def add_phone(data):
name, phone = data
result_name = session.query(Contact).filter(
Contact.contact_name == name).first()
result_phone = session.query(Phone).filter(Phone.phone == phone).first()
if result_name and result_phone:
info = '\n\tEntered contact with phone already exist'
else:
try:
new_contact_name = Contact(contact_name=name)
session.add(new_contact_name)
session.commit()
new_contact_phone = Phone(phone=phone,
contact_id=new_contact_name.id)
session.add(new_contact_phone)
session.commit()
info = '\n\tContact has been successfully added to addressbook'
except Exception as e:
print(e)
info = '\n\tCheck entered data.'
return info
def get_models():
models = {}
for person in session.query(Person).all():
if person.person not in models:
models[person.person] = []
models[person.person].append(np.array(json.loads(person.marks)))
return models
def show_all_contacts():
result = session.query(Contact.id, Contact.contact_name,
Phone.phone).join(Phone).all()
output = ''
for el in result:
output += f'\t{el[1]} - {el[-1]}\n'
return output
def show_one_contact_data(data):
name, _ = data
result = session.query(
Contact.id, Contact.contact_name,
Phone.phone).join(Phone).filter(Contact.contact_name == name).all()
if result:
output = ''
for el in result:
output += f'\t{el[1]} - {el[-1]}\n'
else:
output = '\tThere is not such contact.'
return output
def delete_contact(data):
name, _ = data
result = session.query(Contact).filter(Contact.contact_name == name).all()
if result:
for _ in range(len(result)):
result = session.query(Contact).filter(
Contact.contact_name == name).first()
id = result.id
if result:
result = session.query(Contact).get(id)
session.delete(result)
session.commit()
output = f'\tAll info for contact {name} were deleted'
else:
output = '\tThere is not such contact.'
return output
def change_phone(data):
name, phone = data
result = session.query(
Contact.id, Contact.contact_name, Phone.phone,
Phone.id).join(Phone).filter(Contact.contact_name == name).all()
contact_phones = {}
if result:
output = ''
for el in result:
contact_phones[el[-1]] = el[2]
output += f'\tContact data: {el[1]} - {el[2]}. Phone id - {el[-1]}\n'
else:
output = '\tThere is not such contact.'
print('=' * 75)
print(output)
user_imput = int(input('Enter phone <id> for phone you want to change: '))
if user_imput in contact_phones.keys():
new_phone = input('Enter new contact phone number: ')
raw_phone = re.search(r'38(097|098|096|068|067|063|068|099)\d{7}',
new_phone)
new_phone = raw_phone.group() if raw_phone else False
if new_phone:
new = session.query(Phone).get(user_imput)
new.phone = new_phone
session.add(new)
session.commit()
output = '\tContact was successfully changed.'
else:
output = '\tInvalid phone format. Repeat command'
else:
output = '\tThere is not such contact. Repeat command'
return output
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--predictor', required=False, help="large or small", default="large")
parser.add_argument('-m', '--model', required=False, help="model name")
parser.add_argument('-l', '--list-models', required=False, help="list trained models", action='count')
parser.add_argument('-r', '--remove-model', required=False, help="remove trained model", type=str)
parser.add_argument('-t', '--train', required=False, help="train model name")
parser.add_argument('-i', '--images', required=False, help="dir with images or list images")
parser.add_argument('-g', '--adjust-gamma', required=False, help="set adjust gamma", type=float, default=1.0)
parser.add_argument('-c', '--calibrate-adjust-gamma', required=False, help="show window for calibrate adjust gamma", action='count')
args = parser.parse_args()
log.info('started')
cap = get_cap()
if args.train:
train(args.train, args.images)
elif args.calibrate_adjust_gamma:
calibrate_adjust(cap, args.adjust_gamma)
else:
if args.model:
marks = [np.array(json.loads(mark.marks)) for mark in session.query(Person.marks).filter(Person.person == args.model).all()]
run(cap, {args.model: marks}, args.adjust_gamma, args.predictor)
else:
models = get_models()
while True:
list(run(cap, models, args.adjust_gamma, args.predictor))