--- 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.



Watch: Object Counting using Ultralytics YOLOv8

## 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 "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 |