well,i also encounter issue like this

from ultralytics.

Please examine the contents of the document carefully !

from ultralytics.

Hello!

Thank you for sharing your code and detailing your issue. Let's work through this together to get your model displaying detections from your laptop's camera.

Firstly, ensure you have the latest versions of torch and ultralytics installed. You can update them using:

pip install --upgrade torch ultralytics

Your code looks mostly correct, but there are a few adjustments we can make to ensure everything runs smoothly. Here's a refined version of your script:

import cv2
from ultralytics import YOLO

# Load the trained YOLOv8 model (adjust the path to your model file)
model = YOLO('box-obb.pt')

# Initialize webcam
cap = cv2.VideoCapture(0)  # 0 is the default device ID for the webcam

if not cap.isOpened():
    print("Error: Could not open webcam.")
    exit()

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    if not ret:
        print("Failed to grab frame")
        break

    # Run YOLOv8 model on the frame
    results = model(frame)

    # Extract the detections
    detections = results[0].boxes.xyxy.cpu().numpy()  # xyxy format (xmin, ymin, xmax, ymax, confidence, class)

    # Loop over detections and draw bounding boxes
    for det in detections:
        xmin, ymin, xmax, ymax, confidence, class_id = det
        if confidence > 0.5:  # Confidence threshold
            cv2.rectangle(frame, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 2)
            label = f"{model.names[int(class_id)]}: {confidence:.2f}"
            cv2.putText(frame, label, (int(xmin), int(ymin) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Display the resulting frame
    cv2.imshow('YOLOv8 Box Detection', frame)

    # Break the loop on 'q' key press
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything is done, release the capture
cap.release()
cv2.destroyAllWindows()

Key Adjustments:

1. Model Inference: Directly use results = model(frame) without converting the frame to RGB, as the model handles this internally.
2. Results Extraction: Access the bounding boxes using results[0].boxes.xyxy.cpu().numpy().

If you still encounter issues, please ensure your environment meets the requirements and try running the script again. If the problem persists, providing any error messages or additional context will help us assist you better.

Feel free to refer to the documentation for more details on prediction modes and settings.

Happy coding! 😊

from ultralytics.

Hello!

Thank you for sharing your code and detailing your issue. Let's work through this together to get your model displaying detections from your laptop's camera.

Firstly, ensure you have the latest versions of torch and ultralytics installed. You can update them using:

pip install --upgrade torch ultralytics

Your code looks mostly correct, but there are a few adjustments we can make to ensure everything runs smoothly. Here's a refined version of your script:

import cv2
from ultralytics import YOLO

# Load the trained YOLOv8 model (adjust the path to your model file)
model = YOLO('box-obb.pt')

# Initialize webcam
cap = cv2.VideoCapture(0)  # 0 is the default device ID for the webcam

if not cap.isOpened():
    print("Error: Could not open webcam.")
    exit()

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    if not ret:
        print("Failed to grab frame")
        break

    # Run YOLOv8 model on the frame
    results = model(frame)

    # Extract the detections
    detections = results[0].boxes.xyxy.cpu().numpy()  # xyxy format (xmin, ymin, xmax, ymax, confidence, class)

    # Loop over detections and draw bounding boxes
    for det in detections:
        xmin, ymin, xmax, ymax, confidence, class_id = det
        if confidence > 0.5:  # Confidence threshold
            cv2.rectangle(frame, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 2)
            label = f"{model.names[int(class_id)]}: {confidence:.2f}"
            cv2.putText(frame, label, (int(xmin), int(ymin) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Display the resulting frame
    cv2.imshow('YOLOv8 Box Detection', frame)

    # Break the loop on 'q' key press
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything is done, release the capture
cap.release()
cv2.destroyAllWindows()

Key Adjustments:

1. **Model Inference**: Directly use `results = model(frame)` without converting the frame to RGB, as the model handles this internally.

2. **Results Extraction**: Access the bounding boxes using `results[0].boxes.xyxy.cpu().numpy()`.

