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| 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). | |