BodySegmentation node

package-lock.json

@@ -1,6 +1,6 @@
 {
   "name": "graphfx",
-  "version": "0.3.3",
+  "version": "0.4.0",
   "lockfileVersion": 1,
   "requires": true,
   "dependencies": {
@@ -3806,6 +3806,11 @@
         "@jridgewell/sourcemap-codec": "1.4.14"
       }
     },
+    "@mediapipe/selfie_segmentation": {
+      "version": "0.1.1675465747",
+      "resolved": "https://registry.npmjs.org/@mediapipe/selfie_segmentation/-/selfie_segmentation-0.1.1675465747.tgz",
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@@ -3830,6 +3835,62 @@
         "@sinonjs/commons": "^1.7.0"
       }
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+    "@tensorflow-models/body-segmentation": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/@tensorflow-models/body-segmentation/-/body-segmentation-1.0.1.tgz",
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+        "@types/seedrandom": "^2.4.28",
+        "@types/webgl-ext": "0.0.30",
+        "@webgpu/types": "0.1.21",
+        "long": "4.0.0",
+        "node-fetch": "~2.6.1",
+        "seedrandom": "^3.0.5"
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@@ -3956,6 +4026,11 @@
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       "dev": true
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+    "@webgpu/types": {
+      "version": "0.1.21",
+      "resolved": "https://registry.npmjs.org/@webgpu/types/-/types-0.1.21.tgz",
+      "integrity": "sha512-pUrWq3V5PiSGFLeLxoGqReTZmiiXwY3jRkIG5sLLKjyqNxrwm/04b4nw7LSmGWJcKk59XOM/YRTUwOzo4MMlow=="
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-      "integrity": "sha1-ibTRmasr7kneFk6gK4nORi1xt2c=",
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@@ -4272,7 +4351,6 @@
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@@ -5925,8 +5999,7 @@
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       "dev": true
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+    "node-fetch": {
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+      "resolved": "https://registry.npmjs.org/node-fetch/-/node-fetch-2.6.9.tgz",
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+      }
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@@ -12285,8 +12369,7 @@
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@@ -12656,6 +12747,11 @@
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+    "seedrandom": {
+      "version": "3.0.5",
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@@ -13105,6 +13201,11 @@
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@@ -13270,6 +13371,20 @@
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+    "webidl-conversions": {
+      "version": "3.0.1",
+      "resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-3.0.1.tgz",
+      "integrity": "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ=="
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+      "requires": {
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package.json

@@ -1,6 +1,6 @@
 {
   "name": "graphfx",
-  "version": "0.3.3",
+  "version": "0.4.0",
   "description": "Graph image processing pipeline",
   "main": "src/index.ts",
   "scripts": {
@@ -29,10 +29,15 @@
     "typescript": "^3.7.3"
   },
   "dependencies": {
+    "@mediapipe/selfie_segmentation": "^0.1.1675465747",
+    "@tensorflow-models/body-segmentation": "^1.0.1",
+    "@tensorflow/tfjs-backend-webgl": "^4.2.0",
+    "@tensorflow/tfjs-converter": "^4.2.0",
+    "@tensorflow/tfjs-core": "^4.2.0",
     "@types/css-font-loading-module": "0.0.2",
     "@types/uuid": "^3.4.6",
-    "uuid": "^3.3.3",
+    "lodash": "^4.17.21",
-    "lodash": "^4.17.21"
+    "uuid": "^3.3.3"
   },
   "jest": {
     "setupFiles": [

