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139 lines
6.5 KiB
139 lines
6.5 KiB
--- |
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comments: true |
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description: Learn how to use Ultralytics YOLO11 for real-time object blurring to enhance privacy and focus in your images and videos. |
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keywords: YOLO11, object blurring, real-time processing, privacy protection, image manipulation, video editing, Ultralytics |
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--- |
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# Object Blurring using Ultralytics YOLO11 🚀 |
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## What is Object Blurring? |
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Object blurring with [Ultralytics YOLO11](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 YOLO11 model capabilities to identify and manipulate objects within a given scene. |
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<p align="center"> |
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<br> |
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ydGdibB5Mds" |
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title="YouTube video player" frameborder="0" |
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
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allowfullscreen> |
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</iframe> |
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<br> |
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<strong>Watch:</strong> Object Blurring using Ultralytics YOLO11 |
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</p> |
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## Advantages of Object Blurring? |
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- **Privacy Protection**: Object blurring is an effective tool for safeguarding privacy by concealing sensitive or personally identifiable information in images or videos. |
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- **Selective Focus**: YOLO11 allows for selective blurring, enabling users to target specific objects, ensuring a balance between privacy and retaining relevant visual information. |
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- **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. |
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!!! example "Object Blurring using YOLO11 Example" |
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=== "Object Blurring" |
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```python |
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import cv2 |
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from ultralytics import YOLO |
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from ultralytics.utils.plotting import Annotator, colors |
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model = YOLO("yolo11n.pt") |
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names = model.names |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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# Blur ratio |
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blur_ratio = 50 |
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# Video writer |
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video_writer = cv2.VideoWriter("object_blurring_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) |
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while cap.isOpened(): |
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success, im0 = cap.read() |
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if not success: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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results = model.predict(im0, show=False) |
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boxes = results[0].boxes.xyxy.cpu().tolist() |
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clss = results[0].boxes.cls.cpu().tolist() |
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annotator = Annotator(im0, line_width=2, example=names) |
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if boxes is not None: |
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for box, cls in zip(boxes, clss): |
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annotator.box_label(box, color=colors(int(cls), True), label=names[int(cls)]) |
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obj = im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] |
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blur_obj = cv2.blur(obj, (blur_ratio, blur_ratio)) |
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im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] = blur_obj |
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cv2.imshow("ultralytics", im0) |
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video_writer.write(im0) |
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if cv2.waitKey(1) & 0xFF == ord("q"): |
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break |
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cap.release() |
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video_writer.release() |
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cv2.destroyAllWindows() |
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``` |
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### Arguments `model.predict` |
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{% include "macros/predict-args.md" %} |
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## FAQ |
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### What is object blurring with Ultralytics YOLO11? |
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Object blurring with [Ultralytics YOLO11](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. YOLO11's real-time processing capabilities make it suitable for applications requiring immediate privacy protection and selective focus adjustments. |
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### How can I implement real-time object blurring using YOLO11? |
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To implement real-time object blurring with YOLO11, follow the provided Python example. This involves using YOLO11 for [object detection](https://www.ultralytics.com/glossary/object-detection) and OpenCV for applying the blur effect. Here's a simplified version: |
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```python |
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import cv2 |
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from ultralytics import YOLO |
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model = YOLO("yolo11n.pt") |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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while cap.isOpened(): |
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success, im0 = cap.read() |
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if not success: |
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break |
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results = model.predict(im0, show=False) |
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for box in results[0].boxes.xyxy.cpu().tolist(): |
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obj = im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] |
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im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] = cv2.blur(obj, (50, 50)) |
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cv2.imshow("YOLO11 Blurring", im0) |
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if cv2.waitKey(1) & 0xFF == ord("q"): |
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break |
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cap.release() |
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cv2.destroyAllWindows() |
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``` |
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### What are the benefits of using Ultralytics YOLO11 for object blurring? |
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Ultralytics YOLO11 offers several advantages for object blurring: |
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- **Privacy Protection**: Effectively obscure sensitive or identifiable information. |
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- **Selective Focus**: Target specific objects for blurring, maintaining essential visual content. |
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- **Real-time Processing**: Execute object blurring efficiently in dynamic environments, suitable for instant privacy enhancements. |
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For more detailed applications, check the [advantages of object blurring section](#advantages-of-object-blurring). |
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### Can I use Ultralytics YOLO11 to blur faces in a video for privacy reasons? |
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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](https://www.ultralytics.com/glossary/opencv) to apply a blur effect. Refer to our guide on [object detection with YOLO11](https://docs.ultralytics.com/models/yolov8/) and modify the code to target face detection. |
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### How does YOLO11 compare to other object detection models like Faster R-CNN for object blurring? |
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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](https://docs.ultralytics.com/models/yolov8/).
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