Object Blurring using Ultralytics YOLO11 🚀
What is Object Blurring?
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
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: 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.
Object Blurring using YOLO11 Example
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()
Arguments model.predict
| Argument | Type | Default | Description |
|---|---|---|---|
source | str | 'ultralytics/assets' | Specifies the data source for inference. Can be an image path, video file, directory, URL, or device ID for live feeds. Supports a wide range of formats and sources, enabling flexible application across different types of input. |
conf | float | 0.25 | Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives. |
iou | float | 0.7 | Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Lower values result in fewer detections by eliminating overlapping boxes, useful for reducing duplicates. |
imgsz | int or tuple | 640 | Defines the image size for inference. Can be a single integer 640 for square resizing or a (height, width) tuple. Proper sizing can improve detection accuracy and processing speed. |
half | bool | False | Enables half-precision (FP16) inference, which can speed up model inference on supported GPUs with minimal impact on accuracy. |
device | str | None | Specifies the device for inference (e.g., cpu, cuda:0 or 0). Allows users to select between CPU, a specific GPU, or other compute devices for model execution. |
max_det | int | 300 | Maximum number of detections allowed per image. Limits the total number of objects the model can detect in a single inference, preventing excessive outputs in dense scenes. |
vid_stride | int | 1 | Frame stride for video inputs. Allows skipping frames in videos to speed up processing at the cost of temporal resolution. A value of 1 processes every frame, higher values skip frames. |
stream_buffer | bool | False | Determines whether to queue incoming frames for video streams. If False, old frames get dropped to accomodate new frames (optimized for real-time applications). If `True', queues new frames in a buffer, ensuring no frames get skipped, but will cause latency if inference FPS is lower than stream FPS. |
visualize | bool | False | Activates visualization of model features during inference, providing insights into what the model is "seeing". Useful for debugging and model interpretation. |
augment | bool | False | Enables test-time augmentation (TTA) for predictions, potentially improving detection robustness at the cost of inference speed. |
agnostic_nms | bool | False | Enables class-agnostic Non-Maximum Suppression (NMS), which merges overlapping boxes of different classes. Useful in multi-class detection scenarios where class overlap is common. |
classes | list[int] | None | Filters predictions to a set of class IDs. Only detections belonging to the specified classes will be returned. Useful for focusing on relevant objects in multi-class detection tasks. |
retina_masks | bool | False | Returns high-resolution segmentation masks. The returned masks (masks.data) will match the original image size if enabled. If disabled, they have the image size used during inference. |
embed | list[int] | None | Specifies the layers from which to extract feature vectors or embeddings. Useful for downstream tasks like clustering or similarity search. |
project | str | None | Name of the project directory where prediction outputs are saved if save is enabled. |
name | str | None | Name of the prediction run. Used for creating a subdirectory within the project folder, where prediction outputs are stored if save is enabled. |
FAQ
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:
- 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.
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.
