def cmd_unlock(args):
if not args.quiet:
print("Unlocking database {0}".format(args.database))
if not args.dry_run:
database = Database.instance(args.database)
mapper = DatabaseMapper(database)
mapper.set_destroy_lock(False)
def main():
training_file = open('training.txt','w')
testing_file = open('testing.txt','w')
training_images = {'blur':list(), 'noblur':list()}
testing_images = {'blur':list(), 'noblur':list()}
parser = argparse.ArgumentParser(description='generates training/testing files for blur')
parser.add_argument('-l', '--limit', type=int, metavar='COUNT', required=False, help='Maximum number of images to use')
parser.add_argument('-r', '--random', action="store_true", default=False, required=False, help='Fetch images ordered randomly if limit is active')
parser.add_argument('--tag_require', action='append', dest='tags_require', default=None, required=False, help='Tag that must be present on selected images')
parser.add_argument('--tag_exclude', action='append', dest='tags_exclude', default=None, required=False, help='Tag that must not be present on selected images')
parser.add_argument('-p', '--percent_training', dest='percent', default=0.25, required=False, help='Tag indicating what percent of images for training')
parser.add_argument('database', help='Name of database to use')
args = parser.parse_args()
db = Database.instance(args.database)
db_mapper = DatabaseMapper(db)
images = db_mapper.get_images_for_analysis(kDomain, limit=args.limit, random=args.random, tags_require=args.tags_require, tags_exclude=args.tags_exclude)
blur_images = list()
noblur_images = list()
for image in images:
if image['annotations'][0]['model'] == 'blur':
blur_images.append(image)
else:
noblur_images.append(image)
random.shuffle(blur_images)
random.shuffle(noblur_images)
blur_training_len = int(len(blur_images)*float(args.percent))
noblur_training_len = int(len(noblur_images)*float(args.percent))
training_images['blur'] = blur_images[:blur_training_len]
testing_images['blur'] = blur_images[blur_training_len:]
training_images['noblur'] = noblur_images[:noblur_training_len]
testing_images['noblur'] = noblur_images[noblur_training_len:]
for file,image_dict in ((training_file,training_images),(testing_file,testing_images)):
for model in image_dict.keys():
for image in image_dict[model]:
file.write('{}\t{}\n'.format(rigor.imageops.find(image), model))
def main():
parser = argparse.ArgumentParser(
description='Runs text detector on relevant images')
parser.add_argument('classifier_file', help='Path to classifier CLF')
parser.add_argument('-l',
'--limit',
type=int,
metavar='COUNT',
required=False,
help='Maximum number of images to use')
parser.add_argument(
'-r',
'--random',
action="store_true",
default=False,
required=False,
help='Fetch images ordered randomly if limit is active')
parser.add_argument('database', help='Database to use')
args = parser.parse_args()
parameters["classifier_file"] = args.classifier_file
i = rigor.runner.Runner('text',
parameters,
limit=args.limit,
random=args.random)
database_mapper = DatabaseMapper(Database.instance(args.database))
for result in i.run():
detected = result[1]
expected = result[2]
image = database_mapper.get_image_by_id(result[0])
cv_image = rigor.imageops.fetch(image)
cv2.polylines(cv_image, expected, True, cv2.RGB(0, 255, 0))
cv2.polylines(cv_image, detected, True, cv2.RGB(255, 255, 0))
cv2.imwrite(".".join((str(image["id"]), image["format"])), cv_image)
def cmd_lock(args):
if not args.quiet:
print("Locking database {0}".format(args.database))
if not args.dry_run:
database = Database.instance(args.database)
mapper = DatabaseMapper(database)
mapper.set_destroy_lock(True)
def cmd_patch(args): database = Database.instance(args.database) mapper = DatabaseMapper(database) start_level = mapper.get_patch_level() + 1 stop_level = None if args.level: stop_level = args.level patch(mapper, args.patch_dir, start_level, stop_level, args.dry_run, args.quiet)
def cmd_clone(args):
if not args.quiet:
print("Cloning database {0} to {1}".format(args.source, args.destination))
if not args.dry_run:
Database.cls().clone(args.source, args.destination)
database = Database.instance(args.destination)
mapper = DatabaseMapper(database)
mapper.set_destroy_lock(False) # new databases are always unlocked
def cmd_patch(args):
database = Database.instance(args.database)
