Faceset Builder is a command line tool for extracting faces from videos and photos.
- Python 3.5+
- CUDA 8+
- cuDNN 5+
- face_recognition
- opencv-python
- numpy
- scipy
- tqdm
- click
Clone this repository
git clone https://github.com/AlexisTheLarge/faceset-builder.git faceset-builder
cd faceset_builderInstall python module and dependencies
python setup.py installCollect faces from src data. The output directory will have faces sorted into directories for easier manual cleaning.
faceset-builder collect <input_dir> <reference_faces_dir> <output_dir>
Compile cleaned faceset to flat directory with sequential naming.
faceset-builder compile <input_dir> <output_dir>
Supported options for the collect command:
Usage: faceset-builder collect [OPTIONS] SOURCE_DIR REFERENCE_DIR OUTPUT_DIR
Go through video files and photographs in OUTPUT_DIR and extract faces
matching those found in REFERENCE_DIR. Extracted faces will be placed in
OUTPUT_DIR sorted into folders corresponding to their source.
Options:
-t, --tolerance FLOAT Threshold for face comparison. Default is 0.5.
--min-face-size INTEGER Minimum size in pixels for faces to extract.
Default is 256.
--crop-size INTEGER Cropped images larger than this will be
scaled down. Must be at least 1.5 times the
size of --min-face-size. Default is 512.
--luminosity-range INTEGER... Range from 0-255 for acceptable face
brightness. Default is 10-245.
--laplacian-threshold FLOAT Threshold for blur detection, values below
this will be considered blurry. 0 means all
images pass, 10 might be a good place to
start. This option is very inconsistent and
is not recommended, use of the --save-invalid
option is strongly recommended in
conjunction. Default is 0.
--one-face Discard any cropped images containing more
than one face.
--mask-faces Attempt to black out unwanted faces.
--save-invalid Duplicates, corrupted files, and faces that
fail validation will be saved to
'<output_dir>/invalid'. Otherwise duplicates
and corrupted files will be deleted, and
invalid faces ignored.
--scan-rate FLOAT Number of frames per second to scan for
reference faces. Default is 0.2 fps (1 frame
every 5 seconds).
--capture-rate FLOAT Number of frames per second to extract when
reference face has been found. Default is 5.
--sample-height INTEGER Height in pixel to downsample the image/frame
to before running facial recognition. Default
is 500. (AFFECTS VRAM)
--batch-size INTEGER Number of video frames to run facial
recognition on per batch. Default is 32.
(AFFECTS VRAM)
--buffer-size INTEGER Number of frames to buffer between each scan.
Default is the number of frames in one scan
interval. (AFFECTS RAM)
--greedy While scanning, consider face detected even
if it's smaller than --min-face-size. Might
capture a few more faces at a potential
performance loss.
-h, --help Show this message and exit.
This script provies an option for blur detection using Laplacian Variance (--laplacian-tolerance). This has been included because some people have expressed interest in such a feature. I can not personally recommend using said option, since I have had very unpredictable results when testing it. It should be considered an experimental feature.
If the extraction process is slow (taking multiple seconds per photo), the problem is most likely that dlib isn't compiled with CUDA support. Install dlib from git (https://github.com/davisking/dlib) in order see more info during the installation about why CUDA support might not be working.
Faceset Builder is licensed under the Mozilla Public License Version 2.0 (see LICENSE)