This application is built with streamlit.
It allows for three major ways of detection:
* Detection by uploading image(s)
* Detection by uploading video(s)
* Detection using webcam
The streamlit library is used for building the interface of the app.
There are two major models(pre-trained) present in this project:
- YOLOv3
- YOLOv8
There are multiple files and folders in this project:
**FOLDERS**
**functions**
This folder contains all the necessary individual functions of the app:
- webcam
This function contains all the necessary codes for opening and displaying the detected images using webcam.
- video_upload
This function contains the codes for
- uploading
- saving
- and detecting videos only.
- image_upload
This function contains the codes to upload and detect images.
- settings
This file contains the necessary settings needed for each functions written in the functions folder to avoid repetition.
- helper
This functions contains various functions that aids the main app file outside the functions folder.
**images**
This folder contains local images used for testing the app.
**weights**
This folder contains the different YOLO weights used in the project.
**uploaded_videos**
This is a `code` generated folder that will store the uploaded videos to be re-read for detection in the upload_video function.
**FILES**
**main**
This is the main file that contains the streamlit interface code and the calling of the various functions in the functions folder.
* Fork the repository/ download the zip file.
* Download the requirements from the requirements.txt file.
* cd into the directory.
* run the following command in your terminal/command prompt 'python -m streamlit run main.py'.
* click on the link (localhost).
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