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)