Ingesting Drone Data into Big Data Platforms
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The document discusses the ingestion of drone data into big data platforms using technologies like Apache NiFi, HDFS, and Apache Hive. It covers metadata extraction from drone images, processing with TensorFlow, and sentiment analysis using Java and Python. The presentation also highlights the functionalities of NiFi in managing data flow and storage effectively.
![1 © Hortonworks Inc. 2011 – 2017. All Rights Reserved
Ingesting Drone Data
into Big Data Platforms
Timothy Spann (@PaasDev)
Oracle Code NYC 2017
https://github.com/tspannhw/IngestingDroneData
[BRK1238-NYC]](https://image.slidesharecdn.com/ingestingdronedatabigdataplatforms21march2017-170321163512/85/Ingesting-Drone-Data-into-Big-Data-Platforms-1-320.jpg)
![7 © Hortonworks Inc. 2011 – 2017. All Rights Reserved
hdfs dfs -get /drone/meta/Bebop2_20160920085308-0400.json
{"date":"2016-09-20T08:53:08","Compression":"JPEG","Exif Version":"2.10","Components Configuration":"YCbCr","file.group":"root","Compression
Type":"Baseline","Image Description":"{ "product_id": "090C", "uuid": "CE112B4276E13339AFBAE10E9ED794E3", "run_date": "2016-09-20T083537-
0400", "filename": "Bebop_2_2016-09-20T085308-0400_CE112B.jpg", "media_date": "2016-09-20T085308-0400" }","Number of
Components":"3","Component 2":"Cb component: Quantization table 1, Sampling factors 1 horiz/1 vert","Focal Length":"1.83 mm","Component 1":"Y component:
Quantization table 0, Sampling factors 2 horiz/2 vert","YCbCr Positioning":"Center of pixel array","tiff:ResolutionUnit":"Inch","uuid":"9576a956-ec32-4314-bc8b-
bd5bb43af4f8","Date/Time Original":"2016:09:20 08:53:08","Shutter Speed Value":"1/249 sec","X Resolution":"72 dots per
inch","tiff:Make":"PARROT","path":"/","Photometric Interpretation":"YCbCr","Component 3":"Cr component: Quantization table 1, Sampling factors 1 horiz/1
vert","Unique Image ID":"[32 bytes]","F-Number":"F2.3","modified":"2016-09-20T08:53:08","Focal Length 35":"6mm","tiff:BitsPerSample":"8","Exposure
Program":"Program action (high-speed program)","GPS Version ID":"2.200","GPS Latitude Ref":"N","meta:creation-date":"2016-09-
20T08:53:08","exif:FNumber":"2.2999999166745724","GPS Altitude Ref":"Sea level","Exposure Time":"8597231/2147483647 sec","GPS Longitude":"-73° 4'
50.41"","Creation-Date":"2016-09-20T08:53:08","ISO Speed Ratings":"426","Make":"PARROT","Orientation":"Top, left side (Horizontal / normal)","Metering
Mode":"(Other)","tiff:Orientation":"1","GPS Longitude Ref":"W","tiff:Software":"Dragon
3.9.0","exif:FocalLength":"1.8300001180412324","filename":"Bebop2_20160920085308-0400.jpg","XMP Value Count":"9","geo:long":"-
73.080669","file.owner":"root","Software":"Dragon 3.9.0","Exif Image Height":"1088 pixels","tiff:YResolution":"72.0","Y Resolution":"72 dots per inch","GPS
Latitude":"40° 45' 16.51"","dc:description":"{ "product_id": "090C", "uuid": "CE112B4276E13339AFBAE10E9ED794E3", "run_date": "2016-09-20T083537-
0400", "filename": "Bebop_2_2016-09-20T085308-0400_CE112B.jpg", "media_date": "2016-09-20T085308-0400" }","geo:lat":"40.754585","FlashPix
Version":"1.00","Data Precision":"8 bits","White Balance":"Flash","tiff:ImageLength":"1088","description":"{ "product_id": "090C", "uuid":
