Data In Motion

[FLaNK] Smart Weather Websocket Application - Kafka Consumer

Part 2 of 2

This is based on Koji Kawamura's excellent GIST:

https://gist.github.com/ijokarumawak/60b9ab2038ef906731ebf4c0eee97176

As part of my Smart Weather Application, I wanted to display weather information as it arrives in a webpage using web sockets. Koji has an excellent NiFi flow that does it. I tweaked it and add some things since I am not using Zeppelin. I am hosting my webpage with NiFi as well.

https://www.datainmotion.dev/2020/11/flank-smart-weather-applications-with.html

We simply supply a webpage that makes a websocket connection to NiFi and NiFi keeps a cache in HBase to know what the client is doing. This cache is updated by consuming from Kafka. We can then feed events as they happen to the page.

Here is the JavaScript for the web page interface to websockets:

<script>
	function sendMessage(type, payload) {
	websocket.send(makeMessage(type, payload));
	}

	function makeMessage(type, payload) {
	return JSON.stringify({
	'type': type,
	'payload': payload
	});
	}

	var wsUri = "ws://edge2ai-1.dim.local:9091/test";

	websocket = new WebSocket(wsUri);
	websocket.onopen = function(evt) {

	sendMessage('publish', {
	"message": document.getElementById("kafkamessage")
	});

	};
	websocket.onerror = function(evt) {console.log('ERR', evt)};
	websocket.onmessage = function(evt) {
	var dataPoints = JSON.parse(evt.data);

	var output = document.getElementById("results");
	var dataBuffer = "<p>";

	for(var i=0;i<dataPoints.length;i++)
	{
	dataBuffer += " <img src=\"" + dataPoints[i].icon_url_base + dataPoints[i].icon_url_name + "\">  " + dataPoints[i].location +
	dataPoints[i].station_id + "@" + dataPoints[i].latitude + ":" +
	dataPoints[i].longitude + "@" + dataPoints[i].observation_time +
	dataPoints[i].temperature_string + "," + dataPoints[i].relative_humidity + "," +
	dataPoints[i].wind_string +"<br>";

	}
	output.innerHTML = output.innerHTML + dataBuffer + "</p><br>";
	};

	</script> Video Walkthrough: https://www.twitch.tv/videos/797412192?es_id=bbacb7cb39 Source Code: https://github.com/tspannhw/SmartWeather/tree/main Kafka Topic weathernj Schema The schema registry has a live Swagger interface to it's REST API NiFi Flow Overview Ingest Via REST All US Weather Data from Zipped XML As Data Streamings In, We Can Govern It Ingested Data is Validated Against It's Schema Then Pushed to Kafka as Avro We consume that Kafka data in store it in Kudu for analytics We host a web page for our Websockets Application in NiFi with 4 simple processors. Listen and Put Web Socket Messages Between NiFi Server and Web Application Kafka Data is Cached for Websocket Applications Set the Port for WebSockets via Jetty Web Server Use HBase As Our Cache We can monitor our Flink SQL application from the Global Flink Dashboard We can query our Weather data store in Apache Kudu via Apache Impala through Apache Hue Kudu Visualizations of Our Weather Data in Cloudera Visual Applications

[FLaNK] Smart Weather Applications with Flink SQL

Sometimes you want to acquire, route, transform, live query and analyze all the weather data in the United States while those reports happen. With FLaNK, it's a trivial process to do.

From Kafka to Kudu for Any Schema of Any Type of Data, No Code, Two Steps

The Schema Registry has full Swagger-ized Runnable REST API Documentation. Integrate, DevOps and Migration in a simple script.

Here's your schemas, upload, edit and compare.

Validating Data Against a Schema With Your Approved Level of Tolerance. You want extra fields allowed, you got it.

Feed that data to beautiful visual applications running in Cloudera Machine Learning.

You like drill down maps, you got them.

Query your data fast with Apache Hue against Apache Kudu tables through Apache Impala.

Let's ingest all the US weather stations even though they are a zipped directory of a ton of XML files.

