publish models

main
Vladimir Mandic 2021-09-25 06:58:43 -04:00
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# human-models
Human library optional models repository
# Human Models
[Human main repository](https://github.com/vladmandic/human)
For model details and credits see [documentation in the main library](https://github.com/vladmandic/human/wiki/Models)
Default models are included in the main library
Optional models are published here to keep the size of the main library contained

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# Human Library: Models
For details see Wiki:
- [**List of Models & Credits**](https://github.com/vladmandic/human/wiki/Models)

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{
"format": "graph-model",
"generatedBy": "https://github.com/oarriaga/face_classification",
"convertedBy": "https://github.com/vladmandic",
"userDefinedMetadata":
{
"signature":
{
"inputs":
{
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},
"outputs":
{
"Identity:0":{"name":"Identity:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"-1"},{"size":"7"}]}}
}
}
},
"modelTopology":
{
"node":
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{"name":"StatefulPartitionedCall/model_1/predictions/Softmax","op":"Softmax","input":["StatefulPartitionedCall/model_1/global_average_pooling2d_1/Mean"],"attr":{"T":{"type":"DT_FLOAT"}}},
{"name":"Identity","op":"Identity","input":["StatefulPartitionedCall/model_1/predictions/Softmax"],"attr":{"T":{"type":"DT_FLOAT"}}}
],
"library": {},
"versions": {}
},
"weightsManifest":
[
{
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"weights": [{"name":"unknown_26","shape":[3,3,128,1],"dtype":"float32"},{"name":"unknown_32","shape":[3,3,256,1],"dtype":"float32"},{"name":"unknown_9","shape":[3,3,64,1],"dtype":"float32"},{"name":"unknown_15","shape":[3,3,128,1],"dtype":"float32"},{"name":"unknown_43","shape":[3,3,256,7],"dtype":"float32"},{"name":"unknown_44","shape":[7],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/global_average_pooling2d_1/Mean/reduction_indices","shape":[2],"dtype":"int32"},{"name":"StatefulPartitionedCall/model_1/conv2d_1/Conv2D_weights","shape":[3,3,1,32],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_1/Conv2D_bn_offset","shape":[32],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_2/Conv2D_weights","shape":[3,3,32,64],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_4/separable_conv2d_weights","shape":[1,1,256,256],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_2/Conv2D_bn_offset","shape":[64],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_3/Conv2D_weights","shape":[1,1,64,128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_4/separable_conv2d_bn_offset","shape":[256],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_3/Conv2D_bn_offset","shape":[128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_1/separable_conv2d_weights","shape":[1,1,64,128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_1/separable_conv2d_bn_offset","shape":[128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_2/separable_conv2d_weights","shape":[1,1,128,128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_2/separable_conv2d_bn_offset","shape":[128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_4/Conv2D_weights","shape":[1,1,128,256],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_4/Conv2D_bn_offset","shape":[256],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_3/separable_conv2d_weights","shape":[1,1,128,256],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_3/separable_conv2d_bn_offset","shape":[256],"dtype":"float32"}]
}
]
}

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import { log } from '../log';
import * as tf from '../../dist/tfjs.esm.js';
import * as profile from '../profile';
export class FaceBoxes {
enlarge: number;
model: any;
config: any;
constructor(model, config) {
this.enlarge = 1.1;
this.model = model;
this.config = config;
}
async estimateFaces(input, config) {
if (config) this.config = config;
const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];
const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);
const castT = resizeT.toInt();
let scores;
let boxes;
if (!config.profile) {
const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);
scores = scoresT.dataSync();
const squeezeT = boxesT.squeeze();
boxes = squeezeT.arraySync();
scoresT.dispose();
boxesT.dispose();
squeezeT.dispose();
numT.dispose();
} else {
const profileData = await tf.profile(() => this.model.executeAsync(castT));
scores = profileData.result[0].dataSync();
const squeezeT = profileData.result[1].squeeze();
boxes = squeezeT.arraySync();
profileData.result.forEach((t) => t.dispose());
profile.run('faceboxes', profileData);
}
castT.dispose();
resizeT.dispose();
for (const i in boxes) {
if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {
const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];
const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];
const box = [
parseInt((boxRaw[0] * input.shape[2]).toString()),
parseInt((boxRaw[1] * input.shape[1]).toString()),
parseInt((boxRaw[2] * input.shape[2]).toString()),
parseInt((boxRaw[3] * input.shape[1]).toString())];
const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);
const image = resized.div([255]);
resized.dispose();
results.push({ confidence: scores[i], box, boxRaw, image });
// add mesh, meshRaw, annotations,
}
}
return results;
}
}
export async function load(config) {
const model = await tf.loadGraphModel(config.face.detector.modelPath);
if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\/(.*)\./)[1]}`);
const faceboxes = new FaceBoxes(model, config);
if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\/(.*)\./)[1]}`);
if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\/(.*)\./)[1]}`);
return faceboxes;
}

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