"CE112B4276E13339AFBAE10E9ED794E3", "run_date": "2016-09-20T083537-0400", "filename": "Bebop_2_2016-09-20T085308-0400_CE112B.jpg",
"media_date": "2016-09-20T085308-0400" }","dcterms:created":"2016-09-20T08:53:08","dcterms:modified":"2016-09-20T08:53:08","Last-Modified":"2016-09-
20T08:53:08","file.permissions":"rwxrwxrwx","exif:ExposureTime":"0.00400339765660623","Last-Save-Date":"2016-09-20T08:53:08","GPS Altitude":"21
metres","absolute.path":"/opt/demo/dronedata/","Color Space":"Undefined","File Size":"853159 bytes","meta:save-date":"2016-09-
20T08:53:08","file.creationTime":"2016-09-20T12:53:10+0000","Date/Time Digitized":"2016:09:20 08:53:08","File Name":"apache-tika-
941370357006559178.tmp","Content-Type":"image/jpeg","Aperture Value":"F2.3","X-Parsed-By":"org.apache.tika.parser.DefaultParser,
org.apache.tika.parser.jpeg.JpegParser","File Modified Date":"Tue Sep 20 23:33:24 UTC 2016","tiff:XResolution":"72.0","file.lastModifiedTime":"2016-09-
20T12:53:10+0000","exif:DateTimeOriginal":"2016-09-20T08:53:08","Date/Time":"2016:09:20 08:53:08","Exif Image Width":"1920 pixels","Image Height":"1088
pixels","Image Width":"1920 pixels","Unknown tag (0xc62f)":"[19 bytes]","Resolution Unit":"Inch","tiff:Model":"Bebop 2","exif:IsoSpeedRatings":"426","Max
Aperture Value":"F2.3","Exposure Mode":"Auto exposure","Model":"Bebop 2","file.lastAccessTime":"2016-09-20T23:33:24+0000","tiff:ImageWidth":"1920","White
Balance Mode":"Auto white balance"}](https://image.slidesharecdn.com/ingestingdronedatabigdataplatforms21march2017-170321163512/85/Ingesting-Drone-Data-into-Big-Data-Platforms-7-320.jpg)
![20 © Hortonworks Inc. 2011 – 2017. All Rights Reserved
python classify_image.py --image_file /opt/demo/dronedata/Bebop2_20160920083655-0400.jpg
solar dish, solar collector, solar furnace (score = 0.98316)
window screen (score = 0.00196)
manhole cover (score = 0.00070)
radiator (score = 0.00041)
doormat, welcome mat (score = 0.00041)
bazel-bin/tensorflow/examples/label_image/label_image --
image=/opt/demo/dronedata/Bebop2_20160920083655-0400.jpg
tensorflow/examples/label_image/main.cc:204] solar dish (577): 0.983162I
tensorflow/examples/label_image/main.cc:204] window screen (912): 0.00196204I
tensorflow/examples/label_image/main.cc:204] manhole cover (763): 0.000704005I
tensorflow/examples/label_image/main.cc:204] radiator (571): 0.000408321I
tensorflow/examples/label_image/main.cc:204] doormat (972): 0.000406186
TensorFlow via Python or C++ Binary (Java Library Is New!)](https://image.slidesharecdn.com/ingestingdronedatabigdataplatforms21march2017-170321163512/85/Ingesting-Drone-Data-into-Big-Data-Platforms-20-320.jpg)
![21 © Hortonworks Inc. 2011 – 2017. All Rights Reserved
/opt/demo/sentiment/run.sh
python /opt/demo/sentiment/sentiment.py "$@”
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import sys
sid = SentimentIntensityAnalyzer()
ss = sid.polarity_scores(sys.argv[1])
print('Compound {0} Negative {1} Neutral {2} Positive {3} '.format(
ss['compound'],ss['neg'],ss['neu'],ss['pos']))
or
if ss['compound'] == 0.00: print('Neutral')
elif ss['compound'] < 0.00: print ('Negative')
else: print('Positive')
Sentiment Analysis via Python
Pretty easy and I will show you
Another example with
TextBlob.
