Mycelium Clamp Connection (CC) and Hyphae Autolysis(HA) Detection under Microscopy Imaging with CCHA YOLO and Streamlit

In order to enable the practical application of the CCHA YOLO model, this has been deployed.

Tracking With Object Detection Demo

https://github.com/lililibin2022/CCHA-YOLO/blob/main/demo%20video.mp4

Demo Pics

Home page

Test on video

Test on uploaded images

## Demo WebApp

This app is up and running on Streamlit cloud server!!! Thanks 'Streamlit' for the community support for the cloud upload. You can check the demo of this web application on the link below. Special thanks to the original developer 'CodingMantras(rs_punia)'!!

yolov8-streamlit-detection-tracking-webapp

Requirements

Python 3.6+ YOLOv8 Streamlit

pip install ultralytics streamlit pytube

Installation

• Clone the repository: git clone https://github.com/CodingMantras/yolov8-streamlit-detection-tracking.git
• Change to the repository directory: cd yolov8-streamlit-detection-tracking
• Create weights, videos, and images directories inside the project.
• Download the pre-trained YOLOv8 weights from (https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) and save them to the weights directory in the same project.

Usage

• Run the app with the following command: streamlit run app.py
• The app should open in a new browser window.

ML Model Config

• Select task (Detection, Segmentation)
• Select model confidence
• Use the slider to adjust the confidence threshold (25-100) for the model.

One the model config is done, select a source.

Detection on images

• The default image with its objects-detected image is displayed on the main page.
• Select a source. (radio button selection Image).
• Upload an image by clicking on the "Browse files" button.
• Click the "Detect Objects" button to run the object detection algorithm on the uploaded image with the selected confidence threshold.
• The resulting image with objects detected will be displayed on the page. Click the "Download Image" button to download the image.("If save image to download" is selected)

Detection in Videos

• Create a folder with name videos in the same directory
• Dump your videos in this folder
• In settings.py edit the following lines.

# video
VIDEO_DIR = ROOT / 'videos' # After creating the videos folder

# Suppose you have four videos inside videos folder
# Edit the name of video_1, 2, 3, 4 (with the names of your video files) 
VIDEO_1_PATH = VIDEO_DIR / 'video_1.mp4' 
VIDEO_2_PATH = VIDEO_DIR / 'video_2.mp4'
VIDEO_3_PATH = VIDEO_DIR / 'video_3.mp4'
VIDEO_4_PATH = VIDEO_DIR / 'video_4.mp4'

# Edit the same names here also.
VIDEOS_DICT = {
    'video_1': VIDEO_1_PATH,
    'video_2': VIDEO_2_PATH,
    'video_3': VIDEO_3_PATH,
    'video_4': VIDEO_4_PATH,
}

# Your videos will start appearing inside streamlit webapp 'Choose a video'.

• Click on Detect Video Objects button and the selected task (detection/segmentation) will start on the selected video.

Detection on RTSP

• Select the RTSP stream button
• Enter the rtsp url inside the textbox and hit Detect Objects button

Detection on YouTube Video URL

• Select the source as YouTube
• Copy paste the url inside the text box.
• The detection/segmentation task will start on the YouTube video url

Acknowledgements

This app is based on the YOLOv8(https://github.com/ultralytics/ultralytics) object detection algorithm. The app uses the Streamlit(https://github.com/streamlit/streamlit) library for the user interface. The original developer of this application is attributed to 'CodingMantras(rs_punia)'. THANKS!!!

Disclaimer

Please note that this project is intended for educational purposes only and should not be used in production environments.