README

This project uses the YOLOv5 model to detect finger in videos and calculates the capillary refill time (pCRT) using the pyCRT library. Below are the steps to install dependencies and run the project.

1. Install Packges

Install Git

Use the default installation options.

https://git-scm.com/downloads

Install Python 3.10 Download and install Python 3.10 from:

https://www.python.org/downloads/release/python-3100/

Important: During installation, make sure to check Add Python 3.10 to PATH.

2. Install pyCRT-dev

⚠️ Important: If you have pyCRT version 1.0.0 installed, uninstall it before continuing:

pip uninstall pyCRT
pip install pyCRT-dev

3. Clone the YOLOv5 repository

Clone the YOLOv5 repository to use the object detection model:

git clone https://github.com/ultralytics/yolov5.git
cd yolov5
python -m pip install -r requirements.txt

4. Install dependencies for the pyCRTYolo package

Navigate to the pyCRTYolo directory:

cd {Your pyCRTYolo directory}

Install the required dependencies:

python -m pip install -r requirements.txt

5. Project Files

This project consists of the following four main Python files:

1. processYolo.py
   Contains the processDetectFinger function, which is used to process object detection using YOLOv5.

2. processLucasKanade.py
   Implements the processLucasKanade function, which is used for optical flow estimation using the Lucas-Kanade method.

3. validationROI.py
   Includes the validateROI and filterROI functions, which are used to validate and filter the regions of interest (ROI) in the video.

4. dataOperation.py
   Provides functions like openTXT (to open text files) and ensure_directories_exist (to ensure the necessary directories exist for saving output).

5. mainProcess.py Contains the main script

The mainProcess.py script is the core of this project. It integrates all the functions from the other files and handles the entire video processing pipeline. This includes detecting objects using YOLOv5, estimating optical flow with the Lucas-Kanade method, validating regions of interest (ROI), and saving the results in the correct format.

Once you run mainProcess.py, it will automatically:

• Process the video file.
• Apply YOLOv5 for object detection.
• Estimate pCRT using the pyCRT library.
• Filter and validate ROIs to ensure the quality of the results.
• Save the processed video and results in the appropriate directories.

6. Processing the Video

To start processing a video, follow these steps:

1. Open mainProcess.py in your code editor.
2. Run the script by executing it in your terminal or through your IDE.

By default, the script will check the timestamps of the files in the Videos directory and process the most recent video.

During the installation of YOLOv5, you might encounter a security prompt asking whether you trust the repository:

The repository ultralytics_yolov5 does not belong to the list of trusted
repositories and as such cannot be downloaded. Do you trust this repository
and wish to add it to the trusted list of repositories (y/N)?

Write y and press ENTER.

7. Save Videos

After processing, the YOLOv5 detection results will be saved in the VideosYoloDetect directory.

Input Video Path: Videos/{videoName}

Output Video Path: VideosYoloDetect/{videoName}_Yolo.mp4

8. Test environment Setup

After installing all dependencies and cloning the repositorieis, run the environment test script to verfy that everytihing is properly set up:

testEnvironment.py