import * as path from 'path';
import * as tf from '@tensorflow/tfjs';
import LearningMobilenetImage from './mobilenet';
import * as fs from 'fs';
const { createCanvas, loadImage } = require('canvas');

let imageTensor1: tf.Tensor3D;
let imageTensor2: tf.Tensor3D;

// 이미지경로를 기준으로
async function ImagePathToTensor(imagePath: string): Promise<tf.Tensor3D> {
  const imageBuffer = fs.readFileSync(imagePath);
  const image = await loadImage(imageBuffer);
  const canvas = createCanvas(image.width, image.height);
  const ctx = canvas.getContext('2d');
  ctx.drawImage(image, 0, 0);
  const imageData = ctx.getImageData(0, 0, image.width, image.height);
  return tf.tensor3d(imageData.data, [imageData.height, imageData.width, 4], 'int32');
}

describe('LearningMobilenetImage', () => {
  const learning = new LearningMobilenetImage({});
 
  const image1Path = path.join(__dirname, '../../public/images/image1.jpeg');
  const image2Path = path.join(__dirname, '../../public/images/image2.jpeg');
  console.log('Resolved path for image1:', image1Path);
  beforeAll(async () => {
    learning.init();
    imageTensor1 = await ImagePathToTensor(image1Path);
    imageTensor2 = await ImagePathToTensor(image2Path);
  });


  it('loads an image and converts it to a tensor', () => {
    expect(imageTensor1).toBeDefined();
    expect(imageTensor1 instanceof tf.Tensor).toBe(true);
    expect(imageTensor2).toBeDefined();
    expect(imageTensor2 instanceof tf.Tensor).toBe(true);
  });

  // test('mobilenet add data', () => {
  //   learning.addData("라벨1", imageTensor1);
  //   learning.addData("라벨1", imageTensor1);
  //   learning.addData("라벨2", imageTensor2);
  //   learning.addData("라벨2", imageTensor2);
  // });
});