QR code color, eval wait for input option, optional tfjs AI models url

src/nodes/AI/BodyPartSegmentation.ts

@@ -1,7 +1,8 @@
 import {
     BooleanVar,
     ImageVar,
-    NumberVar
+    NumberVar,
+    StringVar,
 } from '../io/AbstractIOSet';
 import Node from "../Node";
 import '@tensorflow/tfjs-backend-webgl';
@@ -66,6 +67,9 @@ const inputs = {
         min: 0,
         max: 1,
     } as NumberVar,
+    modelUrl: {
+        type: 'String',
+    } as StringVar,
     ...(flow([
         bodyParts => keyBy(bodyParts, (bodyPart) => bodyPart),
         bodyParts => mapValues(bodyParts, () => ({
@@ -106,6 +110,7 @@ export default class BodyPartSegmentation extends Node<typeof inputs, typeof out
             runtime: 'tfjs',
             modelType: 'general',
             segmentBodyParts: true,
+            modelUrl: this.in.modelUrl.value ? this.in.modelUrl.value : undefined,
         } as BodyPixSegmentationConfig;
         this.segmenter = await createSegmenter(model, segmenterConfig);
     }

src/nodes/AI/BodySegmentation.ts

@@ -1,6 +1,7 @@
 import {
     ImageVar,
-    NumberVar
+    NumberVar,
+    StringVar,
 } from '../io/AbstractIOSet';
 import Node from "../Node";
 import '@tensorflow/tfjs-backend-webgl';
@@ -35,6 +36,9 @@ const inputs = {
         min: 0,
         max: 1,
     } as NumberVar,
+    modelUrl: {
+        type: 'String',
+    } as StringVar,
 }
 
 const outputs = {
@@ -66,7 +70,8 @@ export default class BodySegmentation extends Node<typeof inputs, typeof outputs
         const model = SupportedModels.MediaPipeSelfieSegmentation;
         const segmenterConfig = {
             runtime: 'tfjs',
-            modelType: 'general'
+            modelType: 'general',
+            modelUrl: this.in.modelUrl.value ? this.in.modelUrl.value : undefined,
         } as MediaPipeSelfieSegmentationMediaPipeModelConfig;
         this.segmenter = await createSegmenter(model, segmenterConfig);
     }

src/nodes/AI/FaceFeaturePosition.ts

@@ -1,13 +1,15 @@
 import {
     ImageVar,
-    NumberVar, VariableValueType
+    NumberVar,
+    StringVar,
+    VariableValueType,
 } from '../io/AbstractIOSet';
 import Node from "../Node";
 import '@tensorflow/tfjs-backend-webgl';
 import {Face,
     FaceDetector,
     FaceDetectorInput,
-    MediaPipeFaceDetectorModelConfig,
+    MediaPipeFaceDetectorMediaPipeModelConfig,
     SupportedModels,
     createDetector,
 } from '@tensorflow-models/face-detection';
@@ -22,6 +24,9 @@ const inputs = {
     image: {
         type: 'Image'
     } as ImageVar,
+    modelUrl: {
+        type: 'String',
+    } as StringVar,
 }
 
 const outputs = {
@@ -97,7 +102,8 @@ export default class FaceFeaturePosition extends Node<typeof inputs, typeof outp
             maxFaces: 1,
             modelType: 'short',
             runtime: 'tfjs',
-        } as MediaPipeFaceDetectorModelConfig;
+            detectorModelUrl: this.in.modelUrl.value ? this.in.modelUrl.value : undefined,
+        } as MediaPipeFaceDetectorMediaPipeModelConfig;
         this.detector = await createDetector(model, detectorConfig);
     }
 

src/nodes/Eval.ts

@@ -1,11 +1,16 @@
 import Node from './Node';
 import {
+    ImageVar,
     NumberVar,
     StringVar,
+    BooleanVar,
 } from './io/AbstractIOSet';
 import get from 'lodash/get';
 
 const inputs = {
+    image: {
+        type: 'Image'
+    } as ImageVar,
     i0: {
         type: 'Number',
         default: 0,
@@ -41,16 +46,23 @@ const inputs = {
     formula: {
         type: 'String',
         default: '',
-    } as StringVar
+    } as StringVar,
+    waitForInput: {
+        type: 'Boolean',
+        default: true,
+    } as BooleanVar
 };
 
 const outputs = {
+    image: {
+        type: 'Image'
+    } as ImageVar,
     result: {
         type: 'Number'
     } as NumberVar,
 }
 
-export default class NumberBinaryOperation extends Node<typeof inputs, typeof outputs> {
+export default class Eval extends Node<typeof inputs, typeof outputs> {
     constructor() {
         super('Eval', inputs, outputs);
     }
@@ -61,12 +73,20 @@ export default class NumberBinaryOperation extends Node<typeof inputs, typeof ou
         )
     }
 
+    hasAllConnectedInputsValue() {
+        if (this.in.waitForInput.value) {
+            return super.hasAllConnectedInputsValue();
+        }
+        return true;
+    }
+
     evaluate() {
         try {
             this.out.result.value = eval(this.interpolate());
         } catch (e) {
             console.error(e);
         }
+        this.out.image.value = this.in.image.value;
     }
 
     async _update() {

src/nodes/QRCodeGenerator.ts

@@ -2,6 +2,7 @@ import {
     ImageVar,
     NumberVar,
     StringVar,
+    ColorVar,
 } from './io/AbstractIOSet';
 import Node from './Node';
 import {canvasPool2D} from '../canvas/CanvasPool';
@@ -30,6 +31,10 @@ const inputs = {
         type: 'Number',
         min: 0,
     } as NumberVar,
+    color: {
+        type: 'Color',
+        default: '#000000',
+    } as ColorVar,
 }
 
 const outputs = {
@@ -59,7 +64,12 @@ export default class QRCodeGenerator extends Node<typeof inputs, typeof outputs>
         const encodeHintTypeMap = new Map<EncodeHintType, any>();
         encodeHintTypeMap.set(EncodeHintType.MARGIN, this.in.margin.value);
         encodeHintTypeMap.set(EncodeHintType.ERROR_CORRECTION, QRCodeDecoderErrorCorrectionLevel[this.in.correction.value]);
-        const svgElement = this.writer.write(this.in.text.value, this.in.size.value, this.in.size.value, encodeHintTypeMap);
+        const svgElement: SVGSVGElement = this.writer.write(this.in.text.value, this.in.size.value, this.in.size.value, encodeHintTypeMap);
+
+        for(const element of svgElement.querySelectorAll('[fill]')) {
+            element.setAttribute('fill', this.in.color.value);
+        }
+
         const canvas = canvasPool2D.createCanvas();
         canvas.width = this.in.size.value;
         canvas.height = this.in.size.value;