If you still encounter issues, please ensure your environment meets the requirements and try running the script again. If the problem persists, providing any error messages or additional context will help us assist you better.

Feel free to refer to the documentation for more details on prediction modes and settings.

Happy coding! 😊

I tried your suggestion and adjusted something in my code and here is the error I got

It said AttributeError: "NoneType" object has no attributes 'xyxy'

from ultralytics.

Since no object was detected, result is null. If you want to use OpenCV to show real-time detection results, you can use result[0].plot()

results = model(frame)
img = results[0].plot()
cv2.imshow('YOLOv8 Box Detection', img)

If you want to get the values of xmin, ymin, xmax, ymax, confidence, class_id.

for r in results:
    xmin, ymin, xmax, ymax = r.xyxy.cpu().numpy().tolist()
    confidence = r.conf.cpu().numpy().tolist()[0]
    class_id = int(r.cls.cpu().numpy().tolist()[0])
    class_name = results.name[class_id]

I hope this helps.

from ultralytics.

@sunmooncode hello!

Thank you for your patience and for providing the error details. It looks like the issue arises when no objects are detected, resulting in a NoneType object. Let's address this by ensuring that we handle cases where no detections are made.

Here's an updated version of your script that includes handling for cases where no objects are detected:

import cv2
from ultralytics import YOLO

# Load the trained YOLOv8 model (adjust the path to your model file)
model = YOLO('box-obb.pt')

# Initialize webcam
cap = cv2.VideoCapture(0)  # 0 is the default device ID for the webcam

if not cap.isOpened():
    print("Error: Could not open webcam.")
    exit()

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    if not ret:
        print("Failed to grab frame")
        break

    # Run YOLOv8 model on the frame
    results = model(frame)

    # Check if any detections were made
    if results and results[0].boxes:
        # Extract the detections
        detections = results[0].boxes.xyxy.cpu().numpy()  # xyxy format (xmin, ymin, xmax, ymax, confidence, class)

        # Loop over detections and draw bounding boxes
        for det in detections:
            xmin, ymin, xmax, ymax, confidence, class_id = det
            if confidence > 0.5:  # Confidence threshold
                cv2.rectangle(frame, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 2)
                label = f"{model.names[int(class_id)]}: {confidence:.2f}"
                cv2.putText(frame, label, (int(xmin), int(ymin) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Display the resulting frame
    cv2.imshow('YOLOv8 Box Detection', frame)

    # Break the loop on 'q' key press
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything is done, release the capture
cap.release()
cv2.destroyAllWindows()

Key Adjustments:

1. Check for Detections: Before attempting to access the bounding boxes, we check if results and results[0].boxes are not None.
2. Plotting Results: If you prefer to use the built-in plotting function for visualization, you can use results[0].plot() as suggested:

# Run YOLOv8 model on the frame
results = model(frame)

# Plot the results
img = results[0].plot()

# Display the resulting frame
cv2.imshow('YOLOv8 Box Detection', img)

This should help handle cases where no objects are detected and ensure your script runs smoothly. If you continue to face issues, please ensure your environment is up-to-date with the latest versions of torch and ultralytics. If the problem persists, providing a minimum reproducible example would be very helpful for further investigation. You can refer to our guide on creating a minimum reproducible example.

Happy coding! 😊

from ultralytics.

@sunmooncode hello!

Thank you for your patience and for providing the error details. It looks like the issue arises when no objects are detected, resulting in a NoneType object. Let's address this by ensuring that we handle cases where no detections are made.

