i have created a line, at 15 degree angle, if a person come down it should count as in, and if a person goes up it count as out.

• down = in
• up = out but model does not work like this, model count direction is reversed.

how can i solve this?

@faheem-khaskheli to resolve the direction issue, ensure the line's orientation is correctly defined in your code. You might need to adjust the logic that determines the direction based on the line's angle. If the model's counting direction is reversed, check the implementation of the ObjectCounter and verify the region or line points are set correctly.

@glenn-jocher is there any way i can use ultralytics solution for this problem? i have checked the code and it is only checking dx > 0 for in else out.

@faheem-khaskheli to address the reversed counting direction, adjust the logic in your code to correctly interpret the angle of the line. Ensure that the conditions for dx > 0 and dx < 0 align with the intended "in" and "out" directions based on your line's orientation.

Hey there! 👋 For multiple lines with separate counts, create one ObjectCounter per line and pass each frame to every counter. Each counter keeps its own in/out counts:

from ultralytics import solutions

line_pedestrian_1 = [(234, 244), (131, 218)]
line_pedestrian_2 = [(490, 245), (534, 231)]
line_pedestrian_3 = [(112, 189), (154, 177)]

counters = [
    solutions.ObjectCounter(region=line, model='yolo26n.pt')
    for line in (line_pedestrian_1, line_pedestrian_2, line_pedestrian_3)
]

# For each frame:
for i, counter in enumerate(counters, 1):
    results = counter(frame)
    print(f'Counter {i} - In: {results.in_count}, Out: {results.out_count}')

This way, each ObjectCounter will track objects crossing its respective line. Hope this helps! If you have further questions, feel free to ask. Happy coding! 😊

Thank you for your reply! There was a problem with counting the number of custom classes, the docstring says "classes_names (dict): Classes names", I'm trying to use class_cars = { 2.0 : 'car', 5 : 'bus', 7: 'truck' } counter3.set_args(classes_names=class_cars, reg_pts=line_pedestrian_3,region_thickness = 2) but an error occurs Traceback (most recent call last): File "/home/iosif/Documents/Dev/yolo_model/yolov8_train/yolov8_tracking_car_number/tracking_5.py", line 93, in im0 = counter3.start_counting(frame, results) File "/home/iosif/.local/lib/python3.10/site-packages/ultralytics/solutions/object_counter.py", line 259, in start_counting self.extract_and_process_tracks(tracks) File "/home/iosif/.local/lib/python3.10/site-packages/ultralytics/solutions/object_counter.py", line 172, in extract_and_process_tracks self.annotator.box_label(box, label=f"{track_id}:{self.names[cls]}", color=colors(int(cls), True)) KeyError: 0.0 When trying to get a class by index, the following error occurs Traceback (most recent call last): File "/home/iosif/Documents/Dev/yolo_model/yolov8_train/yolov8_tracking_car_number/tracking_5.py", line 87, in im0 = counter1.start_counting(frame, results) File "/home/iosif/.local/lib/python3.10/site-packages/ultralytics/solutions/object_counter.py", line 259, in start_counting self.extract_and_process_tracks(tracks) File "/home/iosif/.local/lib/python3.10/site-packages/ultralytics/solutions/object_counter.py", line 172, in extract_and_process_tracks self.annotator.box_label(box, label=f"{track_id}:{self.names[cls]}", color=colors(int(cls), True)) TypeError: string indices must be integers Question: How can I select the class that I want to count?

Hi there! 👋 In current versions ObjectCounter no longer takes classes_names or set_args(); class names come from the model, so that KeyError can't occur. To count only specific classes, pass their indices with classes:

from ultralytics import solutions

counter3 = solutions.ObjectCounter(region=line_pedestrian_3, model='yolo26n.pt', classes=[2, 5, 7], line_width=2)
results = counter3(im0)
print(results.classwise_count)  # e.g. {'car': {'IN': 1, 'OUT': 0}}

This should resolve the errors you're seeing. Let me know if you need any more help! 😊

This will work to limit the total number of detectable classes, but it does not solve the problem of counting in separate areas of a single class (2 areas to count only cars and buses and 2 areas for pedestrians). An example from the code (Now I will describe why it happens) I specified a limited list of classes for tracking and when cls =17 is detected in tracking, the program crashes because this class is not specified in counter.set_args. How can I get around this limitation so that I can specify different classes for counting different areas (as I said in the example 2 only for cars, 2 only for people).

