Azure Custom Vision - Local model viewer Sample

Create an object detection model in Azure Custom Vision and run the model locally in a desktop app in minutes. ✨

This is a sample project to use an object detection model, created using Azure Custom Vision, locally in a desktop application. The model is extracted from the "Custom Vision Export to Docker - Linux" feature.

To build an Object Detection project, you can follow this tutorial: Quickstart: Build an object detector with the Custom Vision website

Disclaimer: This demo uses a virtual environment with Python 3.7 as is the required version to run some of the project dependencies.

Prerequisites

🐍 Anaconda

Download and install the latest version of Anaconda.
🪄 Create a local virtual environment

Open Anaconda PowerShell Prompt. Run the following commands to create a virtual environment named [demo]
```
conda create -n demo python=3.7
conda activate demo
```
Once the virtual env is activated the PowerShell window must show the (demo) env.

▶️ Install project dependencies:

Run the following commands to install the required dependencies.

# install latest version of OpenCV
pip install opencv-python

# Install tensorflow and general dependencies
pip install --no-cache-dir numpy~=1.17.5 tensorflow~=2.0.2 flask~=2.1.2 pillow~=7.2.0 protobuf~=3.20.0

# Install image processing library
pip install --no-cache-dir mscviplib

The environment is ready to be used.

✅ Check environment state:

The file 00CheckEnv.py will test if all the requirements are sucessfully installed.

Run the check environment file with the command.
```
python .\00CheckEnv.py
```
The output should be similar to this one:
```
TensorFlow: 2.0.4
OpenCV: 4.8.0
```

Download the Custom Vision model

Once you have you model trained, export and download a Docker Linux version of the model.
Extract the model and, from the app folder, copy the following files to the src folder of this repository.
- labels.txt
- model.pb
- predict.py

Select the right Camera

The file .\src\05CameraTest.py will allow us to identify the right camera to use. Edit the file and change the number in the line [cap = cv2.VideoCapture(0)] until the right camera is in use.
```
import cv2
import time

# access to the camera, change the index to use the right camera
cap = cv2.VideoCapture(0)
```
Navigate to the src folder and run the file 05CameraTest.py with the command.
```
python .\05CameraTest.py
```

Run Locally

The file .\src\10CameraApp.py will use the camera and the exported model to detect object and display the objects in a window. Update the file to use the right.
Navigate to the src folder and run the app with the command.
```
python .\10CameraApp.py
```
Once the app is running, you can press the following keys to enable / disable some features.
- Press D to enable or disable the detection
- Press L to show or hide the labels
- Press F to show or hide the FPS
- Press Q to exit
This is an example of the app running.

Author

👤 Bruno Capuano

Website: https://elbruno.com
Twitter: @elbruno
Github: @elbruno
LinkedIn: @elbruno

🤝 Contributing

Contributions, issues and feature requests are welcome!

Feel free to check issues page.

Show your support

Give a ⭐️ if this project helped you!

📝 License

This project is MIT licensed.

elbruno/CustomVisionPythonLocalViewer