Virtual Webcam Background

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.

Installation

The program needs python 3.5–3.8 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 sudo apt install v4l2loopback-dkms 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.

Python version

Due to the dependencies, the script does not work with tf-nightly. This means you need a python version that is supported by the stable tensorflow packages. When your distribution, for example, only ships python3.9, you need to install python3.8 yourself. You can have a look at this HowTo for installing Python 3.8 on Debian 10.

Troubleshooting

Problems opening the video device

Problems like this

  File "./virtual_webcam.py", line 101, in <module>
    fakewebcam = FakeWebcam(config.get("virtual_video_device"), width, height)
  File "my_venv/lib/python3.8/site-packages/pyfakewebcam/pyfakewebcam.py", line 54, in __init__
    fcntl.ioctl(self._video_device, _v4l2.VIDIOC_S_FMT, self._settings)
OSError: [Errno 22] Invalid argument

mean in most cases, that you're using the wrong video devices. Try to play the stream from the real device using a media player. You can make sure you recognize the loopback device by choosing a high number when loading the kernel module:

modprobe v4l2loopback video_nr=10 exclusive_caps=1 # Creates /dev/video10

Then your webcam will most likely have the device /dev/video0 and the fake cam the device /dev/video10.

Problems using the fake camera in chromium-based browsers

Make sure to load the v4l2loopback kernel module with the exclusive_caps=1 parameter.

Animated GIF support

Animated gifs do NOT work as a background with the image filter, but they work with the video filter.

Configuration

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) or resnet50 (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 for mobilenet and 16 or 32 for resnet50). 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.

Layers

The layers option contains one image source and a list of filters. The image sources are:

• input: The webcam image
• foreground: 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.

Filters

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}]]

Example Layers

A virtual background

- layers:
  - empty: [["image", "background.jpg"]]
  - foreground: []

Blurred background

- layers:
  - input: [["blur", 10]]
  - foreground: []

Blurred background and a moving fog overlay

- layers:
  - input: [["blur", 10]]
  - foreground: []
  - previous: [["image", "images/fog.jpg"], ["roll", 5, 0]]

Filters

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 are LINEAR and NEAREST supported and LINEAR is the default. When you use a pixel art background, it may look better with NEAREST.
• 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 or NEAREST 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 or NEAREST interpolation
• blur: Blur the image.i
  • intensity_x: The intensity in the x direction.
  • intensity_y: The intensity in the y direction. When only intensity_x is given, it will be used for intensity_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 only intensity_x is given, it will be used for intensity_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 and 255)
  • 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 between 0 and 255.
  • 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.

Videos

If you have a video, you can use the video filter:

- "empty": [["video", "my-video.mp4"]]

Image Sequences

Another option are image sequences, that allow for example to use transparent PNGs.

Example

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.

Advanced

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

License

• The project is licensed under GPLv3. A copy of the license can be found in the LICENSE file.
• The the file bodypix_functions.py may be used unter the terms of the MIT license.

Acknowledgements

• 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.