@tensorflow-models/body-segmentation
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1.0.2 • Public • Published

Body Segmentation

This package provides models for running real-time body segmentation.

Currently, we provide 2 model options:

MediaPipe SelfieSegmentation:

MediaPipe SelfieSegmentation segments the prominent humans in the scene. It can run in real-time on both smartphones and laptops. The intended use cases include selfie effects and video conferencing, where the person is close (< 2m) to the camera.

BodyPix:

BodyPix can be used to segment an image into pixels that are and are not part of a person, and into pixels that belong to each of twenty-four body parts. It works for multiple people in an input image or video.


Table of Contents

  1. How to Run It
  2. Example Code and Demos
  3. Output Visualization Utility Functions

How to Run It

In general there are two steps:

You first create a detector by choosing one of the models from SupportedModels, including MediaPipeSelfieSegmentation and BodyPix.

For example:

const model = bodySegmentation.SupportedModels.MediaPipeSelfieSegmentation;
const segmenterConfig = {
  runtime: 'mediapipe', // or 'tfjs'
  solutionPath: 'https://cdn.jsdelivr.net/npm/@mediapipe/selfie_segmentation',
  modelType: 'general'
}
const segmenter = await bodySegmentation.createSegmenter(model, segmenterConfig);

Then you can use the segmenter to segment people in the image.

const people = await segmenter.segmentPeople(image);

The returned segmentation list contains the detected people in the image. Note that it is not necessarily the case that there will be one segmentation per one person. Each model will have its own semantics for the segmentation output.

MediaPipe SelfieSegmentation returns exactly one segmentation corresponding to all people in the input image.

BodyPix returns exactly one segmentation corresponding to all people in the input image if multiSegmentation option is false, and otherwise will return multiple segmentations, one per person.

Example output:

[
  {
    maskValueToLabel: (maskValue: number) => { return 'person' },
    mask: {
      toCanvasImageSource(): ...
      toImageData(): ...
      toTensor(): ...
      getUnderlyingType(): ...
    }
  }
]

The mask key stores an object which provides access to the underlying mask image using the conversion functions toCanvasImageSource, toImageData, and toTensor depending on the desired output type. Note that getUnderlyingType can be queried to determine what is the type being used underneath the hood to avoid expensive conversions (such as from tensor to image data).

The semantics of the RGBA values of the mask is as follows: the image mask is the same size as the input image, where green and blue channels are always set to 0. Different red values denote different body parts (see maskValueToLabel key below). Different alpha values denote the probability of pixel being a body part pixel (0 being lowest probability and 255 being highest).

maskValueToLabel maps a foreground pixel’s red value to the segmented part name of that pixel. Should throw error for unsupported input values. This is not necessarily the same across different models (for example MediaPipeSelfieSegmentation will always return 'person' since it does not distinguish individual body parts).

Refer to each model's documentation for specific configurations for the model and their performance.

MediaPipeSelfieSegmentation MediaPipe Documentation

MediaPipeSelfieSegmentation TFJS Documentation

BodyPix Documentation


Example Code and Demos

You may reference the demos for code examples. Details for how to run the demos are included in the demos/ folder.

Output Visualization Utility Functions

Body Segmentation provides utility functions to help with drawing and compositing using the outputs. These utility functions are based on the ones provided by the deprecated BodyPix Package.

bodySegmentation.toBinaryMask

Given a segmentation or array of segmentations, generates an image with foreground and background color at each pixel determined by the corresponding binary segmentation value at the pixel from the output. In other words, pixels where there is a person will be colored with foreground color and where there is not a person will be colored with background color. This can be used as a mask to crop a person or the background when compositing.

Inputs

  • segmentation Single segmentation or array of segmentations, such as the output from segmentPeople.

  • foreground The foreground color (r,g,b,a) for visualizing pixels that belong to people.

  • background The background color (r,g,b,a) for visualizing pixels that don't belong to people.

  • drawContour Whether to draw the contour around each person's segmentation mask.

