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91 lines
5.4 KiB
91 lines
5.4 KiB
--- |
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comments: true |
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description: Learn to blur objects using Ultralytics YOLOv8 for privacy in images and videos. |
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keywords: Ultralytics, YOLOv8, Object Detection, Object Blurring, Privacy Protection, Image Processing, Video Analysis, AI, Machine Learning |
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--- |
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# Object Blurring using Ultralytics YOLOv8 🚀 |
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## What is Object Blurring? |
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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. |
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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**: YOLOv8 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**: YOLOv8'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 YOLOv8 Example" |
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=== "Object Blurring" |
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```python |
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from ultralytics import YOLO |
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from ultralytics.utils.plotting import Annotator, colors |
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import cv2 |
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model = YOLO("yolov8n.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", |
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cv2.VideoWriter_fourcc(*'mp4v'), |
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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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| Name | Type | Default | Description | |
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|-----------------|----------------|------------------------|----------------------------------------------------------------------------| |
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| `source` | `str` | `'ultralytics/assets'` | source directory for images or videos | |
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| `conf` | `float` | `0.25` | object confidence threshold for detection | |
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| `iou` | `float` | `0.7` | intersection over union (IoU) threshold for NMS | |
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| `imgsz` | `int or tuple` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | |
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| `half` | `bool` | `False` | use half precision (FP16) | |
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| `device` | `None or str` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | |
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| `max_det` | `int` | `300` | maximum number of detections per image | |
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| `vid_stride` | `bool` | `False` | video frame-rate stride | |
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| `stream_buffer` | `bool` | `False` | buffer all streaming frames (True) or return the most recent frame (False) | |
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| `visualize` | `bool` | `False` | visualize model features | |
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| `augment` | `bool` | `False` | apply image augmentation to prediction sources | |
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| `agnostic_nms` | `bool` | `False` | class-agnostic NMS | |
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| `classes` | `list[int]` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] | |
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| `retina_masks` | `bool` | `False` | use high-resolution segmentation masks | |
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| `embed` | `list[int]` | `None` | return feature vectors/embeddings from given layers |
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