After the last update, the choice Masker "none" has gone away... and if no matter which masker I choose, the process halts.
Here is what comes out.
- masker.JPG (19.59 KiB) Viewed 4371 times
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Setting Faceswap backend to NVIDIA
usage: faceswap.py extract [-h] [-C CONFIGFILE]
[-L {INFO,VERBOSE,DEBUG,TRACE}] [-LF LOGFILE] -i
INPUT_DIR -o OUTPUT_DIR [-al ALIGNMENTS_PATH]
[-D {cv2-dnn,mtcnn,s3fd}] [-A {cv2-dnn,fan}]
[-M {unet-dfl,vgg-clear,vgg-obstructed} [{unet-dfl,vgg-clear,vgg-obstructed} ...]]
[-nm {none,clahe,hist,mean}] [-r ROTATE_IMAGES]
[-min MIN_SIZE] [-n NFILTER [NFILTER ...]]
Process exited.[-f FILTER [FILTER ...]] [-l REF_THRESHOLD]
[-een EXTRACT_EVERY_N] [-sz SIZE]
[-si SAVE_INTERVAL] [-dl] [-sp] [-s] [-sf]
Extract the faces from pictures
optional arguments:
-h, --help show this help message and exit
-C CONFIGFILE, --configfile CONFIGFILE
Optionally overide the saved config with the path to a
custom config file.
-L {INFO,VERBOSE,DEBUG,TRACE}, --loglevel {INFO,VERBOSE,DEBUG,TRACE}
Log level. Stick with INFO or VERBOSE unless you need
to file an error report. Be careful with TRACE as it
will generate a lot of data
-LF LOGFILE, --logfile LOGFILE
Path to store the logfile. Leave blank to store in the
faceswap folder
-i INPUT_DIR, --input-dir INPUT_DIR
Input directory or video. Either a directory
containing the image files you wish to process or path
to a video file. NB: This should be the source
video/frames NOT the source faces.
-o OUTPUT_DIR, --output-dir OUTPUT_DIR
Output directory. This is where the converted files
will be saved.
-al ALIGNMENTS_PATH, --alignments ALIGNMENTS_PATH
Optional path to an alignments file. Leave blank if
the alignments file is at the default location.
-D {cv2-dnn,mtcnn,s3fd}, --detector {cv2-dnn,mtcnn,s3fd}
Detector to use. Some of these have configurable
settings in '/config/extract.ini' or 'Settings >
Configure Extract 'Plugins':
- cv2-dnn: A CPU only extractor which is the least
reliable and least resource intensive. Use this if
not using a GPU and time is important. - mtcnn: Good detector. Fast on CPU, faster on GPU.
Uses fewer resources than other GPU detectors but
can often return more false positives. - s3fd: Best detector. Fast on GPU, slow on CPU. Can
detect more faces and fewer false positives than
other GPU detectors, but is a lot more resource
intensive.
-A {cv2-dnn,fan}, --aligner {cv2-dnn,fan}
Aligner to use. - cv2-dnn: A CPU only landmark detector. Faster,
less resource intensive, but less accurate. Only
use this if not using a GPU and time is important. - fan: Best aligner. Fast on GPU, slow on CPU.
-M {unet-dfl,vgg-clear,vgg-obstructed} [{unet-dfl,vgg-clear,vgg-obstructed} ...], --masker {unet-dfl,vgg-clear,vgg-obstructed} [{unet-dfl,vgg-clear,vgg-obstructed} ...]
Additional Masker(s) to use. The masks generated here
will all take up GPU RAM. You can select none, one or
multiple masks, but the extraction may take longer the
more you select. NB: The Extended and Components
(landmark based) masks are automatically generated on
extraction. - vgg-clear: Mask designed to provide smart
segmentation of mostly frontal faces clear of
obstructions. Profile faces and obstructions may
result in sub-par performance. - vgg-obstructed: Mask designed to provide smart
segmentation of mostly frontal faces. The mask
model has been specifically trained to recognize
some facial obstructions (hands and eyeglasses).
