Five Sensors Real-Time with Pulsar and Python on a Pi (FLiP-Py-Pi-BreakoutGarden)

FLiP-Pi-BreakoutGarden

FLiP-Py

The easy way to build Python streaming applications from the edge to cloud.

Code:   https://github.com/tspannhw/FLiP-Pi-BreakoutGarden

Gear / Hardware

• Raspberry Pi 3 Model B Rev 1.2, Bullseye Raspian, armv71
• Pimoroni Breakout Garden Hat
• 1.12" Mono OLED Breakout 128x128 White/Black Screen
• BME680 Air Quality, Temperature, Pressure, Humidity Sensor
• LWM303D 6D0F Motion Sensor (X, Y, Z Axes)
• BH1745 Luminance and Color Sensor
• LTR-559 Light and Proximity Sensor 0.01 lux to 64,000 lux
• VL53L1X Time of Flight (TOF) Sensor

Software / Libraries

• Python 3.9
• Pulsar Python Client 2.10 (avro) pip3 install pulsar-client[avro]
• Python Breakout Garden
• Python PSUTIL https://pypi.org/project/psutil/
• Python LUMA OLED pip3 install --upgrade luma.oled
• Libraries sudo apt-get install python3 python3-pip python3-pil libjpeg-dev zlib1g-dev libfreetype6-dev liblcms2-dev libopenjp2-7 libtiff5 -y

StreamOps

bin/pulsar-admin topics create "persistent://public/default/pi-sensors"

Device Running

VL53L0X_GetDeviceInfo:
Device Name : VL53L1 cut1.1
Device Type : VL53L1
Device ID : 
ProductRevisionMajor : 1
ProductRevisionMinor : 15
{'_required_default': False, '_default': None, '_required': False, 'uuid': 'snr_20220323200032', 'ipaddress': '192.168.1.229', 'cputempf': 99, 'runtime': 154, 'host': 'piups', 'hostname': 'piups', 'macaddress': 'b8:27:eb:4a:4b:61', 'endtime': '1648065632.645613', 'te': '154.00473523139954', 'cpu': 0.0, 'diskusage': '3895.3 MB', 'memory': 21.5, 'rowid': '20220323200032_6a66f9ea-1273-4e5d-b150-9300f6272482', 'systemtime': '03/23/2022 16:00:33', 'ts': 1648065633, 'starttime': '03/23/2022 15:57:58', 'BH1745_red': 112.2, 'BH1745_green': 82.0, 'BH1745_blue': 63.0, 'BH1745_clear': 110.0, 'VL53L1X_distance_in_mm': -1185.0, 'ltr559_lux': 6.65, 'ltr559_prox': 0.0, 'bme680_tempc': 23.6, 'bme680_tempf': 74.48, 'bme680_pressure': 1017.48, 'bme680_humidity': 33.931, 'lsm303d_accelerometer': '-00.08g : -01.00g : +00.01g', 'lsm303d_magnetometer': '+00.06 : +00.30 : +00.07'}
VL53L1X Start Ranging Address 0x29

Consumer

bin/pulsar-client consume "persistent://public/default/pi-sensors" -s "pisnsrgrdnrdr" -n 0

** SQL Consumers **

Pulsar SQL / Presto/Trino

desc pulsar."public/default"."pi-sensors";

