ultralytics/docs/en/guides/object-blurring.md

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true	Learn how to use Ultralytics YOLO11 for real-time object blurring to enhance privacy and focus in your images and videos.	YOLO11, object blurring, real-time processing, privacy protection, image manipulation, video editing, Ultralytics

Object Blurring using Ultralytics YOLO11 🚀

What is Object Blurring?

Object blurring with Ultralytics YOLO11 involves applying a blurring effect to specific detected objects in an image or video. This can be achieved using the YOLO11 model capabilities to identify and manipulate objects within a given scene.

Watch: Object Blurring using Ultralytics YOLO11

Advantages of Object Blurring?

• Privacy Protection: Object blurring is an effective tool for safeguarding privacy by concealing sensitive or personally identifiable information in images or videos.
• Selective Focus: YOLO11 allows for selective blurring, enabling users to target specific objects, ensuring a balance between privacy and retaining relevant visual information.
• Real-time Processing: YOLO11's efficiency enables object blurring in real-time, making it suitable for applications requiring on-the-fly privacy enhancements in dynamic environments.

!!! example "Object Blurring using Ultralytics YOLO"

=== "CLI"

    ```bash
    # Blur the objects
    yolo solutions blur show=True

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

    # Blur the specific classes
    yolo solutions blur classes=[0, 5]
    ```

=== "Python"

    ```python
    import cv2

    from ultralytics import solutions

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

    # Video writer
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
    video_writer = cv2.VideoWriter("object_blurring_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

    # Initialize object blurrer object
    blurrer = solutions.ObjectBlurrer(
        show=True,  # display the output
        model="yolo11n.pt",  # model for object blurring i.e. yolo11m.pt
        # line_width=2,  # width of bounding box.
        # classes=[0, 2],  # count specific classes i.e, person and car with COCO pretrained model.
        # blur_ratio=0.5,  # adjust percentage of blur intensity, the value in range 0.1 - 1.0
    )

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

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

        results = blurrer(im0)

        # print(results")  # access the output

        video_writer.write(results.plot_im)  # write the processed frame.

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

ObjectBlurrer Arguments

Here's a table with the ObjectBlurrer arguments:

{% from "macros/solutions-args.md" import param_table %} {{ param_table(["model", "line_width", "blur_ratio"]) }}

The ObjectBlurrer solution also supports a range of track arguments:

{% from "macros/track-args.md" import param_table %} {{ param_table(["tracker", "conf", "iou", "classes", "verbose", "device"]) }}

Moreover, the following visualization arguments can be used:

{% from "macros/visualization-args.md" import param_table %} {{ param_table(["show", "line_width"]) }}

FAQ

What is object blurring with Ultralytics YOLO11?

Object blurring with Ultralytics YOLO11 involves automatically detecting and applying a blurring effect to specific objects in images or videos. This technique enhances privacy by concealing sensitive information while retaining relevant visual data. YOLO11's real-time processing capabilities make it suitable for applications requiring immediate privacy protection and selective focus adjustments.

How can I implement real-time object blurring using YOLO11?

To implement real-time object blurring with YOLO11, follow the provided Python example. This involves using YOLO11 for object detection and OpenCV for applying the blur effect. Here's a simplified version:

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))

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

# Init ObjectBlurrer
blurrer = solutions.ObjectBlurrer(
    show=True,  # display the output
    model="yolo11n.pt",  # model="yolo11n-obb.pt" for object blurring using YOLO11 OBB model.
    blur_ratio=0.5,  # set blur percentage i.e 0.7 for 70% blurred detected objects
    # line_width=2,  # width of bounding box.
    # classes=[0, 2],  # count specific classes i.e, person and car with COCO pretrained model.
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or processing is complete.")
        break
    results = blurrer(im0)
    video_writer.write(results.plot_im)

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

What are the benefits of using Ultralytics YOLO11 for object blurring?

Ultralytics YOLO11 offers several advantages for object blurring:

• Privacy Protection: Effectively obscure sensitive or identifiable information.
• Selective Focus: Target specific objects for blurring, maintaining essential visual content.
• Real-time Processing: Execute object blurring efficiently in dynamic environments, suitable for instant privacy enhancements.

For more detailed applications, check the advantages of object blurring section.

Can I use Ultralytics YOLO11 to blur faces in a video for privacy reasons?

Yes, Ultralytics YOLO11 can be configured to detect and blur faces in videos to protect privacy. By training or using a pre-trained model to specifically recognize faces, the detection results can be processed with OpenCV to apply a blur effect. Refer to our guide on object detection with YOLO11 and modify the code to target face detection.

How does YOLO11 compare to other object detection models like Faster R-CNN for object blurring?

Ultralytics YOLO11 typically outperforms models like Faster R-CNN in terms of speed, making it more suitable for real-time applications. While both models offer accurate detection, YOLO11's architecture is optimized for rapid inference, which is critical for tasks like real-time object blurring. Learn more about the technical differences and performance metrics in our YOLO11 documentation.