You will need Python 2.7 and
PIP installed.](https://image.slidesharecdn.com/ingestingdronedatabigdataplatforms21march2017-170321163512/85/Ingesting-Drone-Data-into-Big-Data-Platforms-21-320.jpg)
Ingesting Drone Data into Big Data Platforms
- 2. 2 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Agenda • Drones 101 • Metadata Insights • Ingestion Formats: MQTT, Files, REST JSON, Images, FTP and more. • Apache NiFi • Big Data Storage: HDFS, HBase, Phoenix, Hive
- 3. 3 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Code Walk Through • Processing Images with TensorFlow for Image Recognition • Writing a Java 8 Processor for Sentiment Analysis • Writing a Java 8 Processor for analyzing HTML • Writing a Java 8 Microservice for retrieving Phoenix/Hbase data • Writing a Java 8 Microservice for retrieving Hive data • Writing a NiFi flow to wire it all together
- 4. 4 © Hortonworks Inc. 2011 – 2017. All Rights Reserved UAV / Drones A drone is an unmanned aircraft, better known as a unmanned aerial vehicle (UAV) or unmanned aircraft systems (UAS). For enterprise purposes, we are thinking of serious drones that contain high resolution video/still cameras with onboard sensors, LIDAR and GPS. For almost all drones, they are regulated by the Federal Aviation Administration (FAA) and there are strict regulations you must register your drone. If you aren’t prepare for the regulations or cost, keep your drone in your house or under half a pound.
- 6. 6 © Hortonworks Inc. 2011 – 2017. All Rights Reserved MetaData EXIF Geotagging Photographs taken from UAVs, the JPEGs will have extra data stored in EXIF. Apache NiFi can extract this information, which is very helpful. The most important information it contains is GPS information for latitude and longitude. GPS Altitude Ref: Sea level GPS Altitude: 21 metres GPS Longitude: -73 4' 50.41 GPS Latitude: 40 45' 16.51
- 7. 7 © Hortonworks Inc. 2011 – 2017. All Rights Reserved hdfs dfs -get /drone/meta/Bebop2_20160920085308-0400.json {"date":"2016-09-20T08:53:08","Compression":"JPEG","Exif Version":"2.10","Components Configuration":"YCbCr","file.group":"root","Compression Type":"Baseline","Image Description":"{ "product_id": "090C", "uuid": "CE112B4276E13339AFBAE10E9ED794E3", "run_date": "2016-09-20T083537- 0400", "filename": "Bebop_2_2016-09-20T085308-0400_CE112B.jpg", "media_date": "2016-09-20T085308-0400" }","Number of Components":"3","Component 2":"Cb component: Quantization table 1, Sampling factors 1 horiz/1 vert","Focal Length":"1.83 mm","Component 1":"Y component: Quantization table 0, Sampling factors 2 horiz/2 vert","YCbCr Positioning":"Center of pixel array","tiff:ResolutionUnit":"Inch","uuid":"9576a956-ec32-4314-bc8b- bd5bb43af4f8","Date/Time Original":"2016:09:20 08:53:08","Shutter Speed Value":"1/249 sec","X Resolution":"72 dots per inch","tiff:Make":"PARROT","path":"/","Photometric Interpretation":"YCbCr","Component 3":"Cr component: Quantization table 1, Sampling factors 1 horiz/1 vert","Unique Image ID":"[32 bytes]","F-Number":"F2.3","modified":"2016-09-20T08:53:08","Focal Length 35":"6mm","tiff:BitsPerSample":"8","Exposure Program":"Program action (high-speed program)","GPS Version ID":"2.200","GPS Latitude Ref":"N","meta:creation-date":"2016-09- 20T08:53:08","exif:FNumber":"2.2999999166745724","GPS Altitude Ref":"Sea level","Exposure Time":"8597231/2147483647 sec","GPS Longitude":"-73° 4' 50.41"","Creation-Date":"2016-09-20T08:53:08","ISO Speed Ratings":"426","Make":"PARROT","Orientation":"Top, left side (Horizontal / normal)","Metering Mode":"(Other)","tiff:Orientation":"1","GPS Longitude Ref":"W","tiff:Software":"Dragon 3.9.0","exif:FocalLength":"1.8300001180412324","filename":"Bebop2_20160920085308-0400.jpg","XMP