         Column         |   Type    | Extra |                                   Comment                                   
------------------------+-----------+-------+-----------------------------------------------------------------------------
 uuid                   | varchar   |       | ["null","string"]                                                           
 ipaddress              | varchar   |       | ["null","string"]                                                           
 cputempf               | integer   |       | ["null","int"]                                                              
 runtime                | integer   |       | ["null","int"]                                                              
 host                   | varchar   |       | ["null","string"]                                                           
 hostname               | varchar   |       | ["null","string"]                                                           
 macaddress             | varchar   |       | ["null","string"]                                                           
 endtime                | varchar   |       | ["null","string"]                                                           
 te                     | varchar   |       | ["null","string"]                                                           
 cpu                    | real      |       | ["null","float"]                                                            
 diskusage              | varchar   |       | ["null","string"]                                                           
 memory                 | real      |       | ["null","float"]                                                            
 rowid                  | varchar   |       | ["null","string"]                                                           
 systemtime             | varchar   |       | ["null","string"]                                                           
 ts                     | integer   |       | ["null","int"]                                                              
 starttime              | varchar   |       | ["null","string"]                                                           
 bh1745_red             | real      |       | ["null","float"]                                                            
 bh1745_green           | real      |       | ["null","float"]                                                            
 bh1745_blue            | real      |       | ["null","float"]                                                            
 bh1745_clear           | real      |       | ["null","float"]                                                            
 vl53l1x_distance_in_mm | real      |       | ["null","float"]                                                            
 ltr559_lux             | real      |       | ["null","float"]                                                            
 ltr559_prox            | real      |       | ["null","float"]                                                            
 bme680_tempc           | real      |       | ["null","float"]                                                            
 bme680_tempf           | real      |       | ["null","float"]                                                            
 bme680_pressure        | real      |       | ["null","float"]                                                            
 bme680_humidity        | real      |       | ["null","float"]                                                            
 lsm303d_accelerometer  | varchar   |       | ["null","string"]                                                           
 lsm303d_magnetometer   | varchar   |       | ["null","string"]                                                           
 __partition__          | integer   |       | The partition number which the message belongs to                           
 __event_time__         | timestamp |       | Application defined timestamp in milliseconds of when the event occurred    
 __publish_time__       | timestamp |       | The timestamp in milliseconds of when event as published                    
 __message_id__         | varchar   |       | The message ID of the message used to generate this row                     
 __sequence_id__        | bigint    |       | The sequence ID of the message used to generate this row                    
 __producer_name__      | varchar   |       | The name of the producer that publish the message used to generate this row 
 __key__                | varchar   |       | The partition key for the topic                                             
 __properties__         | varchar   |       | User defined properties                                                     
(37 rows)

presto> select * from pulsar."public/default"."pi-sensors";
        uuid        |   ipaddress   | cputempf | runtime | host  | hostname |    macaddress     |      endtime       |         te         | cpu | disk
--------------------+---------------+----------+---------+-------+----------+-------------------+--------------------+--------------------+-----+-----
 snr_20220323180318 | 192.168.1.229 |       99 |       4 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058598.8543017 | 4.47935152053833   | 0.2 | 3895
 snr_20220323180324 | 192.168.1.229 |       99 |      10 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058604.4054732 | 10.03052306175232  | 0.0 | 3895
 snr_20220323180329 | 192.168.1.229 |       99 |      16 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058609.8929565 | 15.518006324768066 | 6.5 | 3895
 snr_20220323180335 | 192.168.1.229 |       99 |      21 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058615.3783045 | 21.00335431098938  | 0.2 | 3895
 snr_20220323180340 | 192.168.1.229 |       99 |      26 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058620.8675282 | 26.49257802963257  | 4.6 | 3895
 snr_20220323180346 | 192.168.1.229 |       99 |      32 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058626.3639522 | 31.989001989364624 | 0.0 | 3895
 snr_20220323180351 | 192.168.1.229 |       99 |      38 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058631.8793604 | 37.50441026687622  | 0.0 | 3895
 snr_20220323180357 | 192.168.1.229 |      100 |      43 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058637.38347   | 43.008519887924194 | 0.0 | 3895
 snr_20220323180402 | 192.168.1.229 |       99 |      49 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058642.8820572 | 48.50710701942444  | 0.0 | 3895
 snr_20220323180408 | 192.168.1.229 |       99 |      54 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058648.3795574 | 54.00460720062256  | 6.2 | 3895
 snr_20220323180413 | 192.168.1.229 |       99 |      59 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058653.8280468 | 59.45309662818909  | 0.0 | 3895
 snr_20220323180419 | 192.168.1.229 |       99 |      65 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058659.3180714 | 64.94312119483948  | 4.9 | 3895
 snr_20220323180424 | 192.168.1.229 |       99 |      70 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058664.8023574 | 70.42740726470947  | 0.0 | 3895
 snr_20220323180430 | 192.168.1.229 |       99 |      76 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058670.286937  | 75.91198682785034  | 0.0 | 3895
 snr_20220323180435 | 192.168.1.229 |       97 |      81 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058675.7804654 | 81.40551519393921  | 0.0 | 3895
 snr_20220323180441 | 192.168.1.229 |       99 |      87 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058681.2751634 | 86.90021324157715  | 0.0 | 3895
 snr_20220323180446 | 192.168.1.229 |       99 |      92 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058686.7713509 | 92.39640069007874  | 5.9 | 3895
 snr_20220323180452 | 192.168.1.229 |       99 |      98 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058692.2672575 | 97.89230728149414  | 0.3 | 3895
 snr_20220323180457 | 192.168.1.229 |       99 |     103 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058697.7704427 | 103.39549255371094 | 5.4 | 3895
 snr_20220323180503 | 192.168.1.229 |       99 |     109 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058703.21333   | 108.83837985992432 | 0.3 | 3895
 snr_20220323180508 | 192.168.1.229 |       99 |     114 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058708.6879904 | 114.31304025650024 | 0.0 | 3895
 snr_20220323180514 | 192.168.1.229 |       99 |     120 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058714.1396198 | 119.76466965675354 | 0.3 | 3895
 snr_20220323180519 | 192.168.1.229 |       99 |     125 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058719.6158638 | 125.24091362953186 | 0.0 | 3895
 snr_20220323180525 | 192.168.1.229 |      100 |     131 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058725.0950723 | 130.72012209892273 | 6.5 | 3895
 snr_20220323180530 | 192.168.1.229 |       99 |     136 | piups | piups    | b8:27:eb:4a:4b:61 | 1648058730.57256   | 136.19760990142822 | 0.0 | 3895
(25 rows)

