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