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comments | description | keywords |
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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 |