Virtual Webcam Background allows you to use a virtual background or to blur the background of your webcam image similar to commercial programs like Zoom.
Tensorflow with BodyPix is used to segment the image into foreground (person) and background using a neural network and v4l2loopback is used to create a virtual webcam.
As the script creates a virtual webcam device it works with any program that can use a v4l2 webcam.
See virtual-webcam.com for more information, image packs and more.
The program needs python3 and is tested with python 3.7.
Install the requirements:
pip install -r requirements.txt
Download the bodypix model:
./get-model.sh
Then install v4l2loopback and load the kernel module:
modprobe v4l2loopback exclusive_caps=1 video_nr=2 # creates /dev/video2
The exclusive_caps
option is needed by some programs, such as chromium.
Then copy config.yaml.example
to config.yaml
and edit the config as needed and run
the virtual_webcam.py
script.
If you have a Nvidia graphics card, you may want to install CUDA for better performance.
To configure the virtual webcam, edit config.yaml
. Most options are applied instantly,
except for width
and height
as the webcam must be reinitialized to change them and
multiplier
and output_stride
as the model must be reloaded to apply them.
width
: The input resolution width.height
: The input resolution height.fps
: The input framerate.mjpeg
: Use mjpeg as input format. This may be faster than the default format.segmentation_threshold
: The threshold for foreground / background segmentation.blur
: Blur factor for the mask to smooth the edges.dilate
: Number of pixels the mask is shrunk to remove spots.erode
: Number of pixels the mask is grown after shrinking to capture the full body image again.real_video_device
: The video device of your webcam, e.g./dev/video0
.average_masks
: Number of masks to average. A higher number will result in afterimages, a smaller number in flickering at the boundary between foreground and background.layers
: A list of videos layers like the input webcam image, the segmented foreground, virtual backgrounds or image overlays.debug_show_mask
: Debug option to show the mask, that can be used to configure blur/dilate/erode correctly.model
:mobilenet
(faster) orresnet50
(more accurate). You need to download the matching model, when you change the parameter.multiplier
: Multiplier parameter of the mobilenet model (0.5, 0.75 or 1.0). You need to download the matching model when you change this parameter.output_stride
: Stride parameter of the model (16 or 8 formobilenet
and 16 or 32 forresnet50
). You need to download the matching model when you change the parameter.internal_resolution
: Resolution factor (between 0.0 and 1.0) for the model input. Smaller is faster and less accurate. Note that 1.0 does not always give the best results.
Note: Input width
and height
are autodetected when they are not set in the config,
but this can lead to bad default values, e.g., 640x480
even when the camera supports
a resolution of 1280x720
.
The layers option contains one image source and a list of filters. The image sources are:
input
: The webcam imageforeground
: The foreground of the image, i.e., the person.previous
: The image composed of all previous layers.empty
: A transparent image.
Each layer has a list of filters, that are applied in the given order. After all filters are applied, the layer is merged with the previous layers.
Each layer has a list of filters. A simple example that converts the background to grayscale and blurs it looks like this:
- input: ["grayscale", "blur"]
Some filters have arguments. To change the blur value in the filter list above, you can use onf of these syntax variants:
- Flat list:
["grayscale", ["blur", 10, 10]]
- Argument list:
["grayscale", ["blur", [10, 10]]]
- Keyword arguments:
["grayscale", ["blur", {intensity_x: 10, intensity_y: 10}]]
- layers:
- empty: [["image", "background.jpg"]]
- foreground: []
- layers:
- input: [["blur", 10]]
- foreground: []
- layers:
- input: [["blur", 10]]
- foreground: []
- previous: [["image", "images/fog.jpg"], ["roll", 5, 0]]
The current filters and their options are:
image
: Returns a static image, e.g., to use a virtual background.image_path
: The path to the image file.interpolation_method
: The interpolation method. Currently areLINEAR
andNEAREST
supported andLINEAR
is the default. When you use a pixel art background, it may look better withNEAREST
.
