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docs/en/guides/object-counting.md
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docs/en/guides/object-counting.md
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---
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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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<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/Ag2e-5_NpS0"
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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> Object Counting using Ultralytics YOLOv8
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</p>
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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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|  |  |
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| Conveyor Belt Packets Counting Using Ultralytics YOLOv8 | Fish Counting in Sea using Ultralytics YOLOv8 |
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!!! Example "Object Counting using YOLOv8 Example"
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=== "Count in Region"
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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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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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# Define region points
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region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
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# Video writer
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video_writer = cv2.VideoWriter("object_counting_output.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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fps,
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(w, h))
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# Init Object Counter
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counter = object_counter.ObjectCounter()
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counter.set_args(view_img=True,
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reg_pts=region_points,
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classes_names=model.names,
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draw_tracks=True)
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False)
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im0 = counter.start_counting(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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=== "Count in Polygon"
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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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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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# Define region points as a polygon with 5 points
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region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360), (20, 400)]
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# Video writer
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video_writer = cv2.VideoWriter("object_counting_output.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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fps,
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(w, h))
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# Init Object Counter
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counter = object_counter.ObjectCounter()
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counter.set_args(view_img=True,
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reg_pts=region_points,
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classes_names=model.names,
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draw_tracks=True)
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False)
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im0 = counter.start_counting(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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=== "Count in Line"
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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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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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# Define line points
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line_points = [(20, 400), (1080, 400)]
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# Video writer
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video_writer = cv2.VideoWriter("object_counting_output.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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fps,
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(w, h))
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# Init Object Counter
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counter = object_counter.ObjectCounter()
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counter.set_args(view_img=True,
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reg_pts=line_points,
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classes_names=model.names,
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draw_tracks=True)
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False)
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im0 = counter.start_counting(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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=== "Specific Classes"
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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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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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line_points = [(20, 400), (1080, 400)] # line or region points
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classes_to_count = [0, 2] # person and car classes for count
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# Video writer
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video_writer = cv2.VideoWriter("object_counting_output.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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fps,
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(w, h))
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# Init Object Counter
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counter = object_counter.ObjectCounter()
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counter.set_args(view_img=True,
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reg_pts=line_points,
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classes_names=model.names,
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draw_tracks=True)
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False,
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classes=classes_to_count)
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im0 = counter.start_counting(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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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 frames with counts |
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| `view_in_counts` | `bool` | `True` | Display in-counts only on video frame |
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| `view_out_counts` | `bool` | `True` | Display out-counts only on video frame |
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| `line_thickness` | `int` | `2` | Increase bounding boxes thickness |
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| `reg_pts` | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
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| `classes_names` | `dict` | `model.model.names` | Dictionary of Class Names |
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| `region_color` | `RGB Color` | `(255, 0, 255)` | Color of the Object counting Region or Line |
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| `track_thickness` | `int` | `2` | Thickness of Tracking Lines |
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| `draw_tracks` | `bool` | `False` | Enable drawing Track lines |
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| `track_color` | `RGB Color` | `(0, 255, 0)` | Color for each track line |
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| `line_dist_thresh` | `int` | `15` | Euclidean Distance threshold for line counter |
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| `count_txt_thickness` | `int` | `2` | Thickness of Object counts text |
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| `count_txt_color` | `RGB Color` | `(0, 0, 0)` | Foreground color for Object counts text |
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| `count_color` | `RGB Color` | `(255, 255, 255)` | Background color for Object counts text |
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| `region_thickness` | `int` | `5` | Thickness for object counter region or line |
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### Arguments `model.track`
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| Name | Type | Default | Description |
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|-----------|---------|----------------|-------------------------------------------------------------|
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| `source` | `im0` | `None` | source directory for images or videos |
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| `persist` | `bool` | `False` | persisting tracks between frames |
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| `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' |
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| `conf` | `float` | `0.3` | Confidence Threshold |
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| `iou` | `float` | `0.5` | IOU Threshold |
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| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
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| `verbose` | `bool` | `True` | Display the object tracking results |
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