Query 20220323_184946_00003_p66fs, FINISHED, 1 node

Spark SQL

val dfPulsar = spark.readStream.format("pulsar").option("service.url", "pulsar://pulsar1:6650").option("admin.url", "http://pulsar1:8080").option("topic", "persistent://public/default/pi-sensors").load()

scala> dfPulsar.printSchema()
root
 |-- uuid: string (nullable = true)
 |-- ipaddress: string (nullable = true)
 |-- cputempf: integer (nullable = true)
 |-- runtime: integer (nullable = true)
 |-- host: string (nullable = true)
 |-- hostname: string (nullable = true)
 |-- macaddress: string (nullable = true)
 |-- endtime: string (nullable = true)
 |-- te: string (nullable = true)
 |-- cpu: float (nullable = true)
 |-- diskusage: string (nullable = true)
 |-- memory: float (nullable = true)
 |-- rowid: string (nullable = true)
 |-- systemtime: string (nullable = true)
 |-- ts: integer (nullable = true)
 |-- starttime: string (nullable = true)
 |-- BH1745_red: float (nullable = true)
 |-- BH1745_green: float (nullable = true)
 |-- BH1745_blue: float (nullable = true)
 |-- BH1745_clear: float (nullable = true)
 |-- VL53L1X_distance_in_mm: float (nullable = true)
 |-- ltr559_lux: float (nullable = true)
 |-- ltr559_prox: float (nullable = true)
 |-- bme680_tempc: float (nullable = true)
 |-- bme680_tempf: float (nullable = true)
 |-- bme680_pressure: float (nullable = true)
 |-- bme680_humidity: float (nullable = true)
 |-- lsm303d_accelerometer: string (nullable = true)
 |-- lsm303d_magnetometer: string (nullable = true)
 |-- __key: binary (nullable = true)
 |-- __topic: string (nullable = true)
 |-- __messageId: binary (nullable = true)
 |-- __publishTime: timestamp (nullable = true)
 |-- __eventTime: timestamp (nullable = true)
 |-- __messageProperties: map (nullable = true)
 |    |-- key: string
 |    |-- value: string (valueContainsNull = true)


