#AzureIoT – How to create an Azure IoT module from an Azure #CustomVision project 👀 2/N

Hi !

In my previous post I wrote about how to create an Azure Custom Vision project. And, once created how to export the project as a Docker image to be used in different platforms. So, let’s take a look at the content of the exported Linux image.

The main docker content is described in the image below. We can find

• Folder app. This folder hosts the computer vision exported model (model.pb), and the python files to perform the object detection, and to host this as as a webserver using flask.
• Folder azure ml. This folder contains a step-by-step instruction on how to use this in Azure ML. Also a python file to analyze and perform the scores on images.
• Root files. dockerfile to build the image, license and readme.

The docker file is very straightforward.

• It’s based on a Python 3.7 slim image
• Install the following prerequisites: numpy, tensorflow, flask, pillow, mscviplib
• Opens the port 80
• Run the python app in the file [app/app.py]

FROM python:3.7-slim

RUN pip install -U pip
RUN pip install --no-cache-dir numpy~=1.17.5 tensorflow~=2.0.2 flask~=1.1.2 pillow~=7.2.0
RUN pip install --no-cache-dir mscviplib==2.200731.16

COPY app /app

# Expose the port
EXPOSE 80

# Set the working directory
WORKDIR /app

# Run the flask server for the endpoints
CMD python -u app.py

And finally, let’s take a look at the [app/app.py]. I cropped some of the code for a better readability.

• Creates a Flask app
• Create an Http POST endpoint [/image] which receive a file to perform the object detection
• Create an Http POST endpoint [/url] which receive a file location (url) to perform the object detection

import json
import os
import io

# Imports for the REST API
from flask import Flask, request, jsonify

# Imports for image procesing
from PIL import Image

# Imports for prediction
from predict import initialize, predict_image, predict_url

app = Flask(__name__)

# 4MB Max image size limit
app.config['MAX_CONTENT_LENGTH'] = 4 * 1024 * 1024 

# Default route just shows simple text
@app.route('/')
def index():
    return 'CustomVision.ai model host harness'

@app.route('/image', methods=['POST'])
def predict_image_handler(project=None, publishedName=None):

@app.route('/url', methods=['POST'])
def predict_url_handler(project=None, publishedName=None):

if __name__ == '__main__':
    # Load and intialize the model
    initialize()

    # Run the server
    app.run(host='0.0.0.0', port=80)

So, we have everything to analyze images !

In my next post, let’s start the update process to update this docker image to run as an Azure IoT Module !

Resources

• Azure Custom Vision
• Azure Custom Vision Documentation

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