Profile faces may result in sub-par performance. - unet-dfl: Mask designed to provide smart
segmentation of mostly frontal faces. The mask
model has been trained by community members and
will need testing for further description. Profile
faces may result in sub-par performance.
The auto generated masks are as follows: - components: Mask designed to provide facial
segmentation based on the positioning of landmark
locations. A convex hull is constructed around the
exterior of the landmarks to create a mask. - extended: Mask designed to provide facial
segmentation based on the positioning of landmark
locations. A convex hull is constructed around the
exterior of the landmarks and the mask is extended
upwards onto the forehead.
(eg:-M unet-dfl vgg-clear,--masker vgg-)
obstructed
-nm {none,clahe,hist,mean}, --normalization {none,clahe,hist,mean}
Performing normalization can help the aligner better
align faces with difficult lighting conditions at an
extraction speed cost. Different methods will yield
different results on different sets. NB: This does not
impact the output face, just the input to the aligner. - none: Don't perform normalization on the face.
- clahe: Perform Contrast Limited Adaptive Histogram
Equalization on the face. - hist: Equalize the histograms on the RGB channels.
- mean: Normalize the face colors to the mean.
-r ROTATE_IMAGES, --rotate-images ROTATE_IMAGES
If a face isn't found, rotate the images to try to
find a face. Can find more faces at the cost of
extraction speed. Pass in a single number to use
increments of that size up to 360, or pass in a list
of numbers to enumerate exactly what angles to check.
-min MIN_SIZE, --min-size MIN_SIZE
Filters out faces detected below this size. Length, in
pixels across the diagonal of the bounding box. Set to
0 for off
-n NFILTER [NFILTER ...], --nfilter NFILTER [NFILTER ...]
Optionally filter out people who you do not wish to
process by passing in an image of that person. Should
be a front portrait with a single person in the image.
Multiple images can be added space separated. NB:
Using face filter will significantly decrease
extraction speed and its accuracy cannot be
guaranteed.
-f FILTER [FILTER ...], --filter FILTER [FILTER ...]
Optionally select people you wish to process by
passing in an image of that person. Should be a front
portrait with a single person in the image. Multiple
images can be added space separated. NB: Using face
filter will significantly decrease extraction speed
and its accuracy cannot be guaranteed.
-l REF_THRESHOLD, --ref_threshold REF_THRESHOLD
For use with the optional nfilter/filter files.
Threshold for positive face recognition. Lower values
are stricter. NB: Using face filter will significantly
decrease extraction speed and its accuracy cannot be
guaranteed.
-een EXTRACT_EVERY_N, --extract-every-n EXTRACT_EVERY_N
Extract every 'nth' frame. This option will skip
frames when extracting faces. For example a value of 1
will extract faces from every frame, a value of 10
will extract faces from every 10th frame.
-sz SIZE, --size SIZE
The output size of extracted faces. Make sure that the
model you intend to train supports your required size.
This will only need to be changed for hi-res models.
-si SAVE_INTERVAL, --save-interval SAVE_INTERVAL
Automatically save the alignments file after a set
amount of frames. By default the alignments file is
only saved at the end of the extraction process. NB:
If extracting in 2 passes then the alignments file
will only start to be saved out during the second
pass. WARNING: Don't interrupt the script when writing
the file because it might get corrupted. Set to 0 to
turn off
-dl, --debug-landmarks
Draw landmarks on the ouput faces for debugging
purposes.
-sp, --singleprocess Don't run extraction in parallel. Will run each part
of the extraction process separately (one after the
other) rather than all at the smae time. Useful if
VRAM is at a premium.
-s, --skip-existing Skips frames that have already been extracted and
exist in the alignments file
-sf, --skip-existing-faces
Skip frames that already have detected faces in the
alignments file
Questions and feedback: https://faceswap.dev/forum
faceswap.py extract: error: argument -M/--masker: invalid choice: 'none' (choose from 'unet-dfl', 'vgg-clear', 'vgg-obstructed')