Here's an updated version of your script that includes handling for cases where no objects are detected:

import cv2
from ultralytics import YOLO

# Load the trained YOLOv8 model (adjust the path to your model file)
model = YOLO('box-obb.pt')

# Initialize webcam
cap = cv2.VideoCapture(0)  # 0 is the default device ID for the webcam

if not cap.isOpened():
    print("Error: Could not open webcam.")
    exit()

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    if not ret:
        print("Failed to grab frame")
        break

    # Run YOLOv8 model on the frame
    results = model(frame)

    # Check if any detections were made
    if results and results[0].boxes:
        # Extract the detections
        detections = results[0].boxes.xyxy.cpu().numpy()  # xyxy format (xmin, ymin, xmax, ymax, confidence, class)

        # Loop over detections and draw bounding boxes
        for det in detections:
            xmin, ymin, xmax, ymax, confidence, class_id = det
            if confidence > 0.5:  # Confidence threshold
                cv2.rectangle(frame, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 2)
                label = f"{model.names[int(class_id)]}: {confidence:.2f}"
                cv2.putText(frame, label, (int(xmin), int(ymin) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Display the resulting frame
    cv2.imshow('YOLOv8 Box Detection', frame)

    # Break the loop on 'q' key press
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything is done, release the capture
cap.release()
cv2.destroyAllWindows()

Key Adjustments:

1. **Check for Detections**: Before attempting to access the bounding boxes, we check if `results` and `results[0].boxes` are not `None`.

2. **Plotting Results**: If you prefer to use the built-in plotting function for visualization, you can use `results[0].plot()` as suggested:

# Run YOLOv8 model on the frame
results = model(frame)

# Plot the results
img = results[0].plot()

# Display the resulting frame
cv2.imshow('YOLOv8 Box Detection', img)

This should help handle cases where no objects are detected and ensure your script runs smoothly. If you continue to face issues, please ensure your environment is up-to-date with the latest versions of torch and ultralytics. If the problem persists, providing a minimum reproducible example would be very helpful for further investigation. You can refer to our guide on creating a minimum reproducible example.

Happy coding! 😊

Hello thank you for your help! I was able to run it on my laptop here is the result

May I ask how to fix it when it detects other objects/scenes as "Parcels" also even if I set the Confidence Thresh hold to "0.95", because currently I only want the model to detect parcels only. Thank you!

from ultralytics.

To improve the model's specificity in detecting only "parcels" and reduce false positives for other objects or scenes, you can consider the following strategies:

Fine-Tune the Model:
If you have a dataset that includes examples of parcels and other objects, you can fine-tune the YOLOv8 model specifically to recognize parcels. This involves training the model on your dataset to learn the unique features of parcels that distinguish them from other objects.
Ensure your dataset is balanced and includes a sufficient number of parcel examples to help the model learn effectively.
Adjust Class Weights:
During training, you can adjust the class weights to give more importance to the "parcel" class. This can help the model focus more on learning the features of parcels and reduce the weight given to other classes, potentially reducing false positives.
You can set higher class weights for the "parcel" class in the training configuration to make the model more sensitive to detecting parcels.
Use Confidence and Thresholds:
You've already set a confidence threshold to 0.95, which is a good start to filter out low-confidence detections. However, you might need to adjust this threshold based on your specific requirements and the trade-off between precision and recall you desire.
Additionally, consider using the iou (Intersection over Union) threshold when filtering detections. This can help in eliminating detections that have a high overlap with other objects but are not classified as "parcels".
Post-Processing:
After the model makes its initial detections, you can apply additional checks in your code to further filter out detections that are not likely to be parcels. For example, you can check the aspect ratio or size of the detected bounding boxes to see if they match the expected dimensions of parcels.
Implementing a shape or size-based filtering step can help reduce false positives.
Evaluate and Iterate:
After applying these changes, thoroughly evaluate the model's performance on a validation set or a test set that includes a variety of scenes and objects.
Analyze the results to identify any remaining issues and iterate on the model configuration or training process as needed.
Here's an example of how you might adjust the class weights in your training configuration:

Example configuration for training with adjusted class weights

train_config = {
"class_weights": {
"0": 1.0, # Background class weight
"1": 5.0, # Parcel class weight (higher weight to prioritize detection)
# Add other class weights as needed
},
# Other training configuration parameters...
}
By implementing these strategies, you should be able to improve the model's ability to specifically detect parcels while reducing false positives for other objects.

from ultralytics.