mapper = DatabaseMapper(database)
start_level = mapper.get_patch_level() + 1
stop_level = None
if args.level:
stop_level = args.level
patch(mapper, args.patch_dir, start_level, stop_level, args.dry_run,
args.quiet)
def cmd_clone(args):
if not args.quiet:
print("Cloning database {0} to {1}".format(args.source,
args.destination))
if not args.dry_run:
Database.cls().clone(args.source, args.destination)
database = Database.instance(args.destination)
mapper = DatabaseMapper(database)
mapper.set_destroy_lock(False) # new databases are always unlocked
def cmd_destroy(args):
if not args.quiet:
print("Destroying database {0}".format(args.database))
database = Database.instance(args.database)
mapper = DatabaseMapper(database)
if mapper.get_destroy_lock():
sys.stderr.write("Error: database is locked\n")
sys.exit(2)
mapper = None
database = None
if not args.dry_run:
Database.cls().drop(args.database)
def cmd_destroy(args):
if not args.quiet:
print("Destroying database {0}".format(args.database))
database = Database.instance(args.database)
mapper = DatabaseMapper(database)
if mapper.get_destroy_lock():
sys.stderr.write("Error: database is locked\n")
sys.exit(2)
mapper = None
database = None
if not args.dry_run:
Database.cls().drop(args.database)
def main():
rigor.domain.money.init(parameters)
logger = rigor.logger.getLogger(__file__)
database_mapper = DatabaseMapper(Database.instance(kDatabase))
logger.debug('Fetching image IDs from database')
images = database_mapper.get_images_for_analysis(kDomain, kLimit, False)
for parameter_set in get_parameters():
timestamp = datetime.utcnow().strftime("{0}-%Y%m%d_%H%M%S%f".format(kDomain))
with open("{0}.params".format(timestamp), "w") as parameter_file:
json.dump(parameter_set, parameter_file)
parameter_file.write("\n")
with open("{0}.results".format(timestamp), "w") as result_file:
image_config = partial(rigor.domain.money.run, parameters=parameter_set)
logger.debug('Processing {0} images'.format(len(images)))
for result in map(image_config, images):
result_file.write("\t".join([str(x) for x in result]))
result_file.write("\n")
def main():
parser = argparse.ArgumentParser(description='Runs text detector on relevant images')
parser.add_argument('classifier_file', help='Path to classifier CLF')
parser.add_argument('-l', '--limit', type=int, metavar='COUNT', required=False, help='Maximum number of images to use')
parser.add_argument('-r', '--random', action="store_true", default=False, required=False, help='Fetch images ordered randomly if limit is active')
parser.add_argument('database', help='Database to use')
args = parser.parse_args()
parameters["classifier_file"] = args.classifier_file
i = rigor.runner.Runner('text', parameters, limit=args.limit, random=args.random)
database_mapper = DatabaseMapper(Database.instance(args.database))
for result in i.run():
detected = result[1]
expected = result[2]
image = database_mapper.get_image_by_id(result[0])
cv_image = rigor.imageops.fetch(image)
cv2.polylines(cv_image, expected, True, cv2.RGB(0, 255, 0))
cv2.polylines(cv_image, detected, True, cv2.RGB(255, 255, 0))
cv2.imwrite(".".join((str(image["id"]), image["format"])), cv_image)
def cmd_create(args):
if not args.quiet:
print("Creating database {0}".format(args.database))
if not args.dry_run:
Database.cls().create(args.database)
stop_level = None
if args.level:
stop_level = args.level
try:
database = Database.instance(args.database)
mapper = DatabaseMapper(database)
patch(mapper, args.patch_dir, 0, stop_level, args.dry_run, True)
except:
# Save exception for later
exc_info = sys.exc_info()
try:
Database.cls().drop(args.database)
except:
pass
raise exc_info[0], exc_info[1], exc_info[2]
def cmd_create(args):
if not args.quiet:
print("Creating database {0}".format(args.database))
if not args.dry_run:
Database.cls().create(args.database)
stop_level = None
if args.level:
stop_level = args.level
try:
database = Database.instance(args.database)
mapper = DatabaseMapper(database)
patch(mapper, args.patch_dir, 0, stop_level, args.dry_run, True)
except:
# Save exception for later
exc_info = sys.exc_info()
try:
Database.cls().drop(args.database)
except:
pass
raise exc_info[0], exc_info[1], exc_info[2]
def __init__(self, database): self._dbmapper = DatabaseMapper(Database.instance(database))
""""
Script to delete ground truth (image, thumbnail, and all!)
