DataTurks

{
	"content": "https://s3-us-west-2.amazonaws.com/com.dataturks.trivia/cars5.mp4",
	"annotation": [{
		"startTime": 0.682497,
		"endTime": 4.764457,
		"label": "Car",
		"shape": "rectangle",
		"positions": [{
			"points": [
				[0.2390829694323144, 0.5667151868025229],

DataTurks

# coding: utf-8
import json
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw
import numpy as np
import cv2

# Find OpenCV version
(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')

DataTurks

#This script has been solely created under dataturks. Copyrights are reserved

#EXAMPLE USAGE
#python3 keras_json_parser.py --json_file "flower.json" --dataset_path "Dataset5/" --train_percentage 80 --validation_percentage 20


import json 
import glob
import urllib.request
import argparse

DataTurks

@echo off
set _DIR_PATH=%1
set _DATATURKS_OUT=dataturks_urls.txt
echo.>%_DATATURKS_OUT%


for /f "tokens=1-2 delims=:" %%a in ('ipconfig^|find "IPv4"') do set ip=%%b
set ip=%ip:~1%

DataTurks

from skimage import draw
from skimage import io
import numpy as np
import urllib.request
import json
import logging
import os
import sys

DataTurks

import spacy
################### Train Spacy NER.###########
def train_spacy():
    TRAIN_DATA = convert_dataturks_to_spacy("dataturks_downloaded.json");
    nlp = spacy.blank('en')  # create blank Language class
    # create the built-in pipeline components and add them to the pipeline
    # nlp.create_pipe works for built-ins that are registered with spaCy
    if 'ner' not in nlp.pipe_names:
        ner = nlp.create_pipe('ner')
        nlp.add_pipe(ner, last=True)

DataTurks

############################################  NOTE  ########################################################
#
#           Creates NER training data in Spacy format from JSON downloaded from Dataturks.
#
#           Outputs the Spacy training data which can be used for Spacy training.
#
############################################################################################################
def convert_dataturks_to_spacy(dataturks_JSON_FilePath):
    try:
        training_data = []

DataTurks

def train_spacy(training_pickle_file):
    
    #read pickle file to load training data
    with open(training_pickle_file, 'rb') as input:
        TRAIN_DATA=pickle.load(input)

    nlp = spacy.blank('en')  # create blank Language class
    # create the built-in pipeline components and add them to the pipeline
    # nlp.create_pipe works for built-ins that are registered with spaCy
    if 'ner' not in nlp.pipe_names:

DataTurks

import argparse
import sys
import os
import json
import logging
import pickle


############################################  NOTE  ########################################################
#

DataTurks

import argparse
import sys
import os
import json
import logging
import requests
from PIL import Image

###################  INSTALLATION NOTE #######################
##############################################################