image_sequence
: Returns images from an image sequence. This can be used for animated backgrounds or overlays.images_path
: The path to a folder containing the images. The folder must only contain images and they must have the correct order when they are sorted by filename.fps
: The frames per second of the animation.interpolation_method
:LINEAR
orNEAREST
interpolation
video
: Returns images from a video. This can be used for animated backgrounds or overlays.video_path
: The path to the video.target_fps
: The target frames per second of image sequence generated from the video. This can be used to reduce the RAM usage.interpolation_method
:LINEAR
orNEAREST
interpolation
blur
: Blur the image.iintensity_x
: The intensity in the x direction.intensity_y
: The intensity in the y direction. When onlyintensity_x
is given, it will be used forintensity_y
as well.
gaussian_blur
: Blur the image using a Gaussian blur. It looks better than normal box blur, but is more CPU intensive.intensity_x
: The intensity in the x direction. Must be an odd value: even values are bumped to the next odd value.intensity_y
: The intensity in the y direction. Must be an odd value: even values are bumped to the next odd value. When onlyintensity_x
is given, it will be used forintensity_y
as well.
grayscale
: Convert the image into a grayscale image.roll
: move an image with a constant speed. This is mostly useful for overlays.speed_x
: Speed in x direction.speed_y
: Speed in y direction.
change_alpha
: Change the transparency of an image.change_alpha
: Alpha value to add (between-255
and255
)alpha_min
,alpha_max
: Transparency levels to clip the resulting alpha value.
colorize
: Change the image to grayscale and then color it with a given color.r
,g
,b
: RGB values.
color_filter
: Change the color levels by multiplying the RGB values with a factor between0
and255
.r
,g
,b
: The factors for the colors red/green/blue.
solid_color
: Fill the image with a single color.r
,g
,b
: RGB values.
flip
: Flip the image horizontally or vertically.horizontal
: Flip horizontally.vertical
: Flip vertically.
stripes
: Add a semi-transparent stripe effect with darker and lighter stripes.width
: Width of a stripe.intensity
: Intensity how much darker/lighter the stripe is.speed
: Speed at which the stripes move across the image.
chroma_key
: Convert a color to transparency (green screen effect).r
,g
,b
: RGB values.fuzz
: Factor for fuzzy matching of similar colors.
If you have a video, you can use the video
filter:
- "empty": [["video", "my-video.mp4"]]
Another option are image sequences, that allow for example to use transparent PNGs.
Example config for loading an image sequence from the folder "animation" and playing it with 5 frames per second:
- empty: [["image_sequence", "frames", 5]]
The program tries to load frames/*.*
and you need to make sure that the folder only contains images and that
the images are ordered correctly when they are sorted by filename.
Example for creating an image sequence from a short video and adding alpha transparency for a green screen effect using ffmpeg and ImageMagick:
mkdir animation
cd animation
ffmpeg -i ../animation.webm -vf fps=10 out%04d.png
mogrify -fuzz 10% -transparent 'rgb(0,129,27)' *
When using the ffmpeg
command, you can change the output framerate using the fps
parameter.
Note that the script loads all images of an animation into RAM scaled to the resolution of your webcam, so using too long animations is not a good idea.
To download other models get the full get-model.sh
script from https://github.com/ajaichemmanam/simple_bodypix_python and run it with one of these combinations:
./get-model.sh bodypix/mobilenet/float/{025,050,075,100}/model-stride{8,16}
./get-model.sh bodypix/resnet50/float/model-stride{16,32}
Example config for mobilenet
:
- model: mobilenet
- multiplier: 0.5
- output_stride: 16
Example config for resnet50
:
- model: resnet50
- output_stride: 16
- The program is inspired by this blog post by Benjamin Elder.
- Linux-Fake-Background-Webcam is a direct implementation of the blog post using docker and nodejs in addition to python.
- simple_bodypix_python was the base for an earlier version of the script.
- The functions in
bodypix_functions.py
are adapted from the body-pix nodejs module.