Value Count":"9","geo:long":"- 73.080669","file.owner":"root","Software":"Dragon 3.9.0","Exif Image Height":"1088 pixels","tiff:YResolution":"72.0","Y Resolution":"72 dots per inch","GPS Latitude":"40° 45' 16.51"","dc:description":"{ "product_id": "090C", "uuid": "CE112B4276E13339AFBAE10E9ED794E3", "run_date": "2016-09-20T083537- 0400", "filename": "Bebop_2_2016-09-20T085308-0400_CE112B.jpg", "media_date": "2016-09-20T085308-0400" }","geo:lat":"40.754585","FlashPix Version":"1.00","Data Precision":"8 bits","White Balance":"Flash","tiff:ImageLength":"1088","description":"{ "product_id": "090C", "uuid": "CE112B4276E13339AFBAE10E9ED794E3", "run_date": "2016-09-20T083537-0400", "filename": "Bebop_2_2016-09-20T085308-0400_CE112B.jpg", "media_date": "2016-09-20T085308-0400" }","dcterms:created":"2016-09-20T08:53:08","dcterms:modified":"2016-09-20T08:53:08","Last-Modified":"2016-09- 20T08:53:08","file.permissions":"rwxrwxrwx","exif:ExposureTime":"0.00400339765660623","Last-Save-Date":"2016-09-20T08:53:08","GPS Altitude":"21 metres","absolute.path":"/opt/demo/dronedata/","Color Space":"Undefined","File Size":"853159 bytes","meta:save-date":"2016-09- 20T08:53:08","file.creationTime":"2016-09-20T12:53:10+0000","Date/Time Digitized":"2016:09:20 08:53:08","File Name":"apache-tika- 941370357006559178.tmp","Content-Type":"image/jpeg","Aperture Value":"F2.3","X-Parsed-By":"org.apache.tika.parser.DefaultParser, org.apache.tika.parser.jpeg.JpegParser","File Modified Date":"Tue Sep 20 23:33:24 UTC 2016","tiff:XResolution":"72.0","file.lastModifiedTime":"2016-09- 20T12:53:10+0000","exif:DateTimeOriginal":"2016-09-20T08:53:08","Date/Time":"2016:09:20 08:53:08","Exif Image Width":"1920 pixels","Image Height":"1088 pixels","Image Width":"1920 pixels","Unknown tag (0xc62f)":"[19 bytes]","Resolution Unit":"Inch","tiff:Model":"Bebop 2","exif:IsoSpeedRatings":"426","Max Aperture Value":"F2.3","Exposure Mode":"Auto exposure","Model":"Bebop 2","file.lastAccessTime":"2016-09-20T23:33:24+0000","tiff:ImageWidth":"1920","White Balance Mode":"Auto white balance"}
- 8. 8 © Hortonworks Inc. 2011 – 2017. All Rights Reserved import paho.mqtt.client as mqtt client = mqtt.Client() client.username_pw_set("username","password") client.connect(“MQTT_Broker", 14162, 60) Sources and Formats { "product_id": "090C", "uuid": "CE112B4276E13339AFBAE10E9ED794E3", "run_date": "2016-09-20T083537-0400", "filename": "Bebop_2_2016-09-20T084230- 0400_CE112B.jpg", "media_date": "2016-09-20T084230-0400" } http://localhost:9999/dronelist
- 9. 9 © Hortonworks Inc. 2011 – 2017. All Rights Reserved ReportingDrone Data Ingest - NiFi pulls in BeBop 2 Drone images - NiFi routes and parses metadata from drone images including geodata - NiFi uses TensorFlow Inception v3 to recognize objects in image - NiFi stores images, metadata and enriched data in Hadoop. - NiFi ingests social and weather feeds - Java 8 Processor Runs Sentiment - Spring Boot App Displays Hive Data - Python Sentiment Analysis scripts
- 10. 10 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Developed by the NSA over the last 8 years. "NSA's innovators work on some of the most challenging national security problems imaginable," "Commercial enterprises could use it to quickly control, manage, and analyze the flow of information from geographically dispersed sites – creating comprehensive situational awareness" -- Linda L. Burger, Director of the NSA NiFi Developed by the National Security Agency