## Example Queries

val pQuery = dfPulsar.selectExpr("*").writeStream.format("console").option("truncate", false).start()

val pQuery = dfPulsar.selectExpr("CAST(__key AS STRING)", 
                                 "CAST(uuid AS STRING)",
                                 "CAST(ipaddress AS STRING)",
                                 "CAST(cputempf AS STRING)",
                                 "CAST(host AS STRING)",
                                 "CAST(cpu AS STRING)",
                                 "CAST(diskusage AS STRING)",
                                 "CAST(memory AS STRING)",
                                 "CAST(systemtime AS STRING)",
                                 "CAST(BH1745_red AS STRING)",
                                 "CAST(BH1745_green AS STRING)",
                                 "CAST(BH1745_blue AS STRING)",
                                 "CAST(BH1745_clear AS STRING)",
                                 "CAST(VL53L1X_distance_in_mm AS STRING)",
                                 "CAST(ltr559_lux AS STRING)",                                 
                                 "CAST(bme680_tempf AS STRING)",
                                 "CAST(bme680_pressure AS STRING)",
                                 "CAST(bme680_humidity AS STRING)")
                                 .as[(String, String, String, String, String, String, String, String,
                                 String, String, String, String, String, String, String, String, String, String)]
            .writeStream.format("csv")
            .option("truncate", "false")
            .option("header", true)
            .option("path", "/opt/demo/pisensordata")
            .option("checkpointLocation", "/tmp/checkpoint")
            .start()

## You could do csv, parquet, json, orc

pQuery.explain()
pQuery.awaitTermination()
pQuery.stop()

// can be "orc", "json", "csv", etc.

     

Example Spark ETL CSV Output

/opt/demo/pisensordata# cat part-00000-0425bfc8-5d25-4143-818c-bc7af5e1d82c-c000.csv
__key,uuid,ipaddress,cputempf,host,cpu,diskusage,memory,systemtime,BH1745_red,BH1745_green,BH1745_blue,BH1745_clear,VL53L1X_distance_in_mm,ltr559_lux,bme680_tempf,bme680_pressure,bme680_humidity
snr_20220324215723,snr_20220324215723,192.168.1.229,95,piups,0.0,3887.5 MB,20.6,03/24/2022 17:57:24,134.2,99.0,75.6,130.0,15.0,6.09,70.66,1006.11,44.737

Flink SQL

CREATE CATALOG pulsar WITH (
   'type' = 'pulsar',
   'service-url' = 'pulsar://pulsar1:6650',
   'admin-url' = 'http://pulsar1:8080',
   'format' = 'json'
);

USE CATALOG pulsar;

SHOW TABLES;


describe `pi-sensors`;
> 
+------------------------+--------+------+-----+--------+-----------+
|                   name |   type | null | key | extras | watermark |
+------------------------+--------+------+-----+--------+-----------+
|                   uuid | STRING | true |     |        |           |
|              ipaddress | STRING | true |     |        |           |
|               cputempf |    INT | true |     |        |           |
|                runtime |    INT | true |     |        |           |
|                   host | STRING | true |     |        |           |
|               hostname | STRING | true |     |        |           |
|             macaddress | STRING | true |     |        |           |
|                endtime | STRING | true |     |        |           |
|                     te | STRING | true |     |        |           |
|                    cpu |  FLOAT | true |     |        |           |
|              diskusage | STRING | true |     |        |           |
|                 memory |  FLOAT | true |     |        |           |
|                  rowid | STRING | true |     |        |           |
|             systemtime | STRING | true |     |        |           |
|                     ts |    INT | true |     |        |           |
|              starttime | STRING | true |     |        |           |
|             BH1745_red |  FLOAT | true |     |        |           |
|           BH1745_green |  FLOAT | true |     |        |           |
|            BH1745_blue |  FLOAT | true |     |        |           |
|           BH1745_clear |  FLOAT | true |     |        |           |
| VL53L1X_distance_in_mm |  FLOAT | true |     |        |           |
|             ltr559_lux |  FLOAT | true |     |        |           |
|            ltr559_prox |  FLOAT | true |     |        |           |
|           bme680_tempc |  FLOAT | true |     |        |           |
|           bme680_tempf |  FLOAT | true |     |        |           |
|        bme680_pressure |  FLOAT | true |     |        |           |
|        bme680_humidity |  FLOAT | true |     |        |           |
|  lsm303d_accelerometer | STRING | true |     |        |           |
|   lsm303d_magnetometer | STRING | true |     |        |           |
+------------------------+--------+------+-----+--------+-----------+

select max(bme680_pressure) as maxpressure, max(bme680_tempf) as maxtemp, max(ltr559_lux) as maxlux, avg(BH1745_red) as avgred,
       max(VL53L1X_distance_in_mm) as maxdistance
from `pi-sensors`

select * from `pi-sensors`;

