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201 lines
10 KiB
201 lines
10 KiB
--- |
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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 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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=== "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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=== "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 incounts only on video frame | |
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| view_out_counts | `bool` | `True` | Display outcounts 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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