/**
 * @license
 * Copyright 2021 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 * as tflite from '@tensorflow/tfjs-tflite';
import {Color, ImageSegmentationResult, ImageSegmenter, Legend} from './common';

/**
 * The base class for all image segmentation TFLite models.
 *
 * @template T The type of inference options.
 */
export class ImageSegmenterTFLite<T> extends ImageSegmenter<T> {
  constructor(private tfliteImageSegmenter: tflite.ImageSegmenter) {
    super();
  }

  async predict(
      img: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,
      infereceOptions?: T): Promise<ImageSegmentationResult> {
    if (!this.tfliteImageSegmenter) {
      throw new Error('source model is not loaded');
    }
    const tfliteResults = this.tfliteImageSegmenter.segment(img);
    if (!tfliteResults) {
      return {
        legend: {},
        width: -1,
        height: -1,
        segmentationMap: undefined,
      };
    }
    const segmentation = tfliteResults[0];
    const legend: Legend = {};
    const colors: Color[] = [];
    for (const coloredLabel of segmentation.coloredLabels) {
      legend[coloredLabel.className || coloredLabel.displayName] = coloredLabel;
      colors.push(coloredLabel);
    }
    const segmentationMap: Uint8ClampedArray =
        new Uint8ClampedArray(segmentation.width * segmentation.height * 4);
    for (let i = 0; i < segmentation.categoryMask.length; i++) {
      const categoryIndex = segmentation.categoryMask[i];
      const color = colors[categoryIndex];
      segmentationMap[i * 4] = color.r;
      segmentationMap[i * 4 + 1] = color.g;
      segmentationMap[i * 4 + 2] = color.b;
      segmentationMap[i * 4 + 3] = 255;
    }
    return {
      legend,
      width: segmentation.width,
      height: segmentation.height,
      segmentationMap,
    };
  }

  cleanUp() {
    if (!this.tfliteImageSegmenter) {
      throw new Error('source model is not loaded');
    }
    this.tfliteImageSegmenter.cleanUp();
  }
}