   

Apache NiFi - Pulsar Consumer. MongoDB Writer.

Data Store - MongoDB

mongo -u debezium -p dbz --authenticationDatabase admin pulsar1:27017/inventory

show databases

db.createCollection("pisensors")

show collections

db.pisensors.find().pretty()

db.pisensors.find().pretty()
{
        "_id" : ObjectId("623b812e5dae8913d42a93ee"),
        "uuid" : "snr_20220323194514",
        "ipaddress" : "192.168.1.229",
        "cputempf" : 100,
        "runtime" : 9,
        "host" : "piups",
        "hostname" : "piups",
        "macaddress" : "b8:27:eb:4a:4b:61",
        "endtime" : "1648064714.7820184",
        "te" : "9.371636629104614",
        "cpu" : 6.5,
        "diskusage" : "3895.4 MB",
        "memory" : 21.4,
        "rowid" : "20220323194514_c9ec900f-05c2-49c4-985f-ddd83e8b15c0",
        "systemtime" : "03/23/2022 15:45:15",
        "ts" : 1648064715,
        "starttime" : "03/23/2022 15:45:05",
        "BH1745_red" : 112.2,
        "BH1745_green" : 83,
        "BH1745_blue" : 64.8,
        "BH1745_clear" : 110,
        "VL53L1X_distance_in_mm" : 31,
        "ltr559_lux" : 6.65,
        "ltr559_prox" : 0,
        "bme680_tempc" : 23.47,
        "bme680_tempf" : 74.25,
        "bme680_pressure" : 1017.71,
        "bme680_humidity" : 34.432,
        "lsm303d_accelerometer" : "-00.08g : -01.01g : +00.01g",
        "lsm303d_magnetometer" : "+00.06 : +00.30 : +00.07"
}

Monitor Everything! Let me see what's going on!?!??!

References

• https://www.academy.streamnative.io/
• https://github.com/tspannhw/PulsarOnRaspberryPi
• https://community.cloudera.com/t5/Community-Articles/IoT-Series-Sensors-Utilizing-Breakout-Garden-Hat-Part-1/ta-p/249262
• https://shop.pimoroni.com/products/breakout-garden-hat?variant=12767628787795
• https://github.com/pimoroni/breakout-garden
• https://github.com/pimoroni/bme680-python
• https://github.com/pimoroni/bh1745-python
• https://github.com/pimoroni/vl53l1x-python
• https://github.com/pimoroni/ltr559-python
• https://github.com/pimoroni/lsm303d-python
• https://github.com/rm-hull/luma.oled
• https://luma-oled.readthedocs.io/en/latest/install.html
• https://github.com/streamnative/examples/blob/master/cloud/python/OAuth2Consumer.py
• https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#output-sinks

Cloudera Edge Management 1.1.0 Release

Cloudera Edge Management 1.1.1 Release

See:   https://docs.cloudera.com/cem/1.1.0/index.html

https://docs.cloudera.com/cem/1.1.0/using-cem/topics/cem-deployment-monitor.html

We can now integrate with Grafana and use Prometheus Metrics:

https://docs.cloudera.com/cem/1.1.0/using-cem/topics/cem-monitoring-grafana-dashboard.html

Google Coral TPU with Edge Devices and MiNiFi

Google Coral TPU with Edge Devices and MiNiFi 

Designing Our Edge AI Flow with Cloudera Edge Flow Manager.

