How to show count in screen using yolov5

[tasyoooo]

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I would like to show the count of objects in the screen using yolov5. I'm using a webcam as the source and i would like to show the objects it counts in the screen also together with the bounding boxes. I've tried adding lines of code in detect.py but nothing works, it always generated an error

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[glenn-jocher]

Hello! 😊 To display the count of detected objects on the screen with YOLOv5, you can modify the code where detection bounding boxes are drawn. Here's a simple way to do it:

1. After objects are detected and before the loop that goes through detections and draws boxes, calculate the count of objects by class.
2. Use OpenCV's cv2.putText() to display the count on the frame.

Here's a basic snippet that you can add/modify in your detect.py file after detection results are processed:

# Assuming 'pred' is the prediction result and 'names' are class names
for i, det in enumerate(pred):  # detections per image
    if len(det):
        # Count objects by class
        count_per_class = Counter(det[:, -1].int().tolist())
        for class_id, count in count_per_class.items():
            label = f"{names[class_id]}: {count}"
            # Display on the top-left corner, you can change the position as needed
            cv2.putText(im0, label, (10, 45 + 30 * class_id), cv2.FONT_HERSHEY_SIMPLEX, 
                        1.25, (255,255,255), 3)

Be sure to adjust im0 (the frame you want to draw on), the position (10, 45 + 30 * class_id), the font size, and the color (255,255,255) to fit your needs.

This will display a count of each detected class on the video frames. Happy coding! 🚀

[tasyoooo]

Hello! 😊 To display the count of detected objects on the screen with YOLOv5, you can modify the code where detection bounding boxes are drawn. Here's a simple way to do it:

1. After objects are detected and before the loop that goes through detections and draws boxes, calculate the count of objects by class.
2. Use OpenCV's cv2.putText() to display the count on the frame.

Here's a basic snippet that you can add/modify in your detect.py file after detection results are processed:

# Assuming 'pred' is the prediction result and 'names' are class names
for i, det in enumerate(pred):  # detections per image
    if len(det):
        # Count objects by class
        count_per_class = Counter(det[:, -1].int().tolist())
        for class_id, count in count_per_class.items():
            label = f"{names[class_id]}: {count}"
            # Display on the top-left corner, you can change the position as needed
            cv2.putText(im0, label, (10, 45 + 30 * class_id), cv2.FONT_HERSHEY_SIMPLEX, 
                        1.25, (255,255,255), 3)

Be sure to adjust im0 (the frame you want to draw on), the position (10, 45 + 30 * class_id), the font size, and the color (255,255,255) to fit your needs.

This will display a count of each detected class on the video frames. Happy coding! 🚀

There is an error occurred:
count_per_class = Counter(det[:, -1].int().tolist())
NameError: name 'Counter' is not defined

[glenn-jocher]

Hello! 😊 It seems you're missing an import statement for the Counter class. You can fix this by adding the following line to the top of your script:

from collections import Counter

This imports the Counter class from the Python collections module, allowing you to count the objects per class as shown in the snippet. Happy coding! 🚀

[tasyoooo]

Hello! 😊 It seems you're missing an import statement for the Counter class. You can fix this by adding the following line to the top of your script:

from collections import Counter

This imports the Counter class from the Python collections module, allowing you to count the objects per class as shown in the snippet. Happy coding! 🚀

it works now, thank you very much!

[glenn-jocher]

@tasyoooo you're welcome! I'm glad it worked out for you. If you have any more questions or need further assistance, feel free to ask. Happy coding! 🚀