text: "use strict";
  var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
      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) : new P(function (resolve) { resolve(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 (_) 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 };
      }
  };
  var _this = this;
  Object.defineProperty(exports, "__esModule", { value: true });
  var tf = require("@tensorflow/tfjs");
  var checkpoint_loader_1 = require("./checkpoint_loader");
  var checkpoints_1 = require("./checkpoints");
  var mobilenet_1 = require("./mobilenet");
  var model_weights_1 = require("./model_weights");
  var decode_multiple_poses_1 = require("./multi_pose/decode_multiple_poses");
  var decode_single_pose_1 = require("./single_pose/decode_single_pose");
  var util_1 = require("./util");
  var PoseNet = (function () {
      function PoseNet(mobileNet) {
          this.mobileNet = mobileNet;
      }
      PoseNet.prototype.predictForSinglePose = function (input, outputStride) {
          var _this = this;
          if (outputStride === void 0) { outputStride = 16; }
          mobilenet_1.assertValidOutputStride(outputStride);
          return tf.tidy(function () {
              var mobileNetOutput = _this.mobileNet.predict(input, outputStride);
              var heatmaps = _this.mobileNet.convToOutput(mobileNetOutput, 'heatmap_2');
              var offsets = _this.mobileNet.convToOutput(mobileNetOutput, 'offset_2');
              return { heatmapScores: heatmaps.sigmoid(), offsets: offsets };
          });
      };
      PoseNet.prototype.predictForMultiPose = function (input, outputStride) {
          var _this = this;
          if (outputStride === void 0) { outputStride = 16; }
          return tf.tidy(function () {
              var mobileNetOutput = _this.mobileNet.predict(input, outputStride);
              var heatmaps = _this.mobileNet.convToOutput(mobileNetOutput, 'heatmap_2');
              var offsets = _this.mobileNet.convToOutput(mobileNetOutput, 'offset_2');
              var displacementFwd = _this.mobileNet.convToOutput(mobileNetOutput, 'displacement_fwd_2');
              var displacementBwd = _this.mobileNet.convToOutput(mobileNetOutput, 'displacement_bwd_2');
              return {
                  heatmapScores: heatmaps.sigmoid(),
                  offsets: offsets,
                  displacementFwd: displacementFwd,
                  displacementBwd: displacementBwd
              };
          });
      };
      PoseNet.prototype.estimateSinglePose = function (input, imageScaleFactor, flipHorizontal, outputStride) {
          if (imageScaleFactor === void 0) { imageScaleFactor = 0.5; }
          if (flipHorizontal === void 0) { flipHorizontal = false; }
          if (outputStride === void 0) { outputStride = 16; }
          return __awaiter(this, void 0, void 0, function () {
              var _a, height, width, resizedHeight, resizedWidth, _b, heatmapScores, offsets, pose, scaleY, scaleX;
              var _this = this;
              return __generator(this, function (_c) {
                  switch (_c.label) {
                      case 0:
                          mobilenet_1.assertValidOutputStride(outputStride);
                          mobilenet_1.assertValidScaleFactor(imageScaleFactor);
                          _a = util_1.getInputTensorDimensions(input), height = _a[0], width = _a[1];
                          resizedHeight = util_1.getValidResolution(imageScaleFactor, height, outputStride);
                          resizedWidth = util_1.getValidResolution(imageScaleFactor, width, outputStride);
                          _b = tf.tidy(function () {
                              var inputTensor = util_1.toResizedInputTensor(input, resizedHeight, resizedWidth, flipHorizontal);
                              return _this.predictForSinglePose(inputTensor, outputStride);
                          }), heatmapScores = _b.heatmapScores, offsets = _b.offsets;
                          return [4, decode_single_pose_1.decodeSinglePose(heatmapScores, offsets, outputStride)];
                      case 1:
                          pose = _c.sent();
                          scaleY = height / resizedHeight;
                          scaleX = width / resizedWidth;
                          heatmapScores.dispose();
                          offsets.dispose();
                          return [2, util_1.scalePose(pose, scaleY, scaleX)];
                  }
              });
          });
      };
      PoseNet.prototype.estimateMultiplePoses = function (input, imageScaleFactor, flipHorizontal, outputStride, maxDetections, scoreThreshold, nmsRadius) {
          if (imageScaleFactor === void 0) { imageScaleFactor = 0.5; }
