طمس الأجسام باستخدام Ultralytics YOLO11 🚀

ما هو تعتيم الكائن؟

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.

شاهد: Object Blurring using Ultralytics YOLO11

مزايا طمس الكائن؟

• حماية الخصوصية: يعد تعتيم الكائن أداة فعالة لحماية الخصوصية عن طريق إخفاء المعلومات الحساسة أو الشخصية في الصور أو مقاطع الفيديو.
• 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()

الحجج model.predict

الأسئلة المتداولة

ما هو تمويه الأجسام مع 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.

كيف يمكنني تنفيذ تمويه الأجسام في الوقت الحقيقي باستخدام 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()

ما هي مزايا استخدام Ultralytics YOLO11 لطمس الأجسام؟

Ultralytics يوفر YOLO11 العديد من المزايا لطمس الأجسام:

• حماية الخصوصية: إخفاء المعلومات الحساسة أو القابلة للتحديد بشكل فعال.
• تركيز انتقائي: استهداف كائنات محددة للتشويش، مع الحفاظ على المحتوى المرئي الأساسي.
• معالجة في الوقت الحقيقي: تنفيذ تشويش الكائنات بكفاءة في البيئات الديناميكية، ومناسبة لتحسينات الخصوصية الفورية.

لمزيد من التطبيقات الأكثر تفصيلاً، راجع قسم مزايا تمويه الأجسام.

هل يمكنني استخدام Ultralytics YOLO11 لتمويه الوجوه في الفيديو لأسباب تتعلق بالخصوصية؟

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.

كيف يقارن YOLO11 بنماذج اكتشاف الأجسام الأخرى مثل Faster R-CNN لتعتيم الأجسام؟

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.

📅 تم إنشاؤه منذ 10 أشهر ✏️ تم التحديث منذ 1 شهر

التعليقات