Configure Your Remote Process Group to Send Data to Your NiFi Cluster

Monitor Your Agents From the Events Screen

Let's grab all the new images and then delete on completion

We have Input and Output Ports to have Bidirectional communication with 0-n MiNiFi agents

Our NiFi flow to process calls from MiNiFi Agents running Coral TPUs

We run a query to check the TensorFlow Lite classification results and send out a slack message.

Let's push JSON data to a Kafka Cluster in AWS

EFM Series: Using MiNiFi Agents on Raspberry Pi 4 with Intel Movidius Neural Compute Stick 2, Apache NiFi and AI

EFM Series:   Using MiNiFi Agents on Raspberry Pi 4 with Intel Movidius Neural Compute Stick 2, Apache NiFi and AI

The good news is Raspberry Pi 4 can run MiNiFi Java Agents, Intel Movidius Neural Compute Stick 2 and AI libraries.   You can now use this 4GB of RAM device to run IoT with AI on the edge.

Flow From MiNiFi Agent Running OpenVino, SysLog Tail and Grabbing WebCam Images

Configure The Execution of OpenVino Python Applications on RPI 4

Events Returning from Raspberry Pi 4

Models Used

Download model using downloader.

Github

https://github.com/tspannhw/minifi-rpi4-ncc2

DATE=$(date +"%Y-%m-%d_%H%M")
fswebcam -q -r 1280x720 --no-banner /opt/demo/images/$DATE.jpg

python3 /opt/intel/openvino/build/test.py /opt/demo/images/$DATE.jpg

Software

• Apache NiFi
• Apache NiFi - MiNiFi Agents
• TensorFlow
• OpenVino
• Python 3
• FSWEBCAM
• OpenCV DNN
• PSUTIL

Python Libraries

pip3 install getmac

pip3 install psutil

pip3 install --upgrade pip
pip3 install --upgrade setuptools

• Intel Movidius Neural Compute Stick 2
• Raspberry Pi 4 with 4GB RAM

Hosting

We can run On-Premise on VMs, Containers, K8 or Bare metal.   Or on own of the clouds such as Google.

Resources

• https://github.com/movidius/ncappzoo
• https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_raspbian.html
• https://github.com/movidius/ncappzoo/blob/master/tensorflow/README.md
• https://software.intel.com/en-us/articles/python3-sbc-and-ncs2
• https://github.com/opencv/open_model_zoo
• https://download.01.org/opencv/2019/open_model_zoo/R2/20190628_180000_models_bin/
• https://docs.openvinotoolkit.org/latest/_intel_models_index.html
• https://docs.openvinotoolkit.org/latest/_intel_models_person_detection_retail_0013_description_person_detection_retail_0013.html
• https://docs.openvinotoolkit.org/latest/_intel_models_person_detection_action_recognition_0006_description_person_detection_action_recognition_0006.html
• https://docs.openvinotoolkit.org/latest/_intel_models_pedestrian_and_vehicle_detector_adas_0001_description_pedestrian_and_vehicle_detector_adas_0001.html
• https://github.com/opencv/open_model_zoo/blob/master/tools/downloader/README.md
• https://github.com/opencv/open_model_zoo/blob/master/demos/README.md
• https://github.com/opencv/open_model_zoo/blob/master/demos/gaze_estimation_demo/README.md
• https://github.com/opencv/open_model_zoo/blob/master/demos/security_barrier_camera_demo/README.md
• https://github.com/opencv/open_model_zoo/blob/master/demos/pedestrian_tracker_demo/README.md
• https://github.com/opencv/open_model_zoo/blob/master/demos/text_detection_demo/README.md
• https://github.com/movidius/ncappzoo/tree/master/apps/simple_classifier_py
• https://download.01.org/openvinotoolkit/2018_R5/open_model_zoo/text-detection-0001/FP32/
• http://docs.openvinotoolkit.org/latest/_inference_engine_samples_object_detection_sample_ssd_README.html
• https://software.intel.com/en-us/articles/get-started-with-neural-compute-stick
• https://apps.apple.com/us/app/pihelper/id1369930932

Directories

•  /root/inference_engine_samples_build/armv7l/Release
• /opt/intel/openvino