B2Detect
Detect photographs with B2 Stealth Bombers from social media sites using Tensorflow and Apache NiFi
Install / Use
/learn @BrooksIan/B2DetectREADME
B2Detect
Data Science
Object Detection on steaming images using Tensorflow and Apache NiFi
Introduction - B2 Stealth Bomber Social Media Photo Detector <a name="introduction"></a>
The goal of this project is to build an end-to-end project to detect images that contain B2 Stealth Bomber from popular social media sites.
Language: Python
Requirements:
- Python 2.7
- Tensorflow 1.13
Author: Ian R Brooks
Follow: LinkedIn - Ian Brooks PhD
Table of Contents
Links <a name="links"></a>
NiFi Links: <a name="linksNifi"></a>
- Posting Images with Apache NiFi 1.7 and a Custom Processor
- Post Images To Slack Custom Processor
- Great Read On Posting Images to Slack from Apache NiFi Using Custom Processor
- Uploading NiFi Template
TensorFlow Links: <a name="linksTF"></a>
Additional Links: <a name="linksAdd"></a>
Setup Environment - Download Everything! <a name="Setup"></a>
Download Python Libraries <a name="Setup1"></a>
Run at terminal prompt
pip install requests
pip install pillow
pip install numpy
pip install image
pip install Pillow-PIL
Download NiFi Processor <a name="Setup2"></a>
Run at terminal prompt
#Download Post Image Processor nar - Thank You Tim Spann!
wget https://github.com/tspannhw/nifi-postimage-processor/releases/download/1.0/nifi-postimage-nar-1.0.nar \
-O /usr/hdf/current/nifi/lib/nifi-postimage-nar-1.0.nar
Download Project <a name="Setup3"></a>
Download the project using the git url for here.
Upload NiFi Template <a name="Setup4"></a>
NiFi flow template is called B2DetectFlow_BaseTemplate.xml
Once the template has been loaded, you should see the following NiFi flow
NiFi Configuration <a name="NifiConfig"></a>
Set Social Seacher API Token in InvokeHTTP processor
Set Social Searcher token value
http://api.social-searcher.com/v2/search?q=B2+Stealth+Bomber&type=photo&key=<YOUR TOKEN VALUE HERE>
SSL Context Configuration in InvokeHTTP processor
Post Image Processor Configuration
Update Slack API Token in URL value:
https://slack.com/api/files.upload?token= <YOUR KEY HERE> &channels=b2detect&filename=${absolute.path}${filename}&files:write:user&pretty=1
Put Slack Processor Configuration
Update Webhook URL value
Tensorflow Serving Using Docker <a name="TFServe"></a>
Run at terminal prompt. Note the path need to point to the location of saved_model directory in this github repo.
docker pull tensorflow/serving
#Adding the Version number on model target path is VERY important!
docker run -p 8900:8500 -p 8501:8501 --mount type=bind,source=/saved_model,target=/models/saved_model/1 \
-e MODEL_NAME=saved_model -t tensorflow/serving &
Configure Execute Stream Command Processor <a name="modelcall"></a>
-
Download (or copy) callTFModel.py python script to the path set in the Exectute Stream Command processor, which is used to call the Tensorflow model.
-
In callTFModel.py, set the URL of the Tensorflow Serving Docker container
import PIL.Image
from PIL import ImageDraw
import numpy
import requests
import time
import json
import sys
imagePath = str(sys.argv[1])
threshold=0.95
timeTheashold = 2.5
image = PIL.Image.open(imagePath)
image_np = numpy.array(image)
draw = ImageDraw.Draw(image)
payload = {"instances": [image_np.tolist()]}
start = time.time()
res = requests.post("<URL OF DOCKER CONTAINER>:8501/v1/models/saved_model:predict", json=payload)
processTime = time.time()-start
jsonStr= json.dumps(res.json())
jsonDict = json.loads(jsonStr)
predScore = jsonDict['predictions'][0]['detection_scores'][0]
if((predScore >= threshold) and (timeTheashold >= processTime)):
response = {"response":"B2Found","confidence": predScore , "duration": processTime }
else:
response = {"response":"B2NotFound","confidence":predScore,"duration":processTime}
jsonresponse = json.dumps(response)
print(jsonresponse)
Results - Images Are Posted To Slack Channel<a name="Result"></a>