Weather Ingest is Easy Automagically

View All Your Topic Data Enabled by Schema Registry Even in Avro Format

Reference:

https://www.datainmotion.dev/2020/07/ingesting-all-weather-data-with-apache.html

Source:

Build

https://github.com/tspannhw/ApacheConAtHome2020/blob/main/scripts/setup.sh

Query

https://github.com/tspannhw/ApacheConAtHome2020/blob/main/scripts/flink.sh

SQL

INSERT INTO weathernj
	SELECT `location`, station_id,latitude,longitude,observation_time,weather,
	temperature_string, temp_f,temp_c,relative_humidity,wind_string,wind_dir,wind_degrees,wind_mph,
	wind_kt, pressure_in,dewpoint_string,dewpoint_f,dewpoint_c
	FROM weather
	WHERE
	`location` is not null and `location` <> 'null' and trim(`location`) <> '' and `location` like '%NJ';

Kafka Insert

https://github.com/tspannhw/ApacheConAtHome2020/blob/main/flinksql/weathernj.sql

Schemas

https://github.com/tspannhw/ApacheConAtHome2020/blob/main/schemas/weathernj.avsc

https://github.com/tspannhw/ApacheConAtHome2020/blob/main/schemas/weather.avsc

Example Slack Output

12:56

=========================================================
http://forecast.weather.gov/images/wtf/small/ovc.pngLocation Cincinnati/Northern Kentucky International Airport, KY Station KCVG
Temperature: 49.0 F (9.4 C)
Humdity: 83
Wind East at 3.5 MPH (3 KT)
Overcast
Dewpoint 44.1 F (6.7 C)Observed at Tue, 27 Oct 2020 11:52:00 -0400---- tracking info ---- UUID: 2cb6bd67-148c-497d-badf-dfffb4906b89
Kafka offset: 0
Kafka Timestamp: 1603818351260
=========================================================

[FLaNK] Streaming EdgeAI on the new NVIDIA Jetson Nano 2GB with MiNiFi Agents To FLaNK Applications

Unboxing: https://www.youtube.com/watch?v=dVGEtWYkP2c&feature=youtu.be

Plug Into Community AI Apps: https://youtu.be/2T8CG7lDkcU

Education Projects: https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/education-projects/

I am not patient enough to shoot an unboxing video, I was too excited to get this superb machine running. The NVIDIA Jetson Nano 2GB is now available for purchase for only $59!!!

Source and Setup: https://github.com/tspannhw/SettingUpAJetsonNano2GB/blob/main/README.md

The 2GB version of NVIDIA Jetson Nano is great, you really don't miss anything that was removed. I have copied over my MiNiFi agent and code from other Jetson Nanos, Xavier NX and TX1 and it all works fine. The speed is fine for most needs especially for development and prototyping. I prefer the Xavier, but at this price you can't go wrong. I am definitely going to be getting Jetson Nanos instead of other devices for most IoT / Edge AI use cases. I have used my NVidia Jetson 2GB for demos for a number of events including ApacheCon, BeamSummit, Open Source Summit and AI Dev World.

https://www.linkedin.com/pulse/2020-streaming-edge-ai-events-tim-spann/

I installed the fswebcam to capture still images and build up a directory of them to process.

See my first article: https://www.datainmotion.dev/2020/10/unveiling-nvidia-jetson-nano-2gb-and.html

You must install and run: https://github.com/dusty-nv/jetson-inference. You get great libraries, tutorials, documentation and examples. I usually build my apps starting from one of these examples and use one of the excellent NVIDIA pre-built models. This rapidly accelerates my development and deployment of EdgeAI applications whether they are IoT or other purposes. This is working with standard Raspberry Pi plug in cameras and the excellent Logitech USB web cameras that I have used with all my other NVIDIA devices.

At this price point, there seems no reason that every developer in every company should have one. It's a great place to test out Edge AI applications and run classifications at a decent speed. This is a real machine despite it.

