_characters=auto_characters)
logger.info('Done')
except Exception as e:
logger.exception(e)