Jack Northrup BlogBlocks

## ffmpeg_script.sh
#!/bin/bash
#----------------------------------------------------------------
# SETTINGS

#input_dir="/mnt/HDD500/collections/newdownloads/512x512"  # Replace this by a path to your folder /path/to/your/folder
input_dir="$(pwd)"  #use the current directory the script can be copied to and run from /usr/local/bin run from
n_files=30                        # Replace this by a number of images
#files=`ls ${input_dir}/*.jpg | head -${n_files}`  # Change the file type to the correct type of your images
files=$(ls -1 "${input_dir}"/*.jpg | shuf -n ${n_files})  # pick thirty random *.jpg
#output_file="video.mp4"           # Name of output video

## mkgallery.sh
#!/bin/bash

# Define the directory containing the images
dir=$(pwd)

# Create the HTML header
cat <<EOF > gallery.html
<!DOCTYPE html>
<html>
<head>

## mkgallery.sh
#!/bin/bash

# Define the directory containing the images
dir=$(pwd)

# Create the HTML header
cat <<EOF > gallery.html
<!DOCTYPE html>
<html>
<head>

## memeMaker.sh
#!/bin/bash
if [[ $# < 3 ]] ; then
    echo 'Correct usage:'
    echo 'ffmpeg <video-in> "The Text To Use" <capture-second>'
    echo '<capture-second> Capture what second in the video'
    echo 'Example:'
    echo 'ffmpeg video.mp4 "The Text To Use" 30'
    exit 0
fi

## snd.py
from gtts import gTTS
tts = gTTS('hello')
tts.save('hello.mp3')

## gensentence.py
import random
Noun = ['baby ','owl ','rat ','hamster ','Blue Bird ','giant ', 'mongoose ', 'snake ','tiger ',\
        'leprechaun ','octopus ','frog ','fat old toad ','skinny little spidermonkey ','hipo ','chipmunk ',\
       'goose ','rabbit ','platapus ','tiny bird ','bear ','rhinoceros ','cat ','deer ','zebra ',\
       'old woman ','cheerleader ']
Verb = ['ran with ','jumped over ','ate ','watched ','hugged ','smelled like ','watched ','hugged and kissed  ',\
        'kissed ','sat on the back of ','ran behind ','threw rocks at ','is feeding ','fell in love with ',\
       'introduced me to ','hid from ','kicked at ','tickled ','snuggled ','washed ','chased ',\
        'tried to communicate with ','felt sorry for ','gossiped about ','lied to ']
Adjective = ['ugly ','fat ','mean ','angry ','smiling ','wierd looking ','tired ','lazy ','stupid ',\

## NED.py
#!/usr/local/bin/python
import sys
import sqlite3
conn = sqlite3.connect("~/NED.db")
conn.text_factory = str
c = conn.cursor()
if len(sys.argv) < 3:
     print "\n******* NED - Notes Editor **************"
     print "Not enough options were passed."
     print "NED requires 2 arguments. the first -H , -R , -I , -D or -S .\nThe second can be a period."

## EDITOR.py
#!/usr/local/bin/python
#EDITOR.py
print ("""
for usage:
import EDITOR
EDITOR.Help()
-------------
also can be used as :
from EDITOR import *
Help()

## ChatterBotDatabaseEditor.py
from bson.objectid import ObjectId
import pymongo
import re
def textUpdate():
    client = pymongo.MongoClient()
    mydb = myclient["chatterbot-database"]
    mycol = mydb["statements"]
    ID = input("ObjectId: ")
    Id = ObjectId(''+ID+'')
    TEXT = str(input("text: "))

## LuaData2Numpy.py
# Example from test.text:
#    This is a full set/block of information it takes up two lines.
"""
kolbrin4/kolbrin4-BASE_BASE_GRU_current-learning-rate_0.002000000000_rnn-size_64,_train_loss_2.72439270,_epoch_0.46_evaluationloss_2.6701.t7
BASE :200:431000 epoch 0.464, train_loss = 2.72439270, grad/param norm = 4.4864e-01, time/batch = 1.8300s

The block above is parsed to get this information. There are hundreds ans sometime thousands of line.
This information is used to create graphs of the model building process.

