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

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true	Learn how to use Ultralytics YOLOv8 for real-time object blurring to enhance privacy and focus in your images and videos.	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 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.

Watch: Object Blurring using Ultralytics YOLOv8

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 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:

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.

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 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.