  • foregroundThresholdProbability The minimum probability to color a pixel as foreground rather than background.

  • foregroundMaskValues The red channel integer values that represent foreground (for more information refer to the mask output documentation).

Returns

An ImageData with the same and width height of the input segmentations, with color and opacity at each pixel determined by the corresponding binary segmentation value at the pixel from the output.

MaskImageData

Given the input shown in the first image above, if it is run through the API's segmentPeople method followed by toBinaryMask, An ImageData that either looks like the second image above if setting foregroundColor to {r: 0, g: 0, b: 0, a: 0} and backgroundColor to {r: 0, g: 0, b: 0, a: 255} (by default), or the third image if if setting foregroundColor to {r: 0, g: 0, b: 0, a: 255} and backgroundColor to {r: 0, g: 0, b: 0, a: 0}. This can be used to mask either the person or the background using the method drawMask.

Example usage

const img = document.getElementById('image');

const segmenter = await bodySegmentation.createSegmenter(bodySegmentation.SupportedModels.MediaPipeSelfieSegmentation);
const segmentation = await segmenter.segmentPeople(img);

// The mask image is an binary mask image with a 1 where there is a person and
// a 0 where there is not.
const coloredPartImage = await bodySegmentation.toBinaryMask(segmentation);
const opacity = 0.7;
const flipHorizontal = false;
const maskBlurAmount = 0;
const canvas = document.getElementById('canvas');
// Draw the mask image on top of the original image onto a canvas.
// The colored part image will be drawn semi-transparent, with an opacity of
// 0.7, allowing for the original image to be visible under.
bodySegmentation.drawMask(
    canvas, img, coloredPartImage, opacity, maskBlurAmount,
    flipHorizontal);

bodySegmentation.toColoredMask

Given a segmentation or array of segmentations, and a function mapping the red pixel values (representing body part labels) to colours, generates an image with the corresponding color for each part at each pixel, and background color used where there is no part.

Inputs

  • segmentation Single segmentation or array of segmentations, such as the output from segmentPeople.

  • maskValueToColor A function mapping red channel mask values to colors to use in output image (for more information refer to the mask output documentation). If using bodyPix with segmentBodyParts on, then bodySegmentation.bodyPixMaskValueToRainbowColor can be used as a default.

  • background The background color (r,g,b,a) for visualizing pixels that don't belong to people.

  • foregroundThresholdProbability The minimum probability to color a pixel as foreground rather than background.

Returns

An ImageData with the same width and height of the input segmentations, with the corresponding color for each part at each pixel, and the background color where there is no part.

Example usage

const img = document.getElementById('image');

const segmenter = await bodySegmentation.createSegmenter(bodySegmentation.SupportedModels.BodyPix);
const segmentation = await segmenter.segmentPeople(img, {multiSegmentation: false, segmentBodyParts: true});

// The colored part image is an rgb image with a corresponding color from the
// rainbow colors for each part at each pixel, and black pixels where there is
// no part.
const coloredPartImage = await bodySegmentation.toColoredMask(segmentation, bodySegmentation.bodyPixMaskValueToRainbowColor, {r: 255, g: 255, b: 255, a: 255}));
const opacity = 0.7;
const flipHorizontal = false;
const maskBlurAmount = 0;
const canvas = document.getElementById('canvas');
// Draw the colored part image on top of the original image onto a canvas.
// The colored part image will be drawn semi-transparent, with an opacity of
// 0.7, allowing for the original image to be visible under.
bodySegmentation.drawMask(
    canvas, img, coloredPartImage, opacity, maskBlurAmount,
    flipHorizontal);

toColoredMask

Given the input shown in the first image above, if it is run through the api using the BodyPix model and the segmentPeople method, followed by toColoredMask, then a 'spectral' or 'rainbow' color scale in toColoredMask will produce an ImageData that looks like the second image or the third image above.

bodySegmentation.drawMask

Draws an image onto a canvas and draws an ImageData containing a mask on top of it with a specified opacity; The ImageData is typically generated using toBinaryMask or toColoredMask.