It appears you're encountering an issue where the model misidentifies other objects as "parcels," even after setting a high confidence threshold. To address this and focus the model on parcel detection, consider the following steps:

Improve Model Training

• Gather More Data: Collect images of parcels under various conditions (different lighting, angles, sizes, etc.) and include these in your training set.
• Precise Annotation: Ensure your annotations are precise, covering all parts of the parcel. Also, include examples of non-parcel objects in your training set and label them accordingly.

Post-Processing

• Class Filtering: If your model outputs multiple classes, filter out detections that do not belong to the 'Parcel' class. Modify the code to only draw bounding boxes for detections classified as 'Parcel'.

for det in detections:
    xmin, ymin, xmax, ymax, confidence, class_id = det
    if confidence > 0.95 and int(class_id) == parcel_class_id:  # Assuming parcel_class_id is known
        # Draw bounding box and label

from ultralytics.

@1716775457damn hello!

Thank you for your detailed comment. It sounds like you're encountering an issue where the model misidentifies other objects as "parcels," even after setting a high confidence threshold. Let's explore some strategies to improve the model's performance and focus on parcel detection.

Improve Model Training

1. Gather More Data:

   • Diverse Conditions: Collect images of parcels under various conditions (different lighting, angles, sizes, etc.) to ensure the model learns to generalize well.
   • Balanced Dataset: Include a balanced number of non-parcel objects in your training set and label them accordingly. This helps the model distinguish between parcels and other objects.

2. Precise Annotation:

   • Accurate Labels: Ensure your annotations are precise, covering all parts of the parcel. Misannotations can confuse the model and lead to incorrect predictions.
   • Variety of Examples: Include a variety of parcel shapes, sizes, and packaging types in your dataset to improve the model's robustness.

Post-Processing

1. Class Filtering:
   • Filter Non-Parcel Classes: If your model outputs multiple classes, filter out detections that do not belong to the 'Parcel' class. Modify the code to only draw bounding boxes for detections classified as 'Parcel'.

Here's an example of how you can implement class filtering in your code:

parcel_class_id = 1  # Replace with the actual class ID for parcels

for det in detections:
    xmin, ymin, xmax, ymax, confidence, class_id = det
    if confidence > 0.95 and int(class_id) == parcel_class_id:
        cv2.rectangle(frame, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 2)
        label = f"{model.names[int(class_id)]}: {confidence:.2f}"
        cv2.putText(frame, label, (int(xmin), int(ymin) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

Additional Tips

1. Adjust Class Weights:

   • During training, you can adjust the class weights to give more importance to the "parcel" class. This can help the model focus more on learning the features of parcels and reduce the weight given to other classes, potentially reducing false positives.

2. Evaluate and Iterate:

   • After applying these changes, thoroughly evaluate the model's performance on a validation set or a test set that includes a variety of scenes and objects. Analyze the results to identify any remaining issues and iterate on the model configuration or training process as needed.

By implementing these strategies, you should be able to improve the model's ability to specifically detect parcels while reducing false positives for other objects.

If you continue to face issues, please ensure your environment is up-to-date with the latest versions of torch and ultralytics. If the problem persists, providing a minimum reproducible example would be very helpful for further investigation. You can refer to our guide on creating a minimum reproducible example.

Happy coding! 😊

from ultralytics.

@1716775457damn hello!

Thank you for your detailed comment. It sounds like you're encountering an issue where the model misidentifies other objects as "parcels," even after setting a high confidence threshold. Let's explore some strategies to improve the model's performance and focus on parcel detection.

Improve Model Training

1. **Gather More Data**:
   
   * **Diverse Conditions**: Collect images of parcels under various conditions (different lighting, angles, sizes, etc.) to ensure the model learns to generalize well.
   * **Balanced Dataset**: Include a balanced number of non-parcel objects in your training set and label them accordingly. This helps the model distinguish between parcels and other objects.

2. **Precise Annotation**:
   
   * **Accurate Labels**: Ensure your annotations are precise, covering all parts of the parcel. Misannotations can confuse the model and lead to incorrect predictions.
   * **Variety of Examples**: Include a variety of parcel shapes, sizes, and packaging types in your dataset to improve the model's robustness.

