2017通訊大賽「聯發科技物聯網開發競賽」決賽團隊29強出爐！作品都在11月24日頒獎典禮進行展示

import * as fs from 'fs';
	import * as cv from '../../';
	import {
	lccs,
	centerLetterInImage,
	saveConfusionMatrix
	} from './OCRTools';
	const trainDataPath = '../../data/ocr/traindata';
	const testDataPath = '../../data/ocr/testdata';
	const outPath = '../../data/ocr';
	const SVMFile = 'lcletters.xml';
	const hog = new cv.HOGDescriptor({
	winSize: new cv.Size(40, 40),
	blockSize: new cv.Size(20, 20),
	blockStride: new cv.Size(10, 10),
	cellSize: new cv.Size(10, 10),
	L2HysThreshold: 0.2,
	nbins: 9,
	gammaCorrection: true,
	signedGradient: true
	});
	const svm = new cv.SVM({
	kernelType: cv.ml.SVM.RBF,
	c: 12.5,
	gamma: 0.50625
	});
	const computeHOGDescriptorFromImage = (img: cv.Mat, isIorJ: boolean) => {
	let im = img;
	if (im.rows !== 40 || im.cols !== 40) {
	im = im.resize(40, 40);
	}
	// center the letter
	im = centerLetterInImage(img, isIorJ);
	if (!img) {
	return null;
	}
	return hog.compute(im);
	};
	const trainSVM = (trainDataFiles: string[][], isAuto: boolean = false) => {
	// make hog features of trainingData and label it
	console.log('make features');
	const samples: number[][] = [];
	const labels: number[] = [];
	trainDataFiles.forEach((files, label) => {
	files.forEach((file) => {
	const img = cv.imread(file);
	const isIorJ = label === 8 || label === 9;
	const desc = computeHOGDescriptorFromImage(img, isIorJ);
	if (!desc) {
	return;
	}
	samples.push(desc);
	labels.push(label);
	});
	});
	// train the SVM
	console.log('training');
	const trainData = new cv.TrainData(
	new cv.Mat(samples, cv.CV_32F),
	cv.ml.ROW_SAMPLE,
	new cv.Mat([labels], cv.CV_32S)
	);
	svm[isAuto ? 'trainAuto' : 'train'](trainData);
	};
	const data = lccs.map((letter) => {
	const trainDataDir = `${trainDataPath}/${letter}`;
	const testDataDir = `${testDataPath}/${letter}`;
	const train = fs.readdirSync(trainDataDir).map(file => `${trainDataDir}/${file}`);
	const test = fs.readdirSync(testDataDir).map(file => `${testDataDir}/${file}`);
	return ({ train, test });
	});
	const trainDataFiles = data.map(classData => classData.train);
	const testDataFiles = data.map(classData => classData.test);
	const numTrainImagesPerClass = trainDataFiles[0].length;
	const numTestImagesPerClass = testDataFiles[0].length;
	console.log('train data per class:', numTrainImagesPerClass);
	console.log('test data per class:', numTestImagesPerClass);
	trainSVM(trainDataFiles, false);
	svm.save(`${outPath}/${SVMFile}`);
	svm.load(`${outPath}/${SVMFile}`);
	// compute prediction error for each letter
	const errs = Array(26).fill(0);
	testDataFiles.forEach((files, label) => {
	files.forEach((file) => {
	const img = cv.imread(file);
	const isIorJ = label === 8 || label === 9;
	const desc = computeHOGDescriptorFromImage(img, isIorJ);
	if (!desc) {
	throw new Error(`Computing HOG descriptor failed for file: ${file}`);
	}
	const predictedLabel = svm.predict(desc);
	if (label !== predictedLabel) {
	errs[label] += 1;
	}
	});
	});
	console.log('prediction result:');
	errs.forEach((err, l) => console.log(lccs[l], err, 1 - (err / numTestImagesPerClass)));
	console.log('average: ', 1 - (errs.reduce((e1, e2) => e1 + e2) / (lccs.length * numTestImagesPerClass)));
	saveConfusionMatrix(
	testDataFiles,
	(img, isIorJ) => svm.predict(computeHOGDescriptorFromImage(img, isIorJ)),
	numTestImagesPerClass,
	`${outPath}/confusionmatrix.csv`
	);