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()