Bird Watching With Python and TensorFlowJS ( Part 3 )

In this series, we will continue building a small system to capture pictures of my back yard and detect if we see anything. In the future, we want to search the database for birds. This post will focus on the problem of detecting objects in the image and storing them into a database. Check out part 1 and part 2 to more context on this project.

TensorFlow.js is an open-source JavaScript library developed by Google’s TensorFlow team. It enables machine learning and deep learning tasks to be performed directly in web browsers and Node.js environments using JavaScript or TypeScript. TensorFlow.js brings the power of TensorFlow, a popular machine learning framework, to the JavaScript ecosystem, making it accessible for web developers and data scientists.

Under the TensorFlow Js Framework, you have access to the COCOS-SSD model that detects 80 classes of common objects. The output response reports a list of objects found in the image, a confidence factor, bounding boxes to point to each object. Check out this video for an example.

In the following code, we import some of our dependencies. This includes
– TFJS – TensorflowJs
– cocosSSd – TensorFlow model for common object detection
– amqp – A library for connecting to rabbitMQ
– supabase/supabase-js – To log data of objects found, we will send our data to Supabase
– azure/storage-blob – To download pictures from Azure blob storage, we add a client library to connect to the cloud

const tf = require("@tensorflow/tfjs-node")
const amqp = require('amqplib');
const cocosSSd = require("@tensorflow-models/coco-ssd")
const { createCanvas, loadImage } = require('canvas');
const { createClient } = require('@supabase/supabase-js');
const { BlobServiceClient } = require("@azure/storage-blob");
const { v1: uuidv1 } = require("uuid");
var fs = require('fs');

My friend Javier got me excited about trying out If you’re looking for a simple document or relational database solution with an easy api, it’s pretty cool. This code will grab some details from the environment and setup a supabase client.

const supabaseUrl = process.env.SUPABASEURL;
const supabaseKey = process.env.SUPABASEKEY;
const supabase = createClient(supabaseUrl, supabaseKey)

To learn more about Supabase, check out

In our situation, the job-processor program and the watcher program will probably run on two different machines. I will try to run the watcher process on a RaspberryPi. The job processor will probably run on some other machine. The watch program takes pictures and stores the files into Microsoft Azure blob storage. The watcher signals the job processor by sending a message through rabbitMQ.

Let’s setup the connection to Azure Blob storage.


  throw Error('Azure Storage Connection string not found');

const containerName = "picturesblobstorage";
const blobServiceClient = BlobServiceClient.fromConnectionString(AZURE_BLOB_STORAGE_CONNECTION_STRING);
const containerClient = blobServiceClient.getContainerClient(containerName);

When we want to download a file from Azure blob storage, we leverage our container client.

async function downloadPictureFromBlobStorage(fileName)
    const blobClient = containerClient.getBlobClient(fileName);
    console.log(`Downloading blob ${fileName} to ${fileName}`);
    const downloadBlockBlobResponse = await blobClient.downloadToFile(fileName);
    console.log(`Downloaded ${downloadBlockBlobResponse.contentLength} bytes`);
    return true;
  } catch (err) {
    return false;

Let’s setup our class for getting insight from our object detection algorithm. In the following class, the “makeCanvasFromFilePath” method loads the picture into memory as a canvas. Using CocosSSD mode, we detect objects in the image using the predict method.

class ObjectDetection 
        this.model = null;

    async predict(image)
            this.model = await cocosSSd.load();

        const canvas = await this.makeCanvasFromFilePath(image);    
        const predictions = await this.model.detect(canvas);

        return { predictions: predictions }

    async makeCanvasFromFilePath(image) {
        const img = await loadImage(image);
        const canvas = createCanvas(img.width, img.height);
        const ctx = canvas.getContext('2d');
        ctx.drawImage(img, 0, 0);
        return canvas;

const objectDetection = new ObjectDetection();

Let’s configure RabbitMQ

// RabbitMQ connection URL
const rabbitmqUrl = 'amqp://localhost';

// Queue name to consume messages from
const queueName = 'review-picture-queue';

The “processJsonMessage” method is the heart of this nodeJs script. At a high level, the system does the following tasks.
– Read a JSON message from the watcher program.
– Download the picture from Azure blob storage.
– Run object detection on the file.
– Store findings into database ( Supabase )

// Create a function to process JSON messages
async function processJsonMessage(message) {
  try {
    const json = JSON.parse(message.content.toString());
    // Replace this with your custom processing logic for the JSON data
    console.log('Received JSON:', json);

    // need function to download file from blob storage 
    const fileDownloaded = await downloadPictureFromBlobStorage(json.fileName);
      // Run TF prediction ...
      const response = await objectDetection.predict(json.fileName);

      // Store data in supabase ....
      const { error } = await supabase.from('watch_log').insert({ file_name: json.fileName, json: response })    
        console.log("error object defined");


      console.log("Error downloading file from blob storage");

  } catch (error) {
    console.error('Error processing JSON message:', error.message);

Here’s some sample data captured as JSON:

  "predictions": [
      "bbox": [
      "class": "person",
      "score": 0.6217759847640991

In this last section, we connect ourselves to RabbitMQ so that we can start to accept work.

