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():
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():
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))
