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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
63 lines
4.2 KiB
Markdown
63 lines
4.2 KiB
Markdown
---
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comments: true
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description: Object Counting 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 using Ultralytics YOLOv8 🚀
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## What is Object Counting?
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Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLOv8 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and deep learning capabilities.
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## Advantages of Object Counting?
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- **Resource Optimization:** Object counting facilitates efficient resource management by providing accurate counts, and optimizing resource allocation in applications like inventory management.
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- **Enhanced Security:** Object counting enhances security and surveillance by accurately tracking and counting entities, aiding in proactive threat detection.
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- **Informed Decision-Making:** Object counting offers valuable insights for decision-making, optimizing processes in retail, traffic management, and various other domains.
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## Real World Applications
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| Logistics | Aquaculture |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------:|
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| Conveyor Belt Packets Counting Using Ultralytics YOLOv8 | Fish Counting in Sea using Ultralytics YOLOv8 |
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## Example
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```python
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from ultralytics import YOLO
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from ultralytics.solutions import object_counter
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import cv2
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model = YOLO("yolov8n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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counter = object_counter.ObjectCounter() # Init Object Counter
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region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
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counter.set_args(view_img=True, reg_pts=region_points,
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classes_names=model.names, draw_tracks=True)
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while cap.isOpened():
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success, frame = cap.read()
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if not success:
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exit(0)
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tracks = model.track(frame, persist=True, show=False)
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counter.start_counting(frame, tracks)
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```
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???+ tip "Region is Movable"
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You can move the region anywhere in the frame by clicking on its edges
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### Optional Arguments `set_args`
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| Name | Type | Default | Description |
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|-----------------|---------|--------------------------------------------------|---------------------------------------|
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| view_img | `bool` | `False` | Display the frame with counts |
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| line_thickness | `int` | `2` | Increase the thickness of count value |
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| reg_pts | `list` | `(20, 400), (1080, 404), (1080, 360), (20, 360)` | Region Area Points |
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| classes_names | `dict` | `model.model.names` | Classes Names Dict |
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| region_color | `tuple` | `(0, 255, 0)` | Region Area Color |
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| track_thickness | `int` | `2` | Tracking line thickness |
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