"use strict";
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/**
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* @license
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* Copyright 2018 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
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step((generator = generator.apply(thisArg, _arguments || [])).next());
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});
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};
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var __generator = (this && this.__generator) || function (thisArg, body) {
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var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
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return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
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function verb(n) { return function (v) { return step([n, v]); }; }
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function step(op) {
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if (f) throw new TypeError("Generator is already executing.");
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while (_) try {
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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;
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if (y = 0, t) op = [op[0] & 2, t.value];
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switch (op[0]) {
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case 0: case 1: t = op; break;
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case 4: _.label++; return { value: op[1], done: false };
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case 5: _.label++; y = op[1]; op = [0]; continue;
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case 7: op = _.ops.pop(); _.trys.pop(); continue;
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default:
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if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
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if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
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}
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op = body.call(thisArg, _);
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} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
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}
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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var non_max_suppression_impl_1 = require("../backends/non_max_suppression_impl");
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var engine_1 = require("../engine");
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var tensor_util_env_1 = require("../tensor_util_env");
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var util = require("../util");
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var operation_1 = require("./operation");
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/**
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* Bilinear resize a batch of 3D images to a new shape.
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*
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* @param images The images, of rank 4 or rank 3, of shape
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* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
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* @param size The new shape `[newHeight, newWidth]` to resize the
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* images to. Each channel is resized individually.
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* @param alignCorners Defaults to False. If true, rescale
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* input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
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* corners of images and resized images. If false, rescale by
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* `new_height / height`. Treat similarly the width dimension.
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*/
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/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
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function resizeBilinear_(images, size, alignCorners) {
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if (alignCorners === void 0) { alignCorners = false; }
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var $images = tensor_util_env_1.convertToTensor(images, 'images', 'resizeBilinear');
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util.assert($images.rank === 3 || $images.rank === 4, function () { return "Error in resizeBilinear: x must be rank 3 or 4, but got " +
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("rank " + $images.rank + "."); });
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util.assert(size.length === 2, function () { return "Error in resizeBilinear: new shape must 2D, but got shape " +
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(size + "."); });
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var batchImages = $images;
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var reshapedTo4D = false;
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if ($images.rank === 3) {
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reshapedTo4D = true;
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batchImages =
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$images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
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}
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var newHeight = size[0], newWidth = size[1];
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var forward = function (backend, save) {
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save([batchImages]);
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return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);
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};
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var backward = function (dy, saved) {
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return {
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x: function () { return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.resizeBilinearBackprop(dy, saved[0], alignCorners); }, {}); }
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};
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};
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var res = engine_1.ENGINE.runKernelFunc(forward, { x: batchImages }, backward, 'ResizeBilinear', { alignCorners: alignCorners, newHeight: newHeight, newWidth: newWidth });
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if (reshapedTo4D) {
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return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
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}
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return res;
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}
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/**
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* NearestNeighbor resize a batch of 3D images to a new shape.
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*
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* @param images The images, of rank 4 or rank 3, of shape
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* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
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* @param size The new shape `[newHeight, newWidth]` to resize the
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* images to. Each channel is resized individually.
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* @param alignCorners Defaults to False. If true, rescale
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* input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
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* corners of images and resized images. If false, rescale by
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* `new_height / height`. Treat similarly the width dimension.
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*/
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/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
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function resizeNearestNeighbor_(images, size, alignCorners) {
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if (alignCorners === void 0) { alignCorners = false; }
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var $images = tensor_util_env_1.convertToTensor(images, 'images', 'resizeNearestNeighbor');
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util.assert($images.rank === 3 || $images.rank === 4, function () { return "Error in resizeNearestNeighbor: x must be rank 3 or 4, but got " +
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("rank " + $images.rank + "."); });
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util.assert(size.length === 2, function () {
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return "Error in resizeNearestNeighbor: new shape must 2D, but got shape " +
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(size + ".");
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});
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util.assert($images.dtype === 'float32' || $images.dtype === 'int32', function () { return '`images` must have `int32` or `float32` as dtype'; });
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var batchImages = $images;
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var reshapedTo4D = false;
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if ($images.rank === 3) {
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reshapedTo4D = true;
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batchImages =
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$images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
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}
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var newHeight = size[0], newWidth = size[1];
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var forward = function (backend, save) {
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save([batchImages]);
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return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);
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};
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var backward = function (dy, saved) {
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return {
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batchImages: function () { return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.resizeNearestNeighborBackprop(dy, saved[0], alignCorners); }, {}); }
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};
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};
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var res = engine_1.ENGINE.runKernelFunc(forward, { batchImages: batchImages }, backward);
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if (reshapedTo4D) {
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return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
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}
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return res;
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}
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/**
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* Performs non maximum suppression of bounding boxes based on
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* iou (intersection over union).
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*
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* @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
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* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
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* the bounding box.
