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---
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comments: true
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description: Object Counting in Different Region using Ultralytics YOLOv8
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keywords: Ultralytics, YOLOv8, Object Detection, Object Counting, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
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---
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# Object Counting in Different Regions using Ultralytics YOLOv8 🚀
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## What is Object Counting in Regions?
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[Object counting](https://docs.ultralytics.com/guides/object-counting/) in regions with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) 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.
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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/okItf1iHlV8"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> Ultralytics YOLOv8 Object Counting in Multiple & Movable Regions
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</p>
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## Advantages of Object Counting in Regions?
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- **Precision and Accuracy:** Object counting in regions with advanced computer vision ensures precise and accurate counts, minimizing errors often associated with manual counting.
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- **Efficiency Improvement:** Automated object counting enhances operational efficiency, providing real-time results and streamlining processes across different applications.
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- **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.
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## Real World Applications
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| Retail | Market Streets |
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|:------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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|  |  |
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| People Counting in Different Region using Ultralytics YOLOv8 | Crowd Counting in Different Region using Ultralytics YOLOv8 |
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## Steps to Run
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### Step 1: Install Required Libraries
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Begin by cloning the Ultralytics repository, installing dependencies, and navigating to the local directory using the provided commands in Step 2.
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```bash
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# Clone Ultralytics repo
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git clone https://github.com/ultralytics/ultralytics
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# Navigate to the local directory
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cd ultralytics/examples/YOLOv8-Region-Counter
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```
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### Step 2: Run Region Counting Using Ultralytics YOLOv8
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Execute the following basic commands for inference.
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???+ tip "Region is Movable"
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During video playback, you can interactively move the region within the video by clicking and dragging using the left mouse button.
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```bash
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# Save results
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python yolov8_region_counter.py --source "path/to/video.mp4" --save-img
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# Run model on CPU
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python yolov8_region_counter.py --source "path/to/video.mp4" --device cpu
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# Change model file
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python yolov8_region_counter.py --source "path/to/video.mp4" --weights "path/to/model.pt"
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# Detect specific classes (e.g., first and third classes)
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python yolov8_region_counter.py --source "path/to/video.mp4" --classes 0 2
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# View results without saving
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python yolov8_region_counter.py --source "path/to/video.mp4" --view-img
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```
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### Optional Arguments
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| Name | Type | Default | Description |
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|----------------------|--------|--------------|--------------------------------------------|
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| `--source` | `str` | `None` | Path to video file, for webcam 0 |
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| `--line_thickness` | `int` | `2` | Bounding Box thickness |
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| `--save-img` | `bool` | `False` | Save the predicted video/image |
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| `--weights` | `str` | `yolov8n.pt` | Weights file path |
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| `--classes` | `list` | `None` | Detect specific classes i.e. --classes 0 2 |
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| `--region-thickness` | `int` | `2` | Region Box thickness |
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| `--track-thickness` | `int` | `2` | Tracking line thickness |
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