kcuric / surveillance-system Goto Github PK

Implement a class whose methods will be running on the Raspberry PI devices. Class should be "plug n' play" meaning user should be able to instantiate the user class and call the run() method - class should do all the rest.

Client class should use the following developed modules:

• Recorder (class for recording video)
• Streamer (class for transferring the footage)
• Detector (class for motion detection)

Implement the Flask web application that will be running on the created GCP VM Instance.

Write the documentation for the project.

Create the pipenv files so the virtual environment can be used.
Needed dependencies on the client side:

• OpenCV
• ...

Implement a face detection class via OpenCV in Python.
Class name: "Detector"
Store the class source code inside the "modules" folder.

Streamer is currently dependent on the Recorder class - this should not be the case. Both Recorder and Streamer should be instantiated inside the client script.

On the server side implement email sending to the user if detection has occurred on the camera. User should get the following info:

• date and time when did the detection occurred
• picture in which the face was detected

Send the email to your own email address or make a new email address for this purpose.

Implement signal handling for the client and the server side. Stopping the execution should free all used resources (ports, objects, etc.).

Implement video capture via OpenCV.
Class name: "Recorder".
Store the class source code inside the "modules" folder.

Implement a class that enables sending images from the client (Raspberry PI) to the server (GCP VM Instance).
Class name: "Streamer".
Store the class source code inside the "modules" folder.

Create a GCP VM Instance that will be running the Flask app. Configure the VM Instance.

Install dependencies on the GCP VM Instance. Python, Python modules, etc..
Dependencies of the GCP VM Instance (known for now):

• Flask