@pedmindset yes, it is possible to count objects crossing multiple regions using Ultralytics YOLO11. You can utilize the RegionCounter class to define multiple regions and track objects across them. The objects' movement through these regions can be tracked using persistent IDs, enabling you to determine which object crossed which regions. For implementation details, refer to the Region Counting Guide.

@amulyaamaggididivami Hi, can you please try reversing the region coordinates, like mentioned? I believe this will work.

region_points = [(1134, 416), (1135, 714)]

I have tried that, but it is not working

@amulyaamaggididivami, well, in that case, you can clone the ultralytics repository and build it locally with the changes in the code line here: https://github.com/ultralytics/ultralytics/blob/fa5d0bffbd63adfc302e9c118ffe5caa6447a044/ultralytics/solutions/object_counter.py#L135

Edit: Can you please also try using the region points, rather than line points?

Sure, thanks

@SudhanshuBlaze IN/OUT for line counters is image-axis based, not compass: for a mostly vertical line, IN = motion to the right (+x) and OUT = left; for a mostly horizontal line, IN = down (+y) and OUT = up; you can relabel to N/S/E/W post-process like this:

def label_dirs(results, p1, p2):
    (x1, y1), (x2, y2) = p1, p2
    vertical = abs(x1 - x2) < abs(y1 - y2)
    return ({"eastbound": results.in_count, "westbound": results.out_count} if vertical
            else {"southbound": results.in_count, "northbound": results.out_count})

Details of the IN/OUT logic are in the count_objects method of the ObjectCounter reference.

@morussk Hi, thanks for sharing the details. The counting in region feature relies on object tracking rather than standalone predictions. As a result, detections with a confidence score below 0.25 are ignored by default.

However, this behaviour can be modified by adjusting the track_high_thresh parameter. Currently, the Solutions class doesn’t support adjusting tracking configuration core options directly, but you can adjust it by cloning the code and modifying the tracker's configurations file.

Note: We always recommend using the latest version of the Ultralytics Python package. You can update it by running: pip install -U ultralytics.

Thanks.

@dimasfadhila for region-based object counting in the Ultralytics example, we use models pre-trained on the COCO dataset by default, which includes 80 common object classes. You can see the full class list in our COCO dataset documentation. If you need to count custom objects, you'll want to train on your own dataset using YOLO's training mode.

@rejens RegionCounter returns a results object, so call it on each frame and read the per-region counts from region_counts:

results = region(im0)
print(results.region_counts)  # e.g. {'Region#01': 3}
im0 = results.plot_im

The counts are for the current frame (they reset on every frame). Let me know if you need further clarification!

@Darkbutbright yes, you can customize the text information, font size and position of the count by modifying the solution source code. For ObjectCounter, the counts are drawn in its display_counts() method (in ultralytics/solutions/object_counter.py) through the SolutionAnnotator, so you can adjust the text rendering there. You can also call the solution and draw your own text on the returned plot_im with cv2.putText(), using results.in_count and results.out_count. Let me know if you need further clarification!

Thank you for your patient answer, but I really don't know where to get the detailed instructions (documents) of the solutions class. Could you please tell me how to get it (including more clear instructions for using other classes of yolo)?

@Darkbutbright thank you for your question! You can find detailed instructions and documentation for the solutions class and other YOLO functionalities in the Ultralytics Docs. For specific examples, such as region counting or object tracking, check the solutions guide or the respective sections in the repository. Let me know if you need assistance with a particular use case!

@kemalfrk to implement object counting in specific regions using mouse events, you can modify the region points dynamically by capturing mouse click events and updating the region coordinates in real-time. You can refer to the Ultralytics YOLOv8 region counting example for guidance on how to set up and use the RegionCounter. If you have further questions or need additional customization, feel free to ask!

@MuhammedEnesGurkan to count only sheep, use the --classes argument to specify the class index for sheep in your model. For example, if sheep are class 2, run: python yolov8_region_counter.py --source "path/to/video.mp4" --classes 2. Adjust the class index as needed based on your model's class mapping.