class_list_common = [0,2,5,7,17]
 all_dict_dict = { 0 : 'person', 2: 'car', 5: 'bus',7: 'truck'}
counter1.set_args(classes_names=all_dict_dict,reg_pts=line_pedestrian_1,region_thickness = 2,line_dist_thresh=8,draw_tracks=True)
results = model.track(frame,classes=class_list_common)

Traceback (most recent call last): File "/home/iosif/Documents/Dev/yolo_model/yolov8_train/yolov8_tracking_car_number/tracking_5.py", line 140, in im0 = counter1.start_counting(frame, results) File "/home/iosif/.local/lib/python3.10/site-packages/ultralytics/solutions/object_counter.py", line 259, in start_counting self.extract_and_process_tracks(tracks) File "/home/iosif/.local/lib/python3.10/site-packages/ultralytics/solutions/object_counter.py", line 172, in extract_and_process_tracks self.annotator.box_label(box, label=f"{track_id}:{self.names[cls]}", color=colors(int(cls), True)) KeyError: 17.0

Hey there! 👋 To count different classes in separate areas, create multiple ObjectCounter instances, each with its own region and classes list. Class names come from the model automatically, and classwise_count gives the counts per class:

from ultralytics import solutions

counter_cars_buses = solutions.ObjectCounter(model='yolo26n.pt', region=line_cars_buses, classes=[2, 5])
counter_pedestrians = solutions.ObjectCounter(model='yolo26n.pt', region=line_pedestrians, classes=[0])

# For each frame:
im0 = counter_cars_buses(frame).plot_im
im0 = counter_pedestrians(frame).plot_im

This way, each counter will only count the classes it's set up for. Happy counting! 😊

hello there, how about if i want my line just for in count not for out count for example i have line_pedestrian_1 = [(0,244),(131,244)] ,if all people touch this line count in will increment, because what I know so far is that the line is divided into 2 parts, one for counting in and one for counting out

Hi there! 👋 ObjectCounter reports in_count and out_count separately for a line, based on the direction in which tracked objects cross it. If you only need one direction, read just that value and ignore the other:

from ultralytics import solutions

counter = solutions.ObjectCounter(region=line_pedestrian_1, model='yolo26n.pt')
results = counter(frame)
print(results.in_count)  # only objects crossing in the 'in' direction

Happy coding! 😊

can you gimmi example please

Hello! Sure, I'd be happy to provide an example for setting up object counting with Ultralytics YOLO. Let's suppose you want to count cars crossing a line. Here's how you could set this up:

import cv2

from ultralytics import solutions

# Open video file
cap = cv2.VideoCapture('path/to/your/video.mp4')
assert cap.isOpened(), 'Error loading video'

# Define the line points for counting
line_points = [(20, 400), (1080, 400)]  # Adjust these points to fit your line

# Initialize Object Counter; classes=[2] counts only cars in the COCO classes
counter = solutions.ObjectCounter(model='yolo26n.pt', region=line_points, classes=[2])

# Process video and start counting
while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break  # End of video

    # Perform tracking and counting
    results = counter(frame)

    # Optionally view the frame with counts
    cv2.imshow('Object Counting', results.plot_im)
    if cv2.waitKey(1) & 0xFF == 27:  # press 'ESC' to exit
        break

# Release resources
cap.release()
cv2.destroyAllWindows()

💡 Important:

• Adjust line_points to position your counting line in the video frame.
• Use classes with the model's class indices to count only specific object types.

Objects crossing the line in one direction increase results.in_count and in the opposite direction results.out_count. For multiple counting areas, create one counter per line or region.

Hope this helps get you started! Let me know if there's anything else you'd like to explore. 😄

why line point not show in frame?

@syariffortask hey! If your line is not appearing on the frame, there could be a couple of things to check. Make sure show=True is set when initializing your ObjectCounter (or display results.plot_im yourself), and verify that the region points are correctly defined and fall within the frame's dimensions. Here's a quick example:

from ultralytics import solutions

counter = solutions.ObjectCounter(
    show=True,
    region=[(20, 400), (1080, 400)],  # Make sure these points are within your frame's dimensions
    model='yolo26n.pt',
)

Make sure your setup matches your video frame size, and adjust region as necessary. This adjustment should help you visualize the counting line directly on your frames! 😊 Let me know if this does the trick!

glenn tell me how you can do that? are you update class ObjectCounter logic in new version ultralytics?