          if (flipHorizontal === void 0) { flipHorizontal = false; }
          if (outputStride === void 0) { outputStride = 16; }
          if (maxDetections === void 0) { maxDetections = 5; }
          if (scoreThreshold === void 0) { scoreThreshold = .5; }
          if (nmsRadius === void 0) { nmsRadius = 20; }
          return __awaiter(this, void 0, void 0, function () {
              var _a, height, width, resizedHeight, resizedWidth, _b, heatmapScores, offsets, displacementFwd, displacementBwd, poses, scaleY, scaleX;
              var _this = this;
              return __generator(this, function (_c) {
                  switch (_c.label) {
                      case 0:
                          mobilenet_1.assertValidOutputStride(outputStride);
                          mobilenet_1.assertValidScaleFactor(imageScaleFactor);
                          _a = util_1.getInputTensorDimensions(input), height = _a[0], width = _a[1];
                          resizedHeight = util_1.getValidResolution(imageScaleFactor, height, outputStride);
                          resizedWidth = util_1.getValidResolution(imageScaleFactor, width, outputStride);
                          _b = tf.tidy(function () {
                              var inputTensor = util_1.toResizedInputTensor(input, resizedHeight, resizedWidth, flipHorizontal);
                              return _this.predictForMultiPose(inputTensor, outputStride);
                          }), heatmapScores = _b.heatmapScores, offsets = _b.offsets, displacementFwd = _b.displacementFwd, displacementBwd = _b.displacementBwd;
                          return [4, decode_multiple_poses_1.decodeMultiplePoses(heatmapScores, offsets, displacementFwd, displacementBwd, outputStride, maxDetections, scoreThreshold, nmsRadius)];
                      case 1:
                          poses = _c.sent();
                          heatmapScores.dispose();
                          offsets.dispose();
                          displacementFwd.dispose();
                          displacementBwd.dispose();
                          scaleY = height / resizedHeight;
                          scaleX = width / resizedWidth;
                          return [2, util_1.scalePoses(poses, scaleY, scaleX)];
                  }
              });
          });
      };
      PoseNet.prototype.dispose = function () {
          this.mobileNet.dispose();
      };
      return PoseNet;
  }());
  exports.PoseNet = PoseNet;
  function load(multiplier) {
      if (multiplier === void 0) { multiplier = 1.01; }
      return __awaiter(this, void 0, void 0, function () {
          var possibleMultipliers, mobileNet;
          return __generator(this, function (_a) {
              switch (_a.label) {
                  case 0:
                      if (tf == null) {
                          throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please " +
                              "also include @tensorflow/tfjs on the page before using this model.");
                      }
                      possibleMultipliers = Object.keys(checkpoints_1.checkpoints);
                      tf.util.assert(typeof multiplier === 'number', function () { return "got multiplier type of " + typeof multiplier + " when it should be a " +
                          "number."; });
                      tf.util.assert(possibleMultipliers.indexOf(multiplier.toString()) >= 0, function () { return "invalid multiplier value of " + multiplier + ".  No checkpoint exists for that " +
                          ("multiplier. Must be one of " + possibleMultipliers.join(',') + "."); });
                      return [4, exports.mobilenetLoader.load(multiplier)];
                  case 1:
                      mobileNet = _a.sent();
                      return [2, new PoseNet(mobileNet)];
              }
          });
      });
  }
  exports.load = load;
  exports.mobilenetLoader = {
      load: function (multiplier) { return __awaiter(_this, void 0, void 0, function () {
          var checkpoint, checkpointLoader, variables, weights;
          return __generator(this, function (_a) {
              switch (_a.label) {
                  case 0:
                      checkpoint = checkpoints_1.checkpoints[multiplier];
                      checkpointLoader = new checkpoint_loader_1.CheckpointLoader(checkpoint.url);
                      return [4, checkpointLoader.getAllVariables()];
                  case 1:
                      variables = _a.sent();
                      weights = new model_weights_1.ModelWeights(variables);
                      return [2, new mobilenet_1.MobileNet(weights, checkpoint.architecture)];
              }
          });
      }); },
  };
  //# sourceMappingURL=posenet_model.js.map