wildlife-watch | Wildlife monitoring is essential for keeping track

by saubury Python Version: Current License: No License

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kandi X-RAY | wildlife-watch Summary

wildlife-watch is a Python library. wildlife-watch has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Wildlife monitoring is essential for keeping track of animal movement patterns & population change. The tricky part however can be finding & identifying every species. This project is a demonstration of using a raspberry Pi and camera, Apache Kafka, Kafka Connect to identify and classify animals. Using ksqlDB to see population trends over time and the display of real-time analytics on this data with Kibana dashboards. Plus instant alerting using Telegram to send me a push notification to my phone if we discover a rare animal.

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wildlife-watch has a low active ecosystem.

It has 15 star(s) with 4 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

wildlife-watch has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of wildlife-watch is current.

Quality

wildlife-watch has no bugs reported.

Security

wildlife-watch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

wildlife-watch does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

wildlife-watch releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

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wildlife-watch Key Features

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wildlife-watch Examples and Code Snippets

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Vulnerabilities

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Install wildlife-watch

This code has been tested on a Raspberry Pi 4 and MacBook Pro (Intel) with Python 3.7.4 and pip 22.1.2. This example uses TensorFlow Lite with Python on a Raspberry Pi to perform real-time object detection using images streamed from the Pi Camera. Much inspiration taken from the TensorFlow Lite Python object detection example.
Use a virtual python environment to keep dependancies seperate.
These instructions are for running Kafka, Kafka Connect and Kibana locally using docker containers.
Create Kafka topics and populate with dummy data.
Connect to ksqlDB server and create streams.
Ensure the animals and zoo topics are sent from kafka to elastic.
To call Telegram you must use and create your own bot. Although we're not using this connector, these instructions for creating a bot are great steps to follow. You'll need to know the bot url and chat_id.
And then edit 07_teaddybear-telegram-sink.json updating the bot url and chat_id.
Establish Kafka connect sink to HTTP for Telegram.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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