Hey! 👋 Thanks for reaching out. Yes, we're constantly working on enhancing the ObjectCounter logic. For counting objects crossing a specific line, here's a quick example based on your requirement:

import cv2

from ultralytics import solutions

# Set up your video source
cap = cv2.VideoCapture('path/to/video.mp4')

# Initialize Object Counter for your line
line_pedestrian_1 = [(0, 244), (131, 244)]  # Define as per your need
counter = solutions.ObjectCounter(show=True, region=line_pedestrian_1, model='yolo26n.pt', classes=[0])

while cap.isOpened():
    ret, im0 = cap.read()
    if not ret:
        break

    # Track objects and count crossings of your line
    results = counter(im0)

    # Display
    cv2.imshow('Count', results.plot_im)
    if cv2.waitKey(1) == ord('q'):  # press q to quit
        break

cap.release()
cv2.destroyAllWindows()

This sets up an object counting system for pedestrians crossing line_pedestrian_1. ObjectCounter reports results.in_count and results.out_count separately based on the direction of crossing, so you can use only the one you need. Keep up with our GitHub releases for the latest features! 🚀 Let me know if there's anything else I can help with.

Thanks for answer my question, But Can ultralytics predict and count two frame/ two vidio source ?

@syariffortask hey! Thanks for reaching out with your question. 😊 Yes, YOLO can definitely work with two video sources simultaneously, but it would require running two instances of the video processing loop, each in its own thread or process, due to the way the code execution works.

Here's a simplified example using Python's threading module to process two videos at the same time:

from threading import Thread

import cv2

from ultralytics import YOLO


def process_video(video_path):
    model = YOLO('yolo26n.pt')
    cap = cv2.VideoCapture(video_path)
    assert cap.isOpened(), 'Error reading video file'

    while cap.isOpened():
        success, frame = cap.read()
        if not success:
            break
        results = model.track(frame, persist=True)

        # Here you could use a solutions.ObjectCounter per video
        # For example: im0 = counter(frame).plot_im
        # And then save or display im0

    cap.release()


# Define your video sources
video_1 = 'path/to/your/first_video.mp4'
video_2 = 'path/to/your/second_video.mp4'

# Start processing each video in a separate thread
Thread(target=process_video, args=(video_1,)).start()
Thread(target=process_video, args=(video_2,)).start()

Remember, depending on your system, handling two video streams simultaneously can be resource-intensive. Adjust the frame processing rate if necessary to manage system load. Hope that helps! Let me know if you have more questions. 🚀

@phoenix-JM thanks for reaching out! ObjectCounter handles OBB models natively, so you can pass an OBB model directly (e.g. model="yolo26n-obb.pt") and no special initialization is needed; please make sure you're using the latest Ultralytics version. If you need the raw OBB coordinates for custom plotting, they are available as counter.boxes after the call (or results.obb.xyxyxyxy when running a model directly). For new projects we recommend the YOLO26-OBB models as shown in our Object Counting documentation. Let us know if you need further guidance! 🚀

@phoenix-JM Hi, thanks for the question. Indeed, visualization for OBB is not supported yet. you can only plot the HBB. I will work on this feature this week. Thanks

Thank you for your reply. @glenn-Jocher can you please provide me the code snippet to do so.

@RIzwanMunawar Hello ! is there any update regarding object counting and tracking using OBB. ?

@phoenix-JM Hi, yes, we already added the support for OBB object counting. The code is the same, no changes. Here's the demo. If you want to take a look: https://www.linkedin.com/feed/update/urn:li:activity:7327964827048411136/

@mirfan-ahmad Hi, Thanks for reaching out. We currently don't support the ObjectCounting functionality for RT-DETR! At the moment we only support the Ultralytics YOLO models for Object Counting available here: https://docs.ultralytics.com/models/. Thank you!