I was facilitating data journeys at the NetHope Global Summit today and I thought these $59 devices could be great for non-profits to use for many data collection and analytics purposes in the field. https://www.nethopeglobalsummit.org/agenda-2020#sz-tab-44134 I am exploring some use cases to see if I can pre build some easy applications that an NGO could just pick up and run with. Let's see what develops. A $59 GPU edge device enables some new applications at an affordable cost. $59 won't give me a lot of cloud, but I can get a powerful small data collection device that runs ML, DL, cameras, MiNiFi Agents, Python and Java. With 2 Gigabytes of fast RAM and a GPU, one is limited by their imagination.

Example Application

Output Exampl Datae:

{"uuid": "nano_uuid_cmq_20201026202757", "ipaddress": "192.168.1.169", "networktime": 47.7275505065918, "detectleft": 1.96746826171875, "detectconfidence": 52.86550521850586, "cputemp": "34.0", "gputemp": "30.0", "gputempf": "86", "cputempf": "93", "runtime": "169", "host": "nano5", "filename": "/opt/demo/images/out_iue_20201026202757.jpg", "host_name": "nano5", "macaddress": "00:e0:4c:49:d8:b7", "end": "1603744246.924455", "te": "169.4200084209442", "systemtime": "10/26/2020 16:30:46", "cpu": 9.9, "diskusage": "37100.4 MB", "memory": 91.5, "id": "20201026202757_64d69a82-88d8-45f8-be06-1b836cb6cc84"}

Below is some example output for running a Python script to classify a webcamera (a low end Logi webcam, but you can use a Raspberry Pi camera). We would be best served by running this continuously outputting log messages and images for MiNiFi agents to scoop up and send to a server for routing, transformation and processing.

root@nano5:/opt/demo/minifi-jetson-nano# jetson_clocks

root@nano5:/opt/demo/minifi-jetson-nano# python3 detect.py

[gstreamer] initialized gstreamer, version 1.14.5.0

[gstreamer] gstCamera -- attempting to create device v4l2:///dev/video0

[gstreamer] gstCamera -- found v4l2 device: HD Webcam C615

[gstreamer] v4l2-proplist, device.path=(string)/dev/video0, udev-probed=(boolean)false, device.api=(string)v4l2, v4l2.device.driver=(string)uvcvideo, v4l2.device.card=(string)"HD\ Webcam\ C615", v4l2.device.bus_info=(string)usb-70090000.xusb-3.2, v4l2.device.version=(uint)264588, v4l2.device.capabilities=(uint)2216689665, v4l2.device.device_caps=(uint)69206017;

[gstreamer] gstCamera -- found 30 caps for v4l2 device /dev/video0

[gstreamer] [0] video/x-raw, format=(string)YUY2, width=(int)1920, height=(int)1080, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction)5/1;

[gstreamer] [1] video/x-raw, format=(string)YUY2, width=(int)1600, height=(int)896, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 15/2, 5/1 };

[gstreamer] [2] video/x-raw, format=(string)YUY2, width=(int)1280, height=(int)720, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 10/1, 15/2, 5/1 };

[gstreamer] [3] video/x-raw, format=(string)YUY2, width=(int)960, height=(int)720, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [4] video/x-raw, format=(string)YUY2, width=(int)1024, height=(int)576, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [5] video/x-raw, format=(string)YUY2, width=(int)800, height=(int)600, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [6] video/x-raw, format=(string)YUY2, width=(int)864, height=(int)480, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [7] video/x-raw, format=(string)YUY2, width=(int)800, height=(int)448, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [8] video/x-raw, format=(string)YUY2, width=(int)640, height=(int)480, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [9] video/x-raw, format=(string)YUY2, width=(int)640, height=(int)360, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [10] video/x-raw, format=(string)YUY2, width=(int)432, height=(int)240, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [11] video/x-raw, format=(string)YUY2, width=(int)352, height=(int)288, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [12] video/x-raw, format=(string)YUY2, width=(int)320, height=(int)240, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [13] video/x-raw, format=(string)YUY2, width=(int)176, height=(int)144, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [14] video/x-raw, format=(string)YUY2, width=(int)160, height=(int)120, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [15] image/jpeg, width=(int)1920, height=(int)1080, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [16] image/jpeg, width=(int)1600, height=(int)896, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [17] image/jpeg, width=(int)1280, height=(int)720, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [18] image/jpeg, width=(int)960, height=(int)720, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [19] image/jpeg, width=(int)1024, height=(int)576, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [20] image/jpeg, width=(int)800, height=(int)600, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [21] image/jpeg, width=(int)864, height=(int)480, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [22] image/jpeg, width=(int)800, height=(int)448, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [23] image/jpeg, width=(int)640, height=(int)480, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [24] image/jpeg, width=(int)640, height=(int)360, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [25] image/jpeg, width=(int)432, height=(int)240, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [26] image/jpeg, width=(int)352, height=(int)288, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [27] image/jpeg, width=(int)320, height=(int)240, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [28] image/jpeg, width=(int)176, height=(int)144, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] [29] image/jpeg, width=(int)160, height=(int)120, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 24/1, 20/1, 15/1, 10/1, 15/2, 5/1 };