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* @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
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* @param maxOutputSize The maximum number of boxes to be selected.
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* @param iouThreshold A float representing the threshold for deciding whether
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* boxes overlap too much with respect to IOU. Must be between [0, 1].
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* Defaults to 0.5 (50% box overlap).
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* @param scoreThreshold A threshold for deciding when to remove boxes based
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* on score. Defaults to -inf, which means any score is accepted.
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* @return A 1D tensor with the selected box indices.
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*/
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/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
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function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
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if (iouThreshold === void 0) { iouThreshold = 0.5; }
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if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
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var $boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
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var $scores = tensor_util_env_1.convertToTensor(scores, 'scores', 'nonMaxSuppression');
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var inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
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maxOutputSize = inputs.maxOutputSize;
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iouThreshold = inputs.iouThreshold;
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scoreThreshold = inputs.scoreThreshold;
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var attrs = { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold };
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return engine_1.ENGINE.runKernelFunc(function (b) { return b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); }, { boxes: $boxes, scores: $scores }, null /* grad */, 'NonMaxSuppressionV3', attrs);
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}
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/** This is the async version of `nonMaxSuppression` */
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function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
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if (iouThreshold === void 0) { iouThreshold = 0.5; }
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if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
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return __awaiter(this, void 0, void 0, function () {
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var $boxes, $scores, inputs, boxesAndScores, boxesVals, scoresVals, res;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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$boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
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$scores = tensor_util_env_1.convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
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inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
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maxOutputSize = inputs.maxOutputSize;
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iouThreshold = inputs.iouThreshold;
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scoreThreshold = inputs.scoreThreshold;
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return [4 /*yield*/, Promise.all([$boxes.data(), $scores.data()])];
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case 1:
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boxesAndScores = _a.sent();
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boxesVals = boxesAndScores[0];
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scoresVals = boxesAndScores[1];
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res = non_max_suppression_impl_1.nonMaxSuppressionV3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
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if ($boxes !== boxes) {
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$boxes.dispose();
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}
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if ($scores !== scores) {
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$scores.dispose();
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}
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return [2 /*return*/, res];
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}
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});
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});
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}
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/**
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* Performs non maximum suppression of bounding boxes based on
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* iou (intersection over union).
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*
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* This op also supports a Soft-NMS mode (c.f.
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* Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
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* of other overlapping boxes, therefore favoring different regions of the image
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* with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`
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* parameter to be larger than 0.
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*
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* @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
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* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
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* the bounding box.
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* @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
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* @param maxOutputSize The maximum number of boxes to be selected.
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* @param iouThreshold A float representing the threshold for deciding whether
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* boxes overlap too much with respect to IOU. Must be between [0, 1].
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* Defaults to 0.5 (50% box overlap).
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* @param scoreThreshold A threshold for deciding when to remove boxes based
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* on score. Defaults to -inf, which means any score is accepted.
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* @param softNmsSigma A float representing the sigma parameter for Soft NMS.
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* When sigma is 0, it falls back to nonMaxSuppression.
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* @return A map with the following properties:
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* - selectedIndices: A 1D tensor with the selected box indices.
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* - selectedScores: A 1D tensor with the corresponding scores for each
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* selected box.
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*/
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/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
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function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
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if (iouThreshold === void 0) { iouThreshold = 0.5; }
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if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
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if (softNmsSigma === void 0) { softNmsSigma = 0.0; }
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var $boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
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var $scores = tensor_util_env_1.convertToTensor(scores, 'scores', 'nonMaxSuppression');
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var inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
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maxOutputSize = inputs.maxOutputSize;
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iouThreshold = inputs.iouThreshold;
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scoreThreshold = inputs.scoreThreshold;
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softNmsSigma = inputs.softNmsSigma;
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var attrs = { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold, softNmsSigma: softNmsSigma };
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var result = engine_1.ENGINE.runKernel('NonMaxSuppressionV5', { boxes: $boxes, scores: $scores }, attrs);
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return { selectedIndices: result[0], selectedScores: result[1] };
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}
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/** This is the async version of `nonMaxSuppressionWithScore` */
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function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
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if (iouThreshold === void 0) { iouThreshold = 0.5; }
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if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
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if (softNmsSigma === void 0) { softNmsSigma = 0.0; }
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return __awaiter(this, void 0, void 0, function () {
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var $boxes, $scores, inputs, boxesAndScores, boxesVals, scoresVals, res;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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$boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
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$scores = tensor_util_env_1.convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
