def colored_status(status): parser = config.default_config().parser if not parser.has_section("colors"): return status for option in parser.options("colors"): if status.strip().lower() == option: return colored(status, parser.get("colors", option), attrs=["bold"]) return status
def colored_status(status): parser = config.default_config().parser if not parser.has_section('colors'): return status for option in parser.options('colors'): if status.strip().lower() == option: return colored(status, parser.get('colors', option), attrs=['bold']) return status
import webbrowser import sys import os sys.path.append(os.path.join(os.path.dirname(__file__), "..")) import lib.config as config if __name__ == '__main__': if len(sys.argv) <= 1: print 'Usage: python %s [issueKey]' % __file__ sys.exit(0) issueKey = sys.argv[1] host = config.default_config().get('jira_default', 'host') webbrowser.open('https://%s/browse/%s' % (host, issueKey))
along with this program. If not, see <http://www.gnu.org/licenses/>. ***The feature extraction methods impelementation, they are similar to OpenSfM sofware (https://github.com/mapillary/OpenSfM/blob/master/opensfm/features.py)*** ***The configuration file (lib.config) is the OpenSfM's one.*** """ import numpy as np import PIL from lib.utils import message import cv2 as cv from PIL import ExifTags, Image from lib.config import default_config config = default_config() class Image: ''' Name: Image Description: Image class: 1) Calculates image's camera matrix. 2) Extracts image's feature points. Functions: --- Setters --- set_imagename: Set image's name. set_focal: Set focal length (mm). set_width: Set image's width. set_height: Set image's height.