Streamlit TF Real Time Object Detection

Interactive Web App that performs object detection within a video stream.

Dependencies

Running the application can be done following the instructions above:

1. To create a Python Virtual Environment (virtualenv) to run the code, type:

   python3 -m venv my-env

2. Activate the new environment:

   • Windows: my-env\Scripts\activate.bat
   • macOS and Linux: source my-env/bin/activate

3. Install all the dependencies from requirements.txt:

   pip install -r requirements.txt

If you're a conda user, you can create an environment from the environment.yml file using the Terminal or an Anaconda Prompt for the following steps:

1. Create the environment from the environment.yml file:

   conda env create -f environment.yml

2. Activate the new environment:

   • Windows: activate obj-det
   • macOS and Linux: source activate obj-det

3. Verify that the new environment was installed correctly:

   conda list

You can also clone the environment through the environment manager of Anaconda Navigator.

It's mandatory to download at least a TensorFlow object detection model. Several models can be found in th TF Model Zoo. The models should be placed in the trained_model/ folder.

Note: If you already have installed a version of TF Object Detection, you might want to erase the object_detection/ folder.

Use

Within the virtual environment:

streamlit run main.py

A web application will open in the prompted URL. The options there are:

• Select which classes to detect: Multiselect to choose which of the classes that the model's been trained on (available on label_map.pbtxt) are going to be used in the inference. This is, only detections of the selected classes will appear in the resulting bounding boxes.
• Select which camera signal to use: If more than a camera is connected to the computer, the user can select which signal to be fed to the model.
• Select which model to use: Any TensorFlow model can be used to make predictions out of the video signal. Just select it from the trained_model/ directory. Be sure that the model has a frozen_inference_graph.pb on it.

Acknowledgments

• TF Object Detection API

License

This project is licensed under the MIT License - see the LICENSE.md file for details