mirfan899

import os
import sys
import dlib
import matplotlib.pyplot as plt
from PIL import Image

# adjust these variables as necessary

# dirname is the directory relative to the script where the files to detect a face and crop live
dirname = "source"

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#!/usr/bin/env python

import os
import xml.parsers.expat
from xml.sax.saxutils import escape
from optparse import OptionParser
from math import log10


# How much data we process at a time

mirfan899

#!/usr/bin/env python

#based off this gist https://gist.github.com/benallard/8042835
#modified it a little so it worked for my needs. (mainly encoding issues and bigger file chunks)

import os
import xml.parsers.expat
from xml.sax.saxutils import escape
from optparse import OptionParser
from math import log10

mirfan899

Ubuntu 20.04 on a 15" Retina MacBook Pro (Mid-2014)

These are notes from my efforts to get Ubuntu 20.04 installed on my older MacBook Pro. I'm making this gist public in the hopes that it's helpful to others.

I did a Minimal install, but selected the option to install additional 3rd-party drivers.

Wifi doesn't work during the install (because it requires a 3rd-party driver), so you won't be able to choose to download updates while installing. No big deal, run a software update after the install.

The installer takes about 25 minutes to complete. Post-install, most things work. The only driver I had to manually install was for the FaceTime camera. More on that below.

mirfan899

import os

nltkdata_wn = '/path/to/nltk_data/corpora/wordnet/'
wn31 = "http://wordnetcode.princeton.edu/wn3.1.dict.tar.gz"
if not os.path.exists(nltkdata_wn+'wn3.0'):
    os.mkdir(nltkdata_wn+'wn3.0')
os.system('mv '+nltkdata_wn+"* "+nltkdata_wn+"wn3.0/")
if not os.path.exists('wn3.1.dict.tar.gz'):
    os.system('wget '+wn31)
os.system("tar zxf wn3.1.dict.tar.gz -C "+nltkdata_wn)

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function import_extract_first {
  echo "name:ID(Concept)" > concepts.txt
  cat data-concept-instance-relations.txt | cut -d $'\t' -f 1 | sort | uniq >> concepts.txt
  
  echo "name:ID(Instance)" > instances.txt
  cat data-concept-instance-relations.txt | cut -d $'\t' -f 2 | sort | uniq >> instances.txt

  echo $':END_ID(Concept)\t:START_ID(Instance)	relations:int' > is_a.hdr
  
  $NEO4J_HOME/bin/neo4j-import --into concepts.db --id-type string --delimiter TAB --bad-tolerance 13000000 --skip-duplicate-nodes true --skip-bad-relationships true \

mirfan899

https://drive.google.com/drive/folders/14rA9c0cpK2XptZVDNi7-24jooPEJWKgm?usp=sharing

mirfan899

import math
import torch
from transformers import BertTokenizer, BertModel, BertForMaskedLM, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer


# Load pre-trained model (weights)
model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
model.eval()
# Load pre-trained model tokenizer (vocabulary)
tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')

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import cv2 # opencv
import numpy as np

font_scale = 1.5
font = cv2.FONT_HERSHEY_PLAIN

# set the rectangle background to white
rectangle_bgr = (255, 255, 255)
# make a black image
img = np.zeros((500, 500))

mirfan899

# -*- coding: utf-8 -*-
import os
import uuid

from flask import Flask, render_template, redirect, url_for, request
from flask_uploads import UploadSet, configure_uploads, IMAGES, patch_request_class
from flask_wtf import FlaskForm
from flask_wtf.file import FileField, FileRequired, FileAllowed
from wtforms import SubmitField