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Posting Images with Apache NiFi 1.7 and a Custom Processor
I have been using a shell script for this since Apache NiFi did not have a good way to natively post an image to HTTP servers su such as the model server for Apache MXNet.
So I wrote a quick and dirty processor that posts an image there and gathers the headers, result body, status text and status code and returns them to you as attributes.
In this example I am download images from picsum.photos free photo service.
To use this new processor, download to your lib directory and restart Apache NiFi, then you can add the PostImageProcessor.
Eclipse For Building My Processor
Configure the Post Image Processor with your URL, fieldname, imagename and image type.
MXNet Model Server Results
The Attribute Results From the Data
Results
Example Results
post.header
{Server=[Werkzeug/0.14.1 Python/3.6.6], Access-Control-Allow-Origin=[*], Content-Length=[396], Date=[Fri, 05 Oct 2018 17:47:22 GMT], Content-Type=[application/json]}
post.results
{"prediction":[[{"probability":0.24173378944396973,"class":"n02281406 sulphur butterfly, sulfur butterfly"},{"probability":0.19173663854599,"class":"n02190166 fly"},{"probability":0.052654966711997986,"class":"n02280649 cabbage butterfly"},{"probability":0.05147545784711838,"class":"n03485794 handkerchief, hankie, hanky, hankey"},{"probability":0.048753462731838226,"class":"n02834397 bib"}]]}
post.status
OK
post.statuscode
200 Results from HTTP Posting an Image to MXNet Model Server
[INFO 2018-10-05 13:47:22,217 PID:88561 /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/mms/serving_frontend.py:predict_callback:467] Request input: data should be image with jpeg format.
[INFO 2018-10-05 13:47:22,218 PID:88561 /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/mms/request_handler/flask_handler.py:get_file_data:137] Getting file data from request.
[INFO 2018-10-05 13:47:22,262 PID:88561 /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/mms/serving_frontend.py:predict_callback:510] Response is text.
[INFO 2018-10-05 13:47:22,262 PID:88561 /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/mms/request_handler/flask_handler.py:jsonify:159] Jsonifying the response: {'prediction': [[{'probability': 0.24173378944396973, 'class': 'n02281406 sulphur butterfly, sulfur butterfly'}, {'probability': 0.19173663854599, 'class': 'n02190166 fly'}, {'probability': 0.052654966711997986, 'class': 'n02280649 cabbage butterfly'}, {'probability': 0.05147545784711838, 'class': 'n03485794 handkerchief, hankie, hanky, hankey'}, {'probability': 0.048753462731838226, 'class': 'n02834397 bib'}]]}
[INFO 2018-10-05 13:47:22,263 PID:88561 /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/werkzeug/_internal.py:_log:88] 127.0.0.1 - - [05/Oct/2018 13:47:22] "POST /squeezenet/predict HTTP/1.1" 200 -Example HTTP Server
https://github.com/awslabs/mxnet-model-server
Source Code For Processor
https://github.com/tspannhw/nifi-postimage-processor
Pre-Built NAR To Install
https://github.com/tspannhw/nifi-postimage-processor/releases/tag/1.0