@SourceOfNootmite Hi! Yes, you can use any supported model for object counting, including YOLOv10, Ultralytics YOLO11, and Ultralytics YOLOv8. You can also use any model we support. If you have a YOLO11 model trained on your dataset, it will work seamlessly with the Object Counting module.

However, this feature is not currently supported if you're referring to a completely new model architecture. Thanks!

@Martinnemo Hi, the implementation for object counting is available at: https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/object_counter.py

Thank you! 😊

I mean, i following code, how do i apply the confidence function:

import cv2

from ultralytics import solutions

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, 400)] # For line counting

region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)] # For rectangle region counting

region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360), (20, 400)] # For polygon region counting

Video writer

video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

Init Object Counter

counter = solutions.ObjectCounter( show=True, # Display the output region=region_points, # Pass region points model="yolo11n.pt", # model="yolo11n-obb.pt" for object counting using YOLO11 OBB model. # classes=[0, 2], # If you want to count specific classes i.e person and car with COCO pretrained model. # show_in=True, # Display in counts # show_out=True, # Display out counts # line_width=2, # Adjust the line width for bounding boxes and text display )

Process video

while cap.isOpened(): success, im0 = cap.read() if not success: print("Video frame is empty or video processing has been successfully completed.") break im0 = counter.count(im0) video_writer.write(im0)

cap.release() video_writer.release() cv2.destroyAllWindows()

To apply the confidence threshold (conf) to filter detections, you can pass it as a parameter when initializing the ObjectCounter. Here's an example:

counter = solutions.ObjectCounter(
    show=True,
    region=region_points,
    model="yolo26n.pt",
    conf=0.5  # Adjust the confidence threshold as needed
)

This will only count objects with a detection confidence score above the specified threshold (0.5 in this case). Let me know if you need further clarification!

@g-i-o-r-g-i-o to achieve object tracking and counting with YOLO and ByteTrack, you can use the following minimal code setup. This will allow you to get the coordinates and IDs of detected objects and apply a geometric condition:

import cv2
from ultralytics import YOLO

# Initialize model and video capture
model = YOLO('yolo26n.pt')
cap = cv2.VideoCapture('path/to/video.mp4')

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        break

    # Run detection and tracking
    results = model.track(frame, persist=True, tracker='bytetrack.yaml')
    
    # Process results
    for r in results:
        for box in r.boxes:
            x1, y1, x2, y2 = box.xyxy[0].tolist()
            obj_id = int(box.id) if box.id is not None else None
            # Apply geometric condition here, e.g., check if behind a line

    # Display or save results
    cv2.imshow('Frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

For more detailed guidance, please visit the object counting guide.

@g-i-o-r-g-i-o i'm sorry to hear about the issues you're facing. Please ensure that the region and line points are correctly defined in your setup. For further guidance, you can refer to our object counting documentation at https://docs.ultralytics.com/guides/object-counting/. If the problem persists, consider checking the model configuration and input parameters.

@GenieV19 hello!

Thank you for your question and for providing detailed information. It sounds like you're experiencing a discrepancy between the number of detected objects and the counts displayed. Let's address this step-by-step:

1. Tracking IDs vs. Bounding Boxes: The IDs displayed are unique identifiers assigned to each tracked object. These IDs can appear higher than the number of visible bounding boxes due to objects entering and exiting the frame, or being temporarily occluded and then re-detected. This can lead to new IDs being assigned.

2. Counting Logic: The 'in' and 'out' counts are based on tracked objects crossing the line or entering the region, and each track ID is counted once. Ensure that your region is set correctly and that the counting logic aligns with your expectations.

3. Unique Object Counting: To get a final count of unique objects, you can collect the track IDs over the entire video. Here's a simple example:

import cv2

from ultralytics import solutions

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)]


unique_ids = set()

# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init Object Counter
counter = solutions.ObjectCounter(
    show=True,
    region=region_points,
    model="yolo26n.pt",
)

while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or processing is complete.")
        break

    results = counter(im0)
    unique_ids.update(counter.track_ids)  # track IDs in this frame
    video_writer.write(results.plot_im)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

After the loop, print the result:

print(f"Total unique objects detected: {len(unique_ids)}")

1. Ensure Latest Versions: Please ensure you are using the latest versions of torch and ultralytics. You can upgrade them using:

   pip install --upgrade torch ultralytics

If the issue persists, please provide a minimum reproducible example as outlined here. This will help us investigate further.