[gstreamer] gstCamera -- selected device profile: codec=mjpeg format=unknown width=1280 height=720

[gstreamer] gstCamera pipeline string:

[gstreamer] v4l2src device=/dev/video0 ! image/jpeg, width=(int)1280, height=(int)720 ! jpegdec ! video/x-raw ! appsink name=mysink

[gstreamer] gstCamera successfully created device v4l2:///dev/video0

[gstreamer] opening gstCamera for streaming, transitioning pipeline to GST_STATE_PLAYING

[gstreamer] gstreamer changed state from NULL to READY ==> mysink

[gstreamer] gstreamer changed state from NULL to READY ==> capsfilter1

[gstreamer] gstreamer changed state from NULL to READY ==> jpegdec0

[gstreamer] gstreamer changed state from NULL to READY ==> capsfilter0

[gstreamer] gstreamer changed state from NULL to READY ==> v4l2src0

[gstreamer] gstreamer changed state from NULL to READY ==> pipeline0

[gstreamer] gstreamer changed state from READY to PAUSED ==> capsfilter1

[gstreamer] gstreamer changed state from READY to PAUSED ==> jpegdec0

[gstreamer] gstreamer changed state from READY to PAUSED ==> capsfilter0

[gstreamer] gstreamer stream status CREATE ==> src

[gstreamer] gstreamer changed state from READY to PAUSED ==> v4l2src0

[gstreamer] gstreamer changed state from READY to PAUSED ==> pipeline0

[gstreamer] gstreamer stream status ENTER ==> src

[gstreamer] gstreamer message new-clock ==> pipeline0

[gstreamer] gstreamer message stream-start ==> pipeline0

[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> capsfilter1

[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> jpegdec0

[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> capsfilter0

[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> v4l2src0

[gstreamer] gstCamera -- onPreroll

[gstreamer] gstCamera -- map buffer size was less than max size (1382400 vs 1382407)

[gstreamer] gstCamera recieve caps: video/x-raw, format=(string)I420, width=(int)1280, height=(int)720, interlace-mode=(string)progressive, multiview-mode=(string)mono, multiview-flags=(GstVideoMultiviewFlagsSet)0:ffffffff:/right-view-first/left-flipped/left-flopped/right-flipped/right-flopped/half-aspect/mixed-mono, pixel-aspect-ratio=(fraction)1/1, chroma-site=(string)mpeg2, colorimetry=(string)1:4:0:0, framerate=(fraction)30/1

[gstreamer] gstCamera -- recieved first frame, codec=mjpeg format=i420 width=1280 height=720 size=1382407

RingBuffer -- allocated 4 buffers (1382407 bytes each, 5529628 bytes total)

[gstreamer] gstreamer changed state from READY to PAUSED ==> mysink

[gstreamer] gstreamer message async-done ==> pipeline0

[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> mysink

[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> pipeline0

RingBuffer -- allocated 4 buffers (14745600 bytes each, 58982400 bytes total)

jetson.inference -- detectNet loading build-in network 'ssd-mobilenet-v2'

detectNet -- loading detection network model from:

-- model networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff

-- input_blob 'Input'

-- output_blob 'NMS'

-- output_count 'NMS_1'

-- class_labels networks/SSD-Mobilenet-v2/ssd_coco_labels.txt

-- threshold 0.500000

-- batch_size 1

[TRT] TensorRT version 7.1.3

[TRT] loading NVIDIA plugins...

