yolov7-object-tracking

New Features

• Added Label for Every Track
• Code can run on Both (CPU & GPU)
• Video/WebCam/External Camera/IP Stream Supported

Coming Soon

• Development of streamlit dashboard for Object Tracking

Ready to Use Google Colab

• https://colab.research.google.com/drive/1xrB76UQ_LaVaBAxfTi8-a9dIcazmxD5b?usp=sharing

Steps to run Code

1. Clone the repository.

   git clone https://github.com/RizwanMunawar/yolov7-object-tracking.git
   

2. Goto the cloned folder.

   cd yolov7-object-tracking
   

3. Create a virtual envirnoment (Recommended, If you dont want to disturb python packages)

   For Anaconda

   # Create the virtural envirnment
   conda create -n yolov7objtracking python=3.10
   
   # Activate the virtural envirnment
   conda activate yolov7objtracking
   

   For Linux Users

   # Create the virtural envirnment
   python3 -m venv yolov7objtracking
   
   # Activate the virtural envirnment
   source yolov7objtracking/bin/activate
   

   For Window Users

   # Create the virtural envirnment
   python3 -m venv yolov7objtracking
   
   # Activate the virtural envirnment
   cd yolov7objtracking
   cd Scripts
   activate
   cd ..
   cd ..
   

4. Update pip and install libraries

   # Upgrade pip with mentioned command below.
   pip install --upgrade pip
   
   # Install requirements with mentioned command below.
   pip install -r requirements.txt
   

5. Run the script

   Select the appropirate command from the following list of command according to your need. (by default, pretrained yolov7 weights will be automatically downloaded into the working directory if they don't already exist).

   # for detection only
   python detect.py --weights yolov7.pt --source "your video.mp4"
   
   #if you want to change source file
   python detect_and_track.py --weights yolov7.pt --source "your video.mp4"
   
   #for WebCam
   python detect_and_track.py --weights yolov7.pt --source 0
   
   #for External Camera
   python detect_and_track.py --weights yolov7.pt --source 1
   
   #For LiveStream (Ip Stream URL Format i.e "rtsp://username:pass@ipaddress:portno/video/video.amp")
   python detect_and_track.py --source "your IP Camera Stream URL" --device 0
   
   #for specific class (person)
   python detect_and_track.py --weights yolov7.pt --source "your video.mp4" --classes 0
   
   #for colored tracks 
   python detect_and_track.py --weights yolov7.pt --source "your video.mp4" --colored-trk
   
   #for saving tracks centroid, track id and bbox coordinates
   python detect_and_track.py --weights yolov7.pt --source "your video.mp4" --save-txt --save-bbox-dim
   

6. Output file will be created in the working-dir/runs/detect/obj-tracking with original filename.

Results

References

• https://github.com/WongKinYiu/yolov7
• https://github.com/abewley/sort

My Medium Articles

• https://muhammadrizwanmunawarvisionai.blogspot.com/2023/04/maximizing-efficiency-on-construction.html 🔥
• https://muhammadrizwanmunawarvisionai.blogspot.com/2023/03/instance-segmentation-vs-semantic.html ✅
• https://muhammadrizwanmunawarvisionai.blogspot.com/2023/03/mastering-image-classification.html 🔥
• https://muhammadrizwanmunawarvisionai.blogspot.com/2023/03/object-detection-in-agriculture.html ✅
• https://muhammadrizwanmunawarvisionai.blogspot.com/2023/03/techniques-for-accurate-data-annotation.html ✅
• https://muhammadrizwanmunawarvisionai.blogspot.com/2023/03/object-tracking-using-bytetrack.html 🔥
• https://muhammadrizwanmunawarvisionai.blogspot.com/2023/03/pose-estimation-computer-vision.html ✅
• https://medium.com/augmented-startups/yolov7-training-on-custom-data-b86d23e6623
• https://medium.com/augmented-startups/roadmap-for-computer-vision-engineer-45167b94518c
• https://medium.com/augmented-startups/yolor-or-yolov5-which-one-is-better-2f844d35e1a1
• https://medium.com/augmented-startups/train-yolor-on-custom-data-f129391bd3d6
• https://medium.com/augmented-startups/develop-an-analytics-dashboard-using-streamlit-e6282fa5e0f

For more details, you can reach out to me on Medium or can connect with me on LinkedIn