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inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
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maxOutputSize = inputs.maxOutputSize;
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iouThreshold = inputs.iouThreshold;
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scoreThreshold = inputs.scoreThreshold;
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softNmsSigma = inputs.softNmsSigma;
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return [4 /*yield*/, Promise.all([$boxes.data(), $scores.data()])];
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case 1:
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boxesAndScores = _a.sent();
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boxesVals = boxesAndScores[0];
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scoresVals = boxesAndScores[1];
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res = non_max_suppression_impl_1.nonMaxSuppressionV5(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
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if ($boxes !== boxes) {
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$boxes.dispose();
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}
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if ($scores !== scores) {
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$scores.dispose();
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}
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return [2 /*return*/, res];
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}
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});
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});
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}
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function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
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if (iouThreshold == null) {
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iouThreshold = 0.5;
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}
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if (scoreThreshold == null) {
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scoreThreshold = Number.NEGATIVE_INFINITY;
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}
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if (softNmsSigma == null) {
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softNmsSigma = 0.0;
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}
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var numBoxes = boxes.shape[0];
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maxOutputSize = Math.min(maxOutputSize, numBoxes);
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util.assert(0 <= iouThreshold && iouThreshold <= 1, function () { return "iouThreshold must be in [0, 1], but was '" + iouThreshold + "'"; });
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util.assert(boxes.rank === 2, function () { return "boxes must be a 2D tensor, but was of rank '" + boxes.rank + "'"; });
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util.assert(boxes.shape[1] === 4, function () {
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return "boxes must have 4 columns, but 2nd dimension was " + boxes.shape[1];
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});
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util.assert(scores.rank === 1, function () { return 'scores must be a 1D tensor'; });
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util.assert(scores.shape[0] === numBoxes, function () { return "scores has incompatible shape with boxes. Expected " + numBoxes + ", " +
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("but was " + scores.shape[0]); });
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util.assert(0 <= softNmsSigma && softNmsSigma <= 1, function () { return "softNmsSigma must be in [0, 1], but was '" + softNmsSigma + "'"; });
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return { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold, softNmsSigma: softNmsSigma };
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}
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/**
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* Extracts crops from the input image tensor and resizes them using bilinear
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* sampling or nearest neighbor sampling (possibly with aspect ratio change)
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* to a common output size specified by crop_size.
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*
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* @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,
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* where imageHeight and imageWidth must be positive, specifying the
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* batch of images from which to take crops
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* @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is
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* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized
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* coordinates of the box in the boxInd[i]'th image in the batch
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* @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range
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* `[0, batch)` that specifies the image that the `i`-th box refers to.
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* @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`
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* specifying the size to which all crops are resized to.
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* @param method Optional string from `'bilinear' | 'nearest'`,
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* defaults to bilinear, which specifies the sampling method for resizing
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* @param extrapolationValue A threshold for deciding when to remove boxes based
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* on score. Defaults to 0.
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* @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`
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*/
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/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
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function cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {
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var $image = tensor_util_env_1.convertToTensor(image, 'image', 'cropAndResize');
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var $boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');
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var $boxInd = tensor_util_env_1.convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');
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method = method || 'bilinear';
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extrapolationValue = extrapolationValue || 0;
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var numBoxes = $boxes.shape[0];
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util.assert($image.rank === 4, function () { return 'Error in cropAndResize: image must be rank 4,' +
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("but got rank " + $image.rank + "."); });
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util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, function () { return "Error in cropAndResize: boxes must be have size [" + numBoxes + ",4] " +
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("but had shape " + $boxes.shape + "."); });
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util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, function () { return "Error in cropAndResize: boxInd must be have size [" + numBoxes + "] " +
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("but had shape " + $boxes.shape + "."); });
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util.assert(cropSize.length === 2, function () { return "Error in cropAndResize: cropSize must be of length 2, but got " +
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("length " + cropSize.length + "."); });
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util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, function () { return "cropSize must be atleast [1,1], but was " + cropSize; });
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util.assert(method === 'bilinear' || method === 'nearest', function () { return "method must be bilinear or nearest, but was " + method; });
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var forward = function (backend, save) {
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return backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);
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};
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var res = engine_1.ENGINE.runKernelFunc(forward, { images: $image, boxes: $boxes, boxInd: $boxInd }, null /* der */, 'CropAndResize', { method: method, extrapolationValue: extrapolationValue, cropSize: cropSize });
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return res;
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}
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exports.resizeBilinear = operation_1.op({ resizeBilinear_: resizeBilinear_ });
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exports.resizeNearestNeighbor = operation_1.op({ resizeNearestNeighbor_: resizeNearestNeighbor_ });
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exports.nonMaxSuppression = operation_1.op({ nonMaxSuppression_: nonMaxSuppression_ });
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exports.nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
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exports.nonMaxSuppressionWithScore = operation_1.op({ nonMaxSuppressionWithScore_: nonMaxSuppressionWithScore_ });
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exports.nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
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exports.cropAndResize = operation_1.op({ cropAndResize_: cropAndResize_ });
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//# sourceMappingURL=image_ops.js.map
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