Inputs

  • canvas The canvas to be drawn onto.
  • image The original image to apply the mask to.
  • maskImage An ImageData containing the mask. Ideally this should be generated by toBinaryMask or toColoredMask.
  • maskOpacity The opacity when drawing the mask on top of the image. Defaults to 0.7. Should be a float between 0 and 1.
  • maskBlurAmount How many pixels to blur the mask by. Defaults to 0. Should be an integer between 0 and 20.
  • flipHorizontal If the result should be flipped horizontally. Defaults to false.

Example usage

const img = document.getElementById('image');

const segmenter = await bodySegmentation.createSegmenter(bodySegmentation.SupportedModels.MediaPipeSelfieSegmentation);
const segmentation = await segmenter.segmentPeople(img);

// Convert the segmentation into a mask to darken the background.
const foregroundColor = {r: 0, g: 0, b: 0, a: 0};
const backgroundColor = {r: 0, g: 0, b: 0, a: 255};
const backgroundDarkeningMask = await bodySegmentation.toBinaryMask(
    segmentation, foregroundColor, backgroundColor);

const opacity = 0.7;
const maskBlurAmount = 3;
const flipHorizontal = false;
const canvas = document.getElementById('canvas');
// Draw the mask onto the image on a canvas.  With opacity set to 0.7 and
// maskBlurAmount set to 3, this will darken the background and blur the
// darkened background's edge.
await bodySegmentation.drawMask(
    canvas, img, backgroundDarkeningMask, opacity, maskBlurAmount, flipHorizontal);

drawMask

The above shows drawing a mask generated by toBinaryMask on top of an image and canvas using drawMask. In this case, segmentationThreshold was set to a lower value of 0.25, making the mask include more pixels. The top two images show the mask drawn on top of the image, and the second two images show the mask blurred by setting maskBlurAmount to 9 before being drawn onto the image, resulting in a smoother transition between the person and the masked background.

bodySegmentation.drawPixelatedMask

Draws an image onto a canvas and draws an ImageData containing a mask on top of it with a specified opacity; The ImageData is typically generated using toColoredMask. Different from drawMask, this rendering function applies the pixelation effect to the body part segmentation prediction. This allows a user to display low resolution body part segmentation and thus offers an aesthetic interpretation of the body part segmentation prediction.

Inputs

  • canvas The canvas to be drawn onto.
  • image The original image to apply the mask to.
  • maskImage An ImageData containing the mask. Ideally this should be generated by toColoredMask.
  • maskOpacity The opacity when drawing the mask on top of the image. Defaults to 0.7. Should be a float between 0 and 1.
  • maskBlurAmount How many pixels to blur the mask by. Defaults to 0. Should be an integer between 0 and 20.
  • flipHorizontal If the result should be flipped horizontally. Defaults to false.
  • pixelCellWidth The width of each pixel cell. Default to 10 px.

Example usage

const img = document.getElementById('image');

const segmenter = await bodySegmentation.createSegmenter(bodySegmentation.SupportedModels.BodyPix);
const segmentation = await segmenter.segmentPeople(img, {multiSegmentation: false, segmentBodyParts: true});

// The colored part image is an rgb image with a corresponding color from the
// rainbow colors for each part at each pixel, and white pixels where there is
// no part.
const coloredPartImage = await bodySegmentation.toColoredMask(segmentation, bodySegmentation.bodyPixMaskValueToRainbowColor, {r: 255, g: 255, b: 255, a: 255}));
const opacity = 0.7;
const flipHorizontal = false;
const maskBlurAmount = 0;
const pixelCellWidth = 10.0;
const canvas = document.getElementById('canvas');
// Draw the pixelated colored part image on top of the original image onto a
// canvas.  Each pixel cell's width will be set to 10 px. The pixelated colored
// part image will be drawn semi-transparent, with an opacity of 0.7, allowing
// for the original image to be visible under.
await bodySegmentation.drawPixelatedMask(
    canvas, img, coloredPartImage, opacity, maskBlurAmount,
    flipHorizontal, pixelCellWidth);

drawPixelatedMask

The pixelation effect is applied to part image using drawPixelatedMask; the result is shown in the image above.

bodySegmentation.drawBokehEffect

Given a segmentation or array of segmentations, and an image, draws the image with its background blurred onto a canvas.

