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

---
comments: true
description: Learn how to use Ultralytics YOLOv8 for real-time object blurring to enhance privacy and focus in your images and videos.
keywords: YOLOv8, object blurring, real-time processing, privacy protection, image manipulation, video editing, Ultralytics
---

# Object Blurring using Ultralytics YOLOv8 🚀

## What is Object Blurring?

Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves applying a blurring effect to specific detected objects in an image or video. This can be achieved using the YOLOv8 model capabilities to identify and manipulate objects within a given scene.

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ydGdibB5Mds"
    title="YouTube video player" frameborder="0"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
    allowfullscreen>
  </iframe>
  <br>
  <strong>Watch:</strong> Object Blurring using Ultralytics YOLOv8
</p>

## 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**: YOLOv8 allows for selective blurring, enabling users to target specific objects, ensuring a balance between privacy and retaining relevant visual information.
- **Real-time Processing**: YOLOv8'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 YOLOv8 Example"

    === "Object Blurring"

        ```python
        import cv2

        from ultralytics import YOLO
        from ultralytics.utils.plotting import Annotator, colors

        model = YOLO("yolov8n.pt")
        names = model.names

        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))

        # Blur ratio
        blur_ratio = 50

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

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

            results = model.predict(im0, show=False)
            boxes = results[0].boxes.xyxy.cpu().tolist()
            clss = results[0].boxes.cls.cpu().tolist()
            annotator = Annotator(im0, line_width=2, example=names)

            if boxes is not None:
                for box, cls in zip(boxes, clss):
                    annotator.box_label(box, color=colors(int(cls), True), label=names[int(cls)])

                    obj = im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])]
                    blur_obj = cv2.blur(obj, (blur_ratio, blur_ratio))

                    im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] = blur_obj

            cv2.imshow("ultralytics", im0)
            video_writer.write(im0)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break

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

### Arguments `model.predict`

| Name            | Type           | Default                | Description                                                                |
| --------------- | -------------- | ---------------------- | -------------------------------------------------------------------------- |
| `source`        | `str`          | `'ultralytics/assets'` | source directory for images or videos                                      |
| `conf`          | `float`        | `0.25`                 | object confidence threshold for detection                                  |
| `iou`           | `float`        | `0.7`                  | intersection over union (IoU) threshold for NMS                            |
| `imgsz`         | `int or tuple` | `640`                  | image size as scalar or (h, w) list, i.e. (640, 480)                       |
| `half`          | `bool`         | `False`                | use half precision (FP16)                                                  |
| `device`        | `None or str`  | `None`                 | device to run on, i.e. cuda device=0/1/2/3 or device=cpu                   |
| `max_det`       | `int`          | `300`                  | maximum number of detections per image                                     |
| `vid_stride`    | `bool`         | `False`                | video frame-rate stride                                                    |
| `stream_buffer` | `bool`         | `False`                | buffer all streaming frames (True) or return the most recent frame (False) |
| `visualize`     | `bool`         | `False`                | visualize model features                                                   |
| `augment`       | `bool`         | `False`                | apply image augmentation to prediction sources                             |
| `agnostic_nms`  | `bool`         | `False`                | class-agnostic NMS                                                         |
| `classes`       | `list[int]`    | `None`                 | filter results by class, i.e. classes=0, or classes=[0,2,3]                |
| `retina_masks`  | `bool`         | `False`                | use high-resolution segmentation masks                                     |
| `embed`         | `list[int]`    | `None`                 | return feature vectors/embeddings from given layers                        |

## FAQ

### What is object blurring with Ultralytics YOLOv8?

Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) 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. YOLOv8'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 YOLOv8?

To implement real-time object blurring with YOLOv8, follow the provided Python example. This involves using YOLOv8 for object detection and OpenCV for applying the blur effect. Here's a simplified version:

```python
import cv2

from ultralytics import YOLO

model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")

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

    results = model.predict(im0, show=False)
    for box in results[0].boxes.xyxy.cpu().tolist():
        obj = im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])]
        im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] = cv2.blur(obj, (50, 50))

    cv2.imshow("YOLOv8 Blurring", im0)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()
```

### What are the benefits of using Ultralytics YOLOv8 for object blurring?

Ultralytics YOLOv8 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](#advantages-of-object-blurring).

### Can I use Ultralytics YOLOv8 to blur faces in a video for privacy reasons?

Yes, Ultralytics YOLOv8 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 YOLOv8](https://docs.ultralytics.com/models/yolov8) and modify the code to target face detection.

### How does YOLOv8 compare to other object detection models like Faster R-CNN for object blurring?

Ultralytics YOLOv8 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, YOLOv8'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 [YOLOv8 documentation](https://docs.ultralytics.com/models/yolov8).