- 11. 11 © Hortonworks Inc. 2011 – 2017. All Rights Reserved • For agile and immediate creation, configuration, control of dataflowsVisual Command and Control • Ensures trust of your dataData Lineage (Provenance) • Because not all data is of equal importanceData Prioritization • Since not all senders/receivers/connections work perfectly all the timeData Buffering/Back-Pressure • Adapt to different situations with different requirementsControl Latency vs Throughput • Security of data, and data accessSecure Control Plane/Data Plane • ScalabilityScale out Clustering • Ecosystem flexibility and growthExtensibility Apache NiFi: Designed for 8 challenges of global enterprise dataflow
- 12. 12 © Hortonworks Inc. 2011 – 2017. All Rights Reserved FlowFile • Unit of data moving through the system • Content + Attributes (key/value pairs) Processor • Performs the work, can access FlowFiles Connection • Links between processors • Queues that can be dynamically prioritized Terminology
- 13. 13 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Typical NiFi Cluster Logical View 13
- 15. 15 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Hadoop Distributed File System (HDFS) § Fault-Tolerance § Multiple copies provide performance boost § Replication Level is configurable § Full checksums § Rack awareness § Files split into blocks distributed on three* servers § Commodity Hardware § Near limitless horizontal scalability § Looks like Linux File System § WebUI Hadoop Scalable Storage and Compute Hive LLAP High Performance SQL Data Mart
- 16. 16 © Hortonworks Inc. 2011 – 2017. All Rights Reserved What Are Apache HBase and Phoenix? Flexible Schema Millisecond Latency SQL and NoSQL Interfaces Store and Process Petabytes of Data Scale out on Commodity Servers Integrated with YARN 100% Open Source On Top of HDFS YARN : Data Operating System HBase RegionServer 1 ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° N HDFS (Permanent Data Storage) HBase RegionServer HBase RegionServer Flexible Schema Extreme Low Latency Directly Integrated with Hadoop SQL and NoSQL Interfaces
- 17. 17 © Hortonworks Inc. 2011 – 2017. All Rights Reserved What Are Apache Phoenix and HBase? Apache HBase is distributed database modeled after Google’s BigTable and designed to provide real-time access to data in Hadoop. Apache Phoenix provides an ANSI SQL interface to HBase. Features: • Real-Time Data Management for Hadoop • PB+ Scale • ANSI SQL Interface • Secondary Indexes • Cross-DC Replication • Fine-Grained Security • Develop in JDBC, ODBC, .NET, and more
- 18. 18 © Hortonworks Inc. 2011 – 2017. All Rights Reserved What Is Apache Hive? Apache Hive is a SQL data warehouse infrastructure that delivers fast, scalable SQL processing on Hadoop and in the Cloud. Features: • Extensive SQL:2011 Support • ACID Transactions • In-Memory Caching • Cost-Based Optimizer • User-Based Dynamic Security • Replication and Disaster Recovery • JDBC and ODBC Support • Compatible with every major BI Tool • Proven at 300+ PB Scale • On Top of HDFS
- 20. 20 © Hortonworks Inc. 2011 – 2017. All Rights Reserved python classify_image.py --image_file /opt/demo/dronedata/Bebop2_20160920083655-0400.jpg solar dish, solar collector, solar furnace (score = 0.98316) window screen (score = 0.00196) manhole cover (score = 0.00070) radiator (score = 0.00041) doormat, welcome mat (score = 0.00041) bazel-bin/tensorflow/examples/label_image/label_image -- image=/opt/demo/dronedata/Bebop2_20160920083655-0400.jpg tensorflow/examples/label_image/main.cc:204] solar dish (577): 0.983162I tensorflow/examples/label_image/main.cc:204] window screen (912): 0.00196204I tensorflow/examples/label_image/main.cc:204] manhole cover (763): 0.000704005I tensorflow/examples/label_image/main.cc:204] radiator (571): 0.000408321I tensorflow/examples/label_image/main.cc:204] doormat (972): 0.000406186 TensorFlow via Python or C++ Binary (Java Library Is New!)
