ultralytics/docs/en/guides/region-counting.md

comments	description	keywords
true	Object Counting in Different Region using Ultralytics YOLOv8	Ultralytics, YOLOv8, Object Detection, Object Counting, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK

Object Counting in Different Regions using Ultralytics YOLOv8 🚀

What is Object Counting in Regions?

Object counting in regions with Ultralytics YOLOv8 involves precisely determining the number of objects within specified areas using advanced computer vision. This approach is valuable for optimizing processes, enhancing security, and improving efficiency in various applications.

Watch: Ultralytics YOLOv8 Object Counting in Multiple & Movable Regions

Advantages of Object Counting in Regions?

• Precision and Accuracy: Object counting in regions with advanced computer vision ensures precise and accurate counts, minimizing errors often associated with manual counting.
• Efficiency Improvement: Automated object counting enhances operational efficiency, providing real-time results and streamlining processes across different applications.
• Versatility and Application: The versatility of object counting in regions makes it applicable across various domains, from manufacturing and surveillance to traffic monitoring, contributing to its widespread utility and effectiveness.

Real World Applications

Retail	Market Streets
People Counting in Different Region using Ultralytics YOLOv8	Crowd Counting in Different Region using Ultralytics YOLOv8

Steps to Run

Step 1: Install Required Libraries

Begin by cloning the Ultralytics repository, installing dependencies, and navigating to the local directory using the provided commands in Step 2.

# Clone Ultralytics repo
git clone https://github.com/ultralytics/ultralytics

# Navigate to the local directory
cd ultralytics/examples/YOLOv8-Region-Counter

Step 2: Run Region Counting Using Ultralytics YOLOv8

Execute the following basic commands for inference.

???+ tip "Region is Movable"

During video playback, you can interactively move the region within the video by clicking and dragging using the left mouse button.

# Save results
python yolov8_region_counter.py --source "path/to/video.mp4" --save-img

# Run model on CPU
python yolov8_region_counter.py --source "path/to/video.mp4" --device cpu

# Change model file
python yolov8_region_counter.py --source "path/to/video.mp4" --weights "path/to/model.pt"

# Detect specific classes (e.g., first and third classes)
python yolov8_region_counter.py --source "path/to/video.mp4" --classes 0 2

# View results without saving
python yolov8_region_counter.py --source "path/to/video.mp4" --view-img

Optional Arguments

Name	Type	Default	Description
--source	str	None	Path to video file, for webcam 0
--line_thickness	int	2	Bounding Box thickness
--save-img	bool	False	Save the predicted video/image
--weights	str	yolov8n.pt	Weights file path
--classes	list	None	Detect specific classes i.e. --classes 0 2
--region-thickness	int	2	Region Box thickness
--track-thickness	int	2	Tracking line thickness