/**
 * NanoDet object detection model implementation
 *
 * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)
 */

import { log, now } from '../util/util';
import * as tf from '../../dist/tfjs.esm.js';
import { loadModel } from '../tfjs/load';
import { constants } from '../tfjs/constants';
import { labels } from './labels';
import type { ObjectResult, ObjectType, Box } from '../result';
import type { GraphModel, Tensor } from '../tfjs/types';
import type { Config } from '../config';
import { env } from '../util/env';

let model: GraphModel;
let last: Array<ObjectResult> = [];
let lastTime = 0;
let skipped = Number.MAX_SAFE_INTEGER;
let inputSize = 0;

const scaleBox = 2.5; // increase box size

export async function load(config: Config): Promise<GraphModel> {
  if (!model || env.initial) {
    model = await loadModel(config.object.modelPath);
    const inputs = Object.values(model.modelSignature['inputs']);
    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config.debug) log('cached model:', model['modelUrl']);
  return model;
}

async function process(res: Tensor[], outputShape: [number, number], config: Config) {
  let id = 0;
  let results: Array<ObjectResult> = [];
  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects
    // find scores, boxes, classes
    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors
      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704
      // find boxes and scores output depending on stride
      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));
      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));
      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride
      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself
      const scores = await scoresT.array(); // optionally use exponential scores or just as-is
      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)
        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class
          const score = scores[i][j]; // get score for current position
          if (score > (config.object.minConfidence || 0) && j !== 61) {
            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1
            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1
            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores
            const [x, y] = [
              cx - (scaleBox / strideSize * boxOffset[0]),
              cy - (scaleBox / strideSize * boxOffset[1]),
            ];
            const [w, h] = [
              cx + (scaleBox / strideSize * boxOffset[2]) - x,
              cy + (scaleBox / strideSize * boxOffset[3]) - y,
            ];
            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1
            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords
            const box = [ // results normalized to input image pixels
              boxRaw[0] * outputShape[0],
              boxRaw[1] * outputShape[1],
              boxRaw[2] * outputShape[0],
              boxRaw[3] * outputShape[1],
            ];
            const result = {
              id: id++,
              // strideSize,
              score: Math.round(100 * score) / 100,
              class: j + 1,
              label: labels[j].label as ObjectType,
              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],
              // centerRaw: [cx, cy],
              box: box.map((a) => Math.trunc(a)) as Box,
              boxRaw,
            };
            results.push(result);
          }
        }
      }
    });
  }
  // deallocate tensors
  res.forEach((t) => tf.dispose(t));

  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of
  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)
  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms
  const nmsScores = results.map((a) => a.score);
  let nmsIdx: Array<number> = [];
  if (nmsBoxes && nmsBoxes.length > 0) {
    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);
    nmsIdx = await nms.data();
    tf.dispose(nms);
  }

  // filter & sort results
  results = results
    .filter((_val, idx) => nmsIdx.includes(idx))
    .sort((a, b) => (b.score - a.score));

  return results;
}

export async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {
  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);
  const skipFrame = skipped < (config.object.skipFrames || 0);
  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {
    skipped++;
    return last;
  }
  skipped = 0;
  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;
  return new Promise(async (resolve) => {
    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];
    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);
    const norm = tf.div(resize, constants.tf255);
    const transpose = norm.transpose([0, 3, 1, 2]);
    tf.dispose(norm);
    tf.dispose(resize);

    let objectT;
    if (config.object.enabled) objectT = model.execute(transpose);
    lastTime = now();
    tf.dispose(transpose);

    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);
    last = obj;
    resolve(obj);
  });
}