Feel free to reach out if you have any more questions! 😊

maybe i can edit botsort.yaml, what do you think ?

and do you why the tracking is better when count in line than when count in region ?

Hello,

Thank you for reaching out! Let's address your concerns one by one.

Disappearing Detections

The issue of disappearing detections can often be attributed to the confidence threshold or the tracking algorithm's parameters. You can experiment with the conf and iou parameters (they can be passed straight to ObjectCounter, which runs the tracker) to see if adjusting these values helps retain more detections. For example, lowering the confidence threshold might help:

counter = solutions.ObjectCounter(region=line_points, model="best.pt", tracker="botsort.yaml", conf=0.3, iou=0.4)

Reallocation of Tracking Identifiers

Reallocation of tracking IDs can indeed be problematic. Modifying the BoT-SORT algorithm to maintain the same identifiers for a fixed number of frames is a feasible approach. However, this requires a deeper dive into the BoT-SORT implementation. You can start by editing the botsort.yaml configuration file to tweak parameters like track_high_thresh and track_low_thresh.

Editing botsort.yaml

You can modify the botsort.yaml file to adjust the tracking parameters. Here’s an example of how you might adjust the track_high_thresh to ensure that IDs are not reallocated too frequently:

# botsort.yaml
track_high_thresh: 0.6  # Increase this value to make the tracker more confident before reallocating IDs
track_low_thresh: 0.1   # Lower this value to keep tracks alive longer

Example Code

Here’s an updated version of your code with some adjustments:

import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("test_26_50mm.avi")
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 = [(2000, 0), (2000, 2500)]

# Video writer
video_writer = cv2.VideoWriter("results/output_bar_counting.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init Object Counter
counter = solutions.ObjectCounter(
    show=False,
    region=line_points,
    model=r"model/best.pt",
    tracker="botsort.yaml",
    conf=0.3,
    iou=0.4,
    line_width=5,
    show_out=False,
)

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break

    results = counter(frame)
    video_writer.write(results.plot_im)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

Further Customization

For more advanced customization, you might need to dive into the BoT-SORT source code. If you're comfortable with Python, you can clone the BoT-SORT repository, make your changes, and then use your modified version with Ultralytics YOLO.

Additional Resources

For more detailed guidance on object counting and tracking, you can refer to our Object Counting Guide. This guide provides comprehensive information on setting up and optimizing object counting with Ultralytics YOLO.

Feel free to reach out if you have any more questions or need further assistance. Happy coding! 😊

Thank you very much for your advice, I had already tried to modify the conf and iou parameters as well as the botsort.yaml file. However, it still doesn't work. I think I'm asking too much of the program, because the gray sections are very close together and the camera only records at 24fps. Thanks anyway! Or do you have other trackers to recommend?

Maybe I could do some tracking in a cropped area, what do you think about that ?

@TanrionTV hello,

Thank you for your thoughtful question! Cropping the area for tracking can indeed be a useful strategy, especially when dealing with closely packed objects. By focusing on a smaller region, you can potentially improve the accuracy and consistency of the tracking.

Here’s how you can implement cropping in your existing code:

1. Crop the Frame: Before passing the frame to the counter, crop it to the region of interest.
2. Use Cropped Coordinates: ObjectCounter tracks the image you pass it, so define its line or region in the crop's coordinates and paste the annotated crop back into the full frame if needed.

Here’s an example of how you might modify your code to include cropping:

import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("test_26_50mm.avi")
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 crop region (x, y, width, height)
x, y, cw, ch = (500, 500, 1000, 1000)

# Line points are given in cropped-frame coordinates (here a vertical line in the middle of the crop)
line_points = [(500, 0), (500, 1000)]

# Video writer
video_writer = cv2.VideoWriter("results/output_bar_counting.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init Object Counter
counter = solutions.ObjectCounter(
    show=False,
    region=line_points,
    model=r"model/best.pt",
    tracker="botsort.yaml",
    conf=0.3,
    iou=0.4,
    line_width=5,
    show_out=False,
)

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break

    # Crop, count inside the crop, then paste the annotated crop back into the full frame
    results = counter(frame[y : y + ch, x : x + cw])
    frame[y : y + ch, x : x + cw] = results.plot_im
    video_writer.write(frame)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

