def __init__(
self,
root_folder: Path,
split: str = "validation",
transform: Callable = None,
):
"""
Object Detection dataset.
[extended_summary]
:param root_folder:
:param split:
:param transform:
"""
super().__init__()
self.transform = transform
images_folder = root_folder / "images"
if split == "train":
all_folders = images_folder.glob(f"{split}_0" + r"[0-9]")
all_images = chain(
*[folder.glob(r"*.jpg") for folder in all_folders]
)
else:
images_folder /= split
all_images = images_folder.glob(r"*.jpg")
bbox_csv_filepath = root_folder.joinpath(
"annotations", "boxes", f"{split}-annotations-bbox.csv"
)
indices = tuple(
BBOX_INDICES[key] for key in (
"LabelName",
"XMin",
"XMax",
"YMin",
"YMax",
)
)
self.box_labels = multicolumn_csv_to_dict(
bbox_csv_filepath, value_cols=indices, one_to_n_mapping=True
)
images_with_labels = set(self.box_labels.keys())
self.images = [
image_path for image_path in all_images
if image_path.stem in images_with_labels
]
self.label_name_to_class_description = csv_to_dict(
root_folder.joinpath(
"annotations", "metadata", "class-descriptions-boxable.csv"
),
discard_header=False,
)
self.label_name_to_id = bidict(
zip(
self.label_name_to_class_description.keys(),
range(len(self.label_name_to_class_description.keys())),
)
)
def output_to_text(user_settings, output_settings, m_dir):
import os
# user_settings : path to csv of user settings e.g. config/settings/default.csv
# output_settings : path to csv of output settings e.g. config/settings/output_settings.csv
# output_dir : path to *.mat files
user = csv_to_dict(user_settings)
ott = csv_to_dict(output_settings)
assert ott.has_key('singleFilename')
# outfile = ott['singleFilename']
outfile = "out.txt"
matfiles = [m_dir+"/"+f for f in os.listdir(m_dir) if f.endswith(".mat")]
test = matfiles[0]
fields = [x[0] for x in sio.whosmat(test)]
print fields
# print test
# d = sio.loadmat(test)
data = sio.loadmat(test)
print type(data)
delim = ","
def points(author_data, conference, journal):
author_pubs = csv_to_dict(author_data)
author = create_author_dict()
for k, v in author_pubs.items():
if k in conference.keys():
author[conference.get(k)] += int(v)
elif k in journal.keys():
author[journal.get(k)] += int(v) * 2
else:
print("This {author_data.get(k)} does not exists!",
"\nPlease correct the input file")
result = calc_pub(author)
return result
def init_tables(self):
# read data files
self.books = csv_to_dict('csv/books.csv')
self.characters = csv_to_dict('csv/characters.csv')
self.places = csv_to_dict('csv/places.csv')
self.verbs = csv_to_dict('csv/verbs.csv')
self.parts = csv_to_dict('csv/parts.csv')
self.adjectives = csv_to_dict('csv/adjectives.csv')
"""
Proof of Concept
Read ports.csv and print out extra commands to run based on open port.
"""
from utils import csv_to_dict
from commands_by_port import commands
nmap_dict = csv_to_dict("/root/pentests/home/ports.csv")
for row in nmap_dict:
if row['port'] in commands:
print("# " + commands[row['port']]['header'])
print("# ***************************")
for command in commands[row['port']]['commands']:
print(command.format(ip=row['ipv4']))
# -*- coding: utf-8 -*-s
__author__ = 'jorgesaldivar'
import parser_rsssf, utils
if __name__ == '__main__':
dict_championships = []
meta_championships = utils.csv_to_dict('../data/campeonatos.csv')
for meta_championship in meta_championships:
if int(meta_championship['year']) <= 2007:
print('Championship %s', meta_championship['name'])
dict_championships.append(parser_rsssf.get_data(meta_championship))
else:
print('here')
print('Finished!')
ET.SubElement(bndbox,
"ymin").text = str(int(float(label_tokens[3])))
ET.SubElement(bndbox,
"xmax").text = str(int(float(label_tokens[4])))
ET.SubElement(bndbox,
"ymax").text = str(int(float(label_tokens[5])))
if contains:
raw_string = ET.tostring(root, "utf-8")
reparsed = xml.dom.minidom.parseString(raw_string)
file = open(os.path.join(save_path, jpg + ".xml"), "w")
file.write(reparsed.toprettyxml(indent="\t"))
file.close()
if __name__ == "__main__":
# result_dir = "/home/sakulaki/code/yolo-pre-trained/darknet/results"
# classes_list = ["ASCUS", "LSIL", "ASCH", "HSIL", "SCC"]
# dict_pic_info = get_predictions_result(result_dir, classes_list)
# img_size = 608
# save_path = "/home/sakulaki/dataset/realtest/608/XB1800118"
# det = 0.3
# prediction_convert(dict_pic_info, classes_list, img_size, save_path, det)
csv_file = "D:/2018-08-13-test_jpg/2018-08-13-14_15_41/2018-08-13-14_15_41_s.csv"
dict_ = csv_to_dict(csv_file)
classes = ["ASCUS", "LSIL", "ASCH", "HSIL", "SCC"]
img_size = 608
save_path = "D:/2018-08-13-test_jpg/2018-08-13-14_15_41/xmls"
det = 0.9
prediction_convert(dict_, classes, img_size, save_path, det)
import sys
from utils import csv_to_dict
from core import run
if __name__ == "__main__":
conference = csv_to_dict("../data/conference_qualis.csv")
journal = csv_to_dict("../data/journal_qualis.csv")
path = '.'
if len(sys.argv) > 1:
persons = sys.argv[1:]
run(persons, conference, journal)
else:
sys.exit(1)
"""if sys.argv[1] == '-h' or sys.argv[1] == '--help' or sys.argv[1] == '-H':
print('This file require 3 parameters, that follows:')
print('The first represents witch file do you want to execute, in order:')
print('\t 1 - papers_data_creation.py this file generate a new csv,',
'where each line represents a paper.')
print('\t 2 - split_program this file generate a new csv, where each',
'line represents a "programa".')
print('\t 3 - qualis_by_year this file create a new csv file, where',
'each line represent a researcher.')
print('\n The second parameter represent the input file and the location')
print('\n The last parameter represents the output file and the location')
else:
dataIn = sys.argv[2]
dataOut = name_the_file(sys.argv[3], sys.argv[1].split('.')[0])
num = int(sys.argv[1])
if num == 1:
papers_data_creation.run(dataIn, dataOut)
elif num == 2:
def get_stats(self):
"""
Returns the status message of the database as dictionary.
"""
return utils.csv_to_dict(self.proto.stat())