"""
import argparse
import rigor.imageops
from rigor.dbmapper import DatabaseMapper
from rigor.database import Database
parser = argparse.ArgumentParser(
description='Deletes ground truth (image, thumbnail, and all!)')
parser.add_argument('database', help='Name of database to use')
parser.add_argument('delete_ids',
metavar='delete_id',
nargs='+',
type=int,
help='ID(s) of images to delete')
args = parser.parse_args()
db = Database.instance(args.database)
db_mapper = DatabaseMapper(db)
for image_id in args.delete_ids:
image = db_mapper.get_image_by_id(image_id)
print("OBLITERATING {}".format(image['id']))
rigor.imageops.destroy_image(db, image)
""""
Script to delete ground truth (image, thumbnail, and all!)
"""
import argparse
import rigor.imageops
from rigor.dbmapper import DatabaseMapper
from rigor.database import Database
parser = argparse.ArgumentParser(description='Deletes ground truth (image, thumbnail, and all!)')
parser.add_argument('database', help='Name of database to use')
parser.add_argument('delete_ids', metavar='delete_id', nargs='+', type=int, help='ID(s) of images to delete')
args = parser.parse_args()
db = Database.instance(args.database)
db_mapper = DatabaseMapper(db)
for image_id in args.delete_ids:
image = db_mapper.get_image_by_id(image_id)
print("OBLITERATING {}".format(image['id']))
rigor.imageops.destroy_image(db, image)
def main():
training_file = open('training.txt', 'w')
testing_file = open('testing.txt', 'w')
training_images = {'blur': list(), 'noblur': list()}
testing_images = {'blur': list(), 'noblur': list()}
parser = argparse.ArgumentParser(
description='generates training/testing files for blur')
parser.add_argument('-l',
'--limit',
type=int,
metavar='COUNT',
required=False,
help='Maximum number of images to use')
parser.add_argument(
'-r',
'--random',
action="store_true",
default=False,
required=False,
help='Fetch images ordered randomly if limit is active')
parser.add_argument('--tag_require',
action='append',
dest='tags_require',
default=None,
required=False,
help='Tag that must be present on selected images')
parser.add_argument('--tag_exclude',
action='append',
dest='tags_exclude',
default=None,
required=False,
help='Tag that must not be present on selected images')
parser.add_argument(
'-p',
'--percent_training',
dest='percent',
default=0.25,
required=False,
help='Tag indicating what percent of images for training')
parser.add_argument('database', help='Name of database to use')
args = parser.parse_args()
db = Database.instance(args.database)
db_mapper = DatabaseMapper(db)
images = db_mapper.get_images_for_analysis(kDomain,
limit=args.limit,
random=args.random,
tags_require=args.tags_require,
tags_exclude=args.tags_exclude)
blur_images = list()
noblur_images = list()
for image in images:
if image['annotations'][0]['model'] == 'blur':
blur_images.append(image)
else:
noblur_images.append(image)
random.shuffle(blur_images)
random.shuffle(noblur_images)
blur_training_len = int(len(blur_images) * float(args.percent))
noblur_training_len = int(len(noblur_images) * float(args.percent))
training_images['blur'] = blur_images[:blur_training_len]
testing_images['blur'] = blur_images[blur_training_len:]
training_images['noblur'] = noblur_images[:noblur_training_len]
testing_images['noblur'] = noblur_images[noblur_training_len:]
for file, image_dict in ((training_file, training_images),
(testing_file, testing_images)):
for model in image_dict.keys():
for image in image_dict[model]:
file.write('{}\t{}\n'.format(rigor.imageops.find(image),
model))