Alternative Trackers

If BoT-SORT is not meeting your needs, you might want to try ByteTrack, which is another robust tracker supported by Ultralytics. You can switch to ByteTrack by changing the tracker parameter:

counter = solutions.ObjectCounter(region=line_points, model="model/best.pt", tracker="bytetrack.yaml", conf=0.3, iou=0.4)

Additional Tips

• Frame Rate: Given that your camera records at 24fps, ensure that your model and tracker can handle this frame rate efficiently. Sometimes, reducing the frame rate can help improve tracking stability.
• Experiment with Parameters: Continue experimenting with different conf and iou values, as well as tracker-specific parameters in the YAML files.

For more detailed guidance on object counting and tracking, you can refer to our Object Counting Guide.

Feel free to reach out if you have any more questions or need further assistance. Happy coding! 😊

thank you ! but i have this error : track.boxes.xyxy[:, [0, 2]] += x ~~~~~~~~~~~~~~~~^^^^^^^^^^^ RuntimeError: Inplace update to inference tensor outside InferenceMode is not allowed.You can make a clone to get a normal tensor before doing inplace update.See https://github.com/pytorch/rfcs/pull/17 for more details.

Hello,

Thank you for your detailed feedback and for sharing your code! 😊

The inplace tensor update error comes from editing the tracked boxes after inference. In current versions you don't need to: ObjectCounter runs the tracker itself, so you pass it the cropped image with the line defined in the crop's coordinates and paste the annotated crop back. Here’s how you can modify your code:

import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("test_26_50mm.avi")
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 crop region (x, y, width, height)
x, y, cw, ch = (500, 500, 1000, 1000)

# Line points are given in cropped-frame coordinates (here a vertical line in the middle of the crop)
line_points = [(500, 0), (500, 1000)]

# Video writer
video_writer = cv2.VideoWriter("results/output_bar_counting.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init Object Counter
counter = solutions.ObjectCounter(
    show=False,
    region=line_points,
    model=r"model/best.pt",
    tracker="botsort.yaml",
    conf=0.3,
    iou=0.4,
    line_width=5,
    show_out=False,
)

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break

    # Crop, count inside the crop, then paste the annotated crop back into the full frame
    results = counter(frame[y : y + ch, x : x + cw])
    frame[y : y + ch, x : x + cw] = results.plot_im
    video_writer.write(frame)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

Alternative Trackers

If BoT-SORT is not meeting your needs, you might want to try ByteTrack, which is another robust tracker supported by Ultralytics. You can switch to ByteTrack by changing the tracker parameter:

counter = solutions.ObjectCounter(region=line_points, model="model/best.pt", tracker="bytetrack.yaml", conf=0.3, iou=0.4)

Additional Tips

• Frame Rate: Given that your camera records at 24fps, ensure that your model and tracker can handle this frame rate efficiently. Sometimes, reducing the frame rate can help improve tracking stability.
• Experiment with Parameters: Continue experimenting with different conf and iou values, as well as tracker-specific parameters in the YAML files.

For more detailed guidance on object counting and tracking, you can refer to our Object Counting Guide.

Feel free to reach out if you have any more questions or need further assistance. Happy coding! 😊

Thank you for your help! Your fix gives me this error:

    track.boxes.xyxy = track.boxes.xyxy.clone() # Clone the tensor to avoid the inplace update error
    ^^^^^^^^^^^^^^^^
AttributeError : the ‘xyxy’ property of the ‘Boxes’ object has no setter

but I managed to fix it with the following patch:

    # Track the cropped image
    tracks1 = model.track(frame, persist=True, show=False, tracker = ‘bytetrack.yaml’, conf = 0.2,iou=0.4, vid_stride=1, augment=True, retina_masks=False)


    # Adjust the coordinates to the original image
    tracks = deepcopy(tracks1)
    for track in tracks :
        track.boxes.xyxy[ :, [0, 2]] += x
        track.boxes.xyxy[ :, [1, 3]] += y += y

However, even when cropping the image, the tracker reallocates the old identifiers to the new objects. I've also tried using bytetrack by adjusting the parameters but the results are even worse. I'm starting to run out of ideas. Is it possible to send you the video (20mo), maybe you'll see it more clearly than I do.

