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Face blurring software v0.2

What has this project to offer

This is an attempt to create a functional library of tools for blurring faces in videos.

Two different module are supported:

  • Fully manual face (or not only face) blurring
  • Fully automatic face blurring

The manual module supports:

  • Multiple manual annotation of regions to be blurred
  • 1, 5, 10 and 100 frame skipping option (without blurring taken place for those frames that is)
  • Skip for an undefined number of frames (waiting for a key stroke or the end of file)
  • Basic key functionality for the above actions

The automatic module supports:

  • A parameter to choose which bboxes to consider
  • A parameter which controls the size of the blurring area
  • A fully automatic facial blurring

Requirements

  • Manual mode

Mainly openCV.

  • Automatic mode

OpenCV, numpy and tensorflow.

How to use

Manual face blurring

Not technically limited to facial images as the ROI annotation is fully manual.

The module manual_face_blurring_v3.py uses opencv for bounding box definition and KFC opencv tracker for bounding box propagation.

Invoking the module is simple by using:

python3 manual_face_blurring_v3.py "path/to/video" [--verbose]

where verbose is the only optional parameter after the positional parameter for the video location

It is a fully functional module with basic key functionality. Key being used are:

Key pressed Action performed
r Define the ROI for the regions to be blurred. Use Enter after each ROI, twice to add another and once followed by q to end the ROI annotation
s Parses (skips) all remaining frames without applying any blurring. It will be terminated by end of video or another key stroke
1 Skips 1 frame only and stops to wait for a new keystroke (used for fine video search)
5 Skips 5 frames and returns to wait for key input
t The same for 10 frames skipped
h Skips 100 frames and waits for key input
Esc Stops the video processing (any video processed up to this moment remains.

Remarks:

  • For key input only English layout is supported (other locale causes the program to crack).

  • Also ROI definition is a bit awkward since it needs 2 times Enter key stroke for adding another ROI but only 1 time Enter key stroke followed by q key stroke to terminate the ROI annotation.

  • For v0.4 there has been added the option to press 3 times (3x) the Enter to exit the ROI annotation mode and enter tracking mode with the annotated bboxes.

  • This module creates a new video file named old_file_blurred.old_ext and it will complain if a file with that name already exists in folder.

Automatic face blurring

This module uses a tensorflow object detection model from this repository which has trained a mobilenet SSD(single shot multibox detector) trained on WIDERFACE dataset. This is quite small and fast face detector and seems to be quite effective also.

Then a simple gaussian filter is applied to all bboxes with confidence score above a threshold (by default 0.35) and write the frames to a video file.

it can be invoked by using: python3 auto_face_blurring_v2.py "path/to/video"

Optional arguments are:

  • --score-threshold with default value --score-threshold=0.35 which controls the number of bounging boxes to be blurred. All bboxes with confidence score below this threshold are ignored.
  • --enlarge-factor with default value --enlarge-factor=0.1 which controls how much the actual facial bbox will be expanded. Default value equals to 10% for each bounding box in each direction.

Remarks:

  • This module creates a new video file named old_file_blurred_auto.old_ext.