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 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)
""""
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)
