@joshyzou/localskinconditiondetector
Version:
Detects 15 of the most common skin conditions. (local version)
238 lines (224 loc) • 13.5 kB
JavaScript
module.exports = async function everything(path){
const fs = require("fs");
var everyresult;
var __assign = (this && this.__assign) || function () {
__assign = Object.assign || function(t) {
for (var s, i = 1, n = arguments.length; i < n; i++) {
s = arguments[i];
for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p))
t[p] = s[p];
}
return t;
};
return __assign.apply(this, arguments);
};
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
var __spreadArrays = (this && this.__spreadArrays) || function () {
for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;
for (var r = Array(s), k = 0, i = 0; i < il; i++)
for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)
r[k] = a[j];
return r;
};
Object.defineProperty(exports, "__esModule", { value: true });
/*
-------------------------------------------------------------
START OF ACTUAL THING
-------------------------------------------------------------
*/
var tf = require("@tensorflow/tfjs-node");
var fs_1 = require("fs");
var ImageModel = /** @class */ (function () {
function ImageModel(signaturePath) {
var _a;
this.outputKey = "Confidences";
//var signatureData = fs_1.readFileSync(signaturePath, "utf8");
this.signature = signaturePath
this.modelPath = this.signature.filename;
_a = this.signature.inputs.Image.shape.slice(1, 3), this.width = _a[0], this.height = _a[1];
this.outputName = this.signature.outputs[this.outputKey].name;
this.classes = this.signature.classes.Label;
}
ImageModel.prototype.load = function () {
return __awaiter(this, void 0, void 0, function () {
var _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this;
return [4 /*yield*/, tf.loadGraphModel(this.modelPath)];
case 1:
_a.model = _b.sent();
return [2 /*return*/];
}
});
});
};
ImageModel.prototype.dispose = function () {
if (this.model) {
this.model.dispose();
}
};
ImageModel.prototype.predict = function (image) {
var _a, _b;
/*
Given an input image decoded by tensorflow as a tensor,
preprocess the image into pixel values of [0,1], center crop to a square
and resize to the image input size, then run the prediction!
*/
if (!!this.model) {
var _c = image.shape.slice(0, 2), imgHeight = _c[0], imgWidth = _c[1];
// convert image to 0-1
var normalizedImage = tf.div(image, tf.scalar(255));
// make into a batch of 1 so it is shaped [1, height, width, 3]
var reshapedImage = normalizedImage.reshape(__spreadArrays([1], normalizedImage.shape));
// center crop and resize
var top = 0;
var left = 0;
var bottom = 1;
var right = 1;
if (imgHeight != imgWidth) {
// the crops are normalized 0-1 percentage of the image dimension
var size = Math.min(imgHeight, imgWidth);
left = (imgWidth - size) / 2 / imgWidth;
top = (imgHeight - size) / 2 / imgHeight;
right = (imgWidth + size) / 2 / imgWidth;
bottom = (imgHeight + size) / 2 / imgHeight;
}
var croppedImage = tf.image.cropAndResize(reshapedImage, [[top, left, bottom, right]], [0], [this.height, this.width]);
var results = this.model.execute((_a = {}, _a[this.signature.inputs.Image.name] = croppedImage, _a), this.outputName);
var resultsArray_1 = results.dataSync();
return _b = {},
_b[this.outputKey] = this.classes.reduce(function (acc, class_, idx) {
var _a;
return __assign((_a = {}, _a[class_] = resultsArray_1[idx], _a), acc);
}, {}),
_b;
}
else {
console.error("Model not loaded, please await this.load() first.");
}
};
return ImageModel;
}());
function main(imgPath) {
return __awaiter(this, void 0, void 0, function () {
var image, decodedImage, model, results;
return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, fs_1.promises.readFile(imgPath)];
case 1:
image = _a.sent();
decodedImage = tf.node.decodeImage(image, 3);
//model = new ImageModel(__dirname.slice(0, (__dirname.length-8))+"/signature.json");
model = new ImageModel({"doc_id": "a381ef82-d100-4f40-97aa-cb96dba9b4cb", "doc_name": "Skin Detector", "doc_version": "ce7f0dda7a79fc3a7b953c5ea19ecf04", "format": "tf_js", "version": 10, "inputs": {"Image": {"dtype": "float32", "shape": [null, 224, 224, 3], "name": "Image:0"}}, "outputs": {"Confidences": {"dtype": "float32", "shape": [null, 17], "name": "f2dfc9a4-5fed-46d8-a134-dd27efdeef98/dense_2/Softmax:0"}}, "tags": null, "classes": {"Label": ["Acne", "Actinic Keratosis", "Basal Skin Cancer", "Blister", "Cellulitis", "Chickenpox", "Cold_sore", "Keratosis-pilaris", "lupus", "measles", "melanoma", "melesma", "normal", "psoriasis", "ringworm", "rosacea", "vitiligo"]}, "filename":"file://"+ __dirname.slice(0, (__dirname.length-8))+"/model.json"})
return [4 /*yield*/, model.load()];
case 2:
_a.sent();
results = model.predict(decodedImage);
//let arrResult = Object.values(results);
const getMax = object => {
return Object.keys(object).filter(x => {
return object[x] == Math.max.apply(null,
Object.values(object));
});
};
let largest = getMax(results.Confidences)
if (largest[0].toLowerCase().includes("actinic")){
model.dispose();
everyresult = {status: "ok", type: "actinic-keratosis", confidence: results.Confidences[largest[0]]}
}else if (largest[0].toLowerCase().includes("acne")){
model.dispose();
everyresult = {status: "ok", type: "acne", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("basal")){
model.dispose();
everyresult = {status: "ok", type: "basal-cell-cancer", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("blister")){
model.dispose();
everyresult ={status: "ok", type: "blister", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("cellulitis")){
model.dispose();
everyresult ={status: "ok", type: "cellulitis", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("chicken")){
model.dispose();
everyresult ={status: "ok", type: "chickenpox", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("cold")){
model.dispose();
everyresult ={status: "ok", type: "cold-sore", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("keratosis")){
model.dispose();
everyresult ={status: "ok", type: "keratosis-pilaris", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("lupus")){
model.dispose();
everyresult ={status: "ok", type: "lupus", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("measle")){
model.dispose();
everyresult ={status: "ok", type: "measles", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("ringworm")){
model.dispose();
everyresult ={status: "ok", type: "ringworm", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("melanoma")){
model.dispose();
everyresult ={status: "ok", type: "melanoma", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("melesma")){
model.dispose();
everyresult ={status: "ok", type: "melasma", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("psoriasis")){
model.dispose();
everyresult ={status: "ok", type: "psoriasis", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("rosacea")){
model.dispose();
everyresult ={status: "ok", type: "rosacea", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("vitiligo")){
model.dispose();
everyresult ={status: "ok", type: "vitiligo", confidence: results.Confidences[largest[0]]};
}else if (largest[0].toLowerCase().includes("normal")){
model.dispose();
everyresult ={status: "ok", type: "no-conditions", confidence: results.Confidences[largest[0]]};
}else{
model.dispose();
everyresult ={status: "ok", type: "no-conditions", confidence: results.Confidences[largest[0]]};
}
return [2 /*return*/];
}
});
});
}
await main(path);
return everyresult
}