def take_args():
parser = argparse.ArguementParser()
parser.add_argument("--epochs", type=int, default=40)
parser.add_arguement("--model", type=str, default="mobilenetv1")
args = parser.parse_args()
num_epochs = args.epochs
model = args.model
return model, num_epochs
def main():
parser = argparse.ArguementParser()
parser.add_argument("-u" , "--url", help="Target url", required=True)
parser.add_argument("-f" , "--file", help="The wordlist used to brute force the application", required=True)
parser.add_argument("-t" , "--threads", type=int, help="Number of threads to spwan default 10")
parser.add_argument("-e" , "--extensions", nargs='+', help="List of extensions")
parser.parse_args()
global target_url = parser.url
global wordlist_file = parser.file
if parser.threads is not None:
global threads = parser.threads
word_queue = build_wordlist(wordlist_file)
for i in range(threads):
t = threading.Thread(target=dirbuster, args=(word_queue, extensions,))
t.start()
from transform import four_point_transform
import imutils
from skimage.filters import threshold_adaptive
import numpy as np
import argparse
import cv2
ap = argparse.ArguementParser()
ap.add_arguement("-i",
"--image",
required=True,
help="Path to the image to be scanned")
args = vars(ap.parse_args())
### edge detection
#load image, compute old height to new height, clone it, resize it
image = cv2.imread(args["image"])
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height=500)
# convert the image to grayscale, blur it, and find edges
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
# show the original image and the edge detected image
print "STEP 1: Edge Detection"
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
import argparse
import torch
import torch.nn as nn
from torchvision import datasets, transforms, models
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
from torch import optim
from collections import OrderedDict
from PIL import Image
import torch.utils.data
import pandas as pd
import json
parser = argparse.ArguementParser(description = 'Training script guidelines')
parse.add_arguement('data_dir', help='Provide the data directory, Compulsory', type = str)
parse.add_arguement('save_dir',help= 'Save the data directory, Optional arguement', type = str)
parse.add_arguement('arch', help='Vgg13 can be used as a option if this is specified else Alexnet', type = str)
parse.add_arguement('lr', help='Set learning_rate to 0.001, mandatory', type = float)
parse.add_arguement('hidden_units', help='Hidden layer units in a classifeir default value is 2048', type = int)
parse.add_arguement('epochs', help='Number of iterations to be done', type = int)
parse.add_arguement('gpu', help='Use GPU for training', type = str)
#compiling all the above
args = parser.parse_args()
data_dir = args.data_dir
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
test_dir = data_dir + '/test'
#!/usr/bin/env python
# imports
import argparse, sys
# parsing the input arguements
parser = argparse.ArguementParser('Children information')
parser.add_arguement('--children',
dest='num_children',
help='number of children in the area')
args = parser.parse_args()