// Connect to RabbitMQ and consume messages async function consume() { try { const connection = await amqp.connect(rabbitmqUrl); const channel = await connection.createChannel(); await channel.assertQueue(queueName, { durable: false }); console.log(`Waiting for messages in ${queueName}. To exit, press Ctrl+C`); channel.consume(queueName, (message) => { if (message !== null) { processJsonMessage(message); channel.ack(message); } }); } catch (error) { console.error('Error:', error.message); } } consume();

That’s about it. If need to see the completed project files, check out the following github link:

If you’re interested in exploring more tutorials on TensorFlowJs, check out the following links to code labs:
TensorFlowJs Code Labs

If you’re wanting to learn more about TensorFlowJS and Machine Learning stuff, our Orlando Google Developer Group will be organizing a fun 1 day community conference on Oct 14th.

Join us for DevFest Florida – Oct 14

AI | Mobile | Web | Cloud | Community

DevFest Central Florida is a community-run one-day conference aimed to bring technologists, developers, students, tech companies, and speakers together in one location to learn, discuss and experiment with technology.

Bird Watching With Python and TensorFlowJS ( Part 2 )

In this series, we will continue building a small system to capture pictures of my back yard and detect if we see birds. Check out the first post here. In this post, we will focus on the problem of taking pictures every minute or so. For fun, I decided to build this part in Python. To review the system overview, check out my previous blog post here.

The solution for the watcher involves the following major elements and concepts.
– Setup a connection to Azure blob storage. To keep things simple, Azure blob storage enables you to securely store files in Microsoft Azure cloud at low cost.
– Set the time interval for taking pictures
– Setup connection to message queue system. The watch program needs to send a message to an analysis program that will analyze the image content. Please keep in mind that RabbitMQ is simply “email for computer programs.” It’s a way for programs to message each other to do work. I will be running the watcher program on a pretty low-complexity Raspberry PI 2. In my case, I wanted to off-load the image analysis to another computer system with a bit more horse power. In future work, we might move the analysis program to a cloud function. That’s a topic for a future post.

Here’s some pseudo code.
– Setup the program to take pictures
– Loop
– Take a picture
– Store the picture on disk
– Upload the picture to Azure blob storage
– Signal the analysis program to review the picture
– Delete the local copy of the picture
– Wait until we need to take a picture

Setting the stage

Let’s start by sketching out the functions for setting up the blob storage, rabbit message queue, and camera.
At the top of the python file, we need to import the following:

import  cv2
import  time
import  pika
import  json
import  os
from  import  BlobServiceClient

In the following code, we setup the major players of the blob storage, rabbit message queue, and camera.

container_client  =  setup_blob_storage() 
# Set the time interval in seconds
interval  =  60  # every min 
# Initialize the webcam
cap  =  cv2.VideoCapture(0)  

# Check if the webcam is opened successfully

if  not  cap.isOpened():
    print("Error: Could not open the webcam.")
queue_name, connection, channel  =  setup_rabbit_message_queue()

Take a picture

In the later part of the program, we start to loop to take a picture and send the data to the analysis program.

ret, frame  = 
if  not  ret:
    print("Error: Could not read frame from the webcam.")

timestamp, filename = store_picture_on_disk(frame)
print(f"Image captured and saved as {filename}")

Send the picture to Blob Storage

local_file_path  =  filename
blob_name  =  filename 
with  open(local_file_path, "rb") as  data:
    container_client.upload_blob(name=blob_name, data=data)

Signal analysis program to review image using a message

# Prepare a JSON message
message  = {
'fileName': filename,
'timestamp': timestamp,
message_json  =  json.dumps(message)

# Send the JSON message to RabbitMQ
channel.basic_publish(exchange='', routing_key=queue_name, body=message_json)
print(f"Message sent to RabbitMQ: {message_json}")

In the previous code sketches, we have not implemented several key functions. Let’s fill in those functions now. You’ll need to position these functions near the top of your script.