[TRT] Registered plugin creator - ::GridAnchor_TRT version 1

[TRT] Registered plugin creator - ::NMS_TRT version 1

[TRT] Registered plugin creator - ::Reorg_TRT version 1

[TRT] Registered plugin creator - ::Region_TRT version 1

[TRT] Registered plugin creator - ::Clip_TRT version 1

[TRT] Registered plugin creator - ::LReLU_TRT version 1

[TRT] Registered plugin creator - ::PriorBox_TRT version 1

[TRT] Registered plugin creator - ::Normalize_TRT version 1

[TRT] Registered plugin creator - ::RPROI_TRT version 1

[TRT] Registered plugin creator - ::BatchedNMS_TRT version 1

[TRT] Could not register plugin creator - ::FlattenConcat_TRT version 1

[TRT] Registered plugin creator - ::CropAndResize version 1

[TRT] Registered plugin creator - ::DetectionLayer_TRT version 1

[TRT] Registered plugin creator - ::Proposal version 1

[TRT] Registered plugin creator - ::ProposalLayer_TRT version 1

[TRT] Registered plugin creator - ::PyramidROIAlign_TRT version 1

[TRT] Registered plugin creator - ::ResizeNearest_TRT version 1

[TRT] Registered plugin creator - ::Split version 1

[TRT] Registered plugin creator - ::SpecialSlice_TRT version 1

[TRT] Registered plugin creator - ::InstanceNormalization_TRT version 1

[TRT] detected model format - UFF (extension '.uff')

[TRT] desired precision specified for GPU: FASTEST

[TRT] requested fasted precision for device GPU without providing valid calibrator, disabling INT8

[TRT] native precisions detected for GPU: FP32, FP16

[TRT] selecting fastest native precision for GPU: FP16

[TRT] attempting to open engine cache file /usr/local/bin/networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff.1.1.7103.GPU.FP16.engine

[TRT] loading network plan from engine cache... /usr/local/bin/networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff.1.1.7103.GPU.FP16.engine

[TRT] device GPU, loaded /usr/local/bin/networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff

[TRT] Deserialize required 2384046 microseconds.

[TRT]

[TRT] CUDA engine context initialized on device GPU:

[TRT] -- layers 117

[TRT] -- maxBatchSize 1

[TRT] -- workspace 0

[TRT] -- deviceMemory 35449344

[TRT] -- bindings 3

[TRT] binding 0

-- index 0

-- name 'Input'

-- type FP32

-- in/out INPUT

-- # dims 3

-- dim #0 3 (SPATIAL)

-- dim #1 300 (SPATIAL)

-- dim #2 300 (SPATIAL)

[TRT] binding 1

-- index 1

-- name 'NMS'

-- type FP32

-- in/out OUTPUT

-- # dims 3

-- dim #0 1 (SPATIAL)

-- dim #1 100 (SPATIAL)

-- dim #2 7 (SPATIAL)

[TRT] binding 2

-- index 2

-- name 'NMS_1'

-- type FP32

-- in/out OUTPUT

-- # dims 3

-- dim #0 1 (SPATIAL)

-- dim #1 1 (SPATIAL)

-- dim #2 1 (SPATIAL)

[TRT]

[TRT] binding to input 0 Input binding index: 0

[TRT] binding to input 0 Input dims (b=1 c=3 h=300 w=300) size=1080000

[TRT] binding to output 0 NMS binding index: 1

[TRT] binding to output 0 NMS dims (b=1 c=1 h=100 w=7) size=2800

[TRT] binding to output 1 NMS_1 binding index: 2

[TRT] binding to output 1 NMS_1 dims (b=1 c=1 h=1 w=1) size=4

[TRT]

[TRT] device GPU, /usr/local/bin/networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff initialized.

[TRT] W = 7 H = 100 C = 1

[TRT] detectNet -- maximum bounding boxes: 100

[TRT] detectNet -- loaded 91 class info entries

[TRT] detectNet -- number of object classes: 91

detected 0 objects in image