/**
 * @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 {NonMaxSuppressionV4, NonMaxSuppressionV4Attrs, NonMaxSuppressionV4Inputs} from '../../kernel_names';
import {NamedAttrMap} from '../../kernel_registry';
import {Tensor, Tensor1D, Tensor2D} from '../../tensor';
import {NamedTensorMap} from '../../tensor_types';
import {convertToTensor} from '../../tensor_util_env';
import {TensorLike} from '../../types';

import {nonMaxSuppSanityCheck} from '../nonmax_util';
import {op} from '../operation';

/**
 * Asynchronously performs non maximum suppression of bounding boxes based on
 * iou (intersection over union), with an option to pad results.
 *
 * @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.
 * @param padToMaxOutputSize Defalts to false. If true, size of output
 *     `selectedIndices` is padded to maxOutputSize.
 * @return A map with the following properties:
 *     - selectedIndices: A 1D tensor with the selected box indices.
 *     - validOutputs: A scalar denoting how many elements in `selectedIndices`
 *       are valid. Valid elements occur first, then padding.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */
function nonMaxSuppressionPadded_(
    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,
    maxOutputSize: number, iouThreshold = 0.5,
    scoreThreshold = Number.NEGATIVE_INFINITY,
    padToMaxOutputSize = false): NamedTensorMap {
  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');

  const params = nonMaxSuppSanityCheck(
      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,
      null /* softNmsSigma */);
  const $maxOutputSize = params.maxOutputSize;
  const $iouThreshold = params.iouThreshold;
  const $scoreThreshold = params.scoreThreshold;

  const inputs: NonMaxSuppressionV4Inputs = {boxes: $boxes, scores: $scores};
  const attrs: NonMaxSuppressionV4Attrs = {
    maxOutputSize: $maxOutputSize,
    iouThreshold: $iouThreshold,
    scoreThreshold: $scoreThreshold,
    padToMaxOutputSize
  };

  // tslint:disable-next-line: no-unnecessary-type-assertion
  const result = ENGINE.runKernel(
                     NonMaxSuppressionV4, inputs as {} as NamedTensorMap,
                     attrs as {} as NamedAttrMap) as Tensor[];

  return {selectedIndices: result[0], validOutputs: result[1]};
}

export const nonMaxSuppressionPadded = op({nonMaxSuppressionPadded_});