Goddard

' oidgen.vbs 
'  
' THIS CODE AND INFORMATION IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED  
' OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE IMPLIED WARRANTIES OF MERCHANTABILITY AND/OR  
' FITNESS FOR A PARTICULAR PURPOSE. 
' 
' Copyright (c) Microsoft Corporation. All rights reserved 
' Improvements made by Ryein C. Goddard
' 
' This script is not supported under any Microsoft standard support program or service.  

Goddard

#!/bin/sh
### BEGIN INIT INFO
# Provides:          OpenVPN Autoconnect
# Required-Start:    $local_fs $remote_fs $network
# Required-Stop:     $local_fs $remote_fs $network
# Default-Start:     2 3 4 5
# Default-Stop:      0 1 6
# Short-Description: OpenVPN Autoconnect
# Description:       OpenVPN Autoconnect
### END INIT INFO

Goddard

#!/bin/python3

import math
import os
import random
import re
import sys

# Complete the staircase function below.
def staircase(n):

Goddard

#!/bin/python3

import math
import os
import random
import re
import sys

# Complete the diagonalDifference function below.
def diagonalDifference(arr):

Goddard

def draw_lines(img, lines, color=[255, 0, 0], thickness=10):
    """
    NOTE: this is the function you might want to use as a starting point once you want to 
    average/extrapolate the line segments you detect to map out the full
    extent of the lane (going from the result shown in raw-lines-example.mp4
    to that shown in P1_example.mp4).  
    
    Think about things like separating line segments by their 
    slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left
    line vs. the right line.  Then, you can average the position of each of 

Goddard

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright 2016 Massachusetts Institute of Technology

"""Publish a video as ROS messages.
"""

import argparse

Goddard

def gen_test_video(sess, logits, keep_prob, input_image, clip, image_shape): #test_image
    # image = scipy.misc.imresize(scipy.misc.imread(test_image), image_shape)
    image = clip

    im_softmax = sess.run(
        [tf.nn.softmax(logits)],
        {keep_prob: 1.0, input_image: [image]})
    im_softmax = im_softmax[0][:, 1].reshape(image_shape[0], image_shape[1])
    segmentation = (im_softmax > 0.5).reshape(image_shape[0], image_shape[1], 1)
    mask = np.dot(segmentation, np.array([[0, 255, 0, 127]]))

Goddard

import os.path
import tensorflow as tf

import helper
import freeze

import warnings
from distutils.version import LooseVersion
import project_tests as tests

Goddard

import os.path
import tensorflow as tf
import helper
import cv2

from distutils.version import LooseVersion

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

Goddard

import numpy as np  
import matplotlib.pyplot as plt  

def graph(formula, x_range):  
    x = np.array(x_range)  
    y = formula(x)
    plt.plot(x, y)  
    plt.show()  

def my_formula(x):