Post-Processing

1. **Class Filtering**:
   
   * **Filter Non-Parcel Classes**: If your model outputs multiple classes, filter out detections that do not belong to the 'Parcel' class. Modify the code to only draw bounding boxes for detections classified as 'Parcel'.

Here's an example of how you can implement class filtering in your code:

parcel_class_id = 1  # Replace with the actual class ID for parcels

for det in detections:
    xmin, ymin, xmax, ymax, confidence, class_id = det
    if confidence > 0.95 and int(class_id) == parcel_class_id:
        cv2.rectangle(frame, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 2)
        label = f"{model.names[int(class_id)]}: {confidence:.2f}"
        cv2.putText(frame, label, (int(xmin), int(ymin) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

Additional Tips

1. **Adjust Class Weights**:
   
   * During training, you can adjust the class weights to give more importance to the "parcel" class. This can help the model focus more on learning the features of parcels and reduce the weight given to other classes, potentially reducing false positives.

2. **Evaluate and Iterate**:
   
   * After applying these changes, thoroughly evaluate the model's performance on a validation set or a test set that includes a variety of scenes and objects. Analyze the results to identify any remaining issues and iterate on the model configuration or training process as needed.

By implementing these strategies, you should be able to improve the model's ability to specifically detect parcels while reducing false positives for other objects.

If you continue to face issues, please ensure your environment is up-to-date with the latest versions of torch and ultralytics. If the problem persists, providing a minimum reproducible example would be very helpful for further investigation. You can refer to our guide on creating a minimum reproducible example.

Happy coding! 😊

Hello, I have gathered data sets in a real environment scenario and labelled 2 classes, one for parcel and the other class for other objects, is this good enough for fine-tuning and improving the model? I am also currently annotating 650+ images taken in a real environment. The goal is to only detect "parcels" and not other objects.

from ultralytics.

Hello @KennethEladistu,

Thank you for your detailed follow-up! It's great to hear that you've gathered a diverse dataset and are working on precise annotations. Here are some additional tips to ensure your model focuses on detecting parcels effectively:

Dataset and Annotation Tips

1. Balanced Dataset: Ensure that your dataset has a balanced representation of both classes (parcels and other objects). This helps the model learn to distinguish between them effectively.
2. High-Quality Annotations: Continue to ensure that your annotations are accurate and cover all parts of the parcels. Misannotations can lead to confusion during training.

Fine-Tuning the Model

Given your goal to detect only parcels, here are some steps to fine-tune your model:

1. Class Weights: Adjust the class weights during training to give more importance to the parcel class. This can help the model focus more on learning the features of parcels.
2. Training Configuration: Ensure your training configuration is optimized for your dataset. You can adjust parameters such as learning rate, batch size, and epochs based on your dataset's characteristics.

Example Code for Class Filtering

To ensure the model only detects parcels during inference, you can filter out detections that do not belong to the parcel class. Here's an example:

parcel_class_id = 1  # Replace with the actual class ID for parcels

for det in detections:
    xmin, ymin, xmax, ymax, confidence, class_id = det
    if confidence > 0.95 and int(class_id) == parcel_class_id:
        cv2.rectangle(frame, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 2)
        label = f"{model.names[int(class_id)]}: {confidence:.2f}"
        cv2.putText(frame, label, (int(xmin), int(ymin) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

Evaluation and Iteration

1. Validation Set: Use a validation set that includes a variety of scenes and objects to evaluate the model's performance. This helps in identifying any remaining issues.
2. Iterate: Based on the evaluation results, iterate on the model configuration or training process as needed. This might involve adjusting hyperparameters or collecting more data.

Keeping Your Environment Updated

Ensure your environment is up-to-date with the latest versions of torch and ultralytics. This can help avoid potential issues and leverage the latest improvements.

If you continue to face issues, providing a minimum reproducible example would be very helpful for further investigation. You can refer to our guide on creating a minimum reproducible example.

Happy coding! 😊

from ultralytics.