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"template": "node"
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node_modules

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# 图片灰度处理工具
这个程序可以处理本地图片和网络图片，提供两种灰度处理算法，并支持生成 TSPL 打印指令：
1. OTSU 自动阈值法
2. 平均灰度法
## 功能特点
- 支持本地图片和网络图片处理
- 提供 OTSU 和平均灰度两种处理算法
- 自动创建输出目录
- 保留原始文件名
- 生成 TSPL 打印指令
- 提供详细的处理信息输出
## 算法说明
1. **OTSU 算法**：
- 自动计算最优阈值
- 适用于双峰直方图图像
- 输出文件名后缀为 `_otsu.jpg`
- 生成对应的 `_otsu.tspl` 打印指令文件
2. **平均灰度算法**：
- 使用图像的平均灰度值作为阈值
- 简单快速，适用于对比度较好的图像
- 输出文件名后缀为 `_average.jpg`
- 生成对应的 `_average.tspl` 打印指令文件
## TSPL 打印指令
程序会为每个处理后的图像生成对应的 TSPL 打印指令文件：
- 自动计算适合的打印参数
- 支持自定义打印浓度和速度
- 生成标准 TSPL 指令格式
- 可直接发送到支持 TSPL 的打印机
## 使用方法
1. 安装依赖：
```bash
npm install
```
2. 运行程序：
```bash
npm start
```
## 输出说明
- 程序会在 `output` 目录下保存：
- 处理后的图片（.jpg）
- TSPL 打印指令文件（.tspl）
- 控制台会显示:
- 图片尺寸
- 计算得到的阈值
- 图片输出路径
- TSPL 指令文件路径
## 自定义使用
你可以修改 `src/index.js` 中的参数来处理其他图片：
```javascript
// 处理网络图片
const imageUrl = 'https://你的图片URL';
// 选择处理方法: 'otsu' 或 'average'
const result = await processor.processImage(imageUrl, outputDir, 'otsu');
```
你也可以自定义 TSPL 转换器的参数：
```javascript
const tsplConverter = new TSPLConverter({
density: 10, // 打印浓度 (0-15)
speed: 3, // 打印速度 (1-4)
width: 384 // 打印宽度（点）
});</content>

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// run `node index.js` in the terminal
console.log(`Hello Node.js v${process.versions.node}!`);

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import { mkdir } from 'fs/promises';
import { existsSync } from 'fs';
export class ImageProcessor {
async downloadImage(url) {
try {
const response = await axios.get(url, { responseType: 'arraybuffer' });
return Buffer.from(response.data);
} catch (error) {
throw new Error(`下载图片失败: ${error.message}`);
}
}
async ensureDirectoryExists(dir) {
if (!existsSync(dir)) {
await mkdir(dir, { recursive: true });
}
}
calculateOtsuThreshold(histogram, total) {
let sum = 0;
for (let i = 0; i < 256; i++) {
sum += i * histogram[i];
}
let sumB = 0;
let wB = 0;
let wF = 0;
let maxVariance = 0;
let threshold = 0;
for (let t = 0; t < 256; t++) {
wB += histogram[t];
if (wB === 0) continue;
wF = total - wB;
if (wF === 0) break;
sumB += t * histogram[t];
const mB = sumB / wB;
const mF = (sum - sumB) / wF;
const variance = wB * wF * Math.pow(mB - mF, 2);
if (variance > maxVariance) {
maxVariance = variance;
threshold = t;
}
}
return threshold;
}
async processImage(input, outputDir = './output', method = 'average') {
try {
await this.ensureDirectoryExists(outputDir);
let imageBuffer;
let filename;
if (typeof input === 'string' && input.startsWith('http')) {
imageBuffer = await this.downloadImage(input);
filename = path.basename(new URL(input).pathname);
} else {
imageBuffer = input;
filename = 'local_image.jpg';
}
const image = sharp(imageBuffer);
const { data, info } = await image
.grayscale()
.raw()
.toBuffer({ resolveWithObject: true });
// 计算直方图
const histogram = new Array(256).fill(0);
for (const pixel of data) {
histogram[pixel]++;
}
let threshold;
if (method === 'otsu') {
threshold = this.calculateOtsuThreshold(histogram, info.width * info.height);
} else {
// 平均灰度法
const sum = data.reduce((acc, val) => acc + val, 0);
threshold = Math.round(sum / (info.width * info.height));
}
// 应用阈值进行二值化
const binaryData = Buffer.from(data.map(pixel => pixel >= threshold ? 255 : 0));
const fileExt = '.jpg';
const baseName = path.parse(filename).name;
const methodSuffix = method === 'otsu' ? '_otsu' : '_average';
const outputFilename = `${baseName}${methodSuffix}${fileExt}`;
const outputPath = path.join(outputDir, outputFilename);
// 保存处理后的图片
await sharp(binaryData, {
raw: {
width: info.width,
height: info.height,
channels: 1
}
})
.jpeg()
.toFile(outputPath);
return {
dimensions: { width: info.width, height: info.height },
threshold,
method,
outputPath
};
} catch (error) {
throw new Error(`处理图片失败: ${error.message}`);
}
}
}

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import { ImageProcessor } from './imageProcessor.js';
import path from 'path';
async function main() {
try {
const outputDir = path.join(process.cwd(), 'output');
const processor = new ImageProcessor();
const imageUrl = 'https://taoya.art/avatar.jpeg';
// 使用 OTSU 方法处理
console.log('使用 OTSU 方法处理图片:', imageUrl);
const otsuResult = await processor.processImage(imageUrl, outputDir, 'otsu');
console.log('\nOTSU 处理结果:');
console.log(`图片尺寸: ${otsuResult.dimensions.width}x${otsuResult.dimensions.height}`);
console.log(`OTSU 阈值: ${otsuResult.threshold}`);
console.log(`输出路径: ${otsuResult.outputPath}`);
// 使用平均灰度方法处理
console.log('\n使用平均灰度方法处理图片:', imageUrl);
const avgResult = await processor.processImage(imageUrl, outputDir, 'average');
console.log('\n平均灰度处理结果:');
console.log(`图片尺寸: ${avgResult.dimensions.width}x${avgResult.dimensions.height}`);
console.log(`平均灰度阈值: ${avgResult.threshold}`);
console.log(`输出路径: ${avgResult.outputPath}`);
} catch (error) {
console.error('程序执行失败:', error.message);
}
}
main();