@tensorflow-models/body-pix
Version:
Pretrained BodyPix model in TensorFlow.js
152 lines • 9.93 kB
JavaScript
;
/**
* @license
* Copyright 2019 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
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 (g && (g = 0, op[0] && (_ = 0)), _) 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 };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.decodePersonInstancePartMasks = exports.decodePersonInstanceMasks = exports.toPersonKPartSegmentation = exports.toPersonKSegmentation = void 0;
var tf = require("@tensorflow/tfjs-core");
var tfjs_core_1 = require("@tensorflow/tfjs-core");
var decode_multiple_masks_cpu_1 = require("./decode_multiple_masks_cpu");
var decode_multiple_masks_webgl_1 = require("./decode_multiple_masks_webgl");
function toPersonKSegmentation(segmentation, k) {
return tf.tidy(function () { return tf.cast(tf.equal(segmentation, tf.scalar(k)), 'int32'); });
}
exports.toPersonKSegmentation = toPersonKSegmentation;
function toPersonKPartSegmentation(segmentation, bodyParts, k) {
return tf.tidy(function () { return tf.sub(tf.mul(tf.cast(tf.equal(segmentation, tf.scalar(k)), 'int32'), tf.add(bodyParts, 1)), 1); });
}
exports.toPersonKPartSegmentation = toPersonKPartSegmentation;
function isWebGlBackend() {
return (0, tfjs_core_1.getBackend)() === 'webgl';
}
function decodePersonInstanceMasks(segmentation, longOffsets, poses, height, width, stride, _a, padding, minPoseScore, refineSteps, minKeypointScore, maxNumPeople) {
var inHeight = _a[0], inWidth = _a[1];
if (minPoseScore === void 0) { minPoseScore = 0.2; }
if (refineSteps === void 0) { refineSteps = 8; }
if (minKeypointScore === void 0) { minKeypointScore = 0.3; }
if (maxNumPeople === void 0) { maxNumPeople = 10; }
return __awaiter(this, void 0, void 0, function () {
var posesAboveScore, personSegmentationsData, personSegmentations, segmentationsData, longOffsetsData;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
posesAboveScore = poses.filter(function (pose) { return pose.score >= minPoseScore; });
if (!isWebGlBackend()) return [3 /*break*/, 2];
personSegmentations = tf.tidy(function () {
var masksTensorInfo = (0, decode_multiple_masks_webgl_1.decodeMultipleMasksWebGl)(segmentation, longOffsets, posesAboveScore, height, width, stride, [inHeight, inWidth], padding, refineSteps, minKeypointScore, maxNumPeople);
var masksTensor = tf.engine().makeTensorFromDataId(masksTensorInfo.dataId, masksTensorInfo.shape, masksTensorInfo.dtype);
return posesAboveScore.map(function (_, k) { return toPersonKSegmentation(masksTensor, k); });
});
return [4 /*yield*/, Promise.all(personSegmentations.map(function (mask) { return mask.data(); }))];
case 1:
personSegmentationsData =
(_b.sent());
personSegmentations.forEach(function (x) { return x.dispose(); });
return [3 /*break*/, 5];
case 2: return [4 /*yield*/, segmentation.data()];
case 3:
segmentationsData = _b.sent();
return [4 /*yield*/, longOffsets.data()];
case 4:
longOffsetsData = _b.sent();
personSegmentationsData = (0, decode_multiple_masks_cpu_1.decodeMultipleMasksCPU)(segmentationsData, longOffsetsData, posesAboveScore, height, width, stride, [inHeight, inWidth], padding, refineSteps);
_b.label = 5;
case 5: return [2 /*return*/, personSegmentationsData.map(function (data, i) { return ({ data: data, pose: posesAboveScore[i], width: width, height: height }); })];
}
});
});
}
exports.decodePersonInstanceMasks = decodePersonInstanceMasks;
function decodePersonInstancePartMasks(segmentation, longOffsets, partSegmentation, poses, height, width, stride, _a, padding, minPoseScore, refineSteps, minKeypointScore, maxNumPeople) {
var inHeight = _a[0], inWidth = _a[1];
if (minPoseScore === void 0) { minPoseScore = 0.2; }
if (refineSteps === void 0) { refineSteps = 8; }
if (minKeypointScore === void 0) { minKeypointScore = 0.3; }
if (maxNumPeople === void 0) { maxNumPeople = 10; }
return __awaiter(this, void 0, void 0, function () {
var posesAboveScore, partSegmentationsByPersonData, partSegmentations, segmentationsData, longOffsetsData, partSegmentaionData;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
posesAboveScore = poses.filter(function (pose) { return pose.score >= minPoseScore; });
if (!isWebGlBackend()) return [3 /*break*/, 2];
partSegmentations = tf.tidy(function () {
var masksTensorInfo = (0, decode_multiple_masks_webgl_1.decodeMultipleMasksWebGl)(segmentation, longOffsets, posesAboveScore, height, width, stride, [inHeight, inWidth], padding, refineSteps, minKeypointScore, maxNumPeople);
var masksTensor = tf.engine().makeTensorFromDataId(masksTensorInfo.dataId, masksTensorInfo.shape, masksTensorInfo.dtype);
return posesAboveScore.map(function (_, k) {
return toPersonKPartSegmentation(masksTensor, partSegmentation, k);
});
});
return [4 /*yield*/, Promise.all(partSegmentations.map(function (x) { return x.data(); }))];
case 1:
partSegmentationsByPersonData =
(_b.sent());
partSegmentations.forEach(function (x) { return x.dispose(); });
return [3 /*break*/, 6];
case 2: return [4 /*yield*/, segmentation.data()];
case 3:
segmentationsData = _b.sent();
return [4 /*yield*/, longOffsets.data()];
case 4:
longOffsetsData = _b.sent();
return [4 /*yield*/, partSegmentation.data()];
case 5:
partSegmentaionData = _b.sent();
partSegmentationsByPersonData = (0, decode_multiple_masks_cpu_1.decodeMultiplePartMasksCPU)(segmentationsData, longOffsetsData, partSegmentaionData, posesAboveScore, height, width, stride, [inHeight, inWidth], padding, refineSteps);
_b.label = 6;
case 6: return [2 /*return*/, partSegmentationsByPersonData.map(function (data, k) { return ({ pose: posesAboveScore[k], data: data, height: height, width: width }); })];
}
});
});
}
exports.decodePersonInstancePartMasks = decodePersonInstancePartMasks;
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