@tensorflow-models/body-pix
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Pretrained BodyPix model in TensorFlow.js
125 lines • 7.08 kB
JavaScript
;
/**
* @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;
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