59 lines
No EOL
3.3 KiB
JavaScript
59 lines
No EOL
3.3 KiB
JavaScript
"use strict";
|
|
Object.defineProperty(exports, "__esModule", { value: true });
|
|
var tslib_1 = require("tslib");
|
|
var utils_1 = require("../utils");
|
|
var awaitMediaLoaded_1 = require("./awaitMediaLoaded");
|
|
var isMediaElement_1 = require("./isMediaElement");
|
|
var NetInput_1 = require("./NetInput");
|
|
var resolveInput_1 = require("./resolveInput");
|
|
/**
|
|
* Validates the input to make sure, they are valid net inputs and awaits all media elements
|
|
* to be finished loading.
|
|
*
|
|
* @param input The input, which can be a media element or an array of different media elements.
|
|
* @returns A NetInput instance, which can be passed into one of the neural networks.
|
|
*/
|
|
function toNetInput(inputs) {
|
|
return tslib_1.__awaiter(this, void 0, void 0, function () {
|
|
var inputArgArray, getIdxHint, inputArray;
|
|
return tslib_1.__generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0:
|
|
if (inputs instanceof NetInput_1.NetInput) {
|
|
return [2 /*return*/, inputs];
|
|
}
|
|
inputArgArray = Array.isArray(inputs)
|
|
? inputs
|
|
: [inputs];
|
|
if (!inputArgArray.length) {
|
|
throw new Error('toNetInput - empty array passed as input');
|
|
}
|
|
getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; };
|
|
inputArray = inputArgArray.map(resolveInput_1.resolveInput);
|
|
inputArray.forEach(function (input, i) {
|
|
if (!isMediaElement_1.isMediaElement(input) && !utils_1.isTensor3D(input) && !utils_1.isTensor4D(input)) {
|
|
if (typeof inputArgArray[i] === 'string') {
|
|
throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
|
|
}
|
|
throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id");
|
|
}
|
|
if (utils_1.isTensor4D(input)) {
|
|
// if tf.Tensor4D is passed in the input array, the batch size has to be 1
|
|
var batchSize = input.shape[0];
|
|
if (batchSize !== 1) {
|
|
throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
|
|
}
|
|
}
|
|
});
|
|
// wait for all media elements being loaded
|
|
return [4 /*yield*/, Promise.all(inputArray.map(function (input) { return isMediaElement_1.isMediaElement(input) && awaitMediaLoaded_1.awaitMediaLoaded(input); }))];
|
|
case 1:
|
|
// wait for all media elements being loaded
|
|
_a.sent();
|
|
return [2 /*return*/, new NetInput_1.NetInput(inputArray, Array.isArray(inputs))];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
exports.toNetInput = toNetInput;
|
|
//# sourceMappingURL=toNetInput.js.map
|