Skip to content

Register now for

# Object Cropping using Ultralytics YOLO11

## What is Object Cropping?

Object cropping with Ultralytics YOLO11 involves isolating and extracting specific detected objects from an image or video. The YOLO11 model capabilities are utilized to accurately identify and delineate objects, enabling precise cropping for further analysis or manipulation.

Watch: Object Cropping using Ultralytics YOLO

## Advantages of Object Cropping

- Focused Analysis: YOLO11 facilitates targeted object cropping, allowing for in-depth examination or processing of individual items within a scene.
- Reduced Data Volume: By extracting only relevant objects, object cropping helps in minimizing data size, making it efficient for storage, transmission, or subsequent computational tasks.
- Enhanced Precision: YOLO11's object detection accuracy ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis.

## Visuals

Airport Luggage
Suitcases Cropping at airport conveyor belt using Ultralytics YOLO11

Object Cropping using Ultralytics YOLO

CLI Python

    # Crop the objects
    yolo solutions crop show=True

    # Pass a source video
    yolo solutions crop source="path/to/video.mp4"

    # Crop specific classes
    yolo solutions crop classes="[0, 2]"

    import cv2

    from ultralytics import solutions

    cap = cv2.VideoCapture("path/to/video.mp4")
    assert cap.isOpened(), "Error reading video file"

    # Initialize object cropper object
    cropper = solutions.ObjectCropper(
        show=True,  # display the output
        model="yolo11n.pt",  # model for object cropping i.e yolo11x.pt.
        classes=[0, 2],  # crop specific classes i.e. person and car with COCO pretrained model.
        # conf=0.5,  # adjust confidence threshold for the objects.
        # crop_dir="cropped-detections",  # set the directory name for cropped detections
    )

    # Process video
    while cap.isOpened():
        success, im0 = cap.read()

        if not success:
            print("Video frame is empty or processing is complete.")
            break

        results = cropper(im0)

        # print(results)  # access the output

    cap.release()
    cv2.destroyAllWindows()  # destroy all opened windows

### `ObjectCropper` Arguments

Here's a table with the `ObjectCropper` arguments:

Argument | Type | Default | Description
--- | --- | --- | ---
`model` | `str` | `None` | Path to Ultralytics YOLO Model File.
`crop_dir` | `str` | `'cropped-detections'` | Directory name for storing cropped detections.

Moreover, the following visualization arguments are available for use:

Argument | Type | Default | Description
--- | --- | --- | ---
`show` | `bool` | `False` | If `True`, displays the annotated images or videos in a window. Useful for immediate visual feedback during development or testing.
`line_width` | `None or int` | `None` | Specifies the line width of bounding boxes. If `None`, the line width is automatically adjusted based on the image size. Provides visual customization for clarity.

## FAQ

### What is object cropping in Ultralytics YOLO11 and how does it work?

Object cropping using Ultralytics YOLO11 involves isolating and extracting specific objects from an image or video based on YOLO11's detection capabilities. This process allows for focused analysis, reduced data volume, and enhanced precision by leveraging YOLO11 to identify objects with high accuracy and crop them accordingly. For an in-depth tutorial, refer to the object cropping example.

### Why should I use Ultralytics YOLO11 for object cropping over other solutions?

Ultralytics YOLO11 stands out due to its precision, speed, and ease of use. It allows detailed and accurate object detection and cropping, essential for focused analysis and applications needing high data integrity. Moreover, YOLO11 integrates seamlessly with tools like OpenVINO and TensorRT for deployments requiring real-time capabilities and optimization on diverse hardware. Explore the benefits in the guide on model export.

### How can I reduce the data volume of my dataset using object cropping?

By using Ultralytics YOLO11 to crop only relevant objects from your images or videos, you can significantly reduce the data size, making it more efficient for storage and processing. This process involves training the model to detect specific objects and then using the results to crop and save these portions only. For more information on exploiting Ultralytics YOLO11's capabilities, visit our quickstart guide.

### Can I use Ultralytics YOLO11 for real-time video analysis and object cropping?

Yes, Ultralytics YOLO11 can process real-time video feeds to detect and crop objects dynamically. The model's high-speed inference capabilities make it ideal for real-time applications such as surveillance, sports analysis, and automated inspection systems. Check out the tracking and prediction modes to understand how to implement real-time processing.

### What are the hardware requirements for efficiently running YOLO11 for object cropping?

Ultralytics YOLO11 is optimized for both CPU and GPU environments, but to achieve optimal performance, especially for real-time or high-volume inference, a dedicated GPU (e.g., NVIDIA Tesla, RTX series) is recommended. For deployment on lightweight devices, consider using CoreML for iOS or TFLite for Android. More details on supported devices and formats can be found in our model deployment options.

📅 Created 1 year ago ✏️ Updated 6 months ago

## Comments