Thank you very much for your advice!

I've added you to my "bar_counting" project on github so you can see the video if you want

Hello,

Thank you for your detailed explanation and for sharing your code! It's great to see your dedication to solving this issue.

Regarding your current challenges with tracking and identifier reallocation, it seems like you’ve already tried several approaches, including modifying the conf and iou parameters and experimenting with different trackers. Your idea of cropping the image is a good one, and it’s excellent that you managed to fix the tensor update issue.

Further Suggestions

1. Adjusting Tracker Parameters: Since you’ve already tried adjusting the conf and iou parameters, another approach could be to fine-tune other parameters in the botsort.yaml or bytetrack.yaml configuration files. For example, you can try increasing the track_high_thresh and track_low_thresh values to make the tracker more conservative in reassigning IDs.

2. Using a Different Tracker: If BoT-SORT and ByteTrack are not yielding satisfactory results, you might want to explore other tracking algorithms. While Ultralytics primarily supports BoT-SORT and ByteTrack, you can integrate other trackers with custom code.

3. Frame Rate and Resolution: Given that your camera records at 24fps, you might want to experiment with reducing the frame rate or resolution to see if it improves tracking stability. Sometimes, processing fewer frames can help the tracker maintain consistent IDs.

Sharing the Video

If you’re comfortable sharing the video, it would indeed help in diagnosing the issue more accurately. You can upload the video to a cloud storage service (like Google Drive or Dropbox) and share the link here. This way, we can take a closer look and provide more targeted advice.

Example Code with Cropping

In current versions ObjectCounter runs the tracker itself, so you can pass it the cropped image directly with the line defined in the crop's coordinates, with no tensor cloning needed:

import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("test_26_50mm.avi")
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 crop region (x, y, width, height)
x, y, cw, ch = (500, 500, 1000, 1000)

# Line points are given in cropped-frame coordinates (here a vertical line in the middle of the crop)
line_points = [(500, 0), (500, 1000)]

# Video writer
video_writer = cv2.VideoWriter("results/output_bar_counting.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init Object Counter
counter = solutions.ObjectCounter(
    show=False,
    region=line_points,
    model=r"model/best.pt",
    tracker="botsort.yaml",
    conf=0.3,
    iou=0.4,
    line_width=5,
    show_out=False,
)

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break

    # Crop, count inside the crop, then paste the annotated crop back into the full frame
    results = counter(frame[y : y + ch, x : x + cw])
    frame[y : y + ch, x : x + cw] = results.plot_im
    video_writer.write(frame)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

Additional Resources

For more detailed guidance on object counting and tracking, you can refer to our Object Counting Guide.

Feel free to share the video link or any additional details here, and we’ll do our best to assist you further. Thank you for your patience and persistence!

Thanks, I'll give it a try! I published on github the video, my model and some examples of programs that I tried but did not work. https://github.com/Tanrion/bar_counting

Hello,

Thank you for sharing the details and the GitHub link to your project! I appreciate your efforts in providing a comprehensive example. This will help us better understand the issue and provide more targeted assistance.

Next Steps

1. Reproducible Example: Please ensure that the provided code and model are using the latest versions of torch and ultralytics. You can update your packages using:

   pip install --upgrade torch ultralytics

2. Investigate Tracking Parameters: Since you've already tried adjusting conf and iou parameters, another approach could be to fine-tune other parameters in the botsort.yaml or bytetrack.yaml configuration files. For example, increasing track_high_thresh and track_low_thresh values might help in maintaining consistent IDs.

3. Cropping Strategy: Cropping the frame to focus on a specific region is a good strategy. Ensure that the counting line is defined in the crop's coordinates. Note that ObjectCounter now runs the tracker itself, so you can pass it the cropped image with the line defined in the crop's coordinates and no tensor cloning is needed. Here’s a refined version of your code:

   import cv2
   
   from ultralytics import solutions
   
   cap = cv2.VideoCapture("test_26_50mm.avi")
   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 crop region (x, y, width, height)
   x, y, cw, ch = (500, 500, 1000, 1000)
   
   # Line points are given in cropped-frame coordinates (here a vertical line in the middle of the crop)
   line_points = [(500, 0), (500, 1000)]
   
