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chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @license
 * Copyright 2020 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.
 * =============================================================================
 */
import { nonMaxSuppressionV3Impl } from '../../backends/non_max_suppression_impl';
import { convertToTensor } from '../../tensor_util_env';
import { nonMaxSuppSanityCheck } from '../nonmax_util';
import { tensor1d } from '../tensor1d';
/**
 * Performs non maximum suppression of bounding boxes based on
 * iou (intersection over union).
 *
 * This is the async version of `nonMaxSuppression`
 *
 * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
 *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
 *     the bounding box.
 * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
 * @param maxOutputSize The maximum number of boxes to be selected.
 * @param iouThreshold A float representing the threshold for deciding whether
 *     boxes overlap too much with respect to IOU. Must be between [0, 1].
 *     Defaults to 0.5 (50% box overlap).
 * @param scoreThreshold A threshold for deciding when to remove boxes based
 *     on score. Defaults to -inf, which means any score is accepted.
 * @return A 1D tensor with the selected box indices.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */
async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
    maxOutputSize = inputs.maxOutputSize;
    iouThreshold = inputs.iouThreshold;
    scoreThreshold = inputs.scoreThreshold;
    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
    const boxesVals = boxesAndScores[0];
    const scoresVals = boxesAndScores[1];
    // We call a cpu based impl directly with the typedarray data  here rather
    // than a kernel because all kernels are synchronous (and thus cannot await
    // .data()).
    const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
    if ($boxes !== boxes) {
        $boxes.dispose();
    }
    if ($scores !== scores) {
        $scores.dispose();
    }
    return tensor1d(selectedIndices, 'int32');
}
export const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
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