Blur People

Ultralytics Object Blurring is useful for privacy-preserving video and image processing. Rather than hiding the workflow in a solution class, this tutorial exposes the custom blur step as one small operator between native tracking and rendering.

Before	After

Build the pipeline

The default YOLO checkpoint uses COCO class 0 for person, so the tracking operator filters all other detections before ObjectBlurrer receives the native result. The custom operator copies results.orig_img, blurs each box, and calls results.plot(img=image) to draw native annotations over it.

from ml_pipes.core import Pipeline
from ml_pipes.standard import Select
from ml_pipes.ultralytics import yolo

pipeline = Pipeline(
    [
        yolo.Track(
            model="yolo26n.pt",
            persist=True,
            classes=[0],  # COCO person
            tracker="botsort.yaml",
            conf=0.25,
        ),
        Select(0),
        ObjectBlurrer(blur_ratio=0.5),
    ],
    auto_validate=True,
)

ObjectBlurrer is defined in the runnable example. It is ordinary pipeline code, so applications can replace it with another effect or add steps before and after it.

Run it on an image

import cv2

image = cv2.imread("bus.jpg")
if image is None:
    raise RuntimeError("Could not load bus.jpg")

blurred = pipeline(image)
cv2.imwrite("blurred.jpg", blurred)

Run the example

python examples/run_object_blurrer.py
python examples/run_object_blurrer.py --source path/to/image.jpg --show

See run_object_blurrer.py for all command-line options.