An example of applying a bokeh effect:

Bokeh

Inputs

  • canvas The canvas to draw the background-blurred image onto.
  • image The image to blur the background of and draw.
  • segmentation Single segmentation or array of segmentations.
  • foregroundThreshold The minimum probability to color a pixel as foreground rather than background. Defaults to 0.5. Should be a number between 0 and 1.
  • backgroundBlurAmount How many pixels in the background blend into each other. Defaults to 3. Should be an integer between 1 and 20.
  • edgeBlurAmount How many pixels to blur on the edge between the person and the background by. Defaults to 3. Should be an integer between 0 and 20.
  • flipHorizontal If the output should be flipped horizontally. Defaults to false.

Example Usage

const img = document.getElementById('image');

const segmenter = await bodySegmentation.createSegmenter(bodySegmentation.SupportedModels.MediaPipeSelfieSegmentation);
const segmentation = await segmenter.segmentPeople(img);

const foregroundThreshold = 0.5;
const backgroundBlurAmount = 3;
const edgeBlurAmount = 3;
const flipHorizontal = false;

const canvas = document.getElementById('canvas');
// Draw the image with the background blurred onto the canvas. The edge between
// the person and blurred background is blurred by 3 pixels.
await bodySegmentation.drawBokehEffect(
    canvas, img, segmentation, foregroundThreshold, backgroundBlurAmount,
    edgeBlurAmount, flipHorizontal);

bokeh

The above shows the process of applying a 'bokeh' effect to an image (the left-most one) with drawBokehEffect. An inverted mask is generated from a Segmentation. The original image is then drawn onto the canvas, and using the canvas compositing operation destination-over the mask is drawn onto the canvas, causing the background to be removed. The original image is blurred and drawn onto the canvas where it doesn't overlap with the existing image using the compositing operation destination-over. The result is seen in the right-most image.

bodySegmentation.blurBodyPart

Given a segmentation or array of segmentations, and an image, blurs some person body parts (e.g. left face and right face).

An example of applying a body part blur on left_face and right_face body parts for BodyPix model (other body parts can be specified):

three_people_faceblur

Inputs

  • canvas The canvas to draw the body-part blurred image onto.
  • image The image with people to blur the body-part and draw.
  • segmentation Single segmentation or array of segmentations.
  • maskValuesToBlur An array of red channel mask values to blur (representing different body parts, refer to Segmentation interface for more details).
  • foregroundThreshold The minimum probability to color a pixel as foreground rather than background. Defaults to 0.5. Should be a number between 0 and 1.
  • backgroundBlurAmount How many pixels in the background blend into each other. Defaults to 3. Should be an integer between 1 and 20.
  • edgeBlurAmount How many pixels to blur on the edge between the person and the background by. Defaults to 3. Should be an integer between 0 and 20.
  • flipHorizontal If the output should be flipped horizontally. Defaults to false.

Example Usage

const img = document.getElementById('image');

const segmenter = await bodySegmentation.createSegmenter(bodySegmentation.SupportedModels.BodyPix);
const segmentation = await segmenter.segmentPeople(img, {multiSegmentation: false, segmentBodyParts: true});

const foregroundThreshold = 0.5;
const backgroundBlurAmount = 3;
const edgeBlurAmount = 3;
const flipHorizontal = false;
const faceBodyPartIdsToBlur = [0, 1];

const canvas = document.getElementById('canvas');

await bodySegmentation.blurBodyPart(
    canvas, img, partSegmentation, faceBodyPartIdsToBlur, foregroundThreshold,
    backgroundBlurAmount, edgeBlurAmount, flipHorizontal);

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