- 21. 21 © Hortonworks Inc. 2011 – 2017. All Rights Reserved /opt/demo/sentiment/run.sh python /opt/demo/sentiment/sentiment.py "$@” from nltk.sentiment.vader import SentimentIntensityAnalyzer import sys sid = SentimentIntensityAnalyzer() ss = sid.polarity_scores(sys.argv[1]) print('Compound {0} Negative {1} Neutral {2} Positive {3} '.format( ss['compound'],ss['neg'],ss['neu'],ss['pos'])) or if ss['compound'] == 0.00: print('Neutral') elif ss['compound'] < 0.00: print ('Negative') else: print('Positive') Sentiment Analysis via Python Pretty easy and I will show you Another example with TextBlob. You will need Python 2.7 and PIP installed.
- 22. 22 © Hortonworks Inc. 2011 – 2017. All Rights Reserved https://pip.pypa.io/en/latest/installing/ http://www.nltk.org/install.html wget https://bootstrap.pypa.io/get-pip.py python get-pip.py sudo pip install -U nltk sudo pip install -U numpy sudo pip install -U textblob sudo python -m textblob.download_corpora Installing Sentiment Analysis Libraries for Python 2.7 Installing TensorFlow for Python 2.7 See: https://www.tensorflow.org/install/install_mac Installation is getting easier for TensorFlow, but you will need build tools and Python installed. You need to find out if you have a GPU supported by CUDA. If so your performance will be greatly improved.
- 26. 26 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Installation Download the binary from here: http://hortonworks.com/downloads/#dataflow Or here: https://nifi.apache.org/download.html Or on Mac: brew install nifi https://nifi.apache.org/docs/nifi-docs/html/getting-started.html#starting- nifi bin/nifi.sh start
- 27. 27 © Hortonworks Inc. 2011 – 2017. All Rights Reserved à https://hortonworks.com/hadoop-tutorial/learning-ropes-apache-nifi/ à https://github.com/jfrazee/awesome-nifi à https://dzone.com/articles/getting-started-with-apache-nifi-and-hdf à https://nifi.apache.org/docs.html à https://community.hortonworks.com/articles/4356/getting-started-with-nifi- expression-language-and.html à https://github.com/tspannhw/rpi-sensehat-mqtt-nifi à https://github.com/tspannhw/iot-scripts à https://dzone.com/articles/apache-nifi-10-cheatsheet Learning More
- 28. 28 © Hortonworks Inc. 2011 – 2017. All Rights Reserved à https://www.parrot.com/us/drones/parrot-bebop-2#parrot-bebop-2 à https://en.wikipedia.org/wiki/Unmanned_aerial_vehicle à http://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_ima ge/README.md à https://www.tensorflow.org/tutorials/image_recognition à https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/ org/tensorflow/examples/LabelImage.java à https://community.hortonworks.com/content/kbentry/83100/deep-learning-iot- workflows-with-raspberry-pi-mqtt.html à https://sites.google.com/site/pud2gpxkmlcsv/ à http://knowbeforeyoufly.org/ à https://www.faa.gov/uas/getting_started/ Resources
- 29. 29 © Hortonworks Inc. 2011 – 2017. All Rights Reserved à Ken Kranz – Director of UAS Big Data at Cognizant. @kenkranz à Joe Witt - Senior Director of Engineering at Hortonworks. @joewitt26 à Chris Casano – Solution Enngineer Manager at Hortonworks. à Tom McCuch – Sr. Technical Director at Hortonworks. @tmccuch à Ingesting Drone Data into Big Data Platformshttps://github.com/tspannhw/IngestingDroneData Thanks