face-api.js
Version:
JavaScript API for face detection and face recognition in the browser with tensorflow.js
81 lines • 4.42 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var classes_1 = require("../classes");
var ops_1 = require("../ops");
var extractImagePatches_1 = require("./extractImagePatches");
var MtcnnBox_1 = require("./MtcnnBox");
var ONet_1 = require("./ONet");
function stage3(img, inputBoxes, scoreThreshold, params, stats) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var ts, onetInputs, onetOuts, scoresTensor, scores, _a, _b, indices, filteredRegions, filteredBoxes, filteredScores, finalBoxes, finalScores, points, indicesNms;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0:
ts = Date.now();
return [4 /*yield*/, extractImagePatches_1.extractImagePatches(img, inputBoxes, { width: 48, height: 48 })];
case 1:
onetInputs = _c.sent();
stats.stage3_extractImagePatches = Date.now() - ts;
ts = Date.now();
onetOuts = onetInputs.map(function (onetInput) {
var out = ONet_1.ONet(onetInput, params);
onetInput.dispose();
return out;
});
stats.stage3_onet = Date.now() - ts;
scoresTensor = onetOuts.length > 1
? tf.concat(onetOuts.map(function (out) { return out.scores; }))
: onetOuts[0].scores;
_b = (_a = Array).from;
return [4 /*yield*/, scoresTensor.data()];
case 2:
scores = _b.apply(_a, [_c.sent()]);
scoresTensor.dispose();
indices = scores
.map(function (score, idx) { return ({ score: score, idx: idx }); })
.filter(function (c) { return c.score > scoreThreshold; })
.map(function (_a) {
var idx = _a.idx;
return idx;
});
filteredRegions = indices.map(function (idx) {
var regionsData = onetOuts[idx].regions.arraySync();
return new MtcnnBox_1.MtcnnBox(regionsData[0][0], regionsData[0][1], regionsData[0][2], regionsData[0][3]);
});
filteredBoxes = indices
.map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
filteredScores = indices.map(function (idx) { return scores[idx]; });
finalBoxes = [];
finalScores = [];
points = [];
if (filteredBoxes.length > 0) {
ts = Date.now();
indicesNms = ops_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7, false);
stats.stage3_nms = Date.now() - ts;
finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
points = indicesNms.map(function (idx, i) {
return Array(5).fill(0).map(function (_, ptIdx) {
var pointsData = onetOuts[idx].points.arraySync();
return new classes_1.Point(((pointsData[0][ptIdx] * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((pointsData[0][ptIdx + 5] * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
});
});
}
onetOuts.forEach(function (t) {
t.regions.dispose();
t.scores.dispose();
t.points.dispose();
});
return [2 /*return*/, {
boxes: finalBoxes,
scores: finalScores,
points: points
}];
}
});
});
}
exports.stage3 = stage3;
//# sourceMappingURL=stage3.js.map