44,0.002000000000,64,3.01064547,0.23,3.0310,100431000,0.232,3.01064547,7.3745,01,1.6423,
	#!/bin/bash
	#----------------------------------------------------------------
	# SETTINGS

	#input_dir="/mnt/HDD500/collections/newdownloads/512x512" # Replace this by a path to your folder /path/to/your/folder
	input_dir="$(pwd)" #use the current directory the script can be copied to and run from /usr/local/bin run from
	n_files=30 # Replace this by a number of images
	#files=`ls ${input_dir}/*.jpg \| head -${n_files}` # Change the file type to the correct type of your images
	files=$(ls -1 "${input_dir}"/.jpg \| shuf -n ${n_files}) # pick thirty random .jpg
	#output_file="video.mp4" # Name of output video
	#!/bin/bash

	# Define the directory containing the images
	dir=$(pwd)

	# Create the HTML header
	cat <<EOF > gallery.html
	<!DOCTYPE html>
	<html>
	<head>
	#!/bin/bash
	if [[ $# < 3 ]] ; then
	echo 'Correct usage:'
	echo 'ffmpeg <video-in> "The Text To Use" <capture-second>'
	echo '<capture-second> Capture what second in the video'
	echo 'Example:'
	echo 'ffmpeg video.mp4 "The Text To Use" 30'
	exit 0
	fi
	from gtts import gTTS
	tts = gTTS('hello')
	tts.save('hello.mp3')
	import random
	Noun = ['baby ','owl ','rat ','hamster ','Blue Bird ','giant ', 'mongoose ', 'snake ','tiger ',\
	'leprechaun ','octopus ','frog ','fat old toad ','skinny little spidermonkey ','hipo ','chipmunk ',\
	'goose ','rabbit ','platapus ','tiny bird ','bear ','rhinoceros ','cat ','deer ','zebra ',\
	'old woman ','cheerleader ']
	Verb = ['ran with ','jumped over ','ate ','watched ','hugged ','smelled like ','watched ','hugged and kissed ',\
	'kissed ','sat on the back of ','ran behind ','threw rocks at ','is feeding ','fell in love with ',\
	'introduced me to ','hid from ','kicked at ','tickled ','snuggled ','washed ','chased ',\
	'tried to communicate with ','felt sorry for ','gossiped about ','lied to ']
	Adjective = ['ugly ','fat ','mean ','angry ','smiling ','wierd looking ','tired ','lazy ','stupid ',\
	#!/usr/local/bin/python
	import sys
	import sqlite3
	conn = sqlite3.connect("~/NED.db")
	conn.text_factory = str
	c = conn.cursor()
	if len(sys.argv) < 3:
	print "\n***** NED - Notes Editor ************"
	print "Not enough options were passed."
	print "NED requires 2 arguments. the first -H , -R , -I , -D or -S .\nThe second can be a period."
	#!/usr/local/bin/python
	#EDITOR.py
	print ("""
	for usage:
	import EDITOR
	EDITOR.Help()
	-------------
	also can be used as :
	from EDITOR import *
	Help()
	from bson.objectid import ObjectId
	import pymongo
	import re
	def textUpdate():
	client = pymongo.MongoClient()
	mydb = myclient["chatterbot-database"]
	mycol = mydb["statements"]
	ID = input("ObjectId: ")
	Id = ObjectId(''+ID+'')
	TEXT = str(input("text: "))
	# Example from test.text:
	# This is a full set/block of information it takes up two lines.
	"""
	kolbrin4/kolbrin4-BASE_BASE_GRU_current-learning-rate_0.002000000000_rnn-size_64,_train_loss_2.72439270,_epoch_0.46_evaluationloss_2.6701.t7
	BASE :200:431000 epoch 0.464, train_loss = 2.72439270, grad/param norm = 4.4864e-01, time/batch = 1.8300s

	The block above is parsed to get this information. There are hundreds ans sometime thousands of line.
	This information is used to create graphs of the model building process.

	44,0.002000000000,64,3.01064547,0.23,3.0310,100431000,0.232,3.01064547,7.3745,01,1.6423,