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@tensorflow-models/body-pix

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Pretrained BodyPix model in TensorFlow.js

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"use strict"; /** * @license * Copyright 2019 Google LLC. 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.decodeSinglePose = void 0; var keypoints_1 = require("../keypoints"); var argmax2d_1 = require("./argmax2d"); var util_1 = require("./util"); /** * Detects a single pose and finds its parts from part scores and offset * vectors. It returns a single pose detection. It works as follows: * argmax2d is done on the scores to get the y and x index in the heatmap * with the highest score for each part, which is essentially where the * part is most likely to exist. This produces a tensor of size 17x2, with * each row being the y and x index in the heatmap for each keypoint. * The offset vector for each for each part is retrieved by getting the * y and x from the offsets corresponding to the y and x index in the * heatmap for that part. This produces a tensor of size 17x2, with each * row being the offset vector for the corresponding keypoint. * To get the keypoint, each part’s heatmap y and x are multiplied * by the output stride then added to their corresponding offset vector, * which is in the same scale as the original image. * * @param heatmapScores 3-D tensor with shape `[height, width, numParts]`. * The value of heatmapScores[y, x, k]` is the score of placing the `k`-th * object part at position `(y, x)`. * * @param offsets 3-D tensor with shape `[height, width, numParts * 2]`. * The value of [offsets[y, x, k], offsets[y, x, k + numParts]]` is the * short range offset vector of the `k`-th object part at heatmap * position `(y, x)`. * * @param outputStride The output stride that was used when feed-forwarding * through the PoseNet model. Must be 32, 16, or 8. * * @return A promise that resolves with single pose with a confidence score, * which contains an array of keypoints indexed by part id, each with a score * and position. */ function decodeSinglePose(heatmapScores, offsets, outputStride) { return __awaiter(this, void 0, void 0, function () { var totalScore, heatmapValues, _a, scoresBuffer, offsetsBuffer, heatmapValuesBuffer, offsetPoints, offsetPointsBuffer, keypointConfidence, keypoints; return __generator(this, function (_b) { switch (_b.label) { case 0: totalScore = 0.0; heatmapValues = (0, argmax2d_1.argmax2d)(heatmapScores); return [4 /*yield*/, Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()])]; case 1: _a = _b.sent(), scoresBuffer = _a[0], offsetsBuffer = _a[1], heatmapValuesBuffer = _a[2]; offsetPoints = (0, util_1.getOffsetPoints)(heatmapValuesBuffer, outputStride, offsetsBuffer); return [4 /*yield*/, offsetPoints.buffer()]; case 2: offsetPointsBuffer = _b.sent(); keypointConfidence = Array.from((0, util_1.getPointsConfidence)(scoresBuffer, heatmapValuesBuffer)); keypoints = keypointConfidence.map(function (score, keypointId) { totalScore += score; return { position: { y: offsetPointsBuffer.get(keypointId, 0), x: offsetPointsBuffer.get(keypointId, 1) }, part: keypoints_1.PART_NAMES[keypointId], score: score }; }); heatmapValues.dispose(); offsetPoints.dispose(); return [2 /*return*/, { keypoints: keypoints, score: totalScore / keypoints.length }]; } }); }); } exports.decodeSinglePose = decodeSinglePose; //# sourceMappingURL=decode_single_pose.js.map