Object Blurring using Ultralytics YOLO11 🚀

오브젝트 블러란 무엇인가요?

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

오브젝트 블러의 장점은?

• 개인정보 보호: 물체 흐림 효과는 이미지나 동영상에서 민감한 정보나 개인 식별 정보를 숨겨 개인 정보를 보호하는 데 효과적인 도구입니다.
• 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.

import cv2

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

model = YOLO("yolo11n.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()

인수 model.predict

자주 묻는 질문

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 YOLO

model = YOLO("yolo11n.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("YOLO11 Blurring", im0)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

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

Ultralytics YOLO11 offers several advantages for object blurring:

• 개인정보 보호: 민감하거나 식별 가능한 정보를 효과적으로 가릴 수 있습니다.
• 선택적 초점: 특정 개체를 대상으로 흐리게 처리하여 필수 시각적 콘텐츠를 유지합니다.
• 실시간 처리: 동적 환경에서 오브젝트 블러링을 효율적으로 실행하여 즉각적인 프라이버시 향상에 적합합니다.

자세한 적용 사례는 물체 흐림 효과의 장점 섹션에서 확인하세요.

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.

9개월 전 생성됨 ✏️ 업데이트됨 8 일 전

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