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86 lines
5.1 KiB
86 lines
5.1 KiB
--- |
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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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