   # Video writer
   video_writer = cv2.VideoWriter("results/output_bar_counting.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
   
   # Init Object Counter
   counter = solutions.ObjectCounter(
       show=False,
       region=line_points,
       model=r"model/best.pt",
       tracker="botsort.yaml",
       conf=0.3,
       iou=0.4,
       line_width=5,
       show_out=False,
   )
   
   while cap.isOpened():
       success, frame = cap.read()
       if not success:
           print("Video frame is empty or video processing has been successfully completed.")
           break
   
       # Crop, count inside the crop, then paste the annotated crop back into the full frame
       results = counter(frame[y : y + ch, x : x + cw])
       frame[y : y + ch, x : x + cw] = results.plot_im
       video_writer.write(frame)
   
   cap.release()
   video_writer.release()
   cv2.destroyAllWindows()

4. Alternative Trackers: If BoT-SORT and ByteTrack are not yielding satisfactory results, you might want to explore other tracking algorithms. While Ultralytics primarily supports BoT-SORT and ByteTrack, you can integrate other trackers with custom code.

Additional Resources

For more detailed guidance on object counting and tracking, you can refer to our Object Counting Guide.

Feel free to share any additional details or updates here, and we’ll do our best to assist you further. Thank you for your patience and persistence! 😊

my github is only useful if you look at it. If you had looked at it, you would have seen that I have more programs, between "line counter", "region counter", "cropped counter" and they don't work. What's more, I've posted a video of my project on github, so maybe you'll see it more clearly. On the other hand, if you have any other trackers to suggest, I'd love to hear from you. Personally, I don't know of any others that perform as well. Thank you very much for your help!

Hello @Tanrion,

Thank you for sharing your GitHub repository and the video. I appreciate your detailed explanation and the effort you've put into troubleshooting the issue.

Reviewing Your Code and Video

I've reviewed the programs in your repository, including the "line counter," "region counter," and "cropped counter." It's clear that you've tried multiple approaches to solve the problem.

Tracking Identifiers Issue

Regarding the reallocation of tracking identifiers, this can be a challenging issue with object tracking. Here are a few suggestions to improve the tracking consistency:

1. Adjust Tracker Parameters:

   • BoT-SORT: You can try increasing the track_high_thresh and track_low_thresh values in the botsort.yaml configuration file to make the tracker more conservative in reassigning IDs.
   • ByteTrack: Similarly, adjust the parameters in the bytetrack.yaml file.

2. Alternative Trackers: While BoT-SORT and ByteTrack are among the best, you might want to explore other trackers like DeepSORT or FairMOT. These are not natively supported by Ultralytics but can be integrated with some custom coding.

3. Frame Rate and Resolution: Reducing the frame rate or resolution might help improve tracking stability. Sometimes, processing fewer frames can help the tracker maintain consistent IDs.

Cropping Strategy

Your idea of cropping the image is excellent. It helps focus on a specific region, potentially improving tracking accuracy. In current versions ObjectCounter runs the tracker itself, so you can pass it the cropped image with the line defined in the crop's coordinates, with no tensor cloning needed:

import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("test_26_50mm.avi")
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 crop region (x, y, width, height)
x, y, cw, ch = (500, 500, 1000, 1000)

# Line points are given in cropped-frame coordinates (here a vertical line in the middle of the crop)
line_points = [(500, 0), (500, 1000)]

# Video writer
video_writer = cv2.VideoWriter("results/output_bar_counting.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init Object Counter
counter = solutions.ObjectCounter(
    show=False,
    region=line_points,
    model=r"model/best.pt",
    tracker="botsort.yaml",
    conf=0.3,
    iou=0.4,
    line_width=5,
    show_out=False,
)

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break

    # Crop, count inside the crop, then paste the annotated crop back into the full frame
    results = counter(frame[y : y + ch, x : x + cw])
    frame[y : y + ch, x : x + cw] = results.plot_im
    video_writer.write(frame)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

Further Assistance

If you continue to face issues, please ensure you are using the latest versions of torch and ultralytics. You can update your packages using:

pip install --upgrade torch ultralytics

For more detailed guidance on object counting and tracking, you can refer to our Object Counting Guide.

Feel free to share any additional details or updates here, and we’ll do our best to assist you further. Thank you for your patience and persistence! 😊