Add real-world projects in Ultralytics + guides in Docs (#6695)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2023-12-02 03:20:14 +05:00 · 2023-12-02 03:20:14 +05:00 · 8c4094e7d9
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+---
+comments: true
+description: Object Counting Using Ultralytics YOLOv8
+keywords: Ultralytics, YOLOv8, Object Detection, Object Counting, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
+---
+
+# Object Counting using Ultralytics YOLOv8 🚀
+
+## What is Object Counting?
+
+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.
+
+## Advantages of Object Counting?
+
+- **Resource Optimization:** Object counting facilitates efficient resource management by providing accurate counts, and optimizing resource allocation in applications like inventory management.
+- **Enhanced Security:** Object counting enhances security and surveillance by accurately tracking and counting entities, aiding in proactive threat detection.
+- **Informed Decision-Making:** Object counting offers valuable insights for decision-making, optimizing processes in retail, traffic management, and various other domains.
+
+## Real World Applications
+
+|                                                                           Logistics                                                                           |                                                                     Aquaculture                                                                     |
+|:-------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------:|
+| ![Conveyor Belt Packets Counting Using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/70e2d106-510c-4c6c-a57a-d34a765aa757) | ![Fish Counting in Sea using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/c60d047b-3837-435f-8d29-bb9fc95d2191) |
+|                                                    Conveyor Belt Packets Counting Using Ultralytics YOLOv8                                                    |                                                    Fish Counting in Sea using Ultralytics YOLOv8                                                    |
+
+## Example
+
+```python
+from ultralytics import YOLO
+from ultralytics.solutions import object_counter
+import cv2
+
+model = YOLO("yolov8n.pt")
+cap = cv2.VideoCapture("path/to/video/file.mp4")
+
+counter = object_counter.ObjectCounter() # Init Object Counter
+region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
+counter.set_args(view_img=True, reg_pts=region_points,
+                 classes_names=model.model.names, draw_tracks=True)
+
+while cap.isOpened():
+    success, frame = cap.read()
+    if not success:
+        exit(0)
+    tracks = model.track(frame, persist=True, show=False)
+    counter.start_counting(frame, tracks)
+```
+
+???+ tip "Region is Moveable"
+
+    You can move the region anywhere in the frame by clicking on its edges
+
+### Optional Arguments `set_args`
+
+| Name            | Type    | Default                                          | Description                           |
+|-----------------|---------|--------------------------------------------------|---------------------------------------|
+| view_img        | `bool`  | `False`                                          | Display the frame with counts         |
+| line_thickness  | `int`   | `2`                                              | Increase the thickness of count value |
+| reg_pts         | `list`  | `(20, 400), (1080, 404), (1080, 360), (20, 360)` | Region Area Points                    |
+| classes_names   | `dict`  | `model.model.names`                              | Classes Names Dict                    |
+| region_color    | `tuple` | `(0, 255, 0)`                                    | Region Area Color                     |
+| track_thickness | `int`   | `2`                                              | Tracking line thickness               |