针对实时视频流和静态图像实现的对象检测和跟踪算法 .zip

# <span style="color:deepskyblue"> Real-time Object Detection and Tracking with YOLOv8 & Streamlit </span> This repository is an extensive open-source project showcasing the seamless integration of **object detection and tracking** using **YOLOv8** (object detection algorithm), along with **Streamlit** (a popular Python web application framework for creating interactive web apps). The project offers a user-friendly and customizable interface designed to detect and track objects in real-time video streams from sources such as RTSP, UDP, and YouTube URLs, as well as static videos and images. ## <span style="color:deepskyblue">Explore Implementation Details on Medium (3 parts blog series) </span> For a deeper dive into the implementation, check out my three-part blog series on [Medium](https://medium.com/@mycodingmantras), where I detail the [step-by-step process of creating this web application](https://medium.com/@mycodingmantras/building-a-real-time-object-detection-and-tracking-app-with-yolov8-and-streamlit-part-1-30c56f5eb956). ## <span style="color:deepskyblue">WebApp Demo on Streamlit Server</span> Thank you team [Streamlit](<https://github.com/streamlit/streamlit>) for the community support for the cloud upload. This app is up and running on Streamlit cloud server!!! You can check the demo of this web application on this link [yolov8-streamlit-detection-tracking-webapp](https://yolov8-object-detection-and-tracking-app.streamlit.app/) **Note**: In the demo, Due to non-availability of GPUs, you may encounter slow video inferencing. ## <span style="color:deepskyblue"> Tracking With Object Detection Demo</span> <https://user-images.githubusercontent.com/104087274/234874398-75248e8c-6965-4c91-9176-622509f0ad86.mov> ## Overview <https://github.com/user-attachments/assets/85df351a-371c-47e0-91a0-a816cf468d19.mov> ## Demo Pics ### Home page <img src="https://github.com/CodingMantras/yolov8-streamlit-detection-tracking/blob/master/assets/pic1.png" > ### Page after uploading an image and object detection <img src="https://github.com/CodingMantras/yolov8-streamlit-detection-tracking/blob/master/assets/pic3.png" > ### Segmentation task on image <img src="https://github.com/CodingMantras/yolov8-streamlit-detection-tracking/blob/master/assets/segmentation.png" > ## Requirements Python 3.6+ YOLOv8 Streamlit ```bash 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. ```python # 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 <https://user-images.githubusercontent.com/104087274/226178296-684ad72a-fe5f-4589-b668-95c835cd8d8a.mov> ## Acknowledgements This app uses [YOLOv8](<https://github.com/ultralytics/ultralytics>) for object detection algorithm and [Streamlit](<https://github.com/streamlit/streamlit>) library for the user interface. ### Disclaimer This project is intended as a learning exercise and demonstration of integrating various technologies, including: - Streamlit - YoloV8 - Object-Detection on Images And Live Video Streams - Python-OpenCV Please note that this application is not designed or tested for production use. It serves as an educational resource and a showcase of technology integration rather than a production-ready web application. Contributors and users are welcome to explore, learn from, and build upon this project for educational purposes. ### Hit star ⭐ if you like this repo!!!