---
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 | Fish Counting in Sea using Ultralytics YOLOv8 |
!!! Example "Object Counting Example"
=== "Object Counting"
```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"
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.names,
draw_tracks=True)
while cap.isOpened():
success, im0 = cap.read()
if not success:
exit(0)
tracks = model.track(im0, persist=True, show=False)
im0 = counter.start_counting(im0, tracks)
```
=== "Object Counting with 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"
classes_to_count = [0, 2]
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.names,
draw_tracks=True)
while cap.isOpened():
success, im0 = cap.read()
if not success:
exit(0)
tracks = model.track(im0, persist=True,
show=False,
classes=classes_to_count)
im0 = counter.start_counting(im0, tracks)
```
=== "Object Counting with Save Output"
```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"
video_writer = cv2.VideoWriter("object_counting.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
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.names,
draw_tracks=True)
while cap.isOpened():
success, im0 = cap.read()
if not success:
exit(0)
tracks = model.track(im0, persist=True, show=False)
im0 = counter.start_counting(im0, tracks)
video_writer.write(im0)
video_writer.release()
```
???+ 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 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 |
| draw_tracks | `bool` | `False` | Draw Tracks lines |
### 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] |
