@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js

/**
 * @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 { ENGINE } from '../../engine';
import { NonMaxSuppressionV3 } from '../../kernel_names';
import { convertToTensor } from '../../tensor_util_env';
import { nonMaxSuppSanityCheck } from '../nonmax_util';
import { op } from '../operation';
/**
 * Performs non maximum suppression of bounding boxes based on
 * iou (intersection over union).
 *
 * @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'}
 */
function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression', 'float32');
    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression', 'float32');
    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
    maxOutputSize = inputs.maxOutputSize;
    iouThreshold = inputs.iouThreshold;
    scoreThreshold = inputs.scoreThreshold;
    const attrs = { maxOutputSize, iouThreshold, scoreThreshold };
    return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs);
}
export const nonMaxSuppression = op({ nonMaxSuppression_ });
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