src/nodes/AI/BodySegmentation.ts

@@ -0,0 +1,155 @@
+import {
+    ImageVar,
+    NumberVar
+} from '../io/AbstractIOSet';
+import Node from "../Node";
+import '@tensorflow/tfjs-backend-webgl';
+import {
+    SupportedModels,
+    createSegmenter,
+    toBinaryMask,
+    drawMask,
+    BodySegmenter,
+    MediaPipeSelfieSegmentationMediaPipeModelConfig,
+    BodySegmenterInput,
+} from '@tensorflow-models/body-segmentation'
+import {canvasPool2D} from "../../canvas/CanvasPool";
+import {waitForMedia} from "../../utils";
+
+const inputs = {
+    image: {
+        type: 'Image'
+    } as ImageVar,
+    width: {
+        type: 'Number',
+        default: 100,
+        step: 1,
+        min: 1
+    } as NumberVar,
+    height: {
+        type: 'Number',
+        default: 100,
+        step: 1,
+        min: 1,
+    } as NumberVar,
+    // blur mask edges by number of pixels
+    maskBlurAmount: {
+        type: 'Number',
+        default: 0,
+        step: 1,
+    } as NumberVar,
+    // Default to 0.5. The minimum probability to color a pixel as foreground rather than background. The alpha channel integer values will be taken as the probabilities (for more information refer to Segmentation type's documentation).
+    foregroundThreshold: {
+        type: 'Number',
+        default: 0.5,
+        step: 0.1,
+        min: 0,
+        max: 1,
+    } as NumberVar,
+}
+
+const outputs = {
+    image: {
+        type: 'Image'
+    } as ImageVar,
+    originalImage: {
+        type: 'Image'
+    } as ImageVar,
+    width: {
+        type: 'Number',
+    } as NumberVar,
+    height: {
+        type: 'Number',
+    } as NumberVar,
+}
+
+export default class BodySegmentation extends Node<typeof inputs, typeof outputs> {
+    private segmenter: BodySegmenter
+
+    constructor(options={}) {
+        super('BodySegmentation', inputs, outputs);
+    }
+
+    get width() {
+        return this.__in.width.value;
+    }
+
+    get height() {
+        return this.__in.height.value;
+    }
+
+    get image() {
+        if (!this.__in.image.value) {
+            return null;
+        } else {
+            const {width, height} = this.__in.image.value;
+            return {
+                image: this.__in.image.value,
+                top: 0,
+                left: 0,
+                width,
+                height,
+            };
+        }
+    }
+
+    async initSegmenter() {
+        if (this.segmenter) {
+            return;
+        }
+        const model = SupportedModels.MediaPipeSelfieSegmentation;
+        const segmenterConfig = {
+            runtime: 'tfjs',
+            modelType: 'general'
+        } as MediaPipeSelfieSegmentationMediaPipeModelConfig;
+        this.segmenter = await createSegmenter(model, segmenterConfig);
+    }
+
+    async segmentPeople() {
+        await this.initSegmenter();
+        await waitForMedia(this.in.image.value);
+        if (!this.in.image.value) {
+            return;
+        }
+
+        const originalImageCanvas = canvasPool2D.createCanvas();
+        originalImageCanvas.width = this.in.width.value;
+        originalImageCanvas.height = this.in.height.value
+        originalImageCanvas.acquire();
+        const originalCanvasCtx = originalImageCanvas.getContext('2d');
+
+        originalCanvasCtx.drawImage(this.in.image.value as CanvasImageSource | OffscreenCanvas, 0, 0);
+
+        const segmentations = await this.segmenter.segmentPeople(this.in.image.value as BodySegmenterInput);
+
+        const foregroundColor = {r: 0, g: 0, b: 0, a: 0};
+        const backgroundColor = {r: 0, g: 0, b: 0, a: 255};
+        const foregroundThreshold = this.in.maskBlurAmount.value;
+        const backgroundDarkeningMask = await toBinaryMask(
+            segmentations,
+            foregroundColor,
+            backgroundColor,
+            false,
+            foregroundThreshold
+        );
+
+
+        const canvas = canvasPool2D.createCanvas();
+        canvas.acquire();
+        const ctx = canvas.getContext('2d');
+        canvas.width = this.in.width.value;
+        canvas.height = this.in.height.value
+
+        const maskBlurAmount = this.in.maskBlurAmount.value;
+        await drawMask(canvas, canvas as CanvasImageSource, backgroundDarkeningMask, 1, maskBlurAmount);
+
+        this.out.image.value = canvas;
+        this.out.originalImage.value = originalImageCanvas;
+        this.out.width.value = this.in.width.value;
+        this.out.height.value = this.in.height.value;
+    }
+
+    async _update(){
+        await this.segmentPeople();
+    }
+}

src/nodes/index.ts

@@ -26,3 +26,4 @@ export {default as Font} from './Font';
 export {default as Disable} from './Disable';
 // export {default as Synchronize} from './Synchronize';
 export {default as Api} from './Api';
+export {default as BodySegmentation} from './AI/BodySegmentation';