Please use this link to learn about Azure Blob storage, account configuration, and Python code patterns.

container_name  =  "picturesblobstorage"
def  setup_blob_storage():
    connect_str  =  "Get connection string for your Azure storage account"
    blob_service_client  =  BlobServiceClient.from_connection_string(connect_str)
    container_client  =  blob_service_client.get_container_client(container_name)
    return  container_client


Setup connection to message queue system.

def  setup_rabbit_message_queue():
    rabbitmq_host  =  'localhost'
    rabbitmq_port  =  5672
    rabbitmq_username  =  'guest'
    rabbitmq_password  =  'guest'
    queue_name  =  'review-picture-queue'

    # Initialize RabbitMQ connection and channel with authentication
    credentials  =  pika.PlainCredentials(rabbitmq_username, rabbitmq_password)
    connection  =  pika.BlockingConnection(pika.ConnectionParameters(host=rabbitmq_host,port=rabbitmq_port,credentials=credentials))
    channel  =

    # Declare a queue for sending messages
    return  queue_name,connection,channel

To keep this blog post brief, I will not be able to jump into all the details regarding setting up RabbitMQ on your local system. Please refer to this 10-minute video for details on setting up this sub-system.

This blog post does a great job of setting up RabbitMQ with “docker-compose.” It’s a light weight way to setup stuff in your environment.

Easy RabbitMQ Deployment with Docker Compose (


def  store_picture_on_disk(frame):
    timestamp  =  time.strftime("%Y%m%d%H%M%S")
    filename  =  f"image_{timestamp}.jpg"
    cv2.imwrite(filename, frame)
    return  timestamp,filename

In our final blog post, we’ll use NodeJs to load the COCO-SSD model into memory and let it comment upon the image in question.

You can check out the code solution in progress at the following github repository.

Check out object-detection.js to see how how object detection will work. Check out for a completed version of this tutorial.

If you’re wanting to learn more about TensorFlowJS and Machine Learning stuff, our Orlando Google Developer Group will be organizing a fun 1 day community conference on Oct 14th.

Join us for DevFest Florida – Oct 14

AI | Mobile | Web | Cloud | Community

DevFest Central Florida is a community-run one-day conference aimed to bring technologists, developers, students, tech companies, and speakers together in one location to learn, discuss and experiment with technology.

Make A Bird Detector with TensorFlowJs ( Part 1 )

In the Rosario tradition of boldly exploring nature, Sarah and my eldest have gotten into bird watching. It’s been cool to see my son and my wife going on hikes and finding cool birds with a local meetup. My wife gave me a challenge to make a bird watcher device for our yard and our bird house. In her vision, we want to understand when we see the most birds in the back yard and capture great photos. In future work, we might even identify the type of bird. In our post today, I thought we would talk through the high level code I’ve prototyped. This will become a fun family project and give me an opportunity to play with some TensorFlowJs.

In the past 12 years, the industry has exploded with innovations involving machine learning. We see these innovations when we ask our home assistant to play a song, using ChatGPT, or using speech to text. In the domain of bird watching, we might build a machine learning model using pictures of different birds with labels specifying the type of bird. A machine learning(ML) system observes patterns in the input data set(set of bird pictures with labels) and constructs rules or structures so the system can classify future images. In contrast to traditional computer programming, we do not explicitly define the code or rules. We train the model using examples and feedback so it learns. In this case, we want to determine if a picture contains a bird.

In this prototype, I will leverage a pretrained ML system or model called COCO-SSD. The COCO aspect of the model finds 80 different classes of things in the context of the picture. (including birds) The model will estimate if it detects a bird in the picture and a bounding box location for the object. The model makes a best attempt to segment the picture and report on all the objects it can see and provide labels.

This diagram provides an overview of the prototype system.

Major elements

  • Watcher – In this project, Python takes pictures every 5 minutes. Pictures get stored to the file system. The file name of the picture gets stored in a message that eventually gets added to a queue.
  • RabbitMQ – We’re using RabbitMQ with JSON messages to manage our queue plumbing. You can think of RabbitMQ as email for computer programs. You can insert messages into different folders. Job processor programs start executing when they receive messages in these folders. This also enables us to create multi-program solutions in different languages.
  • Job Processor – The job processor, written in JavaScript using NodeJS, monitors the message queue for work. When it receives a file name to process, we load the image into memory and request the machine learning process to review it. The COCO-SSD model will report a list of objects it detects with confidence factors associated. If the system finds a bird, the process will write a database record with the details.
  • Database – For this solution, we’re currently prototyping the solution using Supabase. On many of my weekend projects, I enjoy getting to rapidly create structures and store data in the cloud. Under the hood, it uses PostgresDB and feels pretty scalable. Thank you to my friend Javier who introduced me to this cool tool.

The job processor element using TensorFlowJS to execute the object detection model. TensorFlowJs is a pretty amazing solution for executing ML models in the browser or NodeJS backends. Learn more with the following talk.

In our next post, we’ll dive into the details of the job processor process.

If you’re wanting to learn more about TensorFlowJS and Machine Learning stuff, our Orlando Google Developer Group will be organizing a fun 1 day community conference on Oct 14th.

Join us for DevFest Florida – Oct 14

AI | Mobile | Web | Cloud | Community

DevFest Central Florida is a community-run one-day conference aimed to bring technologists, developers, students, tech companies, and speakers together in one location to learn, discuss and experiment with technology.