@nsfw-filter/nsfwjs
Version:
Detect NSFW content client side
295 lines • 14 kB
JavaScript
"use strict";
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var __generator = (this && this.__generator) || function (thisArg, body) {
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case 0: case 1: t = op; break;
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var __importDefault = (this && this.__importDefault) || function (mod) {
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Object.defineProperty(exports, "__esModule", { value: true });
exports.NSFWJS = exports.load = void 0;
var tf = __importStar(require("@tensorflow/tfjs"));
var nsfw_classes_1 = require("./nsfw_classes");
var gif_frames_1 = __importDefault(require("@nsfw-filter/gif-frames"));
var BASE_PATH = "https://s3.amazonaws.com/ir_public/nsfwjscdn/TFJS_nsfw_mobilenet/tfjs_quant_nsfw_mobilenet/";
var IMAGE_SIZE = 224;
function load(base, options) {
if (base === void 0) { base = BASE_PATH; }
if (options === void 0) { options = { size: IMAGE_SIZE }; }
return __awaiter(this, void 0, void 0, function () {
var nsfwnet;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
if (tf == null) {
throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please " +
"also include @tensorflow/tfjs on the page before using this model.");
}
options.size = options.size || IMAGE_SIZE;
nsfwnet = new NSFWJS(base, options);
return [4, nsfwnet.load()];
case 1:
_a.sent();
return [2, nsfwnet];
}
});
});
}
exports.load = load;
var NSFWJS = (function () {
function NSFWJS(modelPathBaseOrIOHandler, options) {
this.intermediateModels = {};
this.options = options;
this.normalizationOffset = tf.scalar(255);
if (typeof modelPathBaseOrIOHandler === "string") {
this.pathOrIOHandler = modelPathBaseOrIOHandler + "model.json";
}
else {
this.pathOrIOHandler = modelPathBaseOrIOHandler;
}
}
NSFWJS.prototype.load = function () {
return __awaiter(this, void 0, void 0, function () {
var _a, size, type, _b, _c, result;
var _this = this;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
_a = this.options, size = _a.size, type = _a.type;
if (!(type === "graph")) return [3, 2];
_b = this;
return [4, tf.loadGraphModel(this.pathOrIOHandler)];
case 1:
_b.model = _d.sent();
return [3, 4];
case 2:
_c = this;
return [4, tf.loadLayersModel(this.pathOrIOHandler)];
case 3:
_c.model = _d.sent();
this.endpoints = this.model.layers.map(function (l) { return l.name; });
_d.label = 4;
case 4:
result = tf.tidy(function () {
return _this.model.predict(tf.zeros([1, size, size, 3]));
});
return [4, result.data()];
case 5:
_d.sent();
result.dispose();
return [2];
}
});
});
};
NSFWJS.prototype.infer = function (img, endpoint) {
var _this = this;
if (endpoint != null && this.endpoints.indexOf(endpoint) === -1) {
throw new Error("Unknown endpoint " + endpoint + ". Available endpoints: " +
(this.endpoints + "."));
}
return tf.tidy(function () {
if (!(img instanceof tf.Tensor)) {
img = tf.browser.fromPixels(img);
}
var normalized = img
.toFloat()
.div(_this.normalizationOffset);
var resized = normalized;
var size = _this.options.size;
if (img.shape[0] !== size || img.shape[1] !== size) {
var alignCorners = true;
resized = tf.image.resizeBilinear(normalized, [size, size], alignCorners);
}
var batched = resized.reshape([1, size, size, 3]);
var model;
if (endpoint == null) {
model = _this.model;
}
else {
if (_this.model.hasOwnProperty("layers") &&
_this.intermediateModels[endpoint] == null) {
var layer = _this.model.layers.find(function (l) { return l.name === endpoint; });
_this.intermediateModels[endpoint] = tf.model({
inputs: _this.model.inputs,
outputs: layer.output,
});
}
model = _this.intermediateModels[endpoint];
}
return model.predict(batched);
});
};
NSFWJS.prototype.classify = function (img, topk) {
if (topk === void 0) { topk = 5; }
return __awaiter(this, void 0, void 0, function () {
var logits, classes;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
logits = this.infer(img);
return [4, getTopKClasses(logits, topk)];
case 1:
classes = _a.sent();
logits.dispose();
return [2, classes];
}
});
});
};
NSFWJS.prototype.classifyGif = function (gif, config) {
if (config === void 0) { config = { topk: 5 }; }
return __awaiter(this, void 0, void 0, function () {
var frameData, acceptedFrames, totalTimeInMs, i, totalFrames, interval, i, arrayOfPredictions, i, image, predictions;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
frameData = [];
if (!Buffer.isBuffer(gif)) return [3, 2];
return [4, gif_frames_1.default({ url: gif, frames: 'all', outputType: 'jpg' })];
case 1:
frameData = _a.sent();
return [3, 4];
case 2: return [4, gif_frames_1.default({ url: gif.src, frames: 'all', outputType: 'canvas' })];
case 3:
frameData = _a.sent();
_a.label = 4;
case 4:
acceptedFrames = [];
if (typeof config.fps !== 'number') {
acceptedFrames = frameData.map(function (_element, index) { return index; });
}
else {
totalTimeInMs = 0;
for (i = 0; i < frameData.length; i++) {
totalTimeInMs = totalTimeInMs + (frameData[i].frameInfo.delay * 10);
}
totalFrames = Math.floor(totalTimeInMs / 1000 * config.fps);
if (totalFrames <= 1) {
acceptedFrames = [Math.floor(frameData.length / 2)];
}
else if (totalFrames >= frameData.length) {
acceptedFrames = frameData.map(function (_element, index) { return index; });
}
else {
interval = Math.floor(frameData.length / (totalFrames + 1));
for (i = 1; i <= totalFrames; i++) {
acceptedFrames.push(i * interval);
}
}
}
arrayOfPredictions = [];
i = 0;
_a.label = 5;
case 5:
if (!(i < acceptedFrames.length)) return [3, 8];
image = frameData[acceptedFrames[i]].getImage();
return [4, this.classify(image, config.topk)];
case 6:
predictions = _a.sent();
if (typeof config.onFrame === 'function') {
config.onFrame({
index: acceptedFrames[i],
totalFrames: frameData.length,
predictions: predictions,
image: image
});
}
arrayOfPredictions.push(predictions);
_a.label = 7;
case 7:
i++;
return [3, 5];
case 8: return [2, arrayOfPredictions];
}
});
});
};
return NSFWJS;
}());
exports.NSFWJS = NSFWJS;
function getTopKClasses(logits, topK) {
return __awaiter(this, void 0, void 0, function () {
var values, valuesAndIndices, i, topkValues, topkIndices, i, topClassesAndProbs, i;
return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4, logits.data()];
case 1:
values = _a.sent();
valuesAndIndices = [];
for (i = 0; i < values.length; i++) {
valuesAndIndices.push({ value: values[i], index: i });
}
valuesAndIndices.sort(function (a, b) {
return b.value - a.value;
});
topkValues = new Float32Array(topK);
topkIndices = new Int32Array(topK);
for (i = 0; i < topK; i++) {
topkValues[i] = valuesAndIndices[i].value;
topkIndices[i] = valuesAndIndices[i].index;
}
topClassesAndProbs = [];
for (i = 0; i < topkIndices.length; i++) {
topClassesAndProbs.push({
className: nsfw_classes_1.NSFW_CLASSES[topkIndices[i]],
probability: topkValues[i],
});
